From 6483114cdcd6ca227f0fa858f521e613cb973d36 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 21:37:28 +0200 Subject: [PATCH 01/73] Initialize repository --- .github/workflows/ci.yml | 83 + .github/workflows/release.yml | 50 + .gitignore | 223 ++ .pre-commit-config.yaml | 44 + .python-version | 1 + CHANGELOG.md | 52 + CONTRIBUTING.md | 279 +++ LICENSE | 201 ++ Makefile | 158 ++ README.md | 248 +++ THIRD_PARTY_LICENSES.md | 59 + examples/basic_usage.py | 144 ++ examples/custom_backgrounds.py | 283 +++ examples/e_commerce_batch.py | 230 ++ pyproject.toml | 170 ++ src/withoutbg/__init__.py | 18 + src/withoutbg/__version__.py | 3 + src/withoutbg/api.py | 238 +++ src/withoutbg/cli.py | 264 +++ src/withoutbg/core.py | 109 + src/withoutbg/exceptions.py | 31 + src/withoutbg/models.py | 475 +++++ src/withoutbg/py.typed | 0 tests/__init__.py | 0 tests/conftest.py | 225 ++ tests/fixtures/__init__.py | 5 + tests/fixtures/expected_outputs/.gitkeep | 2 + .../expected_outputs/test-ice-cream.png | Bin 0 -> 109699 bytes tests/fixtures/images/.gitkeep | 2 + .../test-ice-cream-api-service-mock-alpha.png | Bin 0 -> 6738 bytes tests/fixtures/images/test-ice-cream.png | Bin 0 -> 122803 bytes tests/fixtures/mock_responses.py | 136 ++ tests/fixtures/mock_responses/.gitkeep | 3 + .../mock_responses/studio_error_401.json | 3 + .../mock_responses/studio_error_402.json | 3 + .../mock_responses/studio_error_429.json | 3 + .../mock_responses/studio_success.json | 3 + .../mock_responses/studio_usage_response.json | 4 + tests/integration/__init__.py | 1 + .../integration/test_real_image_processing.py | 379 ++++ tests/performance/__init__.py | 1 + tests/performance/test_batch_performance.py | 460 ++++ tests/performance/test_benchmarks.py | 462 +++++ tests/performance/test_memory_usage.py | 416 ++++ tests/test_api.py | 357 ++++ tests/test_cli.py | 727 +++++++ tests/test_cli_e2e.py | 585 ++++++ uv.lock | 1841 +++++++++++++++++ 48 files changed, 8981 insertions(+) create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/release.yml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version create mode 100644 CHANGELOG.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 Makefile create mode 100644 README.md create mode 100644 THIRD_PARTY_LICENSES.md create mode 100644 examples/basic_usage.py create mode 100644 examples/custom_backgrounds.py create mode 100644 examples/e_commerce_batch.py create mode 100644 pyproject.toml create mode 100644 src/withoutbg/__init__.py create mode 100644 src/withoutbg/__version__.py create mode 100644 src/withoutbg/api.py create mode 100644 src/withoutbg/cli.py create mode 100644 src/withoutbg/core.py create mode 100644 src/withoutbg/exceptions.py create mode 100644 src/withoutbg/models.py create mode 100644 src/withoutbg/py.typed create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 100644 tests/fixtures/__init__.py create mode 100644 tests/fixtures/expected_outputs/.gitkeep create mode 100644 tests/fixtures/expected_outputs/test-ice-cream.png create mode 100644 tests/fixtures/images/.gitkeep create mode 100644 tests/fixtures/images/test-ice-cream-api-service-mock-alpha.png create mode 100644 tests/fixtures/images/test-ice-cream.png create mode 100644 tests/fixtures/mock_responses.py create mode 100644 tests/fixtures/mock_responses/.gitkeep create mode 100644 tests/fixtures/mock_responses/studio_error_401.json create mode 100644 tests/fixtures/mock_responses/studio_error_402.json create mode 100644 tests/fixtures/mock_responses/studio_error_429.json create mode 100644 tests/fixtures/mock_responses/studio_success.json create mode 100644 tests/fixtures/mock_responses/studio_usage_response.json create mode 100644 tests/integration/__init__.py create mode 100644 tests/integration/test_real_image_processing.py create mode 100644 tests/performance/__init__.py create mode 100644 tests/performance/test_batch_performance.py create mode 100644 tests/performance/test_benchmarks.py create mode 100644 tests/performance/test_memory_usage.py create mode 100644 tests/test_api.py create mode 100644 tests/test_cli.py create mode 100644 tests/test_cli_e2e.py create mode 100644 uv.lock diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..a2c512e --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,83 @@ +name: CI + +on: + push: + branches: [ main, develop ] + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ['3.8', '3.9', '3.10', '3.11', '3.12'] + exclude: + # Reduce matrix for faster CI + - os: windows-latest + python-version: '3.8' + - os: windows-latest + python-version: '3.9' + - os: macos-latest + python-version: '3.8' + - os: macos-latest + python-version: '3.9' + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[dev]" + + - name: Lint with ruff + run: | + ruff check src/ tests/ + + - name: Check formatting with black + run: | + black --check src/ tests/ + + - name: Type check with mypy + run: | + mypy src/ + + - name: Test with pytest + run: | + pytest tests/ --cov=src/withoutbg --cov-report=xml + + - name: Upload coverage to Codecov + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11' + uses: codecov/codecov-action@v3 + with: + file: ./coverage.xml + fail_ci_if_error: true + + security: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: Install bandit + run: pip install bandit[toml] + + - name: Run security scan + run: bandit -r src/ -f json -o bandit-report.json || true + + - name: Upload security results + uses: actions/upload-artifact@v3 + with: + name: bandit-results + path: bandit-report.json \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..ef22f24 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,50 @@ +name: Release + +on: + push: + tags: + - 'v*' + +jobs: + release: + runs-on: ubuntu-latest + permissions: + contents: write + id-token: write # For trusted publishing to PyPI + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + pip install build twine + + - name: Build package + run: python -m build + + - name: Check package + run: twine check dist/* + + - name: Create GitHub Release + uses: actions/create-release@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref }} + release_name: Release ${{ github.ref }} + body: | + Changes in this Release + - See CHANGELOG.md for details + draft: false + prerelease: false + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://upload.pypi.org/legacy/ \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7b7f412 --- /dev/null +++ b/.gitignore @@ -0,0 +1,223 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be added to the global gitignore or merged into this project gitignore. For a PyCharm +# project, it is generally recommended to exclude the project root directory from being +# committed and only include the relevant parts, such as the source code. +.idea/ +*.iml +*.ipr +*.iws + +# VS Code +.vscode/ +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json + +# macOS +.DS_Store +.AppleDouble +.LSOverride +Icon +._* +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Windows +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db +*.tmp +*.temp +*.stackdump +Desktop.ini +$RECYCLE.BIN/ +*.cab +*.msi +*.msix +*.msm +*.msp +*.lnk + +# Linux +*~ + +# Project specific +*.onnx +*.pt +*.pth +bandit-report.json +test_images/ +output/ +models/ +!src/withoutbg/models/ +llmcontext/ + +# API keys and secrets +.env.local +.env.*.local +api_keys.txt + +## Development with LLMs +CLAUDE.md \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..e1845d0 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,44 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + args: ['--maxkb=1000'] + - id: check-merge-conflict + - id: debug-statements + + - repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + language_version: python3 + args: [--line-length=88] + + - repo: https://github.com/charliermarsh/ruff-pre-commit + rev: v0.0.270 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.3.0 + hooks: + - id: mypy + additional_dependencies: [types-requests, types-pillow] + args: [--strict, --ignore-missing-imports] + + - repo: https://github.com/PyCQA/bandit + rev: 1.7.5 + hooks: + - id: bandit + args: [-r, src/, -f, json, -o, bandit-report.json] + pass_filenames: false + + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v3.0.0-alpha.9-for-vscode + hooks: + - id: prettier + types_or: [yaml, markdown, json] \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..bd28b9c --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.9 diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..be203b3 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,52 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + + +## [Unreleased] + +### Added +- Initial project structure +- Core background removal functionality +- CLI interface +- API client for Studio service +- Documentation and examples +- Test suite +- GitHub Actions workflows + +--- + +## Upcoming Features + +### v0.2.0 (Planned) +- [ ] GUI application +- [ ] Improved error messages +- [ ] Performance optimizations +- [ ] Enhanced CLI progress bars + +### Future Considerations +- [ ] Advanced compositing features + +--- + +## Release Notes Format + +Each release will include: +- **Features**: New functionality added +- **Improvements**: Enhancements to existing features +- **Bug Fixes**: Issues resolved +- **Breaking Changes**: API changes requiring user action +- **Deprecations**: Features marked for future removal +- **Security**: Security-related changes + +## Migration Guides + +### From v0.x to v1.0 (Future) +Migration guide will be provided when we reach v1.0 with any breaking changes. + +--- + +*Want to contribute? See our [Contributing Guide](CONTRIBUTING.md)* \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..1ec354d --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,279 @@ +# Contributing to withoutbg + +We welcome contributions to withoutbg! This document provides guidelines for contributing to the project. + +## ๐Ÿค How to Contribute + +### Reporting Issues +- **Bug Reports**: Use our [issue template](https://github.com/withoutbg/withoutbg/issues/new?template=bug_report.md) +- **Feature Requests**: Use our [feature request template](https://github.com/withoutbg/withoutbg/issues/new?template=feature_request.md) +- **Security Issues**: Email security@withoutbg.com instead of creating public issues + +### Code Contributions + +1. **Fork the Repository** + ```bash + git clone https://github.com/your-username/withoutbg.git + cd withoutbg + ``` + +2. **Set Up Development Environment** + ```bash + python -m venv venv + source venv/bin/activate # On Windows: venv\Scripts\activate + pip install -e ".[dev]" + pre-commit install + ``` + +3. **Create a Feature Branch** + ```bash + git checkout -b feature/your-feature-name + ``` + +4. **Make Your Changes** + - Write clean, well-documented code + - Add tests for new functionality + - Update documentation as needed + - Follow our coding standards (see below) + +5. **Test Your Changes** + ```bash + # Run tests + pytest + + # Check code formatting + black src/ tests/ + ruff check src/ tests/ + + # Type checking + mypy src/ + ``` + +6. **Commit Your Changes** + ```bash + git add . + git commit -m "feat: add awesome new feature" + ``` + +7. **Push and Create Pull Request** + ```bash + git push origin feature/your-feature-name + ``` + +## ๐Ÿ“‹ Coding Standards + +### Python Code Style +- **Formatting**: We use [Black](https://black.readthedocs.io/) with 88 character line length +- **Linting**: We use [Ruff](https://docs.astral.sh/ruff/) for fast Python linting +- **Type Hints**: All new code should include type hints +- **Docstrings**: Use Google-style docstrings for all public functions + +### Example Code Style +```python +def remove_background( + input_image: Union[str, Path, Image.Image, bytes], + output_path: Optional[Union[str, Path]] = None, + api_key: Optional[str] = None, +) -> Image.Image: + """Remove background from an image. + + Args: + input_image: Input image as file path, PIL Image, or bytes + output_path: Optional path to save the result + api_key: API key for Studio service + + Returns: + PIL Image with background removed + + Raises: + WithoutBGError: If background removal fails + """ + # Implementation here + pass +``` + +### Commit Message Format +We follow [Conventional Commits](https://www.conventionalcommits.org/): + +``` +[optional scope]: + +[optional body] + +[optional footer(s)] +``` + +**Types:** +- `feat`: New feature +- `fix`: Bug fix +- `docs`: Documentation changes +- `style`: Code style changes (formatting, etc.) +- `refactor`: Code refactoring +- `test`: Adding or updating tests +- `chore`: Maintenance tasks + +**Examples:** +```bash +git commit -m "feat: add batch processing support" +git commit -m "fix: handle RGBA images correctly" +git commit -m "docs: update API examples" +``` + +## ๐Ÿงช Testing Guidelines + +### Writing Tests +- Write tests for all new functionality +- Aim for >80% test coverage +- Use descriptive test names +- Mock external dependencies (API calls, file operations) + +### Test Structure +```python +class TestRemoveBackground: + """Test the remove_background function.""" + + def test_remove_background_with_pil_image(self): + """Test background removal with PIL Image input.""" + # Arrange + input_image = Image.new('RGB', (100, 100), 'red') + + # Act + result = remove_background(input_image) + + # Assert + assert isinstance(result, Image.Image) + assert result.mode == 'RGBA' +``` + +### Running Tests +```bash +# Run all tests +pytest + +# Run with coverage +pytest --cov=src/withoutbg + +# Run specific test file +pytest tests/test_core.py + +# Run with verbose output +pytest -v +``` + +## ๐Ÿ“š Documentation + +### Updating Documentation +- Update docstrings for any changed functions +- Update README.md if adding new features +- Add examples to the `examples/` directory +- Update `docs/` for comprehensive changes + +### Documentation Style +- Use clear, concise language +- Include code examples +- Explain the "why" not just the "how" +- Add links to related concepts + +## ๐ŸŽฏ Areas Where We Need Help + +### High Priority +- **Error Handling**: Better error messages and recovery +- **Documentation**: More examples and tutorials + +### Medium Priority +- **CLI Enhancements**: Additional output formats and options +- **Testing**: Edge cases and integration tests +- **Platform Support**: Windows and macOS compatibility testing +- **Accessibility**: Better error messages for beginners + +### Future Ideas +- **GUI Application**: Desktop app for non-developers +- **Plugin System**: Support for custom preprocessing/postprocessing + +## โšก Development Setup + +### Prerequisites +- Python 3.9+ +- Git +- (Optional) CUDA-compatible GPU for faster local processing + +### Full Development Setup +```bash +# Clone repository +git clone https://github.com/withoutbg/withoutbg.git +cd withoutbg + +# Create virtual environment +python -m venv venv +source venv/bin/activate + +# Install package in development mode +pip install -e ".[dev]" + +# Install pre-commit hooks +pre-commit install + +# Run tests to verify setup +pytest + +# Check code quality +black --check src/ tests/ +ruff check src/ tests/ +mypy src/ +``` + +### IDE Configuration + +#### VS Code +Install the following extensions: +- Python +- Black Formatter +- Ruff +- Pylance + +Add to `.vscode/settings.json`: +```json +{ + "python.defaultInterpreterPath": "./venv/bin/python", + "python.formatting.provider": "black", + "python.linting.enabled": true, + "python.linting.ruffEnabled": true, + "python.typeChecking": "strict" +} +``` + +## ๐Ÿท๏ธ Release Process + +### Version Numbering +We use [Semantic Versioning](https://semver.org/): +- `MAJOR.MINOR.PATCH` +- Major: Breaking changes +- Minor: New features (backward compatible) +- Patch: Bug fixes + +### Release Checklist +1. Update version in `pyproject.toml` +2. Update `CHANGELOG.md` +3. Create git tag: `git tag v1.2.3` +4. Push tag: `git push origin v1.2.3` +5. GitHub Actions will automatically build and publish to PyPI + +## ๐Ÿ“„ License + +By contributing to withoutbg, you agree that your contributions will be licensed under the Apache License 2.0. + +## ๐Ÿ™ Recognition + +Contributors will be recognized in: +- `CONTRIBUTORS.md` file +- GitHub contributors list +- Release notes for significant contributions + +## โ“ Questions? + +- **General Questions**: [GitHub Discussions](https://github.com/withoutbg/withoutbg/discussions) +- **Technical Issues**: [GitHub Issues](https://github.com/withoutbg/withoutbg/issues) +- **Security Concerns**: security@withoutbg.com +- **Other**: contact@withoutbg.com + +Thank you for contributing to withoutbg! ๐ŸŽญ \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e2a06ad --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "page" as the copyright notice for easier identification + within third-party archives. + + Copyright 2024 withoutbg + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..b9a36cc --- /dev/null +++ b/Makefile @@ -0,0 +1,158 @@ +# Makefile for withoutbg project + +.PHONY: help install dev-install test test-unit test-integration test-performance test-coverage lint format type-check clean build docs version-show version-patch version-minor version-major + +# Default target +help: + @echo "Available targets:" + @echo " install Install the package" + @echo " dev-install Install package in development mode with dev dependencies" + @echo " test Run all tests" + @echo " test-unit Run unit tests only" + @echo " test-integration Run integration tests only" + @echo " test-performance Run performance tests only" + @echo " test-coverage Run tests with coverage report" + @echo " lint Run linting (ruff)" + @echo " format Format code (black)" + @echo " type-check Run type checking (mypy)" + @echo " quality Run all quality checks (lint + format + type)" + @echo " clean Clean build artifacts" + @echo " build Build package" + @echo " docs Generate documentation" + @echo " version-show Show current version" + @echo " version-patch Increment patch version (0.1.0 -> 0.1.1)" + @echo " version-minor Increment minor version (0.1.0 -> 0.2.0)" + @echo " version-major Increment major version (0.1.0 -> 1.0.0)" + +# Installation targets +install: + uv sync + +dev-install: + uv sync --dev + +# Testing targets +test: + #uv run pytest --run-real-processing --run-performance + uv run pytest --run-real-processing + +test-unit: + uv run pytest -m "unit" + +test-integration: + uv run pytest -m "integration" --run-real-processing + +test-performance: + uv run pytest -m "performance" --run-performance + +test-api: + uv run pytest -m "api" --run-api + +test-coverage: + uv run pytest --cov=src/withoutbg --cov-report=html --cov-report=term-missing --run-real-processing + +test-fast: + uv run pytest -m "not (performance or integration)" -x + +# Code quality targets +lint: + uv run ruff check src/ tests/ + +format: + uv run black src/ tests/ + +format-check: + uv run black --check src/ tests/ + +type-check: + uv run mypy src/ + +quality: lint format-check type-check + @echo "All quality checks passed!" + +# Fix common issues +fix: + uv run ruff check --fix src/ tests/ + uv run black src/ tests/ + +# Build targets +clean: + rm -rf build/ + rm -rf dist/ + rm -rf *.egg-info/ + rm -rf .pytest_cache/ + rm -rf .coverage + rm -rf htmlcov/ + rm -rf .mypy_cache/ + rm -rf .ruff_cache/ + find . -type d -name __pycache__ -exec rm -rf {} + + find . -type f -name "*.pyc" -delete + +build: clean + uv build + +# Development workflow +dev: dev-install quality test-fast + @echo "Development setup complete!" + +# CI/CD simulation +ci: dev-install quality test-coverage + @echo "CI checks completed!" + +# Documentation (placeholder) +docs: + @echo "Documentation generation not yet implemented" + +# Performance benchmarking +benchmark: + uv run pytest tests/performance/ --run-performance -v + +# Debug helpers +debug-env: + @echo "Python version: $$(python --version)" + @echo "UV version: $$(uv --version)" + @echo "Package info:" + @uv run python -c "import withoutbg; print(f'withoutbg version: {withoutbg.__version__}')" || echo "Package not installed" + +# Testing with different configurations +test-verbose: + uv run pytest -v + +test-quiet: + uv run pytest -q + +test-parallel: + uv run pytest -n auto + +# Security and dependency checks +security: + @echo "Security checks not yet implemented" + +deps-check: + uv run pip list --outdated + +# Version management +version-show: + @echo "Current version: $$(uv run hatch version)" + +version-patch: + uv run hatch version patch + @echo "Version bumped to: $$(uv run hatch version)" + +version-minor: + uv run hatch version minor + @echo "Version bumped to: $$(uv run hatch version)" + +version-major: + uv run hatch version major + @echo "Version bumped to: $$(uv run hatch version)" + +# Release helpers (placeholder) +release-patch: version-patch + @echo "Patch release created: $$(uv run hatch version)" + +release-minor: version-minor + @echo "Minor release created: $$(uv run hatch version)" + +release-major: version-major + @echo "Major release created: $$(uv run hatch version)" \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..36e3aec --- /dev/null +++ b/README.md @@ -0,0 +1,248 @@ +# withoutbg + +**AI-powered background removal with local and cloud options** + +[![PyPI version](https://badge.fury.io/py/withoutbg.svg)](https://badge.fury.io/py/withoutbg) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) +[![CI](https://github.com/withoutbg/withoutbg/workflows/CI/badge.svg)](https://github.com/withoutbg/withoutbg/actions) +[![codecov](https://codecov.io/gh/withoutbg/withoutbg/branch/main/graph/badge.svg)](https://codecov.io/gh/withoutbg/withoutbg) + +Remove backgrounds from images instantly with AI. Choose between local processing (free) or cloud API (best quality). + +## ๐Ÿš€ Quick Start + +```bash +# Install +pip install withoutbg + +# Remove background (local processing) +withoutbg image.jpg + +# Use cloud API for best quality processing +withoutbg image.jpg --api-key sk_your_api_key +``` + +## ๐Ÿ“ฆ Model Tiers + +| Tier | Quality | Usage | License | +|------|---------|-------|---------| +| ๐ŸŸข **Snap** | Good | Local/Free | Apache 2.0 | +| ๐ŸŸก **Focus** | Better | Commercial | Licensed | +| ๐Ÿ”ด **Studio** | Best | Cloud API | SaaS credits | + +## ๐Ÿ’ป Python API + +```python +from withoutbg import remove_background + +# Local processing with Snap model (free) +result = remove_background("input.jpg") +result.save("output.png") + +# Cloud processing with Studio API (best quality) +result = remove_background("input.jpg", api_key="sk_your_key") + +# Batch processing +from withoutbg import remove_background_batch +results = remove_background_batch(["img1.jpg", "img2.jpg"], + output_dir="results/") +``` + +## ๐Ÿ–ฅ๏ธ CLI Usage + +### Basic Usage +```bash +# Process single image +withoutbg photo.jpg + +# Specify output path +withoutbg photo.jpg --output result.png + +# Use different format +withoutbg photo.jpg --format webp --quality 90 +``` + +### Cloud API (Studio) +```bash +# Set API key via environment +export WITHOUTBG_API_KEY="sk_your_api_key" +withoutbg photo.jpg --use-api + +# Or pass directly +withoutbg photo.jpg --api-key sk_your_key +``` + +### Batch Processing +```bash +# Process all images in directory +withoutbg photos/ --batch --output-dir results/ + +# With cloud API for best quality +withoutbg photos/ --batch --use-api --output-dir results/ +``` + +## ๐Ÿ”ง Installation Options + +### Standard Installation +```bash +pip install withoutbg +``` + +### Development +```bash +git clone https://github.com/withoutbg/withoutbg.git +cd withoutbg +pip install -e ".[dev]" +``` + +## ๐ŸŽจ Examples + +### Basic Background Removal +```python +import withoutbg + +# Simple usage +output = withoutbg.remove_background("portrait.jpg") +output.save("portrait-withoutbg.png") +``` + +### E-commerce Product Photos +```python +import withoutbg +from pathlib import Path + +# Process product catalog +product_images = Path("products").glob("*.jpg") +results = withoutbg.remove_background_batch( + list(product_images), + output_dir="catalog-withoutbg/", + api_key="sk_your_key" # Use Studio for best quality +) +``` + +### Social Media Automation +```python +import withoutbg +from PIL import Image + +# Remove background and add custom background +foreground = withoutbg.remove_background("selfie.jpg", api_key="sk_key") +background = Image.open("gradient_bg.jpg") + +# Composite images +background.paste(foreground, (0, 0), foreground) +background.save("social_post.jpg") +``` + +## ๐Ÿ”‘ API Key Setup + +1. **Get API Key**: Visit [withoutbg.com](https://withoutbg.com) to get your API key +2. **Set Environment Variable**: + ```bash + export WITHOUTBG_API_KEY="sk_your_api_key" + ``` +3. **Or pass directly in code**: + ```python + result = withoutbg.remove_background("image.jpg", api_key="sk_your_key") + ``` + +## ๐Ÿ—๏ธ For Developers + +### Local Development +```bash +# Clone repository +git clone https://github.com/withoutbg/withoutbg.git +cd withoutbg + +# Install in development mode +pip install -e ".[dev]" + +# Run tests +pytest + +# Format code +black src/ tests/ +ruff check src/ tests/ + +# Type checking +mypy src/ +``` + +## ๐Ÿ“Š Usage Analytics + +Track your API usage: + +```python +from withoutbg.api import StudioAPI + +api = StudioAPI(api_key="sk_your_key") +usage = api.get_usage() +print(usage) +``` + +## ๐Ÿค Commercial Licensing + +### Focus License +- โœ… Dockerized model for easy deployment +- โœ… Multiple runtime options: ONNX, TensorRT, AWS Inferentia +- โœ… Self-hosted deployment +- โœ… Better quality than Snap +- โœ… Priority support +- โœ… No API dependencies + +[Get Focus License โ†’](https://withoutbg.com/focus) + +### Studio API (Pay-per-use) +- โœ… Best quality processing +- โœ… Best quality results +- โœ… 99.9% uptime SLA +- โœ… Scalable infrastructure + +[Try Studio API โ†’](https://withoutbg.com/remove-background) + +## ๐Ÿ“š Documentation + +- **[API Reference](https://withoutbg.com/documentation)** - Complete API documentation +- **[Model Guide]()** - Choosing the right model +- **[Integration Examples]()** - Real-world usage patterns +- **[Quality Benchmarks]()** - Quality metrics and comparisons + +## ๐Ÿ› Issues & Support + +- **Bug Reports**: [GitHub Issues](https://github.com/withoutbg/withoutbg/issues) +- **Feature Requests**: [GitHub Discussions](https://github.com/withoutbg/withoutbg/discussions) +- **Commercial Support**: [contact@withoutbg.com](mailto:contact@withoutbg.com) + +## ๐Ÿค— Hugging Face + +Find our models on Hugging Face: +- **[withoutbg/snap](https://huggingface.co/withoutbg/snap)** - Open source Snap model +- **[Demo Widget](https://huggingface.co/spaces/withoutbg/demo)** - Try online + + +## ๐Ÿ“„ License + +This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. + +### Third-Party Components +- **Depth Anything**: Apache 2.0 License + +See [THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md) for complete attribution. + +## ๐ŸŒŸ Contributing + +We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. + +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Add tests +5. Submit a pull request + +## ๐Ÿ“ˆ Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=withoutbg/withoutbg&type=Date)](https://star-history.com/#withoutbg/withoutbg&Date) + +--- + +**[๐ŸŽฏ Get best quality results with withoutbg.com](https://withoutbg.com)** \ No newline at end of file diff --git a/THIRD_PARTY_LICENSES.md b/THIRD_PARTY_LICENSES.md new file mode 100644 index 0000000..ad0f0dd --- /dev/null +++ b/THIRD_PARTY_LICENSES.md @@ -0,0 +1,59 @@ +# Third-Party Licenses + +This project uses the following third-party components: + +## Depth Anything V2 + +**Repository**: https://github.com/DepthAnything/Depth-Anything-V2 +**License**: Apache License 2.0 +**Usage**: Used as part of the background removal pipeline (Small model only) + +``` +Copyright (c) 2024 Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +``` + +## Python Dependencies + +The following Python packages are used by this project. Please refer to their respective licenses: + +- **numpy**: BSD License +- **pillow**: PIL License (MIT-style) +- **onnxruntime**: MIT License +- **requests**: Apache License 2.0 +- **click**: BSD License +- **tqdm**: MIT License +- **huggingface-hub**: Apache License 2.0 + +For complete license text of dependencies, use: +```bash +pip-licenses --format=markdown --output-file=DEPENDENCY_LICENSES.md +``` + +## Attribution Requirements + +As required by the Apache 2.0 license, any distribution of this software must: + +1. Include a copy of the Apache License 2.0 (LICENSE file) +2. Include this THIRD_PARTY_LICENSES.md file +3. Retain all copyright notices from the original authors +4. Include attribution to withoutbg.com in any public usage + +## Model Weights Attribution + +The ONNX model weights included in this distribution are derived from the above mentioned repositories and are subject to their respective licenses. Users redistributing these weights must comply with the original license terms. + +--- + +For questions about licensing, contact: contact@withoutbg.com \ No newline at end of file diff --git a/examples/basic_usage.py b/examples/basic_usage.py new file mode 100644 index 0000000..ff9355c --- /dev/null +++ b/examples/basic_usage.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +"""Basic usage examples for withoutbg.""" + +import withoutbg +from PIL import Image + + +def basic_local_processing(): + """Example: Basic background removal with local Snap model.""" + print("๐ŸŸข Example: Local processing with Snap model") + + # Remove background using local model (free) + result = withoutbg.remove_background("input.jpg") + result.save("output_snap.png") + + print("โœ… Background removed and saved as output_snap.png") + print("๐ŸŽฏ Want best quality? Try withoutbg.com") + + +def api_processing(): + """Example: Background removal with Studio API.""" + print("\n๐Ÿ”ด Example: Cloud processing with Studio API") + + # Set your API key (get one at withoutbg.com) + API_KEY = "sk_your_api_key_here" + + try: + # Remove background using Studio API (best quality) + result = withoutbg.remove_background("input.jpg", api_key=API_KEY) + result.save("output_studio.png") + + print("โœ… Background removed with Studio API") + print("๐ŸŽฏ Best quality processing") + + except withoutbg.APIError as e: + print(f"โŒ API Error: {e}") + print("๐Ÿ’ก Get your API key at https://withoutbg.com") + + +def batch_processing(): + """Example: Process multiple images at once.""" + print("\n๐Ÿ“ฆ Example: Batch processing") + + # List of images to process + image_files = ["photo1.jpg", "photo2.jpg", "photo3.jpg"] + + # Process all images and save to directory + results = withoutbg.remove_background_batch( + image_files, + output_dir="batch_results/" + ) + + print(f"โœ… Processed {len(results)} images") + print("๐Ÿ“ Results saved in batch_results/ directory") + + +def e_commerce_example(): + """Example: E-commerce product photos.""" + print("\n๐Ÿ›๏ธ Example: E-commerce product processing") + + from pathlib import Path + + # Process entire product catalog + product_dir = Path("products/") + if product_dir.exists(): + product_images = list(product_dir.glob("*.jpg")) + + # Use API for best quality in production + results = withoutbg.remove_background_batch( + product_images, + output_dir="catalog-withoutbg/", + api_key="sk_your_key" # Replace with your key + ) + + print(f"โœ… Processed {len(results)} product images") + else: + print("๐Ÿ“ Create a 'products/' directory with images to try this example") + + +def custom_background_example(): + """Example: Add custom background after removal.""" + print("\n๐ŸŽจ Example: Custom background replacement") + + # Remove background + foreground = withoutbg.remove_background("portrait.jpg") + + # Load custom background + background = Image.open("gradient_background.jpg") + + # Resize background to match foreground + background = background.resize(foreground.size, Image.Resampling.LANCZOS) + + # Composite the images + if foreground.mode == 'RGBA': + background.paste(foreground, (0, 0), foreground) # Use alpha as mask + else: + background.paste(foreground, (0, 0)) + + background.save("custom_background_result.jpg") + print("โœ… Custom background applied and saved") + + +def quality_comparison(): + """Example: Compare different model tiers.""" + print("\n๐Ÿ† Example: Quality comparison") + + input_image = "test_image.jpg" + + # Snap model (local, free) + print("Processing with Snap model...") + snap_result = withoutbg.remove_background(input_image) + snap_result.save("comparison_snap.png") + + # Studio model (API, best quality) + print("Processing with Studio API...") + try: + studio_result = withoutbg.remove_background( + input_image, + api_key="sk_your_key" + ) + studio_result.save("comparison_studio.png") + + print("โœ… Comparison complete!") + print("๐Ÿ“Š Compare results:") + print(" - comparison_snap.png (Snap model)") + print(" - comparison_studio.png (Studio API)") + + except withoutbg.APIError: + print("โš ๏ธ Studio API requires valid API key") + + +if __name__ == "__main__": + print("๐ŸŽญ withoutbg Examples") + print("=" * 50) + + # Run examples (comment out as needed) + basic_local_processing() + # api_processing() # Uncomment and add your API key + # batch_processing() + # e_commerce_example() + # custom_background_example() + # quality_comparison() + + print("\n๐ŸŽฏ Get best quality results at https://withoutbg.com") \ No newline at end of file diff --git a/examples/custom_backgrounds.py b/examples/custom_backgrounds.py new file mode 100644 index 0000000..3e77e2e --- /dev/null +++ b/examples/custom_backgrounds.py @@ -0,0 +1,283 @@ +#!/usr/bin/env python3 +"""Custom background replacement examples.""" + +import withoutbg +from PIL import Image, ImageDraw, ImageFilter +import numpy as np +from pathlib import Path + + +def solid_color_background(): + """Replace background with solid color.""" + print("๐ŸŽจ Example: Solid color background") + + # Remove original background + foreground = withoutbg.remove_background("portrait.jpg") + + # Create solid color backgrounds + colors = [ + (255, 255, 255), # White + (0, 0, 0), # Black + (70, 130, 180), # Steel Blue + (255, 182, 193), # Light Pink + (144, 238, 144), # Light Green + ] + + for i, color in enumerate(colors): + # Create background + background = Image.new('RGB', foreground.size, color) + + # Composite + if foreground.mode == 'RGBA': + background.paste(foreground, (0, 0), foreground) + else: + background.paste(foreground, (0, 0)) + + # Save + color_name = ['white', 'black', 'blue', 'pink', 'green'][i] + background.save(f"portrait_{color_name}_bg.jpg", quality=95) + + print("โœ… Solid color backgrounds created") + + +def gradient_background(): + """Create gradient backgrounds.""" + print("\n๐ŸŒˆ Example: Gradient backgrounds") + + foreground = withoutbg.remove_background("portrait.jpg") + width, height = foreground.size + + # Create different gradients + gradients = [ + # Vertical blue to purple + {"start": (0, 100, 255), "end": (128, 0, 255), "direction": "vertical"}, + # Horizontal orange to pink + {"start": (255, 165, 0), "end": (255, 20, 147), "direction": "horizontal"}, + # Diagonal green to blue + {"start": (0, 255, 127), "end": (0, 191, 255), "direction": "diagonal"}, + ] + + for i, grad in enumerate(gradients): + # Create gradient + background = create_gradient(width, height, grad["start"], grad["end"], grad["direction"]) + + # Composite + if foreground.mode == 'RGBA': + background.paste(foreground, (0, 0), foreground) + else: + background.paste(foreground, (0, 0)) + + # Save + background.save(f"portrait_gradient_{i+1}.jpg", quality=95) + + print("โœ… Gradient backgrounds created") + + +def create_gradient(width, height, start_color, end_color, direction): + """Create a gradient image.""" + background = Image.new('RGB', (width, height)) + draw = ImageDraw.Draw(background) + + if direction == "vertical": + for y in range(height): + ratio = y / height + r = int(start_color[0] * (1 - ratio) + end_color[0] * ratio) + g = int(start_color[1] * (1 - ratio) + end_color[1] * ratio) + b = int(start_color[2] * (1 - ratio) + end_color[2] * ratio) + draw.line([(0, y), (width, y)], fill=(r, g, b)) + + elif direction == "horizontal": + for x in range(width): + ratio = x / width + r = int(start_color[0] * (1 - ratio) + end_color[0] * ratio) + g = int(start_color[1] * (1 - ratio) + end_color[1] * ratio) + b = int(start_color[2] * (1 - ratio) + end_color[2] * ratio) + draw.line([(x, 0), (x, height)], fill=(r, g, b)) + + return background + + +def scenic_background(): + """Replace with scenic/nature backgrounds.""" + print("\n๐Ÿž๏ธ Example: Scenic backgrounds") + + foreground = withoutbg.remove_background("portrait.jpg") + + # List of scenic backgrounds (you'd have these images) + scenic_files = [ + "beach_sunset.jpg", + "mountain_landscape.jpg", + "city_skyline.jpg", + "forest_path.jpg" + ] + + for scenic_file in scenic_files: + if Path(scenic_file).exists(): + # Load scenic background + scenic_bg = Image.open(scenic_file) + + # Resize to match foreground + scenic_bg = scenic_bg.resize(foreground.size, Image.Resampling.LANCZOS) + + # Optional: Blur background slightly for depth effect + scenic_bg = scenic_bg.filter(ImageFilter.GaussianBlur(radius=1.5)) + + # Composite + if foreground.mode == 'RGBA': + scenic_bg.paste(foreground, (0, 0), foreground) + else: + scenic_bg.paste(foreground, (0, 0)) + + # Save + output_name = f"portrait_{Path(scenic_file).stem}.jpg" + scenic_bg.save(output_name, quality=95) + + print(f"โœ… Created {output_name}") + else: + print(f"โš ๏ธ Scenic background not found: {scenic_file}") + + +def professional_studio_background(): + """Create professional studio-style backgrounds.""" + print("\n๐Ÿ“ธ Example: Professional studio backgrounds") + + foreground = withoutbg.remove_background("portrait.jpg") + width, height = foreground.size + + # Studio background 1: Soft gray with vignette + studio_bg1 = Image.new('RGB', (width, height), (240, 240, 240)) + + # Add subtle vignette effect + mask = Image.new('L', (width, height), 0) + draw = ImageDraw.Draw(mask) + + # Create radial gradient for vignette + center_x, center_y = width // 2, height // 2 + max_radius = min(width, height) // 2 + + for radius in range(max_radius, 0, -5): + opacity = int(255 * (1 - radius / max_radius) * 0.3) + draw.ellipse([ + center_x - radius, center_y - radius, + center_x + radius, center_y + radius + ], fill=opacity) + + # Apply vignette + vignette = Image.new('RGB', (width, height), (200, 200, 200)) + studio_bg1 = Image.composite(vignette, studio_bg1, mask) + + # Composite with foreground + if foreground.mode == 'RGBA': + studio_bg1.paste(foreground, (0, 0), foreground) + + studio_bg1.save("portrait_studio_gray.jpg", quality=95) + + # Studio background 2: Classic white with shadow + studio_bg2 = Image.new('RGB', (width, height), (255, 255, 255)) + + # Add subtle shadow effect + shadow = Image.new('RGBA', (width, height), (0, 0, 0, 0)) + shadow_draw = ImageDraw.Draw(shadow) + + # Create drop shadow (simplified) + if foreground.mode == 'RGBA': + # Create shadow from alpha channel + alpha = foreground.split()[-1] + shadow_offset = 5 + shadow_img = Image.new('RGBA', (width, height), (0, 0, 0, 0)) + shadow_img.paste((50, 50, 50, 100), (shadow_offset, shadow_offset), alpha) + shadow_img = shadow_img.filter(ImageFilter.GaussianBlur(radius=3)) + + # Composite shadow, then foreground + studio_bg2 = Image.alpha_composite( + studio_bg2.convert('RGBA'), + shadow_img + ).convert('RGB') + studio_bg2.paste(foreground, (0, 0), foreground) + else: + studio_bg2.paste(foreground, (0, 0)) + + studio_bg2.save("portrait_studio_white.jpg", quality=95) + + print("โœ… Professional studio backgrounds created") + + +def social_media_templates(): + """Create social media ready images with backgrounds.""" + print("\n๐Ÿ“ฑ Example: Social media templates") + + foreground = withoutbg.remove_background("portrait.jpg") + + # Instagram square format + instagram_size = (1080, 1080) + instagram_bg = Image.new('RGB', instagram_size, (255, 182, 193)) # Light pink + + # Center the foreground + fg_ratio = min(800 / foreground.width, 800 / foreground.height) + new_fg_size = (int(foreground.width * fg_ratio), int(foreground.height * fg_ratio)) + foreground_resized = foreground.resize(new_fg_size, Image.Resampling.LANCZOS) + + paste_x = (instagram_size[0] - new_fg_size[0]) // 2 + paste_y = (instagram_size[1] - new_fg_size[1]) // 2 + + if foreground_resized.mode == 'RGBA': + instagram_bg.paste(foreground_resized, (paste_x, paste_y), foreground_resized) + else: + instagram_bg.paste(foreground_resized, (paste_x, paste_y)) + + instagram_bg.save("portrait_instagram.jpg", quality=95) + + # LinkedIn banner format + linkedin_size = (1584, 396) + linkedin_bg = Image.new('RGB', linkedin_size, (0, 119, 181)) # LinkedIn blue + + # Position portrait on left side + fg_ratio = min(350 / foreground.width, 350 / foreground.height) + new_fg_size = (int(foreground.width * fg_ratio), int(foreground.height * fg_ratio)) + foreground_resized = foreground.resize(new_fg_size, Image.Resampling.LANCZOS) + + paste_x = 50 + paste_y = (linkedin_size[1] - new_fg_size[1]) // 2 + + if foreground_resized.mode == 'RGBA': + linkedin_bg.paste(foreground_resized, (paste_x, paste_y), foreground_resized) + else: + linkedin_bg.paste(foreground_resized, (paste_x, paste_y)) + + linkedin_bg.save("portrait_linkedin_banner.jpg", quality=95) + + print("โœ… Social media templates created") + print(" - portrait_instagram.jpg (1080x1080)") + print(" - portrait_linkedin_banner.jpg (1584x396)") + + +if __name__ == "__main__": + print("๐ŸŽจ Custom Background Examples") + print("=" * 50) + + # Check if input image exists + if not Path("portrait.jpg").exists(): + print("โŒ Please add a 'portrait.jpg' image to try these examples") + print("๐Ÿ’ก Any portrait photo will work!") + exit(1) + + try: + # Run all examples + solid_color_background() + gradient_background() + scenic_background() + professional_studio_background() + social_media_templates() + + print("\nโœ… All background examples complete!") + print("๐ŸŽฏ Perfect for:") + print(" - E-commerce product photos") + print(" - Professional headshots") + print(" - Social media content") + print(" - Marketing materials") + + except Exception as e: + print(f"โŒ Error: {e}") + print("๐Ÿ’ก Make sure you have a 'portrait.jpg' file in this directory") + + print("\n๐ŸŽฏ Get best quality processing at https://withoutbg.com") \ No newline at end of file diff --git a/examples/e_commerce_batch.py b/examples/e_commerce_batch.py new file mode 100644 index 0000000..8533f11 --- /dev/null +++ b/examples/e_commerce_batch.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 +"""E-commerce batch processing example.""" + +import withoutbg +from pathlib import Path +import time +from PIL import Image +import json + + +def process_product_catalog(): + """Process an entire product catalog for e-commerce.""" + print("๐Ÿ›๏ธ E-commerce Product Catalog Processing") + print("=" * 50) + + # Configuration + input_dir = Path("product_catalog") + output_dir = Path("catalog-withoutbg") + api_key = "sk_your_api_key_here" # Replace with your key + + # Create directories if they don't exist + output_dir.mkdir(exist_ok=True) + + # Find all product images + image_extensions = ['.jpg', '.jpeg', '.png', '.webp'] + product_images = [] + + for ext in image_extensions: + product_images.extend(input_dir.glob(f"*{ext}")) + product_images.extend(input_dir.glob(f"*{ext.upper()}")) + + if not product_images: + print(f"โŒ No images found in {input_dir}") + print("๐Ÿ’ก Create the directory and add some product photos to try this example") + return + + print(f"๐Ÿ“ฆ Found {len(product_images)} product images") + + # Process with timing + start_time = time.time() + + try: + # Option 1: Use API for best quality (recommended for production) + print("๐Ÿ”ด Processing with Studio API (best quality)...") + results = withoutbg.remove_background_batch( + product_images, + output_dir=output_dir, + api_key=api_key + ) + + processing_method = "Studio API" + + except withoutbg.APIError as e: + print(f"โš ๏ธ API Error: {e}") + print("๐ŸŸข Falling back to local Snap model...") + + # Option 2: Fallback to local processing + results = withoutbg.remove_background_batch( + product_images, + output_dir=output_dir + ) + + processing_method = "Local Snap" + + end_time = time.time() + total_time = end_time - start_time + + # Generate report + print("\n๐Ÿ“Š Processing Report") + print("-" * 30) + print(f"Images processed: {len(results)}") + print(f"Processing method: {processing_method}") + print(f"Total time: {total_time:.2f} seconds") + print(f"Average per image: {total_time/len(results):.2f} seconds") + print(f"Output directory: {output_dir}") + + # Save processing report + report = { + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), + "images_processed": len(results), + "processing_method": processing_method, + "total_time_seconds": round(total_time, 2), + "average_time_per_image": round(total_time/len(results), 2), + "input_directory": str(input_dir), + "output_directory": str(output_dir) + } + + with open(output_dir / "processing_report.json", "w") as f: + json.dump(report, f, indent=2) + + print(f"๐Ÿ“„ Report saved: {output_dir}/processing_report.json") + + +def optimize_for_web(): + """Optimize processed images for web use.""" + print("\n๐ŸŒ Web Optimization") + print("-" * 30) + + output_dir = Path("catalog-withoutbg") + web_dir = Path("catalog_web") + web_dir.mkdir(exist_ok=True) + + # Find processed images + processed_images = list(output_dir.glob("*.png")) + + if not processed_images: + print("โŒ No processed images found. Run process_product_catalog() first.") + return + + print(f"๐Ÿ”ง Optimizing {len(processed_images)} images for web...") + + for img_path in processed_images: + try: + # Load image + img = Image.open(img_path) + + # Create different sizes for responsive design + sizes = { + "thumbnail": (150, 150), + "small": (300, 300), + "medium": (600, 600), + "large": (1200, 1200) + } + + stem = img_path.stem + + for size_name, dimensions in sizes.items(): + # Resize maintaining aspect ratio + img_copy = img.copy() + img_copy.thumbnail(dimensions, Image.Resampling.LANCZOS) + + # Save as WebP for better compression + webp_path = web_dir / f"{stem}_{size_name}.webp" + img_copy.save(webp_path, "WebP", quality=85, method=6) + + # Also save PNG for compatibility + png_path = web_dir / f"{stem}_{size_name}.png" + img_copy.save(png_path, "PNG", optimize=True) + + except Exception as e: + print(f"โš ๏ธ Failed to process {img_path}: {e}") + + print(f"โœ… Web-optimized images saved to {web_dir}") + + +def generate_html_preview(): + """Generate HTML preview of processed products.""" + print("\n๐Ÿ“„ Generating HTML Preview") + print("-" * 30) + + web_dir = Path("catalog_web") + thumbnail_images = list(web_dir.glob("*_thumbnail.webp")) + + if not thumbnail_images: + print("โŒ No thumbnail images found. Run optimize_for_web() first.") + return + + html_content = """ + + + + + + Product Catalog - Background Removed + + + +
+

๐Ÿ›๏ธ Product Catalog

+

Backgrounds removed with withoutbg

+
AI-Powered
+
+ +
+""" + + for img_path in sorted(thumbnail_images): + product_name = img_path.stem.replace("_thumbnail", "").replace("_", " ").title() + + html_content += f""" +
+ {product_name} +

{product_name}

+
No Background
+
+""" + + html_content += """ +
+ +
+

Processed with withoutbg

+

๐ŸŽฏ Get best quality results with Studio API

+
+ + +""" + + html_path = web_dir / "index.html" + with open(html_path, "w", encoding="utf-8") as f: + f.write(html_content) + + print(f"โœ… HTML preview generated: {html_path}") + print(f"๐ŸŒ Open {html_path} in your browser to view the catalog") + + +if __name__ == "__main__": + print("๐Ÿ›๏ธ E-commerce Batch Processing Example") + print("=" * 50) + + # Step 1: Process product catalog + process_product_catalog() + + # Step 2: Optimize for web + optimize_for_web() + + # Step 3: Generate HTML preview + generate_html_preview() + + print("\nโœ… Complete! Your product catalog is ready for e-commerce.") + print("๐ŸŽฏ Get best quality processing at https://withoutbg.com") \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..eebf166 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,170 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "withoutbg" +dynamic = ["version"] +description = "AI-powered background removal with local and cloud options" +readme = "README.md" +requires-python = ">=3.9" +license = {text = "Apache-2.0"} +authors = [ + {name = "withoutbg", email = "contact@withoutbg.com"} +] +keywords = [ + "background-removal", + "computer-vision", + "image-processing", + "ai", + "onnx" +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Multimedia :: Graphics :: Graphics Conversion", +] +dependencies = [ + "numpy>=1.21.0", + "pillow>=8.0.0", + "onnxruntime>=1.12.0,<1.20.0", + "requests>=2.25.0", + "click>=8.0.0", + "tqdm>=4.60.0", + "huggingface-hub>=0.33.5", +] + +[project.optional-dependencies] +gpu = ["onnxruntime-gpu>=1.12.0,<1.20.0"] +dev = [ + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "black>=22.0.0", + "ruff>=0.1.0", + "mypy>=1.0.0", + "pre-commit>=2.20.0", +] + +[project.urls] +Homepage = "https://withoutbg.com" +Repository = "https://github.com/withoutbg/withoutbg" +Documentation = "https://withoutbg.com/documentation" +"Bug Reports" = "https://github.com/withoutbg/withoutbg/issues" + +[project.scripts] +withoutbg = "withoutbg.cli:main" + +[tool.hatch.version] +path = "src/withoutbg/__version__.py" + +[tool.hatch.envs.default] +dependencies = [ + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "black>=22.0.0", + "ruff>=0.1.0", + "mypy>=1.0.0", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/withoutbg"] + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", + "/README.md", + "/LICENSE", + "/pyproject.toml", +] + +[tool.black] +line-length = 88 +target-version = ['py39'] + +[tool.ruff] +line-length = 88 +target-version = "py39" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade +] + +[tool.mypy] +python_version = "3.9" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_unreachable = true +strict_equality = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +addopts = [ + "--cov=src/withoutbg", + "--cov-report=term-missing", + "--cov-report=html", + "--cov-fail-under=80", + "--strict-markers", + "--disable-warnings" +] +markers = [ + "unit: Unit tests (fast, isolated)", + "integration: Integration tests (slower, multiple components)", + "performance: Performance and benchmark tests (very slow)", + "api: Tests requiring API access (may be skipped)", + "slow: Slow running tests", +] +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::PendingDeprecationWarning", +] + +[tool.coverage.run] +source = ["src"] +omit = ["*/tests/*"] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "def __repr__", + "raise AssertionError", + "raise NotImplementedError", +] + +[dependency-groups] +dev = [ + "black>=24.8.0", + "hatch>=1.14.1", + "mypy>=1.14.1", + "pre-commit>=3.5.0", + "pytest>=8.3.5", + "pytest-cov>=5.0.0", + "ruff>=0.12.5", + "types-pillow>=10.2.0.20240822", + "types-requests>=2.32.4.20250809", +] diff --git a/src/withoutbg/__init__.py b/src/withoutbg/__init__.py new file mode 100644 index 0000000..e086f87 --- /dev/null +++ b/src/withoutbg/__init__.py @@ -0,0 +1,18 @@ +"""withoutbg: AI-powered background removal with local and cloud options.""" + +from .__version__ import __version__ +from .api import StudioAPI +from .core import remove_background, remove_background_batch +from .exceptions import APIError, ModelNotFoundError, WithoutBGError +from .models import SnapModel + +__all__ = [ + "remove_background", + "remove_background_batch", + "SnapModel", + "StudioAPI", + "WithoutBGError", + "ModelNotFoundError", + "APIError", + "__version__", +] diff --git a/src/withoutbg/__version__.py b/src/withoutbg/__version__.py new file mode 100644 index 0000000..15b62ef --- /dev/null +++ b/src/withoutbg/__version__.py @@ -0,0 +1,3 @@ +"""Version information for withoutbg.""" + +__version__ = "0.1.0" diff --git a/src/withoutbg/api.py b/src/withoutbg/api.py new file mode 100644 index 0000000..408ea13 --- /dev/null +++ b/src/withoutbg/api.py @@ -0,0 +1,238 @@ +"""Studio API client for cloud-based background removal.""" + +import base64 +import io +from pathlib import Path +from typing import Any, Optional, Union + +import requests +from PIL import Image + +from .exceptions import APIError + + +class StudioAPI: + """Client for withoutbg Studio API.""" + + def __init__( + self, api_key: Optional[str] = None, base_url: str = "https://api.withoutbg.com" + ): + """Initialize Studio API client. + + Args: + api_key: API key for authentication + base_url: Base URL for API endpoints + """ + self.api_key = api_key + self.base_url = base_url.rstrip("/") + self.session = requests.Session() + + if api_key: + self.session.headers.update({"X-API-Key": api_key}) + + def _encode_image(self, image: Union[str, Path, Image.Image, bytes]) -> str: + """Encode image to base64 for API transmission.""" + if isinstance(image, (str, Path)): + with open(image, "rb") as f: + image_bytes = f.read() + elif isinstance(image, Image.Image): + buffer = io.BytesIO() + image.save(buffer, format="PNG") + image_bytes = buffer.getvalue() + elif isinstance(image, bytes): + image_bytes = image + else: + raise APIError(f"Unsupported image type: {type(image)}") + + return base64.b64encode(image_bytes).decode("utf-8") + + def _decode_image(self, base64_string: str) -> Image.Image: + """Decode base64 string to PIL Image.""" + image_bytes = base64.b64decode(base64_string) + return Image.open(io.BytesIO(image_bytes)) + + def _resize_for_api( + self, image: Image.Image, max_size: int = 1024 + ) -> tuple[Image.Image, tuple[int, int]]: + """Resize image for API transmission while preserving aspect ratio. + + Args: + image: Input PIL Image + max_size: Maximum dimension size (default 1024) + + Returns: + Tuple of (resized_image, original_size) + """ + original_size = image.size + width, height = original_size + + # If image is already smaller than max_size, return as-is + if max(width, height) <= max_size: + return image, original_size + + # Calculate new dimensions preserving aspect ratio + if width > height: + new_width = max_size + new_height = int((height * max_size) / width) + else: + new_height = max_size + new_width = int((width * max_size) / height) + + resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + return resized_image, original_size + + def _apply_alpha_channel( + self, original_image: Image.Image, alpha_image: Image.Image + ) -> Image.Image: + """Apply alpha channel to original image to remove background. + + Args: + original_image: Original RGB/RGBA image + alpha_image: Grayscale alpha channel image + + Returns: + RGBA image with background removed + """ + # Ensure original image is in RGB mode + if original_image.mode != "RGB": + original_image = original_image.convert("RGB") + + # Ensure alpha image is grayscale + if alpha_image.mode != "L": + alpha_image = alpha_image.convert("L") + + # Resize alpha to match original image if needed + if alpha_image.size != original_image.size: + alpha_image = alpha_image.resize( + original_image.size, Image.Resampling.LANCZOS + ) + + # Create RGBA image by adding alpha channel + rgba_image = original_image.copy() + rgba_image.putalpha(alpha_image) + + return rgba_image + + def remove_background( + self, input_image: Union[str, Path, Image.Image, bytes], **kwargs: Any + ) -> Image.Image: + """Remove background using Studio API. + + Args: + input_image: Input image + **kwargs: Additional API parameters + + Returns: + PIL Image with background removed + + Raises: + APIError: If API request fails + """ + if not self.api_key: + raise APIError("API key required for Studio service") + + try: + # Store original image for local alpha application + if isinstance(input_image, (str, Path)): + original_image = Image.open(input_image) + elif isinstance(input_image, Image.Image): + original_image = input_image.copy() + elif isinstance(input_image, bytes): + original_image = Image.open(io.BytesIO(input_image)) + else: + raise APIError(f"Unsupported image type: {type(input_image)}") + + # Resize image for API transmission to optimize latency + api_image, original_size = self._resize_for_api(original_image) + + # Encode resized image + encoded_image = self._encode_image(api_image) + + # Prepare request for base64 endpoint + payload = {"image_base64": encoded_image} + + # Make API request to base64 endpoint + response = self.session.post( + f"{self.base_url}/v1.0/alpha-channel-base64", json=payload, timeout=30 + ) + + if response.status_code == 401: + raise APIError("Invalid API key") + elif response.status_code == 429: + raise APIError("Rate limit exceeded (7 requests per minute)") + elif response.status_code == 402: + raise APIError( + "Insufficient credits. Get more credits at https://withoutbg.com/login" + ) + elif response.status_code == 403: + raise APIError( + "Credits expired. Top up your account to reactivate " + "frozen credits at https://withoutbg.com/login" + ) + elif not response.ok: + try: + error_msg = response.json().get("error", "Unknown API error") + except Exception: + error_msg = f"HTTP {response.status_code}: {response.text}" + raise APIError(f"API request failed: {error_msg}") + + # Decode response + result_data = response.json() + + if "alpha_base64" not in result_data: + raise APIError( + f"Invalid API response format\n\n" + f"More info: sample response: {result_data}" + ) + + # Decode alpha channel + alpha_image = self._decode_image(result_data["alpha_base64"]) + + # Resize alpha channel back to original dimensions if needed + if alpha_image.size != original_size: + alpha_image = alpha_image.resize( + original_size, Image.Resampling.LANCZOS + ) + + # Apply alpha channel to original image locally + return self._apply_alpha_channel(original_image, alpha_image) + + except requests.RequestException as e: + raise APIError(f"Network error: {str(e)}") from e + except Exception as e: + if isinstance(e, APIError): + raise + raise APIError(f"Unexpected error: {str(e)}") from e + + def get_usage(self) -> dict[str, Any]: + """Get current API usage statistics. + + Returns: + Dictionary with usage information + """ + if not self.api_key: + raise APIError("API key required") + + try: + response = self.session.get(f"{self.base_url}/available-credit") + response.raise_for_status() + result: dict[str, Any] = response.json() + return result + + except requests.RequestException as e: + raise APIError(f"Failed to get usage: {str(e)}") from e + + def get_models(self) -> dict[str, Any]: + """Get available models and their capabilities. + + Returns: + Dictionary with model information + """ + try: + response = self.session.get(f"{self.base_url}/v1/models") + response.raise_for_status() + result: dict[str, Any] = response.json() + return result + + except requests.RequestException as e: + raise APIError(f"Failed to get models: {str(e)}") from e diff --git a/src/withoutbg/cli.py b/src/withoutbg/cli.py new file mode 100644 index 0000000..5958c52 --- /dev/null +++ b/src/withoutbg/cli.py @@ -0,0 +1,264 @@ +"""Command-line interface for withoutbg.""" + +import sys +from pathlib import Path +from typing import Any, Optional + +import click +from click._termui_impl import ProgressBar +from PIL import Image + +from . import __version__, remove_background +from .exceptions import WithoutBGError + + +@click.command() +@click.argument("input_path", type=click.Path(exists=True, path_type=Path)) +@click.option( + "--output", + "-o", + type=click.Path(path_type=Path), + help="Output file path (default: adds -withoutbg suffix)", +) +@click.option( + "--api-key", + envvar="WITHOUTBG_API_KEY", + help="API key for Studio service (or set WITHOUTBG_API_KEY env var)", +) +@click.option( + "--use-api", is_flag=True, help="Use Studio API instead of local Snap model" +) +@click.option( + "--model", + default="snap", + type=click.Choice(["snap", "studio"]), + help="Model to use (snap=local, studio=API)", +) +@click.option( + "--batch", + is_flag=True, + help="Process all images in directory (if input is directory)", +) +@click.option( + "--output-dir", + type=click.Path(path_type=Path), + help="Output directory for batch processing", +) +@click.option( + "--format", + type=click.Choice(["png", "jpg", "webp"]), + default="png", + help="Output image format", +) +@click.option("--quality", type=int, default=95, help="Output quality for JPEG (1-100)") +@click.option("--verbose", "-v", is_flag=True, help="Enable verbose output") +@click.version_option(version=__version__) +def main( + input_path: Path, + output: Optional[Path], + api_key: Optional[str], + use_api: bool, + model: str, + batch: bool, + output_dir: Optional[Path], + format: str, + quality: int, + verbose: bool, +) -> None: + """Remove background from images using AI. + + Examples: + + # Process single image with local Snap model + withoutbg image.jpg + + # Use Studio API for best quality processing + withoutbg image.jpg --use-api --api-key sk_... + + # Process all images in directory + withoutbg photos/ --batch --output-dir results/ + + # Specify output format and quality + withoutbg image.jpg --format jpg --quality 90 + """ + + try: + # Determine if using API + using_api = use_api or api_key or model == "studio" + + if using_api and not api_key: + click.echo("Error: API key required when using Studio service", err=True) + click.echo( + "Set WITHOUTBG_API_KEY environment variable or use --api-key", err=True + ) + sys.exit(1) + + # Configure model + actual_model = "studio" if using_api else "snap" + + if verbose: + mode = "Studio API" if using_api else "Local Snap model" + click.echo(f"Using {mode} for processing...") + + # Process images + if batch or input_path.is_dir(): + _process_batch( + input_path, output_dir, api_key, actual_model, format, quality, verbose + ) + else: + _process_single( + input_path, output, api_key, actual_model, format, quality, verbose + ) + + if verbose: + api_msg = "" + if not using_api: + api_msg = " (Want best quality? Try withoutbg.com)" + click.echo(f"โœ… Processing complete!{api_msg}") + + except WithoutBGError as e: + click.echo(f"Error: {e}", err=True) + sys.exit(1) + except KeyboardInterrupt: + click.echo("\nโŒ Processing cancelled", err=True) + sys.exit(1) + except Exception as e: + click.echo(f"Unexpected error: {e}", err=True) + sys.exit(1) + + +def _process_single( + input_path: Path, + output_path: Optional[Path], + api_key: Optional[str], + model: str, + format: str, + quality: int, + verbose: bool, +) -> None: + """Process a single image.""" + + if not output_path: + # Generate output filename + stem = input_path.stem + output_path = input_path.parent / f"{stem}-withoutbg.{format}" + + if verbose: + click.echo(f"Processing: {input_path}") + click.echo(f"Output: {output_path}") + + # Remove background + bar: ProgressBar + with click.progressbar(length=1, label="Removing background") as bar: + result = remove_background(input_path, api_key=api_key, model_name=model) + bar.update(1) + + # Save result + save_kwargs: dict[str, Any] = {} + if format.lower() == "jpg": + # Convert RGBA to RGB for JPEG + if result.mode == "RGBA": + background = Image.new("RGB", result.size, (255, 255, 255)) + background.paste(result, mask=result.split()[-1]) # Use alpha as mask + result = background + save_kwargs["quality"] = quality + save_kwargs["optimize"] = True + elif format.lower() == "webp": + save_kwargs["quality"] = quality + save_kwargs["method"] = 6 # Best compression + + # Map format names to PIL format strings + pil_format = {"jpg": "JPEG", "jpeg": "JPEG", "png": "PNG", "webp": "WEBP"} + result.save( + output_path, + format=pil_format.get(format.lower(), format.upper()), + **save_kwargs, + ) + + +def _process_batch( + input_dir: Path, + output_dir: Optional[Path], + api_key: Optional[str], + model: str, + format: str, + quality: int, + verbose: bool, +) -> None: + """Process multiple images in a directory.""" + + # Find image files + image_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} + + if input_dir.is_file(): + # Single file specified with --batch flag + image_files = [input_dir] + input_dir = input_dir.parent + else: + # Directory specified + image_files = [ + f + for f in input_dir.iterdir() + if f.is_file() and f.suffix.lower() in image_extensions + ] + + if not image_files: + click.echo("No image files found in directory", err=True) + sys.exit(1) + + # Set up output directory + if not output_dir: + output_dir = input_dir / "withoutbg-results" + + output_dir.mkdir(parents=True, exist_ok=True) + + if verbose: + click.echo(f"Found {len(image_files)} images") + click.echo(f"Output directory: {output_dir}") + + # Process images + with click.progressbar(image_files, label="Processing images") as bar: + for image_file in bar: + try: + # Generate output path + output_path = output_dir / f"{image_file.stem}-withoutbg.{format}" + + # Remove background + result = remove_background( + image_file, api_key=api_key, model_name=model + ) + + # Save result + save_kwargs: dict[str, Any] = {} + if format.lower() == "jpg": + if result.mode == "RGBA": + background = Image.new("RGB", result.size, (255, 255, 255)) + background.paste(result, mask=result.split()[-1]) + result = background + save_kwargs["quality"] = quality + save_kwargs["optimize"] = True + elif format.lower() == "webp": + save_kwargs["quality"] = quality + save_kwargs["method"] = 6 + + # Map format names to PIL format strings + pil_format = { + "jpg": "JPEG", + "jpeg": "JPEG", + "png": "PNG", + "webp": "WEBP", + } + result.save( + output_path, + format=pil_format.get(format.lower(), format.upper()), + **save_kwargs, + ) + + except Exception as e: + if verbose: + click.echo(f"\nโŒ Failed to process {image_file}: {e}", err=True) + continue + + +if __name__ == "__main__": + main() diff --git a/src/withoutbg/core.py b/src/withoutbg/core.py new file mode 100644 index 0000000..36143e7 --- /dev/null +++ b/src/withoutbg/core.py @@ -0,0 +1,109 @@ +"""Core background removal functionality.""" + +from pathlib import Path +from typing import Any, Optional, Union + +from PIL import Image + +from .api import StudioAPI +from .exceptions import WithoutBGError +from .models import SnapModel + + +def remove_background( + input_image: Union[str, Path, Image.Image, bytes], + output_path: Optional[Union[str, Path]] = None, + api_key: Optional[str] = None, + model_name: str = "snap", + **kwargs: Any, +) -> Image.Image: + """Remove background from an image. + + Args: + input_image: Input image as file path, PIL Image, or bytes + output_path: Optional path to save the result + api_key: API key for Studio service (uses local Snap model if None) + model_name: Model to use ("snap" for local, "studio" for API) + **kwargs: Additional arguments passed to the model/API + + Returns: + PIL Image with background removed + + Examples: + >>> # Local processing with Snap model + >>> result = remove_background("input.jpg") + + >>> # Cloud processing with Studio API + >>> result = remove_background("input.jpg", api_key="sk_...") + + >>> # Save result directly + >>> remove_background("input.jpg", output_path="output.png") + """ + try: + if api_key or model_name == "studio": + # Use Studio API + api = StudioAPI(api_key) + result = api.remove_background(input_image, **kwargs) + else: + # Use local Snap model + model = SnapModel() + result = model.remove_background(input_image, **kwargs) + + if output_path: + result.save(output_path) + + return result + + except Exception as e: + raise WithoutBGError(f"Background removal failed: {str(e)}") from e + + +def remove_background_batch( + input_images: list[Union[str, Path, Image.Image, bytes]], + output_dir: Optional[Union[str, Path]] = None, + api_key: Optional[str] = None, + model_name: str = "snap", + **kwargs: Any, +) -> list[Image.Image]: + """Remove background from multiple images. + + Args: + input_images: List of input images + output_dir: Directory to save results (optional) + api_key: API key for Studio service + model_name: Model to use + **kwargs: Additional arguments + + Returns: + List of PIL Images with backgrounds removed + """ + results = [] + + for i, input_image in enumerate(input_images): + output_path = None + if output_dir: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Try to get original filename + if isinstance(input_image, (str, Path)): + input_path = Path(input_image) + stem = input_path.stem + suffix = input_path.suffix or ".png" + output_filename = f"{stem}-withoutbg{suffix}" + else: + # For PIL Images or bytes, use numbered fallback + output_filename = f"output_{i:04d}-withoutbg.png" + + output_path = output_dir / output_filename + + result = remove_background( + input_image, + output_path=output_path, + api_key=api_key, + model_name=model_name, + **kwargs, + ) + results.append(result) + + return results diff --git a/src/withoutbg/exceptions.py b/src/withoutbg/exceptions.py new file mode 100644 index 0000000..a862a39 --- /dev/null +++ b/src/withoutbg/exceptions.py @@ -0,0 +1,31 @@ +"""Custom exceptions for withoutbg package.""" + + +class WithoutBGError(Exception): + """Base exception for withoutbg package.""" + + pass + + +class ModelNotFoundError(WithoutBGError): + """Raised when a model file cannot be found or loaded.""" + + pass + + +class APIError(WithoutBGError): + """Raised when API requests fail.""" + + pass + + +class InvalidImageError(WithoutBGError): + """Raised when image input is invalid or corrupted.""" + + pass + + +class ConfigurationError(WithoutBGError): + """Raised when configuration is invalid.""" + + pass diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py new file mode 100644 index 0000000..e6ff83a --- /dev/null +++ b/src/withoutbg/models.py @@ -0,0 +1,475 @@ +"""Local model implementations.""" + +import io +from pathlib import Path +from typing import Any, Optional, Union + +import numpy as np +import onnxruntime as ort # type: ignore +from huggingface_hub import hf_hub_download +from PIL import Image + +from .exceptions import ModelNotFoundError, WithoutBGError + + +class SnapModel: + """Local ONNX-based background removal model (Snap tier).""" + + def __init__( + self, + depth_model_path: Optional[Union[str, Path]] = None, + matting_model_path: Optional[Union[str, Path]] = None, + refiner_model_path: Optional[Union[str, Path]] = None, + ): + """Initialize the Snap model with 3-stage pipeline. + + Args: + depth_model_path: Path to Depth Anything V2 ONNX model. If None, + downloads from HF. + matting_model_path: Path to Matting ONNX model. If None, downloads from HF. + refiner_model_path: Path to Refiner ONNX model. If None, downloads from HF. + """ + self.depth_model_path = depth_model_path or self._get_default_depth_model_path() + self.matting_model_path = ( + matting_model_path or self._get_default_matting_model_path() + ) + self.refiner_model_path = ( + refiner_model_path or self._get_default_refiner_model_path() + ) + + self.depth_session = None + self.matting_session = None + self.refiner_session = None + + self._load_models() + + def _get_default_depth_model_path(self) -> Path: + """Get path to Depth Anything V2 model from Hugging Face.""" + return self._download_from_hf( + "depth_anything_v2_vits_slim.onnx", "Depth Anything V2 model" + ) + + def _get_default_matting_model_path(self) -> Path: + """Get path to Matting model from Hugging Face.""" + return self._download_from_hf("snap_matting_0.1.0.onnx", "Snap matting model") + + def _get_default_refiner_model_path(self) -> Path: + """Get path to Refiner model from Hugging Face.""" + return self._download_from_hf("snap_refiner_0.1.0.onnx", "Snap refiner model") + + def _download_from_hf(self, filename: str, model_name: str) -> Path: + """Download model from Hugging Face Hub with caching. + + Args: + filename: Name of the model file to download + model_name: Human-readable name for error messages + + Returns: + Path to the downloaded model file + + Raises: + ModelNotFoundError: If download fails or HF Hub is not available + """ + + try: + # First try to get from cache + try: + model_path = hf_hub_download( + repo_id="withoutbg/snap", + filename=filename, + cache_dir=None, # Use default cache + local_files_only=True, # Only check cache first + ) + return Path(model_path) + except Exception: + # If not in cache, download it + print(f"Downloading {model_name} from Hugging Face...") + model_path = hf_hub_download( + repo_id="withoutbg/snap", + filename=filename, + cache_dir=None, # Use default cache + local_files_only=False, + ) + print(f"โœ“ {model_name} downloaded successfully") + return Path(model_path) + + except Exception as e: + raise ModelNotFoundError( + f"Failed to download {model_name} from Hugging Face: {str(e)}\n" + f"You can manually download models from: " + f"https://huggingface.co/withoutbg/snap" + ) from e + + def _load_models(self) -> None: + """Load all three ONNX models.""" + try: + # Configure ONNX Runtime for optimal performance + providers = ["CPUExecutionProvider"] + if ort.get_available_providers(): + # Prefer GPU if available + available = ort.get_available_providers() + if "CUDAExecutionProvider" in available: + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] + + # Load Depth Anything V2 model + self.depth_session = ort.InferenceSession( + str(self.depth_model_path), providers=providers + ) + + # Load Matting model + self.matting_session = ort.InferenceSession( + str(self.matting_model_path), providers=providers + ) + + # Load Refiner model + self.refiner_session = ort.InferenceSession( + str(self.refiner_model_path), providers=providers + ) + + except Exception as e: + raise ModelNotFoundError(f"Failed to load models: {str(e)}") from e + + def _constrain_to_multiple_of( + self, + x: float, + ensure_multiple_of: int, + min_val: int = 0, + max_val: Optional[int] = None, + ) -> int: + """Constrain value to be multiple of ensure_multiple_of.""" + y = int(np.round(x / ensure_multiple_of) * ensure_multiple_of) + + if max_val is not None and y > max_val: + y = int(np.floor(x / ensure_multiple_of) * ensure_multiple_of) + + if y < min_val: + y = int(np.ceil(x / ensure_multiple_of) * ensure_multiple_of) + + return y + + def _get_new_size( + self, + orig_width: int, + orig_height: int, + target_width: int, + target_height: int, + ensure_multiple_of: int, + ) -> tuple[int, int]: + """Calculate new size maintaining aspect ratio.""" + scale_height = target_height / orig_height + scale_width = target_width / orig_width + + if scale_width > scale_height: + scale_height = scale_width + else: + scale_width = scale_height + + new_height = self._constrain_to_multiple_of( + scale_height * orig_height, ensure_multiple_of, min_val=target_height + ) + new_width = self._constrain_to_multiple_of( + scale_width * orig_width, ensure_multiple_of, min_val=target_width + ) + + return new_width, new_height + + def _normalize_image(self, img: np.ndarray) -> np.ndarray: + """Normalize image with ImageNet statistics.""" + mean = np.array([0.485, 0.456, 0.406]) + std = np.array([0.229, 0.224, 0.225]) + result: np.ndarray = (img - mean) / std + return result + + def _prepare_image(self, img: np.ndarray) -> np.ndarray: + """Prepare image for model input.""" + img = np.transpose(img, (2, 0, 1)) + img = np.ascontiguousarray(img, dtype=np.float32) + return img + + def _preprocess_for_depth( + self, + image: Image.Image, + target_width: int, + target_height: int, + ensure_multiple_of: int = 1, + interpolation_method: int = Image.LANCZOS, + ) -> np.ndarray: + """ + Transforms an input image to prepare it for depth estimation by + resizing, normalizing, and formatting it. + + Parameters: + - image (PIL.Image.Image): The input image as a PIL Image object. + - target_width (int): The target width for resizing the image. + - target_height (int): The target height for resizing the image. + - ensure_multiple_of (int, optional): Ensures the dimensions of the + resized image are multiples of this value. Defaults to 1. + - interpolation_method (int, optional): The interpolation method to + use for resizing. Defaults to Image.LANCZOS. + + Returns: + - np.ndarray: The transformed image, normalized, and with the correct + dimensions and format for model input. + """ + # Calculate new size + new_width, new_height = self._get_new_size( + image.width, image.height, target_width, target_height, ensure_multiple_of + ) + + # Resize image + resized_pil = image.resize( + (new_width, new_height), resample=Image.Resampling(interpolation_method) + ) + resized_image = np.array(resized_pil).astype(np.float32) / 255.0 + + # Normalize image + normalized_image = self._normalize_image(resized_image) + + # Prepare image + prepared_image = self._prepare_image(normalized_image) + + # Add batch dimension + prepared_image = np.expand_dims(prepared_image, axis=0) + + return prepared_image + + def _estimate_depth( + self, + image: Image.Image, + target_width: int = 518, + target_height: int = 518, + ensure_multiple_of: int = 14, + interpolation_method: int = Image.BICUBIC, + ) -> Image.Image: + """ + Stage 1: Depth estimation using Depth Anything V2 model. + + Parameters: + - image (PIL.Image.Image): The input RGB PIL Image. + - target_width (int, optional): Target width for preprocessing. Defaults to 518. + - target_height (int, optional): Target height for preprocessing. + Defaults to 518. + - ensure_multiple_of (int, optional): Ensures dimensions are multiples + of this value. Defaults to 14. + - interpolation_method (int, optional): PIL interpolation method. + Defaults to Image.BICUBIC. + + Returns: + - PIL.Image.Image: The inverse depth map as a grayscale PIL Image (0-255 range). + """ + # Transform image + img_array = self._preprocess_for_depth( + image, target_width, target_height, ensure_multiple_of, interpolation_method + ) + + # Inference using ONNX + assert self.depth_session is not None, "Depth model not loaded" + ort_inputs = {"image": img_array} # type: ignore[unreachable] + ort_outs = self.depth_session.run(None, ort_inputs) + depth = ort_outs[0] + + # Rescale depth map to 0-255 (inverse depth) + depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 + depth = depth.astype(np.uint8) + depth = depth.squeeze(0) + + # From tensor to image + depth_image = Image.fromarray(depth) + + return depth_image + + def _matting_stage( + self, rgb_image: Image.Image, depth_image: Image.Image + ) -> Image.Image: + """ + Stage 2: Matting using RGBD input (RGB + inverse depth concatenated). + + Parameters: + - rgb_image (PIL.Image.Image): The original RGB image. + - depth_image (PIL.Image.Image): The inverse depth map from stage 1. + + Returns: + - PIL.Image.Image: Alpha channel (A1) as grayscale PIL Image. + """ + # Resize both images to 256x256 for matting model + rgb_resized = rgb_image.resize((256, 256), Image.LANCZOS) + depth_resized = depth_image.resize((256, 256), Image.LANCZOS) + + # Convert to numpy arrays and normalize to [0, 1] + rgb_array = np.array(rgb_resized, dtype=np.float32) / 255.0 + depth_array = np.array(depth_resized, dtype=np.float32) / 255.0 + + # Ensure depth is single channel (grayscale) + if len(depth_array.shape) == 3: + depth_array = depth_array[:, :, 0] + + # Concatenate RGB and depth to create RGBD (4-channel input) + rgbd_array = np.concatenate( + [rgb_array, np.expand_dims(depth_array, axis=2)], axis=2 + ) + + # Prepare for model: transpose to CHW format and add batch dimension + rgbd_tensor = np.transpose(rgbd_array, (2, 0, 1)) + rgbd_tensor = np.expand_dims(rgbd_tensor, axis=0) + rgbd_tensor = np.ascontiguousarray(rgbd_tensor, dtype=np.float32) + + # Run inference through matting model + assert self.matting_session is not None, "Matting model not loaded" + ort_inputs = {"rgbd_input": rgbd_tensor} # type: ignore[unreachable] + ort_outs = self.matting_session.run(None, ort_inputs) + alpha_output = ort_outs[0] + + # Process output: remove batch dimension and convert to grayscale image + alpha_output = alpha_output.squeeze(0) + if len(alpha_output.shape) == 3: + alpha_output = alpha_output[0] + + # Normalize to 0-255 range + alpha_output = np.clip(alpha_output * 255.0, 0, 255).astype(np.uint8) + + # Convert to PIL Image + alpha_image = Image.fromarray(alpha_output, mode="L") + + return alpha_image + + def _refiner_stage( + self, rgb_image: Image.Image, depth_image: Image.Image, alpha1: Image.Image + ) -> Image.Image: + """ + Stage 3: Refine alpha channel using RGB + depth + alpha concatenated input. + + Parameters: + - rgb_image (PIL.Image.Image): The original RGB image. + - depth_image (PIL.Image.Image): The depth map from stage 1. + - alpha1 (PIL.Image.Image): The alpha channel from matting stage. + + Returns: + - PIL.Image.Image: Refined alpha channel (A2) with high detail and resolution. + """ + # Get original image size + original_size = rgb_image.size + + # Scale RGB image to [0, 1] without resizing + rgb_array = np.array(rgb_image, dtype=np.float32) / 255.0 + + # Resize depth and alpha to match RGB image size + depth_resized = depth_image.resize(original_size, Image.LANCZOS) + alpha_resized = alpha1.resize(original_size, Image.LANCZOS) + + # Convert to arrays and scale to [0, 1] + depth_array = np.array(depth_resized, dtype=np.float32) / 255.0 + alpha_array = np.array(alpha_resized, dtype=np.float32) / 255.0 + + # Ensure depth and alpha are single channel + if len(depth_array.shape) == 3: + depth_array = depth_array[:, :, 0] + if len(alpha_array.shape) == 3: + alpha_array = alpha_array[:, :, 0] + + # Concatenate RGB + depth + alpha to create 5-channel input + rgba_depth_array = np.concatenate( + [ + rgb_array, + np.expand_dims(depth_array, axis=2), + np.expand_dims(alpha_array, axis=2), + ], + axis=2, + ) + + # Prepare for model: transpose to CHW format and add batch dimension + input_tensor = np.transpose(rgba_depth_array, (2, 0, 1)) + input_tensor = np.expand_dims(input_tensor, axis=0) + input_tensor = np.ascontiguousarray(input_tensor, dtype=np.float32) + + # Run inference through refiner model + assert self.refiner_session is not None, "Refiner model not loaded" + ort_inputs = {"rgbd_alpha_input": input_tensor} # type: ignore[unreachable] + ort_outs = self.refiner_session.run(None, ort_inputs) + alpha_output = ort_outs[0] + + # Process output: remove batch dimension + alpha_output = alpha_output.squeeze(0) + if len(alpha_output.shape) == 3: + alpha_output = alpha_output[0] + + # Normalize to 0-255 range + alpha_output = np.clip(alpha_output * 255.0, 0, 255).astype(np.uint8) + + # Convert to PIL Image + refined_alpha = Image.fromarray(alpha_output, mode="L") + + return refined_alpha + + def estimate_alpha(self, image: Image.Image) -> Image.Image: + """ + Full 3-stage pipeline: Depth Anything V2 -> Matting -> Refiner. + + Parameters: + - image (PIL.Image.Image): Input RGB image. + + Returns: + - PIL.Image.Image: Final refined alpha channel. + """ + # Stage 1: Depth estimation + depth_map = self._estimate_depth(image) + + # Stage 2: Matting (RGBD -> A1) + alpha1 = self._matting_stage(image, depth_map) + + # Stage 3: Refiner (RGB + depth + alpha -> A2) + alpha2 = self._refiner_stage(image, depth_map, alpha1) + + return alpha2 + + def remove_background( + self, input_image: Union[str, Path, Image.Image, bytes], **kwargs: Any + ) -> Image.Image: + """Remove background from image using local Snap model. + + Args: + input_image: Input image + **kwargs: Additional arguments (unused for Snap model) + + Returns: + PIL Image with background removed + """ + # Load image + if isinstance(input_image, (str, Path)): + image = Image.open(input_image) + elif isinstance(input_image, bytes): + image = Image.open(io.BytesIO(input_image)) + elif isinstance(input_image, Image.Image): + image = input_image.copy() + else: + raise WithoutBGError(f"Unsupported input type: {type(input_image)}") + + # Convert to RGB if needed (model expects RGB only) + if image.mode != "RGB": + image = image.convert("RGB") + + original_size = image.size + + try: + # Run 3-stage pipeline to get final alpha channel + alpha_channel = self.estimate_alpha(image) + + # Resize alpha to original image size + alpha_resized = alpha_channel.resize(original_size, Image.LANCZOS) + + # Convert original image to RGBA + if image.mode != "RGBA": + image = image.convert("RGBA") + + # Apply alpha channel to create final RGBA image + image_array = np.array(image) + alpha_array = np.array(alpha_resized) + + # Replace alpha channel + image_array[:, :, 3] = alpha_array + + result_image = Image.fromarray(image_array, "RGBA") + + return result_image + + except Exception as e: + raise WithoutBGError(f"Model inference failed: {str(e)}") from e diff --git a/src/withoutbg/py.typed b/src/withoutbg/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..4f6f13a --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,225 @@ +"""Shared test configuration and fixtures.""" + +import tempfile +from pathlib import Path + +import numpy as np +import pytest +from PIL import Image + +# Import test data fixtures for shared use across test modules + + +def pytest_configure(config): + """Configure pytest with custom settings.""" + # Add custom markers + config.addinivalue_line("markers", "unit: Unit tests (fast, isolated)") + config.addinivalue_line( + "markers", "integration: Integration tests (slower, multiple components)" + ) + config.addinivalue_line( + "markers", "performance: Performance and benchmark tests (very slow)" + ) + config.addinivalue_line( + "markers", "api: Tests requiring API access (may be skipped)" + ) + config.addinivalue_line("markers", "slow: Slow running tests") + config.addinivalue_line( + "markers", "real_processing: Tests using actual ONNX models (very slow)" + ) + + +def pytest_collection_modifyitems(config, items): + """Modify test collection to add markers automatically.""" + for item in items: + # Auto-mark performance tests + if "performance" in str(item.fspath): + item.add_marker(pytest.mark.performance) + + # Auto-mark integration tests + if "integration" in str(item.fspath): + item.add_marker(pytest.mark.integration) + + # Auto-mark unit tests (default for tests/ directory) + if ( + "test_" in str(item.fspath) + and "integration" not in str(item.fspath) + and "performance" not in str(item.fspath) + ): + item.add_marker(pytest.mark.unit) + + # Auto-mark API tests + if "api" in item.name.lower() or "studio" in item.name.lower(): + item.add_marker(pytest.mark.api) + + +@pytest.fixture(scope="session") +def test_data_dir(): + """Provide path to test data directory.""" + return Path(__file__).parent / "fixtures" + + +@pytest.fixture(scope="session") +def test_images_dir(test_data_dir): + """Provide path to test images directory.""" + return test_data_dir / "images" + + +@pytest.fixture(scope="session") +def expected_outputs_dir(test_data_dir): + """Provide path to expected outputs directory.""" + return test_data_dir / "expected_outputs" + + +@pytest.fixture +def temp_dir(): + """Create a temporary directory for test outputs.""" + with tempfile.TemporaryDirectory() as temp_dir: + yield Path(temp_dir) + + +@pytest.fixture +def sample_test_image(): + """Create a standard test image for general use.""" + return Image.new("RGB", (512, 384), color=(128, 64, 192)) + + +@pytest.fixture +def sample_test_images(): + """Create multiple test images with different properties.""" + return { + "small": Image.new("RGB", (128, 96), color=(255, 0, 0)), + "medium": Image.new("RGB", (512, 384), color=(0, 255, 0)), + "large": Image.new("RGB", (1024, 768), color=(0, 0, 255)), + "portrait": Image.new("RGB", (384, 512), color=(255, 255, 0)), + "landscape": Image.new("RGB", (768, 512), color=(255, 0, 255)), + "square": Image.new("RGB", (512, 512), color=(0, 255, 255)), + } + + +@pytest.fixture(autouse=True) +def cleanup_temp_files(): + """Automatically cleanup temporary files after each test.""" + yield + # This fixture runs after each test to ensure cleanup + # The actual cleanup is handled by tempfile.TemporaryDirectory context managers + + +# Pytest hooks for performance testing +def pytest_runtest_setup(item): + """Setup hook for individual tests.""" + if "performance" in item.keywords: + # Skip performance tests by default unless explicitly requested + if not item.config.getoption("--run-performance", default=False): + pytest.skip("Performance tests skipped (use --run-performance to run)") + + if "real_processing" in item.keywords: + # Skip real processing tests by default unless explicitly requested + if not item.config.getoption("--run-real-processing", default=False): + pytest.skip( + "Real processing tests skipped (use --run-real-processing to run)" + ) + + +def pytest_addoption(parser): + """Add custom command line options.""" + parser.addoption( + "--run-performance", + action="store_true", + default=False, + help="Run performance tests", + ) + parser.addoption( + "--run-integration", + action="store_true", + default=False, + help="Run integration tests", + ) + parser.addoption( + "--run-api", + action="store_true", + default=False, + help="Run API tests (requires network access)", + ) + parser.addoption( + "--run-real-processing", + action="store_true", + default=False, + help="Run real processing tests (downloads models, very slow)", + ) + + +# Custom assertion helpers +def assert_image_properties( + image, expected_mode=None, expected_size=None, expected_format=None +): + """Helper function to assert image properties.""" + assert isinstance(image, Image.Image), f"Expected PIL Image, got {type(image)}" + + if expected_mode: + assert ( + image.mode == expected_mode + ), f"Expected mode {expected_mode}, got {image.mode}" + + if expected_size: + assert ( + image.size == expected_size + ), f"Expected size {expected_size}, got {image.size}" + + if expected_format: + assert ( + image.format == expected_format + ), f"Expected format {expected_format}, got {image.format}" + + +def assert_processing_result(result, original_image): + """Helper function to assert processing results.""" + assert_image_properties(result, expected_mode="RGBA") + assert result.size == original_image.size, "Result size should match original" + + # Check that alpha channel exists and has variation + result_array = np.array(result) + alpha_channel = result_array[:, :, 3] + assert ( + alpha_channel.min() >= 0 and alpha_channel.max() <= 255 + ), "Alpha values should be in valid range" + + +# Performance testing utilities +class PerformanceTracker: + """Utility class for tracking performance metrics in tests.""" + + def __init__(self): + self.metrics = {} + + def record_metric(self, name, value, unit="seconds"): + """Record a performance metric.""" + self.metrics[name] = {"value": value, "unit": unit} + + def get_metric(self, name): + """Get a recorded metric.""" + return self.metrics.get(name) + + def assert_performance(self, name, max_value, message=None): + """Assert that a performance metric meets requirements.""" + metric = self.get_metric(name) + assert metric is not None, f"Metric {name} not found" + + actual_value = metric["value"] + assert actual_value <= max_value, ( + message + or f"Performance regression: {name} = {actual_value} > {max_value} " + f"{metric['unit']}" + ) + + +@pytest.fixture +def performance_tracker(): + """Provide a performance tracker for tests.""" + return PerformanceTracker() + + +@pytest.fixture +def real_test_image_path(test_images_dir): + """Get path to real test image.""" + return test_images_dir / "test-ice-cream.png" diff --git a/tests/fixtures/__init__.py b/tests/fixtures/__init__.py new file mode 100644 index 0000000..c4e4d25 --- /dev/null +++ b/tests/fixtures/__init__.py @@ -0,0 +1,5 @@ +"""Test fixtures package.""" + +from .mock_responses import create_mock_response, get_mock_api_responses + +__all__ = ["get_mock_api_responses", "create_mock_response"] diff --git a/tests/fixtures/expected_outputs/.gitkeep b/tests/fixtures/expected_outputs/.gitkeep new file mode 100644 index 0000000..6cfc456 --- /dev/null +++ b/tests/fixtures/expected_outputs/.gitkeep @@ -0,0 +1,2 @@ +# Expected output images directory (generated with Midjourney) +# - test-ice-cream.png: 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zG*BM+ITukw2NlDhOSy%K=_s{KDzdj9y?cWNd80000= 400: + mock_response.raise_for_status.side_effect = Exception( + f"HTTP {response_data['status_code']}" + ) + else: + mock_response.raise_for_status.return_value = None + + return mock_response diff --git a/tests/fixtures/mock_responses/.gitkeep b/tests/fixtures/mock_responses/.gitkeep new file mode 100644 index 0000000..4755931 --- /dev/null +++ b/tests/fixtures/mock_responses/.gitkeep @@ -0,0 +1,3 @@ +# Mock API response files directory +# Add JSON files for mocking API responses: +# - studio_success.json \ No newline at end of file diff --git a/tests/fixtures/mock_responses/studio_error_401.json b/tests/fixtures/mock_responses/studio_error_401.json new file mode 100644 index 0000000..345a520 --- /dev/null +++ b/tests/fixtures/mock_responses/studio_error_401.json @@ -0,0 +1,3 @@ +{ + "detail": "Invalid API Key" +} \ No newline at end of file diff --git a/tests/fixtures/mock_responses/studio_error_402.json b/tests/fixtures/mock_responses/studio_error_402.json new file mode 100644 index 0000000..ab3981d --- /dev/null +++ b/tests/fixtures/mock_responses/studio_error_402.json @@ -0,0 +1,3 @@ +{ + "detail": "Insufficient credit. Please top up API credits." +} \ No newline at end of file diff --git a/tests/fixtures/mock_responses/studio_error_429.json b/tests/fixtures/mock_responses/studio_error_429.json new file mode 100644 index 0000000..b46ad63 --- /dev/null +++ b/tests/fixtures/mock_responses/studio_error_429.json @@ -0,0 +1,3 @@ +{ + "detail": "Rate limit (7 request/minute) exceeded" +} \ No newline at end of file diff --git a/tests/fixtures/mock_responses/studio_success.json b/tests/fixtures/mock_responses/studio_success.json new file mode 100644 index 0000000..1ad324c --- /dev/null +++ b/tests/fixtures/mock_responses/studio_success.json @@ -0,0 +1,3 @@ +{ + "alpha_base64": "iVBORw0KGgoAAAANSUhE..." +} \ No newline at end of file diff --git a/tests/fixtures/mock_responses/studio_usage_response.json b/tests/fixtures/mock_responses/studio_usage_response.json new file mode 100644 index 0000000..de34db1 --- /dev/null +++ b/tests/fixtures/mock_responses/studio_usage_response.json @@ -0,0 +1,4 @@ +{ + "credit": 100, + "expiration_date": "2025-07-27" +} \ No newline at end of file diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..c66cd71 --- /dev/null +++ b/tests/integration/__init__.py @@ -0,0 +1 @@ +"""Integration tests package.""" diff --git a/tests/integration/test_real_image_processing.py b/tests/integration/test_real_image_processing.py new file mode 100644 index 0000000..335ef5b --- /dev/null +++ b/tests/integration/test_real_image_processing.py @@ -0,0 +1,379 @@ +"""Real image processing tests using actual ONNX models.""" + +import os +from typing import Optional + +import numpy as np +import pytest +from PIL import Image + +from withoutbg.core import remove_background +from withoutbg.exceptions import ModelNotFoundError, WithoutBGError +from withoutbg.models import SnapModel + + +def assert_alpha_channel_valid(result_image: Image.Image) -> None: + """Validate alpha channel has proper background removal characteristics.""" + assert result_image.mode == "RGBA", f"Expected RGBA, got {result_image.mode}" + + result_array = np.array(result_image) + alpha = result_array[:, :, 3] + + # Alpha should have variation (not all 0 or all 255) + alpha_min, alpha_max = alpha.min(), alpha.max() + assert ( + alpha_min < alpha_max + ), f"Alpha channel lacks variation: min={alpha_min}, max={alpha_max}" + + # Should have some transparency (background removal) + transparent_pixels = np.sum(alpha < 255) + assert transparent_pixels > 0, "No background pixels detected (all opaque)" + + # Should preserve some foreground + opaque_pixels = np.sum(alpha > 0) + assert opaque_pixels > 0, "No foreground pixels preserved (all transparent)" + + # Alpha values should be in valid range + assert 0 <= alpha_min <= 255, f"Invalid alpha min: {alpha_min}" + assert 0 <= alpha_max <= 255, f"Invalid alpha max: {alpha_max}" + + +def calculate_alpha_iou(result: Image.Image, expected: Image.Image) -> float: + """Calculate IoU (Intersection over Union) for alpha channel masks. + + Args: + result: Result image with alpha channel + expected: Expected image with alpha channel + + Returns: + IoU score between 0.0 and 1.0 + """ + assert ( + result.size == expected.size + ), f"Size mismatch: {result.size} vs {expected.size}" + assert result.mode == "RGBA", f"Result must be RGBA, got {result.mode}" + assert expected.mode == "RGBA", f"Expected must be RGBA, got {expected.mode}" + + # Extract alpha channels and normalize to [0,1] + result_alpha = np.array(result)[:, :, 3] / 255.0 + expected_alpha = np.array(expected)[:, :, 3] / 255.0 + + # Binarize by rounding (>0.5 = foreground, <=0.5 = background) + result_binary = (result_alpha > 0.5).astype(np.uint8) + expected_binary = (expected_alpha > 0.5).astype(np.uint8) + + # Calculate IoU + intersection = np.sum(result_binary & expected_binary) + union = np.sum(result_binary | expected_binary) + + # Handle edge case where both masks are empty + if union == 0: + return 1.0 if intersection == 0 else 0.0 + + iou = intersection / union + return float(iou) + + +def assert_alpha_iou( + result: Image.Image, expected: Image.Image, min_iou: float = 0.97 +) -> None: + """Assert that alpha channel IoU meets minimum threshold.""" + iou = calculate_alpha_iou(result, expected) + assert iou >= min_iou, f"Alpha IoU too low: {iou:.4f} < {min_iou}" + + +def assert_images_similar( + result: Image.Image, expected: Image.Image, tolerance: float = 0.15 +) -> None: + """Compare two images with tolerance for minor differences.""" + assert ( + result.size == expected.size + ), f"Size mismatch: {result.size} vs {expected.size}" + assert ( + result.mode == expected.mode + ), f"Mode mismatch: {result.mode} vs {expected.mode}" + + result_array = np.array(result, dtype=np.float32) / 255.0 + expected_array = np.array(expected, dtype=np.float32) / 255.0 + + # Calculate mean absolute difference + diff = np.mean(np.abs(result_array - expected_array)) + assert diff <= tolerance, f"Images too different: MAE={diff:.3f} > {tolerance}" + + +@pytest.fixture(scope="session") +def real_snap_model() -> Optional[SnapModel]: + """Load actual ONNX models if available, skip if not.""" + try: + model = SnapModel() + # Verify models actually loaded + assert model.depth_session is not None, "Depth model not loaded" + assert model.matting_session is not None, "Matting model not loaded" + assert model.refiner_session is not None, "Refiner model not loaded" + return model + except (FileNotFoundError, ModelNotFoundError, Exception) as e: + pytest.skip(f"Real ONNX models not available: {e}") + + +@pytest.mark.integration +@pytest.mark.real_processing +@pytest.mark.skipif( + os.getenv("CI") == "true" and not os.getenv("RUN_REAL_PROCESSING"), + reason="Real processing tests skipped in CI (set RUN_REAL_PROCESSING=1 to enable)", +) +class TestRealImageProcessing: + """Integration tests using actual ONNX models and real images.""" + + def test_ice_cream_processing( + self, real_snap_model, test_images_dir, expected_outputs_dir + ): + """Test processing the ice cream image with real models.""" + input_path = test_images_dir / "test-ice-cream.png" + assert input_path.exists(), f"Test image not found: {input_path}" + + # Process with real model + result = real_snap_model.remove_background(input_path) + + # Validate result properties + assert_alpha_channel_valid(result) + + # Check against expected output with IoU metric + expected_path = expected_outputs_dir / "test-ice-cream.png" + if expected_path.exists(): + expected = Image.open(expected_path) + # Use IoU for interpretable alpha channel comparison + assert_alpha_iou(result, expected, min_iou=0.97) + + def test_core_api_with_real_model(self, test_images_dir): + """Test core API remove_background function with real processing.""" + input_path = test_images_dir / "test-ice-cream.png" + + # Test with default model (should use local Snap) + result = remove_background(input_path) + assert_alpha_channel_valid(result) + + # Result should preserve original dimensions + original = Image.open(input_path) + assert result.size == original.size + + def test_different_input_formats(self, real_snap_model, test_images_dir): + """Test processing with different input formats.""" + input_path = test_images_dir / "test-ice-cream.png" + original_image = Image.open(input_path) + + # Test with PIL Image + result_pil = real_snap_model.remove_background(original_image) + assert_alpha_channel_valid(result_pil) + + # Test with file path (string) + result_path = real_snap_model.remove_background(str(input_path)) + assert_alpha_channel_valid(result_path) + + # Test with Path object + result_pathobj = real_snap_model.remove_background(input_path) + assert_alpha_channel_valid(result_pathobj) + + # Test with bytes + with open(input_path, "rb") as f: + image_bytes = f.read() + result_bytes = real_snap_model.remove_background(image_bytes) + assert_alpha_channel_valid(result_bytes) + + # All results should have same dimensions + assert ( + result_pil.size + == result_path.size + == result_pathobj.size + == result_bytes.size + ) + + @pytest.mark.parametrize( + "format_name,pil_format", + [ + ("JPEG", "JPEG"), + ("PNG", "PNG"), + ("WebP", "WebP"), + ], + ) + def test_different_image_formats( + self, real_snap_model, sample_test_image, format_name, pil_format + ): + """Test processing various image formats.""" + # Convert test image to RGB for JPEG compatibility + if format_name == "JPEG" and sample_test_image.mode == "RGBA": + test_img = sample_test_image.convert("RGB") + else: + test_img = sample_test_image + + # Save to bytes in specific format + import io + + buffer = io.BytesIO() + test_img.save(buffer, format=pil_format) + buffer.seek(0) + + # Process the formatted image + result = real_snap_model.remove_background(buffer.getvalue()) + assert_alpha_channel_valid(result) + + @pytest.mark.parametrize( + "size", + [ + (256, 256), # Small square + (512, 384), # Medium landscape + (384, 512), # Medium portrait + (1024, 768), # Large landscape + ], + ) + def test_different_resolutions(self, real_snap_model, size): + """Test processing different image sizes.""" + # Create test image with specific size + test_img = Image.new("RGB", size, color=(128, 64, 192)) + + # Add some pattern to make it more realistic + import numpy as np + + img_array = np.array(test_img) + # Add gradient pattern + gradient = np.linspace(0, 255, size[0]).astype(np.uint8) + img_array[:, :, 0] = gradient[None, :] # Horizontal gradient in red channel + test_img = Image.fromarray(img_array) + + result = real_snap_model.remove_background(test_img) + + # Should preserve original dimensions + assert result.size == size + assert_alpha_channel_valid(result) + + def test_processing_consistency(self, real_snap_model, test_images_dir): + """Test that multiple runs produce consistent results.""" + input_path = test_images_dir / "test-ice-cream.png" + + # Process same image multiple times + result1 = real_snap_model.remove_background(input_path) + result2 = real_snap_model.remove_background(input_path) + + # Results should be identical (deterministic) - perfect IoU + iou = calculate_alpha_iou(result1, result2) + assert iou == 1.0, f"Processing not deterministic: IoU={iou:.6f} < 1.0" + + def test_pipeline_stages_integration(self, real_snap_model, test_images_dir): + """Test that the 3-stage pipeline produces reasonable outputs.""" + input_path = test_images_dir / "test-ice-cream.png" + original = Image.open(input_path) + + result = real_snap_model.remove_background(original) + + # Validate the result has expected characteristics for ice cream image + result_array = np.array(result) + alpha = result_array[:, :, 3] + + # Should have significant variation in alpha channel + alpha_std = np.std(alpha) + assert alpha_std > 30, f"Alpha channel lacks variation (std={alpha_std:.1f})" + + # Should have both very transparent and very opaque regions + very_transparent = np.sum(alpha < 50) + very_opaque = np.sum(alpha > 200) + + assert very_transparent > 100, "Not enough background removal" + assert very_opaque > 100, "Not enough foreground preservation" + + def test_alpha_iou_metric( + self, real_snap_model, test_images_dir, expected_outputs_dir + ): + """Test alpha IoU metric calculation and interpretation.""" + input_path = test_images_dir / "test-ice-cream.png" + expected_path = expected_outputs_dir / "test-ice-cream.png" + + # Process image + result = real_snap_model.remove_background(input_path) + + if expected_path.exists(): + expected = Image.open(expected_path) + + # Calculate IoU (should be very high since expected was generated + # with same model) + iou = calculate_alpha_iou(result, expected) + print(f"Alpha IoU score: {iou:.4f}") + + # Should be perfect since we generated expected output with same model + assert iou >= 0.99, f"IoU should be near-perfect: {iou:.4f}" + + # Demonstrate interpretability: IoU represents overlap of binary masks + result_alpha = np.array(result)[:, :, 3] / 255.0 + expected_alpha = np.array(expected)[:, :, 3] / 255.0 + + result_binary = (result_alpha > 0.5).astype(np.uint8) + expected_binary = (expected_alpha > 0.5).astype(np.uint8) + + intersection = np.sum(result_binary & expected_binary) + union = np.sum(result_binary | expected_binary) + + print(f"Intersection pixels: {intersection}") + print(f"Union pixels: {union}") + print(f"IoU = {intersection}/{union} = {intersection/union:.4f}") + + def test_error_handling_with_real_model(self, real_snap_model): + """Test error handling with real models.""" + # Test with invalid input + with pytest.raises((WithoutBGError, ValueError, TypeError)): + real_snap_model.remove_background(None) + + # Test with invalid file path + with pytest.raises((FileNotFoundError, WithoutBGError)): + real_snap_model.remove_background("/nonexistent/file.png") + + +@pytest.mark.performance +@pytest.mark.real_processing +class TestRealProcessingPerformance: + """Performance tests for real image processing.""" + + def test_processing_speed_benchmark( + self, real_snap_model, test_images_dir, performance_tracker + ): + """Benchmark processing speed with real models.""" + input_path = test_images_dir / "test-ice-cream.png" + + import time + + start_time = time.time() + result = real_snap_model.remove_background(input_path) + processing_time = time.time() - start_time + + # Record performance metric + performance_tracker.record_metric("processing_time", processing_time, "seconds") + + # Basic validation + assert_alpha_channel_valid(result) + + # Performance assertion (adjust based on your requirements) + performance_tracker.assert_performance( + "processing_time", + max_value=30.0, # 30 seconds max for ice cream image + message=f"Processing too slow: {processing_time:.2f}s", + ) + + def test_memory_usage_real_processing(self, real_snap_model, test_images_dir): + """Test memory usage during real processing.""" + import os + + import psutil + + process = psutil.Process(os.getpid()) + memory_before = process.memory_info().rss / 1024 / 1024 # MB + + input_path = test_images_dir / "test-ice-cream.png" + result = real_snap_model.remove_background(input_path) + + memory_after = process.memory_info().rss / 1024 / 1024 # MB + memory_increase = memory_after - memory_before + + # Basic validation + assert_alpha_channel_valid(result) + + # Memory should not increase excessively (adjust threshold as needed) + assert ( + memory_increase < 500 + ), f"Memory usage too high: {memory_increase:.1f}MB increase" diff --git a/tests/performance/__init__.py b/tests/performance/__init__.py new file mode 100644 index 0000000..a9886d8 --- /dev/null +++ b/tests/performance/__init__.py @@ -0,0 +1 @@ +"""Performance tests package.""" diff --git a/tests/performance/test_batch_performance.py b/tests/performance/test_batch_performance.py new file mode 100644 index 0000000..5d35de2 --- /dev/null +++ b/tests/performance/test_batch_performance.py @@ -0,0 +1,460 @@ +"""Batch processing performance tests.""" + +import gc +import os +import tempfile +import time +from pathlib import Path +from unittest.mock import patch + +import pytest +from PIL import Image + +from withoutbg.core import remove_background_batch + + +@pytest.fixture +def create_test_images(): + """Create temporary test image files.""" + + def _create_images(count=5, size=(256, 256)): + image_files = [] + for i in range(count): + with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_file: + color = (i * 40, i * 30, i * 20) + image = Image.new("RGB", size, color=color) + image.save(tmp_file.name, "JPEG") + image_files.append(tmp_file.name) + return image_files + + return _create_images + + +@pytest.fixture +def mock_processing(): + """Mock the background removal processing.""" + + def _mock_remove_bg(input_image, **kwargs): + # Simulate processing time + time.sleep(0.01) # 10ms simulated processing time + + # Load image to get size + if isinstance(input_image, str): + img = Image.open(input_image) + else: + img = input_image + + # Return mock result + return Image.new("RGBA", img.size, color=(100, 150, 200, 128)) + + return _mock_remove_bg + + +class TestBatchPerformance: + """Test batch processing performance characteristics.""" + + def test_small_batch_performance(self, create_test_images, mock_processing): + """Test performance with small batch sizes.""" + image_files = create_test_images(count=3, size=(256, 256)) + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Measure batch processing time + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / len(image_files) + + # Assertions + assert len(results) == len(image_files) + assert batch_time < 5.0 # Should complete within 5 seconds + assert ( + time_per_image < 2.0 + ) # Each image should take less than 2 seconds + + print( + f"Small batch (3 images): {batch_time:.3f}s total, " + f"{time_per_image:.3f}s per image" + ) + + for result in results: + assert isinstance(result, Image.Image) + assert result.mode == "RGBA" + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_medium_batch_performance(self, create_test_images, mock_processing): + """Test performance with medium batch sizes.""" + image_files = create_test_images(count=10, size=(512, 384)) + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Measure batch processing time + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / len(image_files) + + # Assertions + assert len(results) == len(image_files) + assert batch_time < 15.0 # Should complete within 15 seconds + assert time_per_image < 2.0 # Average time per image + + print( + f"Medium batch (10 images): {batch_time:.3f}s total, " + f"{time_per_image:.3f}s per image" + ) + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_large_batch_performance(self, create_test_images, mock_processing): + """Test performance with large batch sizes.""" + image_files = create_test_images(count=25, size=(400, 300)) + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Measure batch processing time + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + batch_time = end_time - start_time + batch_time / len(image_files) + throughput = len(image_files) / batch_time + + # Assertions + assert len(results) == len(image_files) + assert batch_time < 60.0 # Should complete within 1 minute + assert throughput > 0.4 # Should process at least 0.4 images per second + + print( + f"Large batch (25 images): {batch_time:.3f}s total, " + f"{throughput:.2f} images/sec" + ) + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_batch_size_scaling(self, create_test_images, mock_processing): + """Test how performance scales with batch size.""" + batch_sizes = [1, 5, 10, 20] + performance_data = {} + + for batch_size in batch_sizes: + image_files = create_test_images(count=batch_size, size=(256, 256)) + + try: + with patch( + "withoutbg.core.remove_background", side_effect=mock_processing + ): + # Measure batch processing + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / batch_size + + performance_data[batch_size] = { + "total_time": batch_time, + "time_per_image": time_per_image, + "throughput": batch_size / batch_time, + } + + # Verify results + assert len(results) == batch_size + + print(f"Batch size {batch_size}: {time_per_image:.3f}s per image") + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + # Analyze scaling characteristics + # Time per image should remain relatively stable + times_per_image = [data["time_per_image"] for data in performance_data.values()] + max_time = max(times_per_image) + min_time = min(times_per_image) + + # Time per image shouldn't vary too much across batch sizes + assert max_time / min_time < 3.0 # Max should be less than 3x min + + def test_batch_memory_efficiency(self, create_test_images, mock_processing): + """Test memory efficiency during batch processing.""" + image_files = create_test_images(count=15, size=(512, 384)) + + try: + # Force garbage collection + gc.collect() + + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Process batch and monitor memory + results = remove_background_batch(image_files) + + # Verify processing completed + assert len(results) == len(image_files) + + # Clean up results + del results + gc.collect() + + print(f"Batch memory test completed for {len(image_files)} images") + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_batch_with_output_directory_performance( + self, create_test_images, mock_processing + ): + """Test batch processing performance when saving to output directory.""" + image_files = create_test_images(count=8, size=(400, 300)) + + with tempfile.TemporaryDirectory() as output_dir: + try: + with patch( + "withoutbg.core.remove_background", side_effect=mock_processing + ): + # Measure batch processing with output directory + start_time = time.time() + results = remove_background_batch( + image_files, output_dir=output_dir + ) + end_time = time.time() + + batch_time = end_time - start_time + + # Assertions + assert len(results) == len(image_files) + assert batch_time < 20.0 # Should complete within 20 seconds + + # Check that output directory exists and has correct structure + output_path = Path(output_dir) + assert output_path.exists() + + print(f"Batch with output dir (8 images): {batch_time:.3f}s") + + finally: + # Cleanup input files + for file_path in image_files: + os.unlink(file_path) + + def test_batch_error_handling_performance(self, create_test_images): + """Test batch processing performance when some images fail.""" + image_files = create_test_images(count=6, size=(256, 256)) + + # Add a non-existent file to test error handling + image_files.append("/non/existent/file.jpg") + + def mock_processing_with_errors(input_image, **kwargs): + if not os.path.exists(input_image): + raise FileNotFoundError(f"File not found: {input_image}") + + # Simulate normal processing for valid files + time.sleep(0.01) + img = Image.open(input_image) + return Image.new("RGBA", img.size, color=(100, 150, 200, 128)) + + try: + with patch( + "withoutbg.core.remove_background", + side_effect=mock_processing_with_errors, + ): + # Measure batch processing with errors + start_time = time.time() + + try: + results = remove_background_batch(image_files) + # If no exception is raised, check results + assert len(results) <= len(image_files) # Some may have failed + except Exception: + # Batch processing may raise exception on errors + pass + + end_time = time.time() + batch_time = end_time - start_time + + # Should still complete in reasonable time despite errors + assert batch_time < 10.0 + + print(f"Batch with errors: {batch_time:.3f}s") + + finally: + # Cleanup valid files + for file_path in image_files[:-1]: # Exclude the non-existent file + if os.path.exists(file_path): + os.unlink(file_path) + + def test_batch_different_image_sizes_performance(self, mock_processing): + """Test batch processing performance with mixed image sizes.""" + # Create images of different sizes + sizes = [(200, 200), (400, 300), (600, 450), (300, 400), (512, 384)] + image_files = [] + + for i, size in enumerate(sizes): + with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_file: + color = (i * 45, i * 35, i * 25) + image = Image.new("RGB", size, color=color) + image.save(tmp_file.name, "JPEG") + image_files.append(tmp_file.name) + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Measure batch processing with mixed sizes + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / len(image_files) + + # Assertions + assert len(results) == len(image_files) + assert batch_time < 15.0 # Should complete within 15 seconds + + # Verify different sizes were processed correctly + for _, result in enumerate(results): + assert isinstance(result, Image.Image) + # Note: result size should match original image size + assert result.mode == "RGBA" + + print( + f"Mixed sizes batch: {batch_time:.3f}s total, " + f"{time_per_image:.3f}s per image" + ) + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_batch_processing_consistency(self, create_test_images, mock_processing): + """Test that batch processing produces consistent results.""" + image_files = create_test_images(count=5, size=(300, 300)) + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Process the same batch multiple times + all_results = [] + processing_times = [] + + for _run in range(3): + start_time = time.time() + results = remove_background_batch(image_files) + end_time = time.time() + + processing_times.append(end_time - start_time) + all_results.append(results) + + # Verify each run + assert len(results) == len(image_files) + + # Check consistency of processing times + avg_time = sum(processing_times) / len(processing_times) + max_time = max(processing_times) + min_time = min(processing_times) + + # Processing times should be relatively consistent + assert max_time / min_time < 2.0 # Max shouldn't be more than 2x min + + print( + f"Consistency test - Avg: {avg_time:.3f}s, Min: {min_time:.3f}s, " + f"Max: {max_time:.3f}s" + ) + + finally: + # Cleanup + for file_path in image_files: + os.unlink(file_path) + + def test_batch_processing_with_pil_images(self, mock_processing): + """Test batch processing performance with PIL Image objects.""" + # Create PIL Image objects directly + test_images = [ + Image.new("RGB", (256, 256), color=(i * 50, i * 30, i * 20)) + for i in range(8) + ] + + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Measure batch processing with PIL images + start_time = time.time() + results = remove_background_batch(test_images) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / len(test_images) + + # Assertions + assert len(results) == len(test_images) + assert batch_time < 10.0 # Should be faster with PIL objects + + print( + f"PIL objects batch: {batch_time:.3f}s total, " + f"{time_per_image:.3f}s per image" + ) + + def test_concurrent_batch_processing_simulation( + self, create_test_images, mock_processing + ): + """Simulate concurrent batch processing workloads.""" + # Create multiple small batches + batch1 = create_test_images(count=3, size=(200, 200)) + batch2 = create_test_images(count=4, size=(300, 250)) + batch3 = create_test_images(count=2, size=(400, 300)) + + all_batches = [batch1, batch2, batch3] + + try: + with patch("withoutbg.core.remove_background", side_effect=mock_processing): + # Process batches sequentially (simulating concurrent workload) + total_start_time = time.time() + all_results = [] + + for i, batch in enumerate(all_batches): + start_time = time.time() + results = remove_background_batch(batch) + end_time = time.time() + + all_results.append(results) + batch_time = end_time - start_time + + print( + f"Concurrent batch {i+1} ({len(batch)} images): " + f"{batch_time:.3f}s" + ) + + total_end_time = time.time() + total_time = total_end_time - total_start_time + total_images = sum(len(batch) for batch in all_batches) + + # Assertions + assert len(all_results) == len(all_batches) + assert ( + total_time < 20.0 + ) # All batches should complete within 20 seconds + + overall_throughput = total_images / total_time + print( + f"Overall concurrent throughput: {overall_throughput:.2f} " + f"images/sec" + ) + + finally: + # Cleanup all batches + for batch in all_batches: + for file_path in batch: + os.unlink(file_path) diff --git a/tests/performance/test_benchmarks.py b/tests/performance/test_benchmarks.py new file mode 100644 index 0000000..edcbd5b --- /dev/null +++ b/tests/performance/test_benchmarks.py @@ -0,0 +1,462 @@ +"""Performance benchmarks for withoutbg processing.""" + +import gc +import time +from unittest.mock import Mock, patch + +import numpy as np +import pytest +from PIL import Image + +from withoutbg.core import remove_background, remove_background_batch +from withoutbg.models import SnapModel + + +@pytest.fixture +def benchmark_images(): + """Create test images of various sizes for benchmarking.""" + sizes = [ + (256, 256), # Small + (512, 512), # Medium + (1024, 768), # Large + (2048, 1536), # XL + ] + + images = {} + for size in sizes: + name = f"{size[0]}x{size[1]}" + images[name] = Image.new("RGB", size, color=(128, 64, 192)) + + return images + + +@pytest.fixture +def mock_onnx_setup(): + """Setup mocked ONNX environment for performance testing.""" + with patch("withoutbg.models.ort.InferenceSession") as mock_session: + with patch("pathlib.Path.exists", return_value=True): + # Mock sessions with realistic behavior + sessions = [] + for _ in range(3): + session = Mock() + depth_output = np.random.rand(1, 518, 518).astype(np.float32) + session.run.return_value = [depth_output] + sessions.append(session) + + mock_session.side_effect = sessions + yield sessions + + +class TestProcessingBenchmarks: + """Benchmark processing performance.""" + + def test_small_image_processing_time(self, benchmark_images, mock_onnx_setup): + """Benchmark processing time for small images.""" + image = benchmark_images["256x256"] + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (256, 256), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure processing time + start_time = time.time() + result = remove_background(image) + end_time = time.time() + + processing_time = end_time - start_time + + # Assertions + assert isinstance(result, Image.Image) + assert processing_time < 5.0 # Should complete within 5 seconds + + # Print benchmark result + print(f"Small image (256x256) processing time: {processing_time:.3f}s") + + def test_medium_image_processing_time(self, benchmark_images, mock_onnx_setup): + """Benchmark processing time for medium images.""" + image = benchmark_images["512x512"] + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (512, 512), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure processing time + start_time = time.time() + result = remove_background(image) + end_time = time.time() + + processing_time = end_time - start_time + + # Assertions + assert isinstance(result, Image.Image) + assert processing_time < 10.0 # Should complete within 10 seconds + + print(f"Medium image (512x512) processing time: {processing_time:.3f}s") + + def test_large_image_processing_time(self, benchmark_images, mock_onnx_setup): + """Benchmark processing time for large images.""" + image = benchmark_images["1024x768"] + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (1024, 768), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure processing time + start_time = time.time() + result = remove_background(image) + end_time = time.time() + + processing_time = end_time - start_time + + # Assertions + assert isinstance(result, Image.Image) + assert processing_time < 20.0 # Should complete within 20 seconds + + print(f"Large image (1024x768) processing time: {processing_time:.3f}s") + + def test_xl_image_processing_time(self, benchmark_images, mock_onnx_setup): + """Benchmark processing time for extra large images.""" + image = benchmark_images["2048x1536"] + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (2048, 1536), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure processing time + start_time = time.time() + result = remove_background(image) + end_time = time.time() + + processing_time = end_time - start_time + + # Assertions + assert isinstance(result, Image.Image) + assert processing_time < 30.0 # Should complete within 30 seconds + + print(f"XL image (2048x1536) processing time: {processing_time:.3f}s") + + def test_batch_processing_throughput(self, mock_onnx_setup): + """Benchmark batch processing throughput.""" + # Create multiple test images + test_images = [] + for i in range(10): + image = Image.new("RGB", (512, 384), color=(i * 25, i * 20, i * 15)) + test_images.append(image) + + with patch("withoutbg.core.remove_background") as mock_remove_bg: + # Mock individual processing + mock_results = [] + for image in test_images: + mock_result = Image.new("RGBA", image.size, color=(100, 150, 200, 128)) + mock_results.append(mock_result) + + mock_remove_bg.side_effect = mock_results + + # Measure batch processing time + start_time = time.time() + results = remove_background_batch(test_images) + end_time = time.time() + + total_time = end_time - start_time + throughput = len(test_images) / total_time + + # Assertions + assert len(results) == len(test_images) + assert throughput > 1.0 # Should process at least 1 image per second + + print(f"Batch processing throughput: {throughput:.2f} images/second") + + def test_model_initialization_time(self): + """Benchmark model initialization time.""" + with patch("withoutbg.models.ort.InferenceSession") as mock_session: + with patch("pathlib.Path.exists", return_value=True): + mock_session.return_value = Mock() + + # Measure initialization time + start_time = time.time() + model = SnapModel() + end_time = time.time() + + init_time = end_time - start_time + + # Assertions + assert model is not None + assert init_time < 5.0 # Should initialize within 5 seconds + + print(f"Model initialization time: {init_time:.3f}s") + + def test_preprocessing_performance(self, benchmark_images, mock_onnx_setup): + """Benchmark preprocessing performance.""" + image = benchmark_images["1024x768"] + model = SnapModel() + + # Measure preprocessing time + start_time = time.time() + preprocessed = model._preprocess_for_depth(image, 518, 518, 14) + end_time = time.time() + + preprocessing_time = end_time - start_time + + # Assertions + assert isinstance(preprocessed, np.ndarray) + assert preprocessing_time < 1.0 # Should preprocess within 1 second + + print(f"Preprocessing time (1024x768): {preprocessing_time:.3f}s") + + def test_depth_estimation_performance(self, benchmark_images, mock_onnx_setup): + """Benchmark depth estimation performance.""" + image = benchmark_images["512x512"] + model = SnapModel() + + # Measure depth estimation time + start_time = time.time() + depth_map = model._estimate_depth(image) + end_time = time.time() + + depth_time = end_time - start_time + + # Assertions + assert isinstance(depth_map, Image.Image) + assert depth_time < 2.0 # Should complete within 2 seconds + + print(f"Depth estimation time (512x512): {depth_time:.3f}s") + + def test_concurrent_processing_performance(self, mock_onnx_setup): + """Benchmark concurrent processing simulation.""" + # Create test images + test_images = [ + Image.new("RGB", (256, 256), color=(i * 50, i * 30, i * 20)) + for i in range(5) + ] + + processing_times = [] + + for image in test_images: + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (256, 256), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure individual processing time + start_time = time.time() + result = remove_background(image) + end_time = time.time() + + processing_times.append(end_time - start_time) + assert isinstance(result, Image.Image) + + avg_time = sum(processing_times) / len(processing_times) + max_time = max(processing_times) + min_time = min(processing_times) + + # Assertions + assert avg_time < 5.0 # Average should be under 5 seconds + assert max_time < 10.0 # No single processing should exceed 10 seconds + + print( + f"Concurrent processing - Avg: {avg_time:.3f}s, Min: {min_time:.3f}s, " + f"Max: {max_time:.3f}s" + ) + + def test_api_response_time_simulation(self): + """Benchmark simulated API response time.""" + test_image = Image.new("RGB", (512, 384), color=(128, 64, 192)) + + with patch("requests.Session.post") as mock_post: + # Simulate API response with delay + mock_response = Mock() + mock_response.ok = True + mock_response.status_code = 200 + + # Create mock response image + import base64 + import io + + buffer = io.BytesIO() + result_image = Image.new("RGBA", test_image.size, color=(128, 64, 192, 128)) + result_image.save(buffer, format="PNG") + image_b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + mock_response.json.return_value = {"image": image_b64} + + # Add artificial delay to simulate network + def delayed_response(*args, **kwargs): + time.sleep(0.1) # 100ms simulated network delay + return mock_response + + mock_post.side_effect = delayed_response + + # Measure API processing time + start_time = time.time() + result = remove_background(test_image, api_key="sk_test_key") + end_time = time.time() + + api_time = end_time - start_time + + # Assertions + assert isinstance(result, Image.Image) + assert api_time >= 0.1 # Should include our simulated delay + assert api_time < 5.0 # Should complete within 5 seconds total + + print(f"Simulated API processing time: {api_time:.3f}s") + + +class TestScalabilityBenchmarks: + """Test scalability and resource usage.""" + + def test_memory_scaling_with_image_size(self, mock_onnx_setup): + """Test memory usage scaling with image size.""" + sizes = [(256, 256), (512, 512), (1024, 768), (1536, 1024)] + + for size in sizes: + # Force garbage collection before test + gc.collect() + + test_image = Image.new("RGB", size, color=(100, 150, 200)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", size, color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process image + result = remove_background(test_image) + + # Verify processing completed + assert isinstance(result, Image.Image) + assert result.size == size + + print(f"Successfully processed {size[0]}x{size[1]} image") + + # Clean up + del result + del test_image + gc.collect() + + def test_batch_size_scaling(self, mock_onnx_setup): + """Test performance scaling with batch size.""" + batch_sizes = [1, 5, 10, 20] + + for batch_size in batch_sizes: + # Create batch of test images + test_images = [ + Image.new("RGB", (256, 256), color=(i * 10, i * 15, i * 20)) + for i in range(batch_size) + ] + + with patch("withoutbg.core.remove_background") as mock_remove_bg: + # Mock individual processing + mock_results = [] + for image in test_images: + mock_result = Image.new( + "RGBA", image.size, color=(100, 150, 200, 128) + ) + mock_results.append(mock_result) + + mock_remove_bg.side_effect = mock_results + + # Measure batch processing time + start_time = time.time() + results = remove_background_batch(test_images) + end_time = time.time() + + batch_time = end_time - start_time + time_per_image = batch_time / batch_size + + # Assertions + assert len(results) == batch_size + assert time_per_image < 5.0 # Should average under 5 seconds per image + + print( + f"Batch size {batch_size}: {batch_time:.3f}s total, " + f"{time_per_image:.3f}s per image" + ) + + # Clean up + del results + del test_images + gc.collect() + + def test_repeated_processing_stability(self, mock_onnx_setup): + """Test performance stability over repeated processing.""" + test_image = Image.new("RGB", (512, 384), color=(128, 64, 192)) + processing_times = [] + + # Process same image multiple times + for _i in range(10): + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (512, 384), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + start_time = time.time() + result = remove_background(test_image) + end_time = time.time() + + processing_times.append(end_time - start_time) + assert isinstance(result, Image.Image) + + # Clean up + del result + gc.collect() + + # Analyze stability + avg_time = sum(processing_times) / len(processing_times) + max_time = max(processing_times) + min_time = min(processing_times) + variance = sum((t - avg_time) ** 2 for t in processing_times) / len( + processing_times + ) + + # Assertions for stability + assert variance < 1.0 # Low variance indicates stable performance + assert max_time / min_time < 3.0 # Max shouldn't be more than 3x min + + print(f"Repeated processing - Avg: {avg_time:.3f}s, Variance: {variance:.6f}") + + +@pytest.mark.performance +class TestRegressionBenchmarks: + """Performance regression tests to catch performance degradation.""" + + def test_baseline_performance_regression(self, mock_onnx_setup): + """Test against baseline performance to catch regressions.""" + test_image = Image.new("RGB", (512, 512), color=(128, 64, 192)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (512, 512), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Measure processing time + start_time = time.time() + result = remove_background(test_image) + end_time = time.time() + + processing_time = end_time - start_time + + # Baseline performance thresholds (adjust based on expected performance) + BASELINE_512x512_TIME = 10.0 # seconds + + # Assertions + assert isinstance(result, Image.Image) + assert processing_time < BASELINE_512x512_TIME, ( + f"Performance regression detected: {processing_time:.3f}s > " + f"{BASELINE_512x512_TIME}s" + ) + + print( + f"Baseline test (512x512): {processing_time:.3f}s " + f"(threshold: {BASELINE_512x512_TIME}s)" + ) diff --git a/tests/performance/test_memory_usage.py b/tests/performance/test_memory_usage.py new file mode 100644 index 0000000..93280ee --- /dev/null +++ b/tests/performance/test_memory_usage.py @@ -0,0 +1,416 @@ +"""Memory usage tests for withoutbg processing.""" + +import gc +import os +import sys +import tempfile +from unittest.mock import Mock, patch + +import numpy as np +import pytest +from PIL import Image + +from withoutbg.core import remove_background, remove_background_batch +from withoutbg.models import SnapModel + + +def get_memory_usage(): + """Get current memory usage in MB (approximate).""" + # This is a simplified memory check - in real scenarios you might use psutil + gc.collect() + return sys.getsizeof(gc.get_objects()) / (1024 * 1024) + + +@pytest.fixture +def mock_onnx_setup(): + """Setup mocked ONNX environment for memory testing.""" + with patch("withoutbg.models.ort.InferenceSession") as mock_session: + with patch("pathlib.Path.exists", return_value=True): + # Mock sessions + sessions = [] + for _ in range(3): + session = Mock() + depth_output = np.random.rand(1, 518, 518).astype(np.float32) + session.run.return_value = [depth_output] + sessions.append(session) + + mock_session.side_effect = sessions + yield sessions + + +class TestMemoryUsage: + """Test memory usage patterns.""" + + def test_single_image_memory_usage(self, mock_onnx_setup): + """Test memory usage for single image processing.""" + # Force garbage collection + gc.collect() + initial_memory = get_memory_usage() + + test_image = Image.new("RGB", (1024, 768), color=(128, 64, 192)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (1024, 768), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process image + result = remove_background(test_image) + + # Check memory during processing + peak_memory = get_memory_usage() + memory_increase = peak_memory - initial_memory + + # Assertions + assert isinstance(result, Image.Image) + assert ( + memory_increase < 500 + ) # Should not increase memory by more than 500MB + + print(f"Single image memory increase: {memory_increase:.2f}MB") + + # Clean up + del result + del test_image + gc.collect() + + # Check memory after cleanup + final_memory = get_memory_usage() + memory_after_cleanup = final_memory - initial_memory + + # Memory should be mostly cleaned up + assert memory_after_cleanup < memory_increase / 2 + + def test_large_image_memory_usage(self, mock_onnx_setup): + """Test memory usage with large images.""" + gc.collect() + initial_memory = get_memory_usage() + + # Create large image + large_image = Image.new("RGB", (2048, 1536), color=(100, 150, 200)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (2048, 1536), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process large image + result = remove_background(large_image) + + peak_memory = get_memory_usage() + memory_increase = peak_memory - initial_memory + + # Assertions + assert isinstance(result, Image.Image) + assert result.size == (2048, 1536) + + # Large images should still be manageable + assert memory_increase < 1000 # Should not exceed 1GB increase + + print( + f"Large image (2048x1536) memory increase: {memory_increase:.2f}MB" + ) + + # Clean up + del result + del large_image + gc.collect() + + def test_batch_processing_memory_usage(self, mock_onnx_setup): + """Test memory usage during batch processing.""" + gc.collect() + initial_memory = get_memory_usage() + + # Create batch of images + batch_size = 10 + test_images = [ + Image.new("RGB", (512, 384), color=(i * 25, i * 20, i * 15)) + for i in range(batch_size) + ] + + with patch("withoutbg.core.remove_background") as mock_remove_bg: + # Mock individual processing + mock_results = [] + for image in test_images: + mock_result = Image.new("RGBA", image.size, color=(100, 150, 200, 128)) + mock_results.append(mock_result) + + mock_remove_bg.side_effect = mock_results + + # Process batch + results = remove_background_batch(test_images) + + peak_memory = get_memory_usage() + memory_increase = peak_memory - initial_memory + + # Assertions + assert len(results) == batch_size + + # Memory usage should scale reasonably with batch size + expected_max_memory = batch_size * 50 # Rough estimate: 50MB per image + assert memory_increase < expected_max_memory + + print( + f"Batch processing ({batch_size} images) memory increase: " + f"{memory_increase:.2f}MB" + ) + + # Clean up + del results + del test_images + gc.collect() + + def test_sequential_processing_memory_accumulation(self, mock_onnx_setup): + """Test that sequential processing doesn't accumulate memory.""" + gc.collect() + initial_memory = get_memory_usage() + + memory_readings = [initial_memory] + + # Process multiple images sequentially + for i in range(5): + test_image = Image.new("RGB", (512, 384), color=(i * 50, i * 30, i * 20)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (512, 384), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process image + result = remove_background(test_image) + + # Clean up immediately + del result + del test_image + gc.collect() + + # Record memory + current_memory = get_memory_usage() + memory_readings.append(current_memory) + + # Check memory accumulation + final_memory = memory_readings[-1] + memory_growth = final_memory - initial_memory + + # Should not accumulate significant memory + assert memory_growth < 100 # Should not grow by more than 100MB + + print(f"Sequential processing memory growth: {memory_growth:.2f}MB") + + # Check that memory didn't continuously increase + max_memory = max(memory_readings) + memory_variance = max_memory - min(memory_readings) + assert memory_variance < 200 # Reasonable variance + + def test_model_initialization_memory_usage(self): + """Test memory usage during model initialization.""" + gc.collect() + initial_memory = get_memory_usage() + + with patch("withoutbg.models.ort.InferenceSession") as mock_session: + with patch("pathlib.Path.exists", return_value=True): + mock_session.return_value = Mock() + + # Initialize model + model = SnapModel() + + post_init_memory = get_memory_usage() + memory_increase = post_init_memory - initial_memory + + # Assertions + assert model is not None + assert ( + memory_increase < 100 + ) # Model init should not use excessive memory + + print(f"Model initialization memory increase: {memory_increase:.2f}MB") + + # Clean up + del model + gc.collect() + + def test_preprocessing_memory_efficiency(self, mock_onnx_setup): + """Test memory efficiency of preprocessing operations.""" + gc.collect() + initial_memory = get_memory_usage() + + test_image = Image.new("RGB", (1024, 768), color=(128, 64, 192)) + model = SnapModel() + + # Test preprocessing + preprocessed = model._preprocess_for_depth(test_image, 518, 518, 14) + + post_preprocess_memory = get_memory_usage() + memory_increase = post_preprocess_memory - initial_memory + + # Assertions + assert isinstance(preprocessed, np.ndarray) + assert memory_increase < 50 # Preprocessing should be memory efficient + + print(f"Preprocessing memory increase: {memory_increase:.2f}MB") + + # Clean up + del preprocessed + del test_image + del model + gc.collect() + + def test_memory_leak_detection(self, mock_onnx_setup): + """Test for potential memory leaks in repeated processing.""" + gc.collect() + initial_memory = get_memory_usage() + + # Process the same image multiple times + for _iteration in range(10): + test_image = Image.new("RGB", (256, 256), color=(100, 150, 200)) + + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + mock_alpha = Image.new("L", (256, 256), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process image + result = remove_background(test_image) + + # Immediately clean up + del result + del test_image + gc.collect() + + final_memory = get_memory_usage() + memory_growth = final_memory - initial_memory + + # Should not leak significant memory + assert ( + memory_growth < 50 + ) # Should not grow by more than 50MB after 10 iterations + + print(f"Memory leak test - Growth after 10 iterations: {memory_growth:.2f}MB") + + def test_numpy_array_memory_management(self, mock_onnx_setup): + """Test memory management of numpy arrays in processing.""" + gc.collect() + initial_memory = get_memory_usage() + + test_image = Image.new("RGB", (512, 512), color=(128, 64, 192)) + + # Convert to numpy array and back + image_array = np.array(test_image) + processed_array = image_array.copy() + processed_array[:, :, 0] = 255 # Modify array + + result_image = Image.fromarray(processed_array) + + post_processing_memory = get_memory_usage() + memory_increase = post_processing_memory - initial_memory + + # Clean up arrays + del image_array + del processed_array + del result_image + del test_image + gc.collect() + + final_memory = get_memory_usage() + memory_after_cleanup = final_memory - initial_memory + + # Assertions + assert memory_increase < 100 # Array operations should be reasonable + assert memory_after_cleanup < memory_increase / 2 # Should clean up well + + print( + f"Numpy array memory - Peak: {memory_increase:.2f}MB, " + f"After cleanup: {memory_after_cleanup:.2f}MB" + ) + + def test_image_format_memory_usage(self, mock_onnx_setup): + """Test memory usage with different image formats.""" + formats_memory = {} + + # Test different image formats + formats = ["RGB", "RGBA", "L"] + sizes = [(512, 512), (1024, 768)] + + for fmt in formats: + for size in sizes: + gc.collect() + initial_memory = get_memory_usage() + + # Create image in specific format + if fmt == "RGB": + test_image = Image.new(fmt, size, color=(128, 64, 192)) + elif fmt == "RGBA": + test_image = Image.new(fmt, size, color=(128, 64, 192, 200)) + elif fmt == "L": + test_image = Image.new(fmt, size, color=128) + + # Convert to array and back (simulate processing) + array = np.array(test_image) + result_image = Image.fromarray(array) + + peak_memory = get_memory_usage() + memory_increase = peak_memory - initial_memory + + key = f"{fmt}_{size[0]}x{size[1]}" + formats_memory[key] = memory_increase + + # Clean up + del test_image + del array + del result_image + gc.collect() + + # Print results + for key, memory in formats_memory.items(): + print(f"Format {key} memory usage: {memory:.2f}MB") + + # Basic assertions + for memory in formats_memory.values(): + assert memory < 200 # No format should use excessive memory + + def test_temporary_file_memory_usage(self, mock_onnx_setup): + """Test memory usage when processing temporary files.""" + gc.collect() + initial_memory = get_memory_usage() + + # Create temporary image file + with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_file: + test_image = Image.new("RGB", (512, 384), color=(100, 150, 200)) + test_image.save(tmp_file.name, "JPEG") + + try: + with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch( + "withoutbg.models.SnapModel._refiner_stage" + ) as mock_refiner: + mock_alpha = Image.new("L", (512, 384), color=128) + mock_matting.return_value = mock_alpha + mock_refiner.return_value = mock_alpha + + # Process file + result = remove_background(tmp_file.name) + + peak_memory = get_memory_usage() + memory_increase = peak_memory - initial_memory + + # Assertions + assert isinstance(result, Image.Image) + assert ( + memory_increase < 100 + ) # File processing should be efficient + + print( + f"Temporary file processing memory increase: " + f"{memory_increase:.2f}MB" + ) + + # Clean up + del result + gc.collect() + + finally: + os.unlink(tmp_file.name) diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000..1c68337 --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,357 @@ +"""Integration tests for the StudioAPI class.""" + +import base64 +import io +import json +from pathlib import Path +from unittest.mock import Mock, patch + +import pytest +from PIL import Image + +from src.withoutbg.api import StudioAPI +from src.withoutbg.exceptions import APIError + + +class TestStudioAPI: + """Test cases for StudioAPI integration.""" + + @pytest.fixture + def api_client(self): + """Create StudioAPI client with test API key.""" + return StudioAPI(api_key="test_api_key") + + @pytest.fixture + def test_image_path(self): + """Path to test ice cream image.""" + return Path(__file__).parent / "fixtures" / "images" / "test-ice-cream.png" + + @pytest.fixture + def mock_alpha_path(self): + """Path to mock alpha channel image.""" + return ( + Path(__file__).parent + / "fixtures" + / "images" + / "test-ice-cream-api-service-mock-alpha.png" + ) + + @pytest.fixture + def test_image(self, test_image_path): + """Load test image.""" + return Image.open(test_image_path) + + @pytest.fixture + def mock_alpha_image(self, mock_alpha_path): + """Load mock alpha channel image.""" + return Image.open(mock_alpha_path) + + def _create_mock_alpha_response(self, mock_alpha_image): + """Create mock API response using real alpha channel image.""" + # Convert alpha image to base64 + buffer = io.BytesIO() + mock_alpha_image.save(buffer, format="PNG") + alpha_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + + return {"alpha_base64": alpha_base64} + + @patch("src.withoutbg.api.requests.Session.post") + def test_remove_background_success( + self, mock_post, api_client, test_image, mock_alpha_image + ): + """Test successful background removal with real alpha channel.""" + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + # Test background removal + result = api_client.remove_background(test_image) + + # Verify result + assert isinstance(result, Image.Image) + assert result.mode == "RGBA" + assert result.size == test_image.size + + # Verify API call + mock_post.assert_called_once() + call_args = mock_post.call_args + assert call_args[0][0].endswith("/v1.0/alpha-channel-base64") + assert "image_base64" in call_args[1]["json"] + + @patch("src.withoutbg.api.requests.Session.post") + def test_remove_background_with_file_path( + self, mock_post, api_client, test_image_path, mock_alpha_image + ): + """Test background removal using file path.""" + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + # Test with file path + result = api_client.remove_background(test_image_path) + + # Verify result + assert isinstance(result, Image.Image) + assert result.mode == "RGBA" + + @patch("src.withoutbg.api.requests.Session.post") + def test_remove_background_with_bytes( + self, mock_post, api_client, test_image_path, mock_alpha_image + ): + """Test background removal using bytes input.""" + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + # Load image as bytes + with open(test_image_path, "rb") as f: + image_bytes = f.read() + + # Test with bytes + result = api_client.remove_background(image_bytes) + + # Verify result + assert isinstance(result, Image.Image) + assert result.mode == "RGBA" + + @patch("src.withoutbg.api.requests.Session.post") + def test_image_resizing_for_large_images( + self, mock_post, api_client, mock_alpha_image + ): + """Test that large images are resized for API transmission.""" + # Create large test image + large_image = Image.new("RGB", (2048, 1536), color=(255, 0, 0)) + + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + # Test background removal + result = api_client.remove_background(large_image) + + # Verify result maintains original size + assert result.size == large_image.size + assert result.mode == "RGBA" + + # Verify API was called (image should be resized internally) + mock_post.assert_called_once() + + @patch("src.withoutbg.api.requests.Session.post") + def test_alpha_channel_application( + self, mock_post, api_client, test_image, mock_alpha_image + ): + """Test that alpha channel is correctly applied to original image.""" + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + # Test background removal + result = api_client.remove_background(test_image) + + # Verify alpha channel was applied + assert result.mode == "RGBA" + + # Check that result has alpha channel data + alpha_channel = result.split()[-1] # Get alpha channel + alpha_array = list(alpha_channel.getdata()) + + # Should have varying alpha values (not all 255 or all 0) + unique_alpha_values = set(alpha_array) + assert len(unique_alpha_values) > 1, "Alpha channel should have varying values" + + @patch("src.withoutbg.api.requests.Session.post") + def test_api_error_401_unauthorized(self, mock_post, api_client, test_image): + """Test 401 unauthorized error handling.""" + mock_response = Mock() + mock_response.status_code = 401 + mock_response.ok = False + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Invalid API key"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_api_error_402_insufficient_credits( + self, mock_post, api_client, test_image + ): + """Test 402 insufficient credits error handling.""" + mock_response = Mock() + mock_response.status_code = 402 + mock_response.ok = False + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Insufficient credits"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_api_error_403_expired_credits(self, mock_post, api_client, test_image): + """Test 403 expired credits error handling.""" + mock_response = Mock() + mock_response.status_code = 403 + mock_response.ok = False + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Credits expired"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_api_error_429_rate_limit(self, mock_post, api_client, test_image): + """Test 429 rate limit error handling.""" + mock_response = Mock() + mock_response.status_code = 429 + mock_response.ok = False + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Rate limit exceeded"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_api_error_500_server_error(self, mock_post, api_client, test_image): + """Test 500 server error handling.""" + mock_response = Mock() + mock_response.status_code = 500 + mock_response.ok = False + mock_response.json.return_value = {"error": "Internal server error"} + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="API request failed"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_invalid_response_format(self, mock_post, api_client, test_image): + """Test handling of invalid API response format.""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = {"status": "success"} # Missing alpha_base64 + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Invalid API response format"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.post") + def test_malformed_json_response(self, mock_post, api_client, test_image): + """Test handling of malformed JSON response.""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "", 0) + mock_response.text = "not valid json" + mock_post.return_value = mock_response + + with pytest.raises(APIError, match="Unexpected error"): + api_client.remove_background(test_image) + + def test_no_api_key_error(self): + """Test error when no API key is provided.""" + api_client = StudioAPI() # No API key + test_image = Image.new("RGB", (100, 100)) + + with pytest.raises(APIError, match="API key required"): + api_client.remove_background(test_image) + + @patch("src.withoutbg.api.requests.Session.get") + def test_get_usage_success(self, mock_get, api_client): + """Test successful usage information retrieval.""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "credits_used": 150, + "credits_total": 1000, + "credits_remaining": 850, + } + mock_response.raise_for_status.return_value = None + mock_get.return_value = mock_response + + result = api_client.get_usage() + + assert result["credits_used"] == 150 + assert result["credits_remaining"] == 850 + mock_get.assert_called_once_with("https://api.withoutbg.com/available-credit") + + @patch("src.withoutbg.api.requests.Session.get") + def test_get_models_success(self, mock_get, api_client): + """Test successful models information retrieval.""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "models": ["snap", "studio", "premium"], + "default": "studio", + } + mock_response.raise_for_status.return_value = None + mock_get.return_value = mock_response + + result = api_client.get_models() + + assert "models" in result + assert result["default"] == "studio" + mock_get.assert_called_once_with("https://api.withoutbg.com/v1/models") + + def test_get_usage_no_api_key(self): + """Test usage retrieval without API key.""" + api_client = StudioAPI() # No API key + + with pytest.raises(APIError, match="API key required"): + api_client.get_usage() + + @patch("src.withoutbg.api.requests.Session.post") + def test_network_error_handling(self, mock_post, api_client, test_image): + """Test network error handling.""" + import requests + + mock_post.side_effect = requests.ConnectionError("Connection failed") + + with pytest.raises(APIError, match="Network error"): + api_client.remove_background(test_image) + + def test_unsupported_image_type(self, api_client): + """Test error handling for unsupported image types.""" + with pytest.raises(APIError, match="Unsupported image type"): + api_client.remove_background({"invalid": "type"}) + + @patch("src.withoutbg.api.requests.Session.post") + def test_custom_base_url(self, mock_post, mock_alpha_image): + """Test API client with custom base URL.""" + custom_api = StudioAPI(api_key="test_key", base_url="https://custom.api.com/") + + # Setup mock response + mock_response = Mock() + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = self._create_mock_alpha_response( + mock_alpha_image + ) + mock_post.return_value = mock_response + + test_image = Image.new("RGB", (100, 100)) + result = custom_api.remove_background(test_image) + + # Verify custom URL was used + call_args = mock_post.call_args + assert call_args[0][0] == "https://custom.api.com/v1.0/alpha-channel-base64" + assert isinstance(result, Image.Image) diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..828d404 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,727 @@ +"""Comprehensive tests for the CLI module.""" + +import tempfile +from unittest.mock import patch + +import pytest +from click.testing import CliRunner +from PIL import Image + +from src.withoutbg.cli import main +from src.withoutbg.exceptions import WithoutBGError + + +class TestCLIUnit: + """Unit tests for CLI command parsing and validation.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + def test_version_option(self): + """Test --version option displays version correctly.""" + result = self.runner.invoke(main, ["--version"]) + assert result.exit_code == 0 + assert "version" in result.output.lower() + + def test_help_option(self): + """Test --help option displays help text.""" + result = self.runner.invoke(main, ["--help"]) + assert result.exit_code == 0 + assert "Remove background from images using AI" in result.output + assert "Examples:" in result.output + + def test_help_contains_all_options(self): + """Test help text contains all available options.""" + result = self.runner.invoke(main, ["--help"]) + help_text = result.output + + expected_options = [ + "--output", + "-o", + "--api-key", + "--use-api", + "--model", + "--batch", + "--output-dir", + "--format", + "--quality", + "--verbose", + "-v", + ] + + for option in expected_options: + assert option in help_text + + def test_missing_input_file_error(self): + """Test error when input file is missing.""" + result = self.runner.invoke(main, ["nonexistent.jpg"]) + assert result.exit_code != 0 + assert "does not exist" in result.output + + def test_model_choice_validation(self): + """Test model option accepts only valid choices.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + # Valid choice + result = self.runner.invoke(main, [temp_file.name, "--model", "snap"]) + # Should fail because of processing, not validation + assert "Invalid value" not in result.output + + # Invalid choice + result = self.runner.invoke(main, [temp_file.name, "--model", "invalid"]) + assert result.exit_code != 0 + assert "Invalid value" in result.output + + def test_format_choice_validation(self): + """Test format option accepts only valid choices.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + # Valid choice + result = self.runner.invoke(main, [temp_file.name, "--format", "png"]) + # Should fail because of processing, not validation + assert "Invalid value" not in result.output + + # Invalid choice + result = self.runner.invoke(main, [temp_file.name, "--format", "invalid"]) + assert result.exit_code != 0 + assert "Invalid value" in result.output + + def test_quality_validation(self): + """Test quality option accepts integers.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + # Valid integer + result = self.runner.invoke(main, [temp_file.name, "--quality", "80"]) + # Should fail because of processing, not validation + assert "Invalid value" not in result.output + + # Invalid type + result = self.runner.invoke( + main, [temp_file.name, "--quality", "not_a_number"] + ) + assert result.exit_code != 0 + assert "Invalid value" in result.output + + def test_verbose_flag_parsing(self): + """Test verbose flag is parsed correctly.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + result = self.runner.invoke(main, [temp_file.name, "--verbose"]) + # Should parse without validation error + assert "Invalid value" not in result.output + + def test_batch_flag_parsing(self): + """Test batch flag is parsed correctly.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + result = self.runner.invoke(main, [temp_file.name, "--batch"]) + # Should parse without validation error + assert "Invalid value" not in result.output + + def test_use_api_flag_parsing(self): + """Test use-api flag is parsed correctly.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + result = self.runner.invoke(main, [temp_file.name, "--use-api"]) + # Should fail because no API key provided + assert "API key required" in result.output + + def test_api_key_from_env_var(self): + """Test API key can be provided via environment variable.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + env = {"WITHOUTBG_API_KEY": "test_key"} + result = self.runner.invoke(main, [temp_file.name, "--use-api"], env=env) + # Should not complain about missing API key + assert "API key required" not in result.output + + def test_api_key_option_override(self): + """Test --api-key option overrides environment variable.""" + with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: + env = {"WITHOUTBG_API_KEY": "env_key"} + result = self.runner.invoke( + main, [temp_file.name, "--api-key", "option_key"], env=env + ) + # Should not complain about missing API key + assert "API key required" not in result.output + + +class TestCLIIntegration: + """Integration tests with mocked backends.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + @pytest.fixture + def test_image_file(self, temp_dir): + """Create a test image file.""" + image = Image.new("RGB", (256, 256), color=(255, 0, 0)) + image_path = temp_dir / "test_image.jpg" + image.save(image_path) + return image_path + + @pytest.fixture + def test_images_directory(self, temp_dir): + """Create directory with multiple test images.""" + images_dir = temp_dir / "images" + images_dir.mkdir() + + # Create various test images + formats = [ + ("image1.jpg", "RGB"), + ("image2.png", "RGBA"), + ("image3.webp", "RGB"), + ] + for filename, mode in formats: + image = Image.new(mode, (200, 200), color=(255, 0, 0)) + image.save(images_dir / filename) + + return images_dir + + @patch("src.withoutbg.cli.remove_background") + def test_single_image_processing_snap_model(self, mock_remove_bg, test_image_file): + """Test processing single image with Snap model.""" + # Mock successful processing + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke(main, [str(test_image_file)]) + + assert result.exit_code == 0 + mock_remove_bg.assert_called_once_with( + test_image_file, api_key=None, model_name="snap" + ) + + @patch("src.withoutbg.cli.remove_background") + def test_single_image_processing_with_output_path( + self, mock_remove_bg, test_image_file, temp_dir + ): + """Test processing with custom output path.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + output_path = temp_dir / "custom_output.png" + result = self.runner.invoke( + main, [str(test_image_file), "--output", str(output_path)] + ) + + assert result.exit_code == 0 + assert output_path.exists() + + @patch("src.withoutbg.cli.remove_background") + def test_single_image_processing_studio_api(self, mock_remove_bg, test_image_file): + """Test processing with Studio API.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke( + main, [str(test_image_file), "--model", "studio", "--api-key", "test_key"] + ) + + assert result.exit_code == 0 + mock_remove_bg.assert_called_once_with( + test_image_file, api_key="test_key", model_name="studio" + ) + + @patch("src.withoutbg.cli.remove_background") + def test_use_api_flag_forces_studio_model(self, mock_remove_bg, test_image_file): + """Test that --use-api flag forces Studio model.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke( + main, [str(test_image_file), "--use-api", "--api-key", "test_key"] + ) + + assert result.exit_code == 0 + mock_remove_bg.assert_called_once_with( + test_image_file, api_key="test_key", model_name="studio" + ) + + @patch("src.withoutbg.cli.remove_background") + def test_batch_processing_directory( + self, mock_remove_bg, test_images_directory, temp_dir + ): + """Test batch processing of directory.""" + result_image = Image.new("RGBA", (200, 200), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + output_dir = temp_dir / "output" + result = self.runner.invoke( + main, + [str(test_images_directory), "--batch", "--output-dir", str(output_dir)], + ) + + assert result.exit_code == 0 + assert mock_remove_bg.call_count == 3 # 3 images in directory + assert output_dir.exists() + + @patch("src.withoutbg.cli.remove_background") + def test_batch_processing_creates_default_output_dir( + self, mock_remove_bg, test_images_directory + ): + """Test batch processing creates default output directory.""" + result_image = Image.new("RGBA", (200, 200), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke(main, [str(test_images_directory), "--batch"]) + + assert result.exit_code == 0 + default_output_dir = test_images_directory / "withoutbg-results" + assert default_output_dir.exists() + + @patch("src.withoutbg.cli.remove_background") + def test_output_format_options(self, mock_remove_bg, test_image_file, temp_dir): + """Test different output format options.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + formats = ["png", "jpg", "webp"] + for fmt in formats: + output_path = temp_dir / f"output.{fmt}" + result = self.runner.invoke( + main, + [str(test_image_file), "--output", str(output_path), "--format", fmt], + ) + + assert ( + result.exit_code == 0 + ), f"Format {fmt} failed with output: {result.output}" + assert output_path.exists() + + # Verify the file was saved with correct PIL format + saved_image = Image.open(output_path) + expected_pil_formats = {"png": "PNG", "jpg": "JPEG", "webp": "WEBP"} + expected_format = expected_pil_formats[fmt] + assert ( + saved_image.format == expected_format + ), f"Expected {expected_format}, got {saved_image.format}" + + @patch("src.withoutbg.cli.remove_background") + def test_jpeg_quality_setting(self, mock_remove_bg, test_image_file, temp_dir): + """Test JPEG quality setting.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + output_path = temp_dir / "output.jpg" + result = self.runner.invoke( + main, + [ + str(test_image_file), + "--output", + str(output_path), + "--format", + "jpg", + "--quality", + "80", + ], + ) + + assert ( + result.exit_code == 0 + ), f"JPEG quality test failed with output: {result.output}" + assert output_path.exists() + + @patch("src.withoutbg.cli.remove_background") + def test_verbose_output(self, mock_remove_bg, test_image_file): + """Test verbose output mode.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke(main, [str(test_image_file), "--verbose"]) + + assert result.exit_code == 0 + assert "Using Local Snap model" in result.output + assert "Processing:" in result.output + assert "โœ… Processing complete!" in result.output + + @patch("src.withoutbg.cli.remove_background") + def test_verbose_output_with_api(self, mock_remove_bg, test_image_file): + """Test verbose output with API processing.""" + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke( + main, + [str(test_image_file), "--use-api", "--api-key", "test_key", "--verbose"], + ) + + assert result.exit_code == 0 + assert "Using Studio API" in result.output + + def test_output_filename_generation(self, test_image_file, temp_dir): + """Test automatic output filename generation.""" + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) + mock_remove_bg.return_value = result_image + + result = self.runner.invoke(main, [str(test_image_file)]) + + assert result.exit_code == 0 + # Should create file with -withoutbg suffix + expected_output = ( + test_image_file.parent / f"{test_image_file.stem}-withoutbg.png" + ) + assert expected_output.exists() + + +class TestCLIErrorHandling: + """Test error handling scenarios.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + def test_api_key_required_error(self, temp_dir): + """Test error when API key is required but not provided.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + result = self.runner.invoke(main, [str(image_path), "--use-api"]) + + assert result.exit_code == 1 + assert "API key required" in result.output + + def test_withoutbg_error_handling(self, temp_dir): + """Test handling of WithoutBGError exceptions.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.side_effect = WithoutBGError("Test error") + + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 1 + assert "Error: Test error" in result.output + + def test_keyboard_interrupt_handling(self, temp_dir): + """Test handling of KeyboardInterrupt (Ctrl+C).""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.side_effect = KeyboardInterrupt() + + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 1 + assert "โŒ Processing cancelled" in result.output + + def test_unexpected_error_handling(self, temp_dir): + """Test handling of unexpected exceptions.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.side_effect = ValueError("Unexpected error") + + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 1 + assert "Unexpected error: Unexpected error" in result.output + + def test_batch_processing_no_images_error(self, temp_dir): + """Test error when no images found in directory for batch processing.""" + empty_dir = temp_dir / "empty" + empty_dir.mkdir() + + result = self.runner.invoke(main, [str(empty_dir), "--batch"]) + + assert result.exit_code == 1 + assert "No image files found" in result.output + + @patch("src.withoutbg.cli.remove_background") + def test_batch_processing_partial_failures(self, mock_remove_bg, temp_dir): + """Test batch processing continues on individual failures.""" + # Create test images + images_dir = temp_dir / "images" + images_dir.mkdir() + + for i in range(3): + image = Image.new("RGB", (100, 100)) + image.save(images_dir / f"image{i}.jpg") + + # Mock failure for second image + def side_effect(path, **kwargs): + if "image1" in str(path): + raise ValueError("Processing failed") + return Image.new("RGBA", (100, 100)) + + mock_remove_bg.side_effect = side_effect + + result = self.runner.invoke(main, [str(images_dir), "--batch", "--verbose"]) + + assert result.exit_code == 0 # Should continue processing + assert "โŒ Failed to process" in result.output + + def test_permission_error_handling(self, temp_dir): + """Test handling of permission errors when writing output.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + # Mock save to raise permission error + with patch.object( + Image.Image, "save", side_effect=PermissionError("Permission denied") + ): + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 1 + assert "Unexpected error" in result.output + + +class TestCLIEdgeCases: + """Test edge cases and special scenarios.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + def test_unicode_filename_handling(self, temp_dir): + """Test handling of unicode filenames.""" + # Create image with unicode filename + unicode_filename = "ๆต‹่ฏ•ๅ›พ็‰‡_รฑรกme.jpg" + image_path = temp_dir / unicode_filename + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 0 + + @patch("src.withoutbg.cli.remove_background") + def test_large_image_processing(self, mock_remove_bg, temp_dir): + """Test processing of large images.""" + # Create large test image + large_image = Image.new("RGB", (4000, 3000), color=(255, 0, 0)) + image_path = temp_dir / "large_image.jpg" + large_image.save(image_path) + + mock_remove_bg.return_value = Image.new("RGBA", (4000, 3000)) + + result = self.runner.invoke(main, [str(image_path)]) + + assert result.exit_code == 0 + + def test_corrupted_image_handling(self, temp_dir): + """Test handling of corrupted image files.""" + # Create a file that looks like an image but is corrupted + corrupted_path = temp_dir / "corrupted.jpg" + with open(corrupted_path, "wb") as f: + f.write(b"This is not a valid image file") + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.side_effect = Exception("Cannot identify image file") + + result = self.runner.invoke(main, [str(corrupted_path)]) + + assert result.exit_code == 1 + + @patch("src.withoutbg.cli.remove_background") + def test_various_image_formats(self, mock_remove_bg, temp_dir): + """Test processing of various input image formats.""" + formats = [("test.jpg", "RGB"), ("test.png", "RGBA"), ("test.bmp", "RGB")] + + for filename, mode in formats: + image = Image.new(mode, (100, 100)) + image_path = temp_dir / filename + image.save(image_path) + + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + result = self.runner.invoke(main, [str(image_path)]) + assert result.exit_code == 0 + + def test_single_file_with_batch_flag(self, temp_dir): + """Test using --batch flag with single file.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + result = self.runner.invoke(main, [str(image_path), "--batch"]) + + assert result.exit_code == 0 + + @patch("src.withoutbg.cli.remove_background") + def test_rgba_to_jpg_conversion(self, mock_remove_bg, temp_dir): + """Test conversion from RGBA to JPG format.""" + image_path = temp_dir / "test.png" + Image.new("RGBA", (100, 100), color=(255, 0, 0, 128)).save(image_path) + + # Mock returns RGBA image + mock_remove_bg.return_value = Image.new( + "RGBA", (100, 100), color=(255, 0, 0, 128) + ) + + output_path = temp_dir / "output.jpg" + result = self.runner.invoke( + main, [str(image_path), "--output", str(output_path), "--format", "jpg"] + ) + + assert ( + result.exit_code == 0 + ), f"RGBA to JPG conversion failed with output: {result.output}" + assert output_path.exists() + + # Verify the saved image is RGB (not RGBA) + saved_image = Image.open(output_path) + assert saved_image.mode == "RGB" + + def test_directory_as_input_without_batch_flag(self, temp_dir): + """Test providing directory as input without --batch flag.""" + images_dir = temp_dir / "images" + images_dir.mkdir() + Image.new("RGB", (100, 100)).save(images_dir / "test.jpg") + + with patch("src.withoutbg.cli.remove_background") as mock_remove_bg: + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + result = self.runner.invoke(main, [str(images_dir)]) + + # Should process as batch automatically + assert result.exit_code == 0 + + def test_empty_api_key_environment_variable(self, temp_dir): + """Test behavior with empty API key environment variable.""" + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + env = {"WITHOUTBG_API_KEY": ""} + result = self.runner.invoke(main, [str(image_path), "--use-api"], env=env) + + assert result.exit_code == 1 + assert "API key required" in result.output + + +class TestCLITestUtilities: + """Test helper functions and utilities for CLI testing.""" + + def test_create_test_image_file(self, temp_dir): + """Test utility function for creating test image files.""" + + def create_test_image(path, size=(100, 100), color=(255, 0, 0), mode="RGB"): + """Create a test image file.""" + image = Image.new(mode, size, color) + image.save(path) + return path + + image_path = create_test_image(temp_dir / "test.jpg") + assert image_path.exists() + + # Verify image properties + image = Image.open(image_path) + assert image.size == (100, 100) + assert image.mode == "RGB" + + def test_create_corrupted_file(self, temp_dir): + """Test utility for creating corrupted image files.""" + + def create_corrupted_image_file(path, content=b"corrupted"): + """Create a corrupted image file for testing error handling.""" + with open(path, "wb") as f: + f.write(content) + return path + + corrupted_path = create_corrupted_image_file(temp_dir / "corrupted.jpg") + assert corrupted_path.exists() + + # Verify it can't be opened as valid image + with pytest.raises((OSError, IOError)): + Image.open(corrupted_path) + + def test_verify_output_file_properties(self, temp_dir): + """Test utility for verifying output file properties.""" + + def verify_output_properties( + output_path, expected_format=None, expected_mode=None, expected_size=None + ): + """Verify properties of output image file.""" + assert output_path.exists(), f"Output file {output_path} does not exist" + + image = Image.open(output_path) + + if expected_format: + assert image.format.lower() == expected_format.lower() + if expected_mode: + assert image.mode == expected_mode + if expected_size: + assert image.size == expected_size + + return image + + # Create test output file + test_image = Image.new("RGBA", (200, 150), color=(255, 0, 0, 128)) + output_path = temp_dir / "output.png" + test_image.save(output_path) + + # Test verification + verified_image = verify_output_properties( + output_path, + expected_format="PNG", + expected_mode="RGBA", + expected_size=(200, 150), + ) + assert verified_image.size == (200, 150) + + +class TestCLIPerformance: + """Performance-related tests for CLI operations.""" + + @pytest.mark.performance + @patch("src.withoutbg.cli.remove_background") + def test_batch_processing_performance(self, mock_remove_bg, temp_dir): + """Test performance of batch processing multiple images.""" + import time + + # Create multiple test images + images_dir = temp_dir / "performance_test" + images_dir.mkdir() + + num_images = 10 + for i in range(num_images): + image = Image.new("RGB", (512, 512)) + image.save(images_dir / f"image_{i:03d}.jpg") + + mock_remove_bg.return_value = Image.new("RGBA", (512, 512)) + + runner = CliRunner() + start_time = time.time() + + result = runner.invoke(main, [str(images_dir), "--batch"]) + + end_time = time.time() + processing_time = end_time - start_time + + assert result.exit_code == 0 + assert mock_remove_bg.call_count == num_images + + # Performance assertion (should process 10 images in reasonable time) + # This is quite lenient since we're mocking the actual processing + assert ( + processing_time < 10.0 + ), f"Batch processing took {processing_time:.2f}s, expected < 10s" + + @pytest.mark.performance + @patch("src.withoutbg.cli.remove_background") + def test_cli_startup_performance(self, mock_remove_bg, temp_dir): + """Test CLI startup and initialization performance.""" + import time + + image_path = temp_dir / "test.jpg" + Image.new("RGB", (100, 100)).save(image_path) + + mock_remove_bg.return_value = Image.new("RGBA", (100, 100)) + + runner = CliRunner() + start_time = time.time() + + result = runner.invoke(main, [str(image_path)]) + + end_time = time.time() + total_time = end_time - start_time + + assert result.exit_code == 0 + # CLI should start and process quickly with mocked backend + assert total_time < 5.0, f"CLI execution took {total_time:.2f}s, expected < 5s" diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py new file mode 100644 index 0000000..4c874c0 --- /dev/null +++ b/tests/test_cli_e2e.py @@ -0,0 +1,585 @@ +"""End-to-end tests for CLI with real file processing.""" + +import os +import shutil + +import pytest +from click.testing import CliRunner +from PIL import Image + +from src.withoutbg.cli import main + + +class TestCLIE2E: + """End-to-end tests using real image processing.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + @pytest.fixture + def temp_output_dir(self, temp_dir): + """Create temporary output directory.""" + output_dir = temp_dir / "e2e_outputs" + output_dir.mkdir() + return output_dir + + @pytest.mark.real_processing + def test_e2e_single_image_snap_model(self, real_test_image_path, temp_output_dir): + """Test end-to-end processing of single image with Snap model.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + output_path = temp_output_dir / "output_snap.png" + + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path), "--verbose"] + ) + + # Check command succeeded + assert result.exit_code == 0, f"Command failed with output: {result.output}" + + # Check output file was created + assert output_path.exists(), "Output file was not created" + + # Verify output image properties + output_image = Image.open(output_path) + original_image = Image.open(real_test_image_path) + + assert output_image.mode == "RGBA", "Output should be RGBA" + assert ( + output_image.size == original_image.size + ), "Output size should match input" + + # Check that alpha channel has variation (background was processed) + alpha_channel = output_image.split()[-1] + alpha_values = list(alpha_channel.getdata()) + unique_alpha_values = set(alpha_values) + assert len(unique_alpha_values) > 1, "Alpha channel should have varying values" + + @pytest.mark.real_processing + @pytest.mark.api + def test_e2e_single_image_studio_api(self, real_test_image_path, temp_output_dir): + """Test end-to-end processing with Studio API (if API key available).""" + api_key = os.getenv("WITHOUTBG_API_KEY") + if not api_key: + pytest.skip("API key not available for E2E testing") + + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + output_path = temp_output_dir / "output_studio.png" + + result = self.runner.invoke( + main, + [ + str(real_test_image_path), + "--output", + str(output_path), + "--model", + "studio", + "--api-key", + api_key, + "--verbose", + ], + ) + + # Check command succeeded + assert result.exit_code == 0, f"Command failed with output: {result.output}" + + # Check output file was created + assert output_path.exists(), "Output file was not created" + + # Verify output image properties + output_image = Image.open(output_path) + original_image = Image.open(real_test_image_path) + + assert output_image.mode == "RGBA", "Output should be RGBA" + assert ( + output_image.size == original_image.size + ), "Output size should match input" + + @pytest.mark.real_processing + def test_e2e_batch_processing(self, test_images_dir, temp_output_dir): + """Test end-to-end batch processing.""" + if not test_images_dir.exists(): + pytest.skip("Test images directory not available") + + # Copy test images to temporary directory to avoid modifying originals + temp_input_dir = temp_output_dir / "input_images" + shutil.copytree(test_images_dir, temp_input_dir) + + batch_output_dir = temp_output_dir / "batch_results" + + result = self.runner.invoke( + main, + [ + str(temp_input_dir), + "--batch", + "--output-dir", + str(batch_output_dir), + "--verbose", + ], + ) + + # Check command succeeded + assert result.exit_code == 0, f"Command failed with output: {result.output}" + + # Check output directory was created + assert batch_output_dir.exists(), "Batch output directory was not created" + + # Check that output files were created + input_images = list(temp_input_dir.glob("*.png")) + list( + temp_input_dir.glob("*.jpg") + ) + for input_image in input_images: + expected_output = batch_output_dir / f"{input_image.stem}-withoutbg.png" + assert ( + expected_output.exists() + ), f"Output file {expected_output} was not created" + + # Verify basic properties + output_image = Image.open(expected_output) + assert output_image.mode == "RGBA", "Batch output should be RGBA" + + @pytest.mark.real_processing + def test_e2e_different_output_formats(self, real_test_image_path, temp_output_dir): + """Test end-to-end processing with different output formats.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + formats = [("png", "RGBA"), ("jpg", "RGB"), ("webp", "RGBA")] + + for fmt, expected_mode in formats: + output_path = temp_output_dir / f"output.{fmt}" + + result = self.runner.invoke( + main, + [ + str(real_test_image_path), + "--output", + str(output_path), + "--format", + fmt, + "--quality", + "90", + ], + ) + + assert ( + result.exit_code == 0 + ), f"Failed to process {fmt} format: {result.output}" + assert output_path.exists(), f"Output file for {fmt} format was not created" + + # Verify format-specific properties + output_image = Image.open(output_path) + assert ( + output_image.mode == expected_mode + ), f"{fmt} output should be {expected_mode}" + + # Map format names to PIL's canonical format names + expected_pil_format = { + "jpg": "JPEG", + "jpeg": "JPEG", + "png": "PNG", + "webp": "WEBP", + } + expected_format = expected_pil_format.get(fmt.lower(), fmt.upper()) + assert ( + output_image.format == expected_format + ), f"Output format should be {expected_format}, got {output_image.format}" + + @pytest.mark.real_processing + def test_e2e_large_image_processing(self, temp_output_dir): + """Test end-to-end processing of large images.""" + # Create a large test image + large_image = Image.new("RGB", (2048, 1536), color=(100, 150, 200)) + large_image_path = temp_output_dir / "large_test_image.jpg" + large_image.save(large_image_path) + + output_path = temp_output_dir / "large_output.png" + + result = self.runner.invoke( + main, [str(large_image_path), "--output", str(output_path), "--verbose"] + ) + + assert result.exit_code == 0, f"Large image processing failed: {result.output}" + assert output_path.exists(), "Large image output was not created" + + # Verify output maintains original size + output_image = Image.open(output_path) + assert output_image.size == ( + 2048, + 1536, + ), "Large image output size should match input" + assert output_image.mode == "RGBA", "Large image output should be RGBA" + + @pytest.mark.real_processing + def test_e2e_unicode_filename(self, real_test_image_path, temp_output_dir): + """Test end-to-end processing with unicode filenames.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + # Copy test image to unicode filename + unicode_input_path = temp_output_dir / "ๆต‹่ฏ•ๅ›พ็‰‡_รฑรกme_emoji๐ŸŽจ.png" + shutil.copy2(real_test_image_path, unicode_input_path) + + unicode_output_path = temp_output_dir / "่พ“ๅ‡บๅ›พ็‰‡_rรฉsult_๐ŸŽฏ.png" + + result = self.runner.invoke( + main, + [ + str(unicode_input_path), + "--output", + str(unicode_output_path), + "--verbose", + ], + ) + + assert ( + result.exit_code == 0 + ), f"Unicode filename processing failed: {result.output}" + assert unicode_output_path.exists(), "Unicode filename output was not created" + + # Verify output properties + output_image = Image.open(unicode_output_path) + assert output_image.mode == "RGBA", "Unicode filename output should be RGBA" + + @pytest.mark.real_processing + def test_e2e_progress_indication(self, real_test_image_path, temp_output_dir): + """Test that progress indication works during real processing.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + output_path = temp_output_dir / "progress_test.png" + + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path), "--verbose"] + ) + + assert result.exit_code == 0 + + # Check that verbose output contains expected messages + output = result.output + assert "Processing:" in output, "Should show processing message" + assert "Output:" in output, "Should show output path" + assert "โœ… Processing complete!" in output, "Should show completion message" + + @pytest.mark.real_processing + def test_e2e_automatic_output_naming(self, real_test_image_path, temp_output_dir): + """Test automatic output file naming.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + # Copy test image to temp dir so we can predict output location + input_path = temp_output_dir / "input_test.jpg" + shutil.copy2(real_test_image_path, input_path) + + result = self.runner.invoke(main, [str(input_path)]) + + assert result.exit_code == 0, f"Auto-naming failed: {result.output}" + + # Check that output file was created with expected name + expected_output = temp_output_dir / "input_test-withoutbg.png" + assert expected_output.exists(), "Auto-named output file was not created" + + # Verify output properties + output_image = Image.open(expected_output) + assert output_image.mode == "RGBA", "Auto-named output should be RGBA" + + @pytest.mark.real_processing + def test_e2e_quality_comparison(self, real_test_image_path, temp_output_dir): + """Test that output quality varies with different quality settings.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + qualities = [50, 75, 95] + output_sizes = [] + + for quality in qualities: + output_path = temp_output_dir / f"quality_{quality}.jpg" + + result = self.runner.invoke( + main, + [ + str(real_test_image_path), + "--output", + str(output_path), + "--format", + "jpg", + "--quality", + str(quality), + ], + ) + + assert result.exit_code == 0, f"Quality {quality} processing failed" + assert output_path.exists(), f"Quality {quality} output not created" + + # Record file size + output_sizes.append(output_path.stat().st_size) + + # Higher quality should generally result in larger file sizes + # (though this isn't always guaranteed with different compression algorithms) + assert ( + len(set(output_sizes)) > 1 + ), "Different quality settings should produce different file sizes" + + @pytest.mark.real_processing + def test_e2e_model_comparison(self, real_test_image_path, temp_output_dir): + """Test comparison between different models (if API available).""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + # Test Snap model + snap_output = temp_output_dir / "snap_result.png" + result_snap = self.runner.invoke( + main, + [ + str(real_test_image_path), + "--output", + str(snap_output), + "--model", + "snap", + ], + ) + + assert result_snap.exit_code == 0, "Snap model processing failed" + assert snap_output.exists(), "Snap model output not created" + + # Test Studio API if available + api_key = os.getenv("WITHOUTBG_API_KEY") + if api_key: + studio_output = temp_output_dir / "studio_result.png" + result_studio = self.runner.invoke( + main, + [ + str(real_test_image_path), + "--output", + str(studio_output), + "--model", + "studio", + "--api-key", + api_key, + ], + ) + + assert result_studio.exit_code == 0, "Studio API processing failed" + assert studio_output.exists(), "Studio API output not created" + + # Compare basic properties + snap_image = Image.open(snap_output) + studio_image = Image.open(studio_output) + + assert ( + snap_image.size == studio_image.size + ), "Both models should produce same size output" + assert ( + snap_image.mode == studio_image.mode == "RGBA" + ), "Both should produce RGBA output" + + +class TestCLIE2EErrorHandling: + """E2E tests for error handling with real files.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + @pytest.mark.real_processing + def test_e2e_corrupted_image_file(self, temp_dir): + """Test handling of corrupted image files in real processing.""" + # Create a corrupted file that looks like an image + corrupted_path = temp_dir / "corrupted.jpg" + with open(corrupted_path, "wb") as f: + # Write some binary data that's not a valid image + f.write(b"\xff\xd8\xff\xe0" + b"invalid image data" * 100) + + result = self.runner.invoke(main, [str(corrupted_path), "--verbose"]) + + # Should fail gracefully + assert result.exit_code == 1 + # Error message should be informative + assert "error" in result.output.lower() or "Error" in result.output + + @pytest.mark.real_processing + def test_e2e_unsupported_file_format(self, temp_dir): + """Test handling of unsupported file formats.""" + # Create a text file with image extension + fake_image_path = temp_dir / "fake.jpg" + with open(fake_image_path, "w") as f: + f.write("This is not an image file") + + result = self.runner.invoke(main, [str(fake_image_path)]) + + assert result.exit_code == 1 + assert "error" in result.output.lower() or "Error" in result.output + + @pytest.mark.real_processing + def test_e2e_permission_denied_output(self, real_test_image_path, temp_dir): + """Test handling of permission errors for output files.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + # Create a read-only directory + readonly_dir = temp_dir / "readonly" + readonly_dir.mkdir() + readonly_dir.chmod(0o444) # Read-only + + try: + output_path = readonly_dir / "output.png" + + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path)] + ) + + # Should fail due to permission error + assert result.exit_code == 1 + + finally: + # Restore permissions for cleanup + readonly_dir.chmod(0o755) + + @pytest.mark.real_processing + def test_e2e_disk_space_simulation(self, real_test_image_path, temp_dir): + """Test behavior when output directory has limited space (simulation).""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + # This is a simulation - we can't easily test actual disk space limits + # But we can test that the CLI handles file system errors gracefully + output_path = temp_dir / "output.png" + + # Normal processing should work + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path)] + ) + + assert result.exit_code == 0 + assert output_path.exists() + + +class TestCLIE2EPerformance: + """Performance tests for real CLI operations.""" + + def setup_method(self): + """Set up test fixtures.""" + self.runner = CliRunner() + + @pytest.mark.performance + @pytest.mark.real_processing + def test_e2e_processing_time_snap_model( + self, real_test_image_path, temp_output_dir, performance_tracker + ): + """Test processing time for Snap model.""" + if not real_test_image_path.exists(): + pytest.skip("Real test image not available") + + import time + + output_path = temp_output_dir / "perf_test.png" + + start_time = time.time() + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path)] + ) + end_time = time.time() + + processing_time = end_time - start_time + performance_tracker.record_metric( + "snap_processing_time", processing_time, "seconds" + ) + + assert result.exit_code == 0 + assert output_path.exists() + + # Snap model should process reasonably quickly + # This is generous since it includes model download time on first run + performance_tracker.assert_performance( + "snap_processing_time", + 30.0, # 30 seconds max + "Snap model processing took too long", + ) + + @pytest.mark.performance + @pytest.mark.real_processing + def test_e2e_batch_processing_throughput( + self, test_images_dir, temp_output_dir, performance_tracker + ): + """Test batch processing throughput.""" + if not test_images_dir.exists(): + pytest.skip("Test images directory not available") + + import time + + # Copy test images to avoid modifying originals + temp_input_dir = temp_output_dir / "perf_input" + shutil.copytree(test_images_dir, temp_input_dir) + + batch_output_dir = temp_output_dir / "perf_batch_output" + + start_time = time.time() + result = self.runner.invoke( + main, + [str(temp_input_dir), "--batch", "--output-dir", str(batch_output_dir)], + ) + end_time = time.time() + + total_time = end_time - start_time + + # Count input images + input_images = list(temp_input_dir.glob("*.png")) + list( + temp_input_dir.glob("*.jpg") + ) + num_images = len(input_images) + + if num_images > 0: + time_per_image = total_time / num_images + performance_tracker.record_metric( + "batch_time_per_image", time_per_image, "seconds" + ) + performance_tracker.record_metric("batch_total_time", total_time, "seconds") + + assert result.exit_code == 0 + + # Each image should process in reasonable time + performance_tracker.assert_performance( + "batch_time_per_image", + 20.0, # 20 seconds per image max + "Batch processing too slow per image", + ) + + @pytest.mark.performance + @pytest.mark.real_processing + def test_e2e_memory_usage_large_image(self, temp_output_dir): + """Test memory usage with large images.""" + import os + + import psutil + + # 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Date: Mon, 18 Aug 2025 21:40:49 +0200 Subject: [PATCH 02/73] Fix README badges MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Remove non-functional PyPI and codecov badges, update CI badge to use correct workflow path. ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 36e3aec..2704a59 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,8 @@ **AI-powered background removal with local and cloud options** -[![PyPI version](https://badge.fury.io/py/withoutbg.svg)](https://badge.fury.io/py/withoutbg) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) -[![CI](https://github.com/withoutbg/withoutbg/workflows/CI/badge.svg)](https://github.com/withoutbg/withoutbg/actions) -[![codecov](https://codecov.io/gh/withoutbg/withoutbg/branch/main/graph/badge.svg)](https://codecov.io/gh/withoutbg/withoutbg) +[![CI](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml/badge.svg)](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml) Remove backgrounds from images instantly with AI. Choose between local processing (free) or cloud API (best quality). From fcd15aeb469731cec6601dc9eeb9d0284924d8a1 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 21:43:41 +0200 Subject: [PATCH 03/73] Fix CI: remove Python 3.8 from test matrix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Remove Python 3.8 from GitHub Actions test matrix to match package requirement of Python >=3.9. This resolves the CI error "Package 'withoutbg' requires a different Python: 3.8.18 not in '>=3.9'". ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- .github/workflows/ci.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a2c512e..4e9b771 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,15 +12,11 @@ jobs: strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ['3.8', '3.9', '3.10', '3.11', '3.12'] + python-version: ['3.9', '3.10', '3.11', '3.12'] exclude: # Reduce matrix for faster CI - - os: windows-latest - python-version: '3.8' - os: windows-latest python-version: '3.9' - - os: macos-latest - python-version: '3.8' - os: macos-latest python-version: '3.9' From 6cd35a20d17bcad466b206327d0edd47ed40acb1 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 21:51:09 +0200 Subject: [PATCH 04/73] Fix mypy type checking errors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add types-requests and types-pillow to dev dependencies - Fix PIL resampling filter references (LANCZOS -> Resampling.LANCZOS) - Fix numpy return type annotation by ensuring proper casting - Update deprecated actions in GitHub workflows (upload-artifact@v3 -> @v4, create-release@v1 -> softprops/action-gh-release@v1) ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- .github/workflows/ci.yml | 2 +- .github/workflows/release.yml | 7 ++----- pyproject.toml | 2 ++ src/withoutbg/models.py | 23 +++++++++++++---------- uv.lock | 4 ++++ 5 files changed, 22 insertions(+), 16 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4e9b771..9e55492 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -73,7 +73,7 @@ jobs: run: bandit -r src/ -f json -o bandit-report.json || true - name: Upload security results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: bandit-results path: bandit-report.json \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index ef22f24..d450f55 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -32,12 +32,9 @@ jobs: run: twine check dist/* - name: Create GitHub Release - uses: actions/create-release@v1 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + uses: softprops/action-gh-release@v1 with: - tag_name: ${{ github.ref }} - release_name: Release ${{ github.ref }} + name: Release ${{ github.ref_name }} body: | Changes in this Release - See CHANGELOG.md for details diff --git a/pyproject.toml b/pyproject.toml index eebf166..f3a74e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,6 +50,8 @@ dev = [ "ruff>=0.1.0", "mypy>=1.0.0", "pre-commit>=2.20.0", + "types-requests>=2.32.0", + "types-pillow>=10.2.0", ] [project.urls] diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index e6ff83a..05339f2 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -192,7 +192,7 @@ def _preprocess_for_depth( target_width: int, target_height: int, ensure_multiple_of: int = 1, - interpolation_method: int = Image.LANCZOS, + interpolation_method: int = Image.Resampling.LANCZOS, ) -> np.ndarray: """ Transforms an input image to prepare it for depth estimation by @@ -205,7 +205,7 @@ def _preprocess_for_depth( - ensure_multiple_of (int, optional): Ensures the dimensions of the resized image are multiples of this value. Defaults to 1. - interpolation_method (int, optional): The interpolation method to - use for resizing. Defaults to Image.LANCZOS. + use for resizing. Defaults to Image.Resampling.LANCZOS. Returns: - np.ndarray: The transformed image, normalized, and with the correct @@ -231,7 +231,7 @@ def _preprocess_for_depth( # Add batch dimension prepared_image = np.expand_dims(prepared_image, axis=0) - return prepared_image + return prepared_image.astype(np.float32) def _estimate_depth( self, @@ -239,7 +239,7 @@ def _estimate_depth( target_width: int = 518, target_height: int = 518, ensure_multiple_of: int = 14, - interpolation_method: int = Image.BICUBIC, + interpolation_method: int = Image.Resampling.BICUBIC, ) -> Image.Image: """ Stage 1: Depth estimation using Depth Anything V2 model. @@ -252,7 +252,7 @@ def _estimate_depth( - ensure_multiple_of (int, optional): Ensures dimensions are multiples of this value. Defaults to 14. - interpolation_method (int, optional): PIL interpolation method. - Defaults to Image.BICUBIC. + Defaults to Image.Resampling.BICUBIC. Returns: - PIL.Image.Image: The inverse depth map as a grayscale PIL Image (0-255 range). @@ -292,8 +292,8 @@ def _matting_stage( - PIL.Image.Image: Alpha channel (A1) as grayscale PIL Image. """ # Resize both images to 256x256 for matting model - rgb_resized = rgb_image.resize((256, 256), Image.LANCZOS) - depth_resized = depth_image.resize((256, 256), Image.LANCZOS) + rgb_resized = rgb_image.resize((256, 256), Image.Resampling.LANCZOS) + depth_resized = depth_image.resize((256, 256), Image.Resampling.LANCZOS) # Convert to numpy arrays and normalize to [0, 1] rgb_array = np.array(rgb_resized, dtype=np.float32) / 255.0 @@ -353,8 +353,8 @@ def _refiner_stage( rgb_array = np.array(rgb_image, dtype=np.float32) / 255.0 # Resize depth and alpha to match RGB image size - depth_resized = depth_image.resize(original_size, Image.LANCZOS) - alpha_resized = alpha1.resize(original_size, Image.LANCZOS) + depth_resized = depth_image.resize(original_size, Image.Resampling.LANCZOS) + alpha_resized = alpha1.resize(original_size, Image.Resampling.LANCZOS) # Convert to arrays and scale to [0, 1] depth_array = np.array(depth_resized, dtype=np.float32) / 255.0 @@ -454,7 +454,10 @@ def remove_background( alpha_channel = self.estimate_alpha(image) # Resize alpha to original image size - alpha_resized = alpha_channel.resize(original_size, Image.LANCZOS) + alpha_resized = alpha_channel.resize( + original_size, + Image.Resampling.LANCZOS + ) # Convert original image to RGBA if image.mode != "RGBA": diff --git a/uv.lock b/uv.lock index 67db1a5..7e87890 100644 --- a/uv.lock +++ b/uv.lock @@ -1690,6 +1690,8 @@ dev = [ { name = "pytest" }, { name = "pytest-cov" }, { name = "ruff" }, + { name = "types-pillow" }, + { name = "types-requests" }, ] gpu = [ { name = "onnxruntime-gpu" }, @@ -1724,6 +1726,8 @@ requires-dist = [ { name = "requests", specifier = ">=2.25.0" }, { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.1.0" }, { name = "tqdm", specifier = ">=4.60.0" }, + { name = "types-pillow", marker = "extra == 'dev'", specifier = ">=10.2.0" }, + { name = "types-requests", marker = "extra == 'dev'", specifier = ">=2.32.0" }, ] provides-extras = ["dev", "gpu"] From b97466cde002509ef5c971ec422e73ab189767b6 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 21:54:32 +0200 Subject: [PATCH 05/73] Fix black formatting in models.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- src/withoutbg/models.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index 05339f2..d352a65 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -455,9 +455,8 @@ def remove_background( # Resize alpha to original image size alpha_resized = alpha_channel.resize( - original_size, - Image.Resampling.LANCZOS - ) + original_size, Image.Resampling.LANCZOS + ) # Convert original image to RGBA if image.mode != "RGBA": From 9c968f9a08c01f8fa8afa03da28ae1a0318c2d2b Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:04:19 +0200 Subject: [PATCH 06/73] Fix mypy type checking errors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add explicit type annotation for numpy array to resolve "Returning Any" error. ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- src/withoutbg/models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index d352a65..4cd85c5 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -229,9 +229,9 @@ def _preprocess_for_depth( prepared_image = self._prepare_image(normalized_image) # Add batch dimension - prepared_image = np.expand_dims(prepared_image, axis=0) + prepared_image_batched: np.ndarray = np.expand_dims(prepared_image, axis=0) - return prepared_image.astype(np.float32) + return prepared_image_batched.astype(np.float32) def _estimate_depth( self, From 591dfccc15526f469c494df4766660078510d3cd Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:08:35 +0200 Subject: [PATCH 07/73] Update CI pytest command to match local Makefile MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add --run-real-processing flag to enable more tests and improve coverage matching local development environment. ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 9e55492..e55947d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -47,7 +47,7 @@ jobs: - name: Test with pytest run: | - pytest tests/ --cov=src/withoutbg --cov-report=xml + pytest tests/ --cov=src/withoutbg --cov-report=xml --run-real-processing - name: Upload coverage to Codecov if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11' From eb4547e828198a1775bc6576f4a8fd3b0dda81b2 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:17:04 +0200 Subject: [PATCH 08/73] Add Codecov token to CI workflow MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Use repository token from secrets to avoid rate limiting issues when uploading coverage reports. ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- .github/workflows/ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e55947d..79636d6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -54,6 +54,7 @@ jobs: uses: codecov/codecov-action@v3 with: file: ./coverage.xml + token: ${{ secrets.CODECOV_TOKEN }} fail_ci_if_error: true security: From ea8449b9a7232a3c65fcd0877e9bedc5e02e014f Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:35:01 +0200 Subject: [PATCH 09/73] Fix Windows CI test failures MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Fix file handle leaks by using context managers for PIL Image.open() - Properly close image files to prevent PermissionError during temp cleanup - Fix permission denied test for Windows platform compatibility - Use platform-specific file permission handling in tests ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- src/withoutbg/api.py | 9 ++++++--- src/withoutbg/models.py | 6 ++++-- tests/test_cli_e2e.py | 40 +++++++++++++++++++++++++++++++--------- 3 files changed, 41 insertions(+), 14 deletions(-) diff --git a/src/withoutbg/api.py b/src/withoutbg/api.py index 408ea13..c1759c8 100644 --- a/src/withoutbg/api.py +++ b/src/withoutbg/api.py @@ -49,7 +49,8 @@ def _encode_image(self, image: Union[str, Path, Image.Image, bytes]) -> str: def _decode_image(self, base64_string: str) -> Image.Image: """Decode base64 string to PIL Image.""" image_bytes = base64.b64decode(base64_string) - return Image.open(io.BytesIO(image_bytes)) + with Image.open(io.BytesIO(image_bytes)) as img: + return img.copy() def _resize_for_api( self, image: Image.Image, max_size: int = 1024 @@ -134,11 +135,13 @@ def remove_background( try: # Store original image for local alpha application if isinstance(input_image, (str, Path)): - original_image = Image.open(input_image) + with Image.open(input_image) as img: + original_image = img.copy() elif isinstance(input_image, Image.Image): original_image = input_image.copy() elif isinstance(input_image, bytes): - original_image = Image.open(io.BytesIO(input_image)) + with Image.open(io.BytesIO(input_image)) as img: + original_image = img.copy() else: raise APIError(f"Unsupported image type: {type(input_image)}") diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index 4cd85c5..ad2669c 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -435,9 +435,11 @@ def remove_background( """ # Load image if isinstance(input_image, (str, Path)): - image = Image.open(input_image) + with Image.open(input_image) as img: + image = img.copy() elif isinstance(input_image, bytes): - image = Image.open(io.BytesIO(input_image)) + with Image.open(io.BytesIO(input_image)) as img: + image = img.copy() elif isinstance(input_image, Image.Image): image = input_image.copy() else: diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 4c874c0..282b794 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -420,24 +420,46 @@ def test_e2e_permission_denied_output(self, real_test_image_path, temp_dir): if not real_test_image_path.exists(): pytest.skip("Real test image not available") - # Create a read-only directory + import platform + import stat + + # Create a read-only directory - Windows requires different approach readonly_dir = temp_dir / "readonly" readonly_dir.mkdir() - readonly_dir.chmod(0o444) # Read-only - - try: + + # Use platform-specific permission handling + if platform.system() == "Windows": + # On Windows, create a file first, then make it read-only output_path = readonly_dir / "output.png" - + output_path.touch() + output_path.chmod(stat.S_IREAD) + result = self.runner.invoke( main, [str(real_test_image_path), "--output", str(output_path)] ) - + # Should fail due to permission error assert result.exit_code == 1 - - finally: + # Restore permissions for cleanup - readonly_dir.chmod(0o755) + output_path.chmod(stat.S_IWRITE | stat.S_IREAD) + else: + # Unix-style read-only directory + readonly_dir.chmod(0o444) + + try: + output_path = readonly_dir / "output.png" + + result = self.runner.invoke( + main, [str(real_test_image_path), "--output", str(output_path)] + ) + + # Should fail due to permission error + assert result.exit_code == 1 + + finally: + # Restore permissions for cleanup + readonly_dir.chmod(0o755) @pytest.mark.real_processing def test_e2e_disk_space_simulation(self, real_test_image_path, temp_dir): From 9af3801537c487edebd054dd1263592f8935a423 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:39:44 +0200 Subject: [PATCH 10/73] Fix ruff whitespace linting issues in test_cli_e2e.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- tests/test_cli_e2e.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 282b794..6a1e769 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -426,27 +426,22 @@ def test_e2e_permission_denied_output(self, real_test_image_path, temp_dir): # Create a read-only directory - Windows requires different approach readonly_dir = temp_dir / "readonly" readonly_dir.mkdir() - # Use platform-specific permission handling if platform.system() == "Windows": # On Windows, create a file first, then make it read-only output_path = readonly_dir / "output.png" output_path.touch() output_path.chmod(stat.S_IREAD) - result = self.runner.invoke( main, [str(real_test_image_path), "--output", str(output_path)] ) - # Should fail due to permission error assert result.exit_code == 1 - # Restore permissions for cleanup output_path.chmod(stat.S_IWRITE | stat.S_IREAD) else: # Unix-style read-only directory readonly_dir.chmod(0o444) - try: output_path = readonly_dir / "output.png" From 8cbae65e16e914387a6c7ab30bc0a17f84c401d5 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:44:03 +0200 Subject: [PATCH 11/73] Add Codecov badge to README MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 2704a59..636c154 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![CI](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml/badge.svg)](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml) +[![codecov](https://codecov.io/gh/withoutbg/withoutbg/branch/main/graph/badge.svg)](https://codecov.io/gh/withoutbg/withoutbg) Remove backgrounds from images instantly with AI. Choose between local processing (free) or cloud API (best quality). From 847eaa997542f339e657ed42757755cd485a8481 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 18 Aug 2025 22:59:46 +0200 Subject: [PATCH 12/73] Add PyPI badge to README MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ๐Ÿค– Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 636c154..17997bc 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,7 @@ **AI-powered background removal with local and cloud options** +[![PyPI](https://img.shields.io/pypi/v/withoutbg.svg)](https://pypi.org/project/withoutbg/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![CI](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml/badge.svg)](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml) [![codecov](https://codecov.io/gh/withoutbg/withoutbg/branch/main/graph/badge.svg)](https://codecov.io/gh/withoutbg/withoutbg) From e9c44611b6169470c48a0b7bed47ebc441d70b54 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Fri, 12 Sep 2025 21:24:45 +0200 Subject: [PATCH 13/73] Add visual examples to README Enhance the README by including visual examples of AI background removal, showcasing before and after images for various subjects. --- README.md | 14 ++++++++++++++ examples/images/dog-mugshot.jpg | Bin 0 -> 172736 bytes examples/images/feather.jpg | Bin 0 -> 80256 bytes examples/images/pizza-parody.jpg | Bin 0 -> 86660 bytes examples/images/woman-golden-hour.jpg | Bin 0 -> 131110 bytes 5 files changed, 14 insertions(+) create mode 100644 examples/images/dog-mugshot.jpg create mode 100644 examples/images/feather.jpg create mode 100644 examples/images/pizza-parody.jpg create mode 100644 examples/images/woman-golden-hour.jpg diff --git a/README.md b/README.md index 17997bc..eb41734 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,20 @@ withoutbg image.jpg withoutbg image.jpg --api-key sk_your_api_key ``` +## โœจ Visual Examples + +See the power of AI background removal in action: + +![Woman with confetti - before and after](examples/images/woman-golden-hour.jpg) + +![Dog portrait - before and after](examples/images/dog-mugshot.jpg) + +![Pizza product - before and after](examples/images/pizza-parody.jpg) + +![Feather detail - before and after](examples/images/feather.jpg) + +*Perfect edge detection, hair details, and transparent backgrounds* + ## ๐Ÿ“ฆ Model Tiers | Tier | Quality | Usage | License | diff --git a/examples/images/dog-mugshot.jpg b/examples/images/dog-mugshot.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d92c64cbc12148ceca8e5a34284011ec5e747374 GIT binary patch literal 172736 zcmeFZcUV)~voIWNpa=pgAiao%9y*~ZRip_>Z-x*e9g@&OvmhW@d$QKdteIJLX06T7v7es-47WA4H33vq zRDfHQ8}M@$P@&=H0X@pZG)qjz5Cw{hO3G0v#U-Rc 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UhfR!kYQwe0jnaz0m1Dta&m#pVppCr<3~A$&~VcT6r>_ zPwP)6Q_1~l Path: def _get_default_matting_model_path(self) -> Path: """Get path to Matting model from Hugging Face.""" - return self._download_from_hf("focus_matting_0.1.0.onnx", "Focus matting model") + return self._download_from_hf("focus_matting_1.0.0.onnx", "Focus matting model") def _get_default_refiner_model_path(self) -> Path: """Get path to Refiner model from Hugging Face.""" - return self._download_from_hf("focus_refiner_0.1.0.onnx", "Focus refiner model") + return self._download_from_hf("focus_refiner_1.0.0.onnx", "Focus refiner model") def _download_from_hf(self, filename: str, model_name: str) -> Path: """Download model from Hugging Face Hub with caching. From ed08b6249eebf69efb687ce6ac0b759e2ce2705e Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Mon, 20 Oct 2025 22:11:46 +0200 Subject: [PATCH 18/73] Implement EXIF orientation correction for images in background removal process - Added a new function to apply EXIF orientation to images, ensuring they are displayed correctly after loading. - Integrated the EXIF correction in both the StudioAPI and OpenSourceModel classes to enhance image processing accuracy. - Improved handling of images with various orientation metadata, providing a more reliable user experience. --- src/withoutbg/api.py | 4 +++ src/withoutbg/models.py | 74 ++++++++++++++++++++++++++++++++++++++++- 2 files changed, 77 insertions(+), 1 deletion(-) diff --git a/src/withoutbg/api.py b/src/withoutbg/api.py index 4f14d6f..c93b1a5 100644 --- a/src/withoutbg/api.py +++ b/src/withoutbg/api.py @@ -9,6 +9,7 @@ from PIL import Image from .exceptions import APIError +from .models import _apply_exif_orientation class StudioAPI: @@ -149,6 +150,9 @@ def remove_background( else: raise APIError(f"Unsupported image type: {type(input_image)}") + # Apply EXIF orientation correction right after loading + original_image = _apply_exif_orientation(original_image) + # Resize image for API transmission to optimize latency if progress_callback: progress_callback(0.2) diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index cd6f38a..f310504 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -7,11 +7,80 @@ import numpy as np import onnxruntime as ort # type: ignore from huggingface_hub import hf_hub_download -from PIL import Image +from PIL import Image, ExifTags from .exceptions import ModelNotFoundError, WithoutBGError +def _apply_exif_orientation(image: Image.Image) -> Image.Image: + """Apply EXIF orientation to rotate image correctly. + + Args: + image: PIL Image that may contain EXIF orientation data + + Returns: + PIL Image rotated according to EXIF orientation, or original if no orientation data + """ + try: + # Get EXIF data + exif = image.getexif() + if not exif: + return image + + # Find orientation tag + orientation_key = None + for tag, name in ExifTags.TAGS.items(): + if name == 'Orientation': + orientation_key = tag + break + + if orientation_key is None or orientation_key not in exif: + return image + + orientation = exif[orientation_key] + + # Apply rotation based on orientation value + # EXIF orientation values: + # 1 = Normal (no rotation) + # 2 = Mirrored horizontally + # 3 = Rotated 180ยฐ + # 4 = Mirrored vertically + # 5 = Mirrored horizontally and rotated 90ยฐ CCW + # 6 = Rotated 90ยฐ CW + # 7 = Mirrored horizontally and rotated 90ยฐ CW + # 8 = Rotated 90ยฐ CCW + + if orientation == 2: + # Horizontal mirror + image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + elif orientation == 3: + # 180ยฐ rotation + image = image.rotate(180, expand=True) + elif orientation == 4: + # Vertical mirror + image = image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + elif orientation == 5: + # Horizontal mirror + 90ยฐ CCW + image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + image = image.rotate(90, expand=True) + elif orientation == 6: + # 90ยฐ CW rotation + image = image.rotate(-90, expand=True) + elif orientation == 7: + # Horizontal mirror + 90ยฐ CW + image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + image = image.rotate(-90, expand=True) + elif orientation == 8: + # 90ยฐ CCW rotation + image = image.rotate(90, expand=True) + + return image + + except Exception: + # If any error occurs during EXIF processing, return original image + return image + + class OpenSourceModel: """Local ONNX-based background removal model (Open Source tier).""" @@ -612,6 +681,9 @@ def remove_background( else: raise WithoutBGError(f"Unsupported input type: {type(input_image)}") + # Apply EXIF orientation correction right after loading + image = _apply_exif_orientation(image) + # Convert to RGB if needed (model expects RGB only) if image.mode != "RGB": image = image.convert("RGB") From 0765a77742e089db11f175c47c567dcaa64c22ab Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Tue, 28 Oct 2025 23:24:53 +0100 Subject: [PATCH 19/73] Update refiner model size limit from 1024px to 800px for improved performance - Adjusted the maximum size for the refiner model to 800px on the larger side to optimize processing efficiency. - Updated related comments and documentation to reflect the new size constraint. --- src/withoutbg/models.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index f310504..92db547 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -503,15 +503,15 @@ def _matting_stage( return alpha_image def _calculate_refiner_size(self, original_size: tuple[int, int]) -> tuple[int, int]: - """Calculate optimal size for refiner model (max 1024px on bigger side).""" + """Calculate optimal size for refiner model (max 800px on bigger side).""" width, height = original_size - max_size = 1024 + max_size = 800 - # If both dimensions are already <= 1024, no resizing needed + # If both dimensions are already <= 800, no resizing needed if width <= max_size and height <= max_size: return original_size - # Calculate scale factor to make the bigger side = 1024 + # Calculate scale factor to make the bigger side = 800 scale = max_size / max(width, height) new_width = int(width * scale) new_height = int(height * scale) @@ -524,7 +524,7 @@ def _refiner_stage( """ Stage 3: Refine alpha channel using RGB + alpha concatenated input. - Optimizes performance by resizing large images to max 1024px on the bigger side + Optimizes performance by resizing large images to max 800px on the bigger side for inference, then upscaling the result back to original resolution. Parameters: @@ -537,7 +537,7 @@ def _refiner_stage( # Get original image size original_size = rgb_image.size - # Calculate optimal size for refiner model (max 1024px on bigger side) + # Calculate optimal size for refiner model (max 800px on bigger side) refiner_size = self._calculate_refiner_size(original_size) # Resize RGB image for refiner model if needed From 1838b553a3be79c242a834c2a43b8d51ac6cd295 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Tue, 4 Nov 2025 23:36:22 +0100 Subject: [PATCH 20/73] Restructure repository and enhance documentation for withoutbg project - Introduced a monorepo structure, organizing the codebase into distinct packages for better maintainability and clarity. - Added comprehensive Docker deployment guides and migration instructions to facilitate setup and transition. - Updated .gitignore to manage model files and API keys effectively. - Removed outdated Python version specification and added new configuration files for pre-commit hooks and testing. - Enhanced README and added detailed documentation for both frontend and backend components. - Included various new files for frontend and backend, including Docker configurations, API resources, and UI components. --- .gitignore | 5 +- .python-version | 1 - README.md | 321 +- STRUCTURE.md | 250 + Makefile => apps/Makefile | 0 apps/web/.dockerignore | 104 + apps/web/backend/Dockerfile | 58 + apps/web/backend/app/__init__.py | 11 + apps/web/backend/app/main.py | 138 + apps/web/backend/pyproject.toml | 19 + apps/web/backend/uv.lock | 962 +++ apps/web/docker-compose.yml | 48 + apps/web/frontend/.gitignore | 34 + apps/web/frontend/Dockerfile | 42 + apps/web/frontend/README.md | 120 + apps/web/frontend/index.html | 24 + apps/web/frontend/package-lock.json | 6169 +++++++++++++++++ apps/web/frontend/package.json | 38 + apps/web/frontend/postcss.config.js | 16 + apps/web/frontend/public/logo.png | Bin 0 -> 1356 bytes apps/web/frontend/src/App.css | 15 + apps/web/frontend/src/App.jsx | 75 + 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rename {tests => packages/python/tests}/performance/test_memory_usage.py (100%) rename {tests => packages/python/tests}/test_api.py (100%) rename {tests => packages/python/tests}/test_cli.py (100%) rename {tests => packages/python/tests}/test_cli_e2e.py (100%) rename uv.lock => packages/python/uv.lock (53%) create mode 100644 scripts/.gitkeep diff --git a/.gitignore b/.gitignore index 7b7f412..fe8536b 100644 --- a/.gitignore +++ b/.gitignore @@ -210,10 +210,11 @@ $RECYCLE.BIN/ bandit-report.json test_images/ output/ -models/ -!src/withoutbg/models/ llmcontext/ +# Model files (tracked in git, but .onnx files ignored above) +# models/checkpoints/ contains .onnx files downloaded on first use + # API keys and secrets .env.local .env.*.local diff --git a/.python-version b/.python-version deleted file mode 100644 index bd28b9c..0000000 --- a/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.9 diff --git a/README.md b/README.md index 1eae3df..65e31d5 100644 --- a/README.md +++ b/README.md @@ -4,188 +4,105 @@ [![PyPI](https://img.shields.io/pypi/v/withoutbg.svg)](https://pypi.org/project/withoutbg/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) -[![CI](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml/badge.svg)](https://github.com/withoutbg/withoutbg/actions/workflows/ci.yml) -[![codecov](https://codecov.io/gh/withoutbg/withoutbg/branch/main/graph/badge.svg)](https://codecov.io/gh/withoutbg/withoutbg) Remove backgrounds from images instantly with AI. Choose between local processing (free) or cloud API (best quality). -## ๐Ÿ†• Latest Update: Focus v1.0.0 - -We've just released our most advanced open source model yet! **Focus v1.0.0** delivers significantly improved quality over the previous Snap v0.1.0 model: - -- โœ… **Significantly better edge detail** - Crisp, clean edges on complex objects -- โœ… **Superior hair/fur handling** - Natural-looking results on fine details -- โœ… **Better generalization** - Works great on diverse image types - -*The Focus model is now the default for all local processing.* - ## ๐Ÿš€ Quick Start +### Python SDK + ```bash # Install pip install withoutbg -# Remove background (local processing) -withoutbg image.jpg - -# Use cloud API for best quality processing -withoutbg image.jpg --api-key sk_your_api_key -``` - -## โœจ Visual Examples - -See how our latest **Focus v1.0.0** model handles challenging objects and scenes with superior quality compared to previous versions: - -### ๐ŸŽฏ Challenging Scenarios -*These examples showcase Focus v1.0.0's advanced capabilities in edge cases that previous models struggled with* - -![Portrait 1](examples/images/example1.png) -*Complex hair details and fine edges* - -![Portrait 2](examples/images/example2.png) -*Mixed lighting and subtle shadows* - -![Dandelion](examples/images/example3.png) -*Ultra-fine details and transparency* - -![Spider Web](examples/images/example4.png) -*Intricate patterns and semi-transparent elements* - -![Cat](examples/images/example5.png) -*Fur texture and natural lighting* - -![Mesh Texture](examples/images/example6.png) -*Complex geometric patterns* - -![Knitted Object](examples/images/example7.png) -*Textured materials and depth variations* - -> **๐Ÿ’ก Pro Tip**: Focus v1.0.0 excels at preserving fine details like hair, fur, and transparent objects that were challenging for previous models. - - -## ๐Ÿ’ป Python API - -```python +# Use in Python from withoutbg import remove_background - -# Local processing with Focus model (free) result = remove_background("input.jpg") result.save("output.png") -# Cloud processing with API (best quality) -result = remove_background("input.jpg", api_key="sk_your_key") - -# Batch processing -from withoutbg import remove_background_batch -results = remove_background_batch(["img1.jpg", "img2.jpg"], - output_dir="results/") +# CLI +withoutbg image.jpg ``` -## ๐Ÿ–ฅ๏ธ CLI Usage +### Web Application -### Basic Usage ```bash -# Process single image -withoutbg photo.jpg - -# Specify output path -withoutbg photo.jpg --output result.png +# Clone and run with Docker +git clone https://github.com/withoutbg/withoutbg.git +cd withoutbg +docker-compose -f apps/web/docker-compose.yml up -# Use different format -withoutbg photo.jpg --format webp --quality 90 +# Open browser +open http://localhost:3000 ``` -### Cloud API -```bash -# Set API key via environment -export WITHOUTBG_API_KEY="sk_your_api_key" -withoutbg photo.jpg --use-api +## ๐Ÿ“ฆ Repository Structure -# Or pass directly -withoutbg photo.jpg --api-key sk_your_key -``` - -### Batch Processing -```bash -# Process all images in directory -withoutbg photos/ --batch --output-dir results/ +This is a **monorepo** containing multiple components: -# With cloud API for best quality -withoutbg photos/ --batch --use-api --output-dir results/ +``` +withoutbg/ +โ”œโ”€โ”€ packages/ # Reusable packages +โ”‚ โ””โ”€โ”€ python/ # Core Python SDK (published to PyPI) +โ”‚ +โ”œโ”€โ”€ apps/ # End-user applications +โ”‚ โ””โ”€โ”€ web/ # Web application (React + FastAPI) +โ”‚ +โ”œโ”€โ”€ integrations/ # Third-party tool integrations +โ”‚ โ””โ”€โ”€ (future: GIMP, Photoshop, Figma plugins) +โ”‚ +โ”œโ”€โ”€ models/ # Shared ML model files +โ”‚ โ””โ”€โ”€ checkpoints/ # ONNX model files +โ”‚ +โ”œโ”€โ”€ docs/ # Documentation +โ””โ”€โ”€ scripts/ # Development scripts ``` -## ๐Ÿ”ง Installation Options +### Components -### Standard Installation -```bash -pip install withoutbg -``` +#### ๐Ÿ“š [Python SDK](packages/python/) +Core library for background removal. Published to PyPI. -### Development -```bash -git clone https://github.com/withoutbg/withoutbg.git -cd withoutbg -pip install -e ".[dev]" -``` +- **Install**: `pip install withoutbg` +- **Use**: Python API + CLI +- **Models**: Focus v1.0.0 (local), Cloud API -## ๐ŸŽจ Examples +#### ๐ŸŒ [Web Application](apps/web/) +Modern web interface with drag-and-drop UI. -### Basic Background Removal -```python -import withoutbg +- **Stack**: React 18 + FastAPI + Nginx +- **Deploy**: Docker Compose +- **Features**: Batch processing, live preview -# Simple usage -output = withoutbg.remove_background("portrait.jpg") -output.save("portrait-withoutbg.png") -``` +#### ๐Ÿ”Œ Integrations (Coming Soon) +Plugins for popular creative tools. -### E-commerce Product Photos -```python -import withoutbg -from pathlib import Path - -# Process product catalog -product_images = Path("products").glob("*.jpg") -results = withoutbg.remove_background_batch( - list(product_images), - output_dir="catalog-withoutbg/", - api_key="sk_your_key" # Use for best quality -) -``` +- GIMP plugin +- Photoshop extension +- Figma plugin +- Blender addon -### Social Media Automation -```python -import withoutbg -from PIL import Image +## ๐ŸŽฏ Features -# Remove background and add custom background -foreground = withoutbg.remove_background("selfie.jpg", api_key="sk_key") -background = Image.open("gradient_bg.jpg") +- โœจ **Local Processing**: Free, private, offline with Focus v1.0.0 +- ๐Ÿš€ **Cloud API**: Best quality, scalable, 99.9% uptime +- ๐Ÿ“ฆ **Batch Processing**: Process multiple images efficiently +- ๐ŸŒ **Web Interface**: Beautiful drag-and-drop UI +- ๐Ÿ”ง **CLI Tool**: Command-line automation +- ๐ŸŽจ **Integrations**: Work in your favorite tools -# Composite images -background.paste(foreground, (0, 0), foreground) -background.save("social_post.jpg") -``` +## ๐Ÿ“– Documentation -## ๐Ÿ”‘ API Key Setup +- **[Python SDK Docs](packages/python/README.md)** - API reference and examples +- **[Web App Docs](apps/web/README.md)** - Deployment and development +- **[API Reference](https://withoutbg.com/documentation)** - Cloud API docs -1. **Get API Key**: Visit [withoutbg.com](https://withoutbg.com) to get your API key -2. **Set Environment Variable**: - ```bash - export WITHOUTBG_API_KEY="sk_your_api_key" - ``` -3. **Or pass directly in code**: - ```python - result = withoutbg.remove_background("image.jpg", api_key="sk_your_key") - ``` +## ๐Ÿ› ๏ธ Development -## ๐Ÿ—๏ธ For Developers +### Python Package -### Local Development ```bash -# Clone repository -git clone https://github.com/withoutbg/withoutbg.git -cd withoutbg +cd packages/python # Install in development mode pip install -e ".[dev]" @@ -196,103 +113,48 @@ pytest # Format code black src/ tests/ ruff check src/ tests/ - -# Type checking -mypy src/ ``` -## ๐Ÿ”„ Active Development - -This project is actively maintained with regular updates and improvements: +### Web Application -- **Latest Release**: Focus v1.0.0 (Sep 2025) -- **Development Cycle**: Monthly releases with new features and model improvements -- **Community Driven**: Open to contributions and feature requests -- **Performance Focus**: Continuous optimization for speed and quality - -### Recent Improvements -- ๐Ÿš€ **Focus Model**: Complete rewrite with 4-stage pipeline -- ๐ŸŽฏ **Quality**: Significant improvements in edge detection and fine details -- ๐Ÿ”ง **API**: Enhanced error handling and progress callbacks -- ๐Ÿ“ฆ **Packaging**: Better dependency management and installation - -### Upcoming Features -- ๐Ÿณ **Dockerized Application**: Easy deployment with intuitive web UI -- ๐Ÿค– **Model Retraining**: Continuous improvement with newly generated data - - -## ๐Ÿ“Š Usage Analytics - -Track your API usage: - -```python -from withoutbg.api import StudioAPI - -api = StudioAPI(api_key="sk_your_key") -usage = api.get_usage() -print(usage) +```bash +# Development mode (hot-reload) +docker-compose -f apps/web/docker-compose.yml up + +# Or run components separately +cd apps/web/backend +pip install -e ../../packages/python +pip install -e . +uvicorn app.main:app --reload + +cd apps/web/frontend +npm install +npm run dev ``` -## ๐Ÿ’ผ Commercial - -### API (Pay-per-use) -- โœ… Best quality results -- โœ… 99.9% uptime SLA -- โœ… Scalable infrastructure +## ๐ŸŒŸ Latest Release: Focus v1.0.0 -[Try API โ†’](https://withoutbg.com/remove-background) +Our most advanced open source model with: -## ๐Ÿ“š Documentation +- โœ… **Significantly better edge detail** - Crisp, clean edges +- โœ… **Superior hair/fur handling** - Natural-looking fine details +- โœ… **Better generalization** - Works on diverse image types -- **[API Reference](https://withoutbg.com/documentation)** - Complete API documentation - -## ๐Ÿ› Issues & Support - -- **Bug Reports**: [GitHub Issues](https://github.com/withoutbg/withoutbg/issues) -- **Feature Requests**: [GitHub Discussions](https://github.com/withoutbg/withoutbg/discussions) -- **Commercial Support**: [contact@withoutbg.com](mailto:contact@withoutbg.com) - -## ๐Ÿค— Hugging Face - -### ๐Ÿš€ Latest Model: Focus v1.0.0 -**[withoutbg/focus](https://huggingface.co/withoutbg/focus)** - Our most advanced open source model -- โญ **Recommended**: Best quality and performance -- ๐ŸŽฏ **4-stage pipeline**: Depth โ†’ ISNet โ†’ Matting โ†’ Refiner - -### ๐Ÿ“š Model Archive -**[withoutbg/snap](https://huggingface.co/withoutbg/snap)** - Previous generation model (v0.1.0) -- ๐Ÿ“– **Legacy**: Maintained for compatibility -- ๐Ÿ”„ **Migration**: Easy upgrade path to Focus v1.0.0 - -### ๐Ÿ† Why Choose Focus? -- **Superior Quality**: 30-40% better edge detection -- **Active Development**: Regular updates and improvements -- **Community Support**: Active discussions and contributions - -## โญ Star This Repository - -If you find this project useful, please consider giving it a star! Your support helps us: - -- ๐Ÿš€ **Accelerate development** of new features and models -- ๐Ÿ› **Improve quality** through community feedback and testing -- ๐Ÿ“ˆ **Grow the ecosystem** with better documentation and examples -- ๐ŸŽฏ **Focus on innovation** rather than marketing - -[![GitHub stars](https://img.shields.io/github/stars/withoutbg/withoutbg?style=social)](https://github.com/withoutbg/withoutbg) +See [examples/](packages/python/examples/) for visual comparisons. ## ๐Ÿ“„ License -This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. +Apache License 2.0 - see [LICENSE](LICENSE) ### Third-Party Components -- **Depth Anything**: Apache 2.0 License -- **ISNet**: Apache 2.0 License - [Highly Accurate Dichotomous Image Segmentation](https://github.com/xuebinqin/DIS) +- **Depth Anything V2**: Apache 2.0 License (only vits model) +- **ISNet**: Apache 2.0 License See [THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md) for complete attribution. -## ๐ŸŒŸ Contributing +## ๐Ÿค Contributing -We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. +We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. 1. Fork the repository 2. Create a feature branch @@ -300,10 +162,13 @@ We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) f 4. Add tests 5. Submit a pull request -## ๐Ÿ“ˆ Star History +## ๐Ÿ“ง Support -[![Star History Chart](https://api.star-history.com/svg?repos=withoutbg/withoutbg&type=Date)](https://star-history.com/#withoutbg/withoutbg&Date) +- **Bug Reports**: [GitHub Issues](https://github.com/withoutbg/withoutbg/issues) +- **Feature Requests**: [GitHub Discussions](https://github.com/withoutbg/withoutbg/discussions) +- **Commercial Support**: [contact@withoutbg.com](mailto:contact@withoutbg.com) ---- +## โญ Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=withoutbg/withoutbg&type=Date)](https://star-history.com/#withoutbg/withoutbg&Date) -**[๐ŸŽฏ Get best quality results with withoutbg.com](https://withoutbg.com)** \ No newline at end of file diff --git a/STRUCTURE.md b/STRUCTURE.md new file mode 100644 index 0000000..5124566 --- /dev/null +++ b/STRUCTURE.md @@ -0,0 +1,250 @@ +# Repository Structure + +This document explains the organization of the withoutbg monorepo. + +## Overview + +The repository follows a **monorepo architecture** with clear separation between: + +1. **Reusable packages** - Core libraries that can be published independently +2. **Applications** - End-user facing applications built on top of packages +3. **Integrations** - Third-party tool plugins and extensions +4. **Shared resources** - ML models, documentation, scripts + +## Directory Layout + +``` +withoutbg/ +โ”œโ”€โ”€ packages/ # Reusable, versioned packages +โ”‚ โ””โ”€โ”€ python/ # Core Python SDK +โ”‚ โ”œโ”€โ”€ src/withoutbg/ # Package source code +โ”‚ โ”œโ”€โ”€ tests/ # Unit and integration tests +โ”‚ โ”œโ”€โ”€ pyproject.toml # Package configuration +โ”‚ โ””โ”€โ”€ README.md # Package documentation +โ”‚ +โ”œโ”€โ”€ apps/ # End-user applications +โ”‚ โ””โ”€โ”€ web/ # Web application +โ”‚ โ”œโ”€โ”€ backend/ # FastAPI backend +โ”‚ โ”‚ โ”œโ”€โ”€ app/ # Application code +โ”‚ โ”‚ โ”œโ”€โ”€ Dockerfile # Backend container +โ”‚ โ”‚ โ””โ”€โ”€ pyproject.toml # Backend dependencies +โ”‚ โ”œโ”€โ”€ frontend/ # React frontend +โ”‚ โ”‚ โ”œโ”€โ”€ src/ # React components +โ”‚ โ”‚ โ”œโ”€โ”€ Dockerfile # Frontend container +โ”‚ โ”‚ โ””โ”€โ”€ package.json # NPM dependencies +โ”‚ โ”œโ”€โ”€ nginx/ # Nginx configuration +โ”‚ โ”œโ”€โ”€ docker-compose.yml # Development orchestration +โ”‚ โ””โ”€โ”€ README.md # Web app documentation +โ”‚ +โ”œโ”€โ”€ integrations/ # Third-party tool integrations +โ”‚ โ”œโ”€โ”€ .gitkeep # Placeholder for future plugins +โ”‚ โ””โ”€โ”€ (future: gimp/, photoshop/, figma/, blender/) +โ”‚ +โ”œโ”€โ”€ models/ # Shared ML model files +โ”‚ โ””โ”€โ”€ checkpoints/ # ONNX model files +โ”‚ โ”œโ”€โ”€ depth_anything_v2_vits_slim.onnx +โ”‚ โ”œโ”€โ”€ focus_matting_1.0.2.onnx +โ”‚ โ”œโ”€โ”€ focus_refiner_0.1.0.onnx +โ”‚ โ””โ”€โ”€ isnet.onnx +โ”‚ +โ”œโ”€โ”€ docs/ # Documentation +โ”‚ โ””โ”€โ”€ .gitkeep # Placeholder for docs +โ”‚ +โ”œโ”€โ”€ scripts/ # Development and build scripts +โ”‚ โ””โ”€โ”€ .gitkeep # Placeholder for scripts +โ”‚ +โ”œโ”€โ”€ .github/ # CI/CD workflows +โ”‚ โ””โ”€โ”€ workflows/ +โ”‚ +โ”œโ”€โ”€ README.md # Main repository README +โ”œโ”€โ”€ STRUCTURE.md # This file +โ”œโ”€โ”€ LICENSE # Apache 2.0 License +โ”œโ”€โ”€ CONTRIBUTING.md # Contribution guidelines +โ””โ”€โ”€ CHANGELOG.md # Version history +``` + +## Component Details + +### ๐Ÿ“ฆ packages/python/ + +The **core Python SDK** - independently versioned and published to PyPI. + +**Key files:** +- `src/withoutbg/` - Package source code +- `pyproject.toml` - Dependencies and metadata +- `tests/` - Comprehensive test suite + +**Installation:** +```bash +# From PyPI (end users) +pip install withoutbg + +# From source (development) +cd packages/python +pip install -e ".[dev]" +``` + +**Publishing:** +```bash +cd packages/python +hatch build +hatch publish +``` + +### ๐ŸŒ apps/web/ + +The **web application** - React frontend + FastAPI backend. + +**Backend:** +- Depends on `packages/python/` for core functionality +- FastAPI for REST API +- Docker container for deployment + +**Frontend:** +- React 18 + Vite +- Tailwind CSS for styling +- Nginx for production serving + +**Development:** +```bash +# Run everything with Docker Compose +docker-compose -f apps/web/docker-compose.yml up + +# Or run components separately +cd apps/web/backend && uvicorn app.main:app --reload +cd apps/web/frontend && npm run dev +``` + +### ๐Ÿ”Œ integrations/ + +**Third-party tool plugins** - each integration is independent. + +**Future structure:** +``` +integrations/ +โ”œโ”€โ”€ gimp/ # GIMP plugin +โ”‚ โ”œโ”€โ”€ withoutbg_plugin.py +โ”‚ โ”œโ”€โ”€ install.sh +โ”‚ โ””โ”€โ”€ README.md +โ”œโ”€โ”€ photoshop/ # Photoshop extension +โ”‚ โ”œโ”€โ”€ manifest.json +โ”‚ โ””โ”€โ”€ src/ +โ””โ”€โ”€ figma/ # Figma plugin + โ”œโ”€โ”€ manifest.json + โ””โ”€โ”€ code.ts +``` + +Each integration: +- Has its own README +- Can be installed independently +- Depends on `packages/python/` or web API + +### ๐Ÿง  models/ + +**Shared ML model files** - ONNX models used across components. + +- Checkpoints are downloaded on first use +- Shared between CLI, web app, and integrations +- Large files tracked with Git LFS (optional) + +## Dependency Flow + +``` +โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +โ”‚ packages/python โ”‚ โ† Core library (published to PyPI) +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ โ”‚ + โ”Œโ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ apps/web โ”‚ โ”‚ integrations โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +- **packages/python/** - No dependencies on other repo components +- **apps/web/backend/** - Depends on `packages/python/` +- **integrations/** - Depend on `packages/python/` or web API + +## Versioning Strategy + +Each component has **independent versioning**: + +- **packages/python/**: Semantic versioning (v1.0.0) + - Published to PyPI + - Version in `src/withoutbg/__version__.py` + +- **apps/web/**: Calendar versioning or semantic (v2025.1.0) + - Docker images tagged with version + - Version in `apps/web/backend/pyproject.toml` + +- **integrations/**: Independent per plugin (v0.5.0) + - Each plugin has its own version + - Version in plugin manifest + +## CI/CD Strategy + +Separate workflows for each component: + +``` +.github/workflows/ +โ”œโ”€โ”€ python-package.yml # Test & publish Python package +โ”œโ”€โ”€ web-app.yml # Test & build web app containers +โ””โ”€โ”€ integrations.yml # Test integrations +``` + +## Development Workflow + +### Adding a New Integration + +1. Create directory in `integrations/`: + ```bash + mkdir -p integrations/gimp + cd integrations/gimp + ``` + +2. Add plugin files and README + +3. Plugin depends on: + - `packages/python/` (local processing) + - OR web API (cloud processing) + +### Updating the Python Package + +1. Make changes in `packages/python/src/` +2. Update tests in `packages/python/tests/` +3. Update version in `packages/python/src/withoutbg/__version__.py` +4. Publish to PyPI + +### Updating the Web App + +1. Backend changes in `apps/web/backend/` +2. Frontend changes in `apps/web/frontend/` +3. Test locally with `docker-compose` +4. Build and push Docker images + +## Benefits of This Structure + +### โœ… Clear Separation +- Core library is independent +- Applications build on top +- Integrations are isolated + +### โœ… Independent Releases +- Python package can be published without affecting web app +- Web app can be updated without republishing Python package +- Integrations have their own release cycles + +### โœ… Easy to Extend +- Adding new apps is straightforward +- Adding integrations doesn't affect core +- Each component has focused scope + +### โœ… Better Testing +- Unit tests in `packages/python/tests/` +- Integration tests in `apps/web/` +- E2E tests per integration + +### โœ… Maintainability +- Each component has clear ownership +- Dependencies are explicit +- Documentation is colocated diff --git a/Makefile b/apps/Makefile similarity index 100% rename from Makefile rename to apps/Makefile diff --git a/apps/web/.dockerignore b/apps/web/.dockerignore new file mode 100644 index 0000000..2bd89ed --- /dev/null +++ b/apps/web/.dockerignore @@ -0,0 +1,104 @@ +# Git +.git/ +.gitignore +.gitattributes + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual environments +venv/ +env/ +ENV/ +.venv + +# Testing +.pytest_cache/ +.coverage +htmlcov/ +.tox/ +.hypothesis/ + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ +.DS_Store + +# Node +node_modules/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# Frontend build +frontend/dist/ +frontend/.vite/ + +# Logs +*.log +logs/ + +# Environment files +.env +.env.local +.env.*.local + +# Documentation +*.md +!README.md +docs/ + +# Examples +examples/ + +# Tests +tests/ +test_*.py +*_test.py + +# CI/CD +.github/ +.gitlab-ci.yml + +# Debug +debug_output/ + +# Misc +.cache/ +*.bak +*.tmp +*.temp + + + + + + + + + + diff --git a/apps/web/backend/Dockerfile b/apps/web/backend/Dockerfile new file mode 100644 index 0000000..1545ed1 --- /dev/null +++ b/apps/web/backend/Dockerfile @@ -0,0 +1,58 @@ +# Multi-stage build for withoutbg backend +# Build context should be the repository root: docker build -f apps/web/backend/Dockerfile . + +# Stage 1: Build stage with uv +FROM python:3.12-slim AS builder + +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# Set working directory +WORKDIR /app + +# Copy and install the core withoutbg package +COPY packages/python/pyproject.toml packages/python/uv.lock packages/python/README.md packages/python/ +COPY packages/python/src/ packages/python/src/ +RUN cd packages/python && uv sync --frozen --no-dev + +# Copy and install backend dependencies +COPY apps/web/backend/pyproject.toml apps/web/backend/uv.lock apps/web/backend/README.md apps/web/backend/ +RUN cd apps/web/backend && uv sync --frozen --no-dev + +# Stage 2: Runtime stage +FROM python:3.12-slim + +# Install runtime dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +# Set working directory +WORKDIR /app + +# Copy virtual environments from builder +COPY --from=builder /app/packages/python/.venv /app/packages/python/.venv +COPY --from=builder /app/apps/web/backend/.venv /app/apps/web/backend/.venv + +# Add both virtual environments to PATH +ENV PATH="/app/apps/web/backend/.venv/bin:/app/packages/python/.venv/bin:$PATH" + +# Copy application code +COPY apps/web/backend/ ./backend/ + +# Copy ONNX model checkpoints +COPY models/checkpoints/ ./checkpoints/ + +# Set environment variables +ENV PYTHONUNBUFFERED=1 + +# Expose port +EXPOSE 8000 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \ + CMD python -c "import requests; requests.get('http://localhost:8000/api/health', timeout=5)" || exit 1 + +# Run the application +CMD ["python", "-m", "uvicorn", "backend.app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/apps/web/backend/app/__init__.py b/apps/web/backend/app/__init__.py new file mode 100644 index 0000000..3ffcc3b --- /dev/null +++ b/apps/web/backend/app/__init__.py @@ -0,0 +1,11 @@ +"""Backend application package.""" + + + + + + + + + + diff --git a/apps/web/backend/app/main.py b/apps/web/backend/app/main.py new file mode 100644 index 0000000..f7a8662 --- /dev/null +++ b/apps/web/backend/app/main.py @@ -0,0 +1,138 @@ +"""FastAPI backend for withoutbg web application.""" + +import io +import os +import tempfile +from pathlib import Path +from typing import Optional + +from fastapi import FastAPI, File, UploadFile, HTTPException, Form +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import Response, JSONResponse +from PIL import Image +import uvicorn + +# Import withoutbg package (install via: pip install -e ../../../packages/python) +from withoutbg import remove_background, __version__ +from withoutbg.exceptions import WithoutBGError +from withoutbg.api import StudioAPI + +app = FastAPI( + title="withoutbg API", + description="AI-powered background removal API", + version=__version__, +) + +# CORS middleware for local development and deployment +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # In production, specify your frontend domain + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return { + "status": "healthy", + "version": __version__, + "service": "withoutbg-api" + } + + +@app.post("/api/remove-background") +async def remove_background_endpoint( + file: UploadFile = File(...), + format: str = Form("png"), + quality: int = Form(95), + api_key: Optional[str] = Form(None), +): + """ + Remove background from a single image. + + Args: + file: Image file to process + format: Output format (png, jpg, webp) + quality: Quality for JPEG output (1-100) + api_key: Optional API key for cloud processing + + Returns: + Processed image with background removed + """ + try: + # Validate file type + if not file.content_type or not file.content_type.startswith("image/"): + raise HTTPException(status_code=400, detail="File must be an image") + + # Read uploaded file + contents = await file.read() + input_image = Image.open(io.BytesIO(contents)) + + # Process image + result = remove_background( + input_image, + api_key=api_key, + model_name="api" if api_key else "opensource" + ) + + # Convert result to bytes + output_buffer = io.BytesIO() + + # Handle format conversion + if format.lower() in ["jpg", "jpeg"]: + # Convert RGBA to RGB for JPEG + if result.mode == "RGBA": + rgb_image = Image.new("RGB", result.size, (255, 255, 255)) + rgb_image.paste(result, mask=result.split()[3]) + rgb_image.save(output_buffer, format="JPEG", quality=quality) + else: + result.save(output_buffer, format="JPEG", quality=quality) + media_type = "image/jpeg" + elif format.lower() == "webp": + result.save(output_buffer, format="WEBP", quality=quality) + media_type = "image/webp" + else: # PNG + result.save(output_buffer, format="PNG") + media_type = "image/png" + + output_buffer.seek(0) + + return Response( + content=output_buffer.getvalue(), + media_type=media_type, + headers={ + "Content-Disposition": f"attachment; filename=withoutbg.{format}" + } + ) + + except WithoutBGError as e: + raise HTTPException(status_code=500, detail=str(e)) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Processing failed: {str(e)}") + + +@app.get("/api/usage") +async def get_usage_endpoint(api_key: str): + """ + Get API usage statistics. + + Args: + api_key: API key for cloud service + + Returns: + Usage statistics + """ + try: + api = StudioAPI(api_key) + usage = api.get_usage() + return JSONResponse(content=usage) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to fetch usage: {str(e)}") + + +if 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"/app/apps/web/backend/.venv/bin/python", "-m", "uvicorn", "backend.app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload" ] + networks: + - withoutbg-network + + # Frontend service with Vite dev server + frontend-dev: + image: node:20-alpine + container_name: withoutbg-frontend-dev + working_dir: /app + ports: + - "3000:3000" + environment: + - NODE_ENV=development + - VITE_API_URL=http://localhost:8000/api + volumes: + - ./frontend:/app + - /app/node_modules + command: sh -c "npm install && npm run dev" + depends_on: + - backend + networks: + - withoutbg-network + +networks: + withoutbg-network: + driver: bridge diff --git a/apps/web/frontend/.gitignore b/apps/web/frontend/.gitignore new file mode 100644 index 0000000..e7576f4 --- /dev/null +++ b/apps/web/frontend/.gitignore @@ -0,0 +1,34 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + + + + + + + + + + diff --git a/apps/web/frontend/Dockerfile b/apps/web/frontend/Dockerfile new file mode 100644 index 0000000..f25cb53 --- /dev/null +++ b/apps/web/frontend/Dockerfile @@ -0,0 +1,42 @@ +# Multi-stage build for withoutbg frontend +# Build context should be the repository root: docker build -f apps/web/frontend/Dockerfile . + +# Stage 1: Build React application +FROM node:20-alpine AS builder + +# Set working directory +WORKDIR /app + +# Copy package files +COPY apps/web/frontend/package.json apps/web/frontend/package-lock.json* ./ + +# Install dependencies +RUN npm ci + +# Copy frontend source +COPY apps/web/frontend/ ./ + +# Build the application +RUN npm run build + +# Stage 2: Production stage with Nginx +FROM nginx:1.25-alpine + +# Copy Nginx configuration +COPY apps/web/nginx/nginx.conf /etc/nginx/nginx.conf + +# Copy built React application +COPY --from=builder /app/dist /usr/share/nginx/html + +# Create a simple health check page +RUN echo "healthy" > /usr/share/nginx/html/health.html + +# Expose port +EXPOSE 80 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \ + CMD wget --quiet --tries=1 --spider http://localhost/health || exit 1 + +# Start Nginx +CMD ["nginx", "-g", "daemon off;"] diff --git a/apps/web/frontend/README.md b/apps/web/frontend/README.md new file mode 100644 index 0000000..db7b306 --- /dev/null +++ b/apps/web/frontend/README.md @@ -0,0 +1,120 @@ +# withoutbg Frontend + +React-based web interface for the withoutbg background removal application. + +## Features + +- ๐ŸŽจ Modern, responsive UI with Tailwind CSS +- ๐Ÿ“ค Drag-and-drop image upload +- ๐Ÿ”„ Single and batch processing modes +- ๐Ÿ‘€ Before/after image comparison +- โšก Real-time processing feedback +- ๐Ÿ“ฅ Easy download of results + +## Development + +### Prerequisites + +- Node.js 20 or higher +- npm or yarn + +### Setup + +```bash +# Install dependencies +npm install + +# Start development server +npm run dev + +# Build for production +npm run build + +# Preview production build +npm run preview +``` + +### Environment Variables + +Create a `.env.local` file: + +```bash +VITE_API_URL=http://localhost:8000/api +``` + +## Project Structure + +``` +frontend/ +โ”œโ”€โ”€ public/ # Static assets +โ”œโ”€โ”€ src/ +โ”‚ โ”œโ”€โ”€ components/ # React components (if we add more) +โ”‚ โ”œโ”€โ”€ api.js # API client +โ”‚ โ”œโ”€โ”€ App.jsx # Main application component +โ”‚ โ”œโ”€โ”€ App.css # Component styles +โ”‚ โ”œโ”€โ”€ index.css # Global styles (Tailwind) +โ”‚ โ””โ”€โ”€ main.jsx # Application entry point +โ”œโ”€โ”€ index.html # HTML template +โ”œโ”€โ”€ package.json # Dependencies +โ”œโ”€โ”€ vite.config.js # Vite configuration +โ””โ”€โ”€ tailwind.config.js # Tailwind CSS configuration +``` + +## Technology Stack + +- **React 18**: UI framework +- **Vite**: Build tool and dev server +- **Tailwind CSS**: Utility-first CSS framework +- **react-dropzone**: Drag-and-drop file upload +- **axios**: HTTP client for API requests + +## Available Scripts + +- `npm run dev` - Start development server with hot-reload +- `npm run build` - Build for production +- `npm run preview` - Preview production build locally +- `npm run lint` - Run ESLint + +## API Integration + +The frontend communicates with the FastAPI backend through these endpoints: + +- `GET /api/health` - Health check +- `POST /api/remove-background` - Single image processing +- `POST /api/batch-remove-background` - Batch processing +- `GET /api/usage` - API usage statistics + +See `src/api.js` for API client implementation. + +## Docker + +The frontend is built into a production-ready Docker image with Nginx: + +```bash +# Build +docker build -f ../Dockerfile.frontend -t withoutbg-frontend .. + +# Run +docker run -p 80:80 withoutbg-frontend +``` + +## Contributing + +1. Make changes to the source code +2. Test locally with `npm run dev` +3. Build with `npm run build` to ensure no errors +4. Submit a pull request + +## License + +Apache 2.0 - See parent LICENSE file + + + + + + + + + + diff --git a/apps/web/frontend/index.html b/apps/web/frontend/index.html new file mode 100644 index 0000000..b3e07b0 --- /dev/null +++ b/apps/web/frontend/index.html @@ -0,0 +1,24 @@ + + + + + + + + withoutbg - AI Background Removal + + +

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+ } + } +} diff --git a/apps/web/frontend/package.json b/apps/web/frontend/package.json new file mode 100644 index 0000000..7209a96 --- /dev/null +++ b/apps/web/frontend/package.json @@ -0,0 +1,38 @@ +{ + "name": "withoutbg-frontend", + "version": "1.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview", + "lint": "eslint src --ext js,jsx --report-unused-disable-directives --max-warnings 0" + }, + "dependencies": { + "axios": "^1.7.9", + "lucide-react": "^0.548.0", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-dropzone": "^14.2.10" + }, + "devDependencies": { + "@types/react": "^18.3.12", + "@types/react-dom": "^18.3.1", + "@vitejs/plugin-react": "^4.3.4", + "autoprefixer": "^10.4.20", + "eslint": "^9.15.0", + "eslint-plugin-react": "^7.37.2", + "eslint-plugin-react-hooks": "^5.0.0", + "eslint-plugin-react-refresh": "^0.4.14", + "postcss": "^8.4.49", + "tailwindcss": "^3.4.15", + "vite": "^6.0.1" + } +} + + + + + + diff --git a/apps/web/frontend/postcss.config.js b/apps/web/frontend/postcss.config.js new file mode 100644 index 0000000..8c4ec75 --- /dev/null +++ b/apps/web/frontend/postcss.config.js @@ -0,0 +1,16 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} + + + + + + + + + + diff --git a/apps/web/frontend/public/logo.png b/apps/web/frontend/public/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..5e72ced78bb1312584478228f89e201063505ac7 GIT binary patch literal 1356 zcmV-S1+)5zP)j}oC$PDm%VcB6;4i{hWml|2G0>Ps~9Pcs^i7OXcI2Yo5A%?&ZR9o&lYo*orliY zRFu`RSbUboAiWI+`$}|)``zGu3PS7@Zkcj7n1;#}>Gd!U>swR%5~^Jkzs}^ptsQ*6 z(8cB1(*?6$WF97i(j8yPt-SKJQe%+Cj5IbySpvFK@=0J9Cdg6@7%?sijvE+o_>LV!bilhgr^ltaVS^5e+|e~yR&ihcPSIE#&M zl56K}uC-}W--_!_Fem4aLrWqYBHM>N-oTKEKc|j4PqR)9Fo)RUnqz^7=A}sue2gU2 ze}DujmgudrJGM$VO;3vA7fCKEHS|e(jZR85ermvA%GKCXKP5O^lUi-#TtZ!|psNbX6Am*$VFJ&5gCP@1@tQVqhbZP+5B!bjk821sv z>b15brdcCw%vrNsYUtwU+~Unomm11lF+DV4bE2t#J-%<($@Q9Wn4IWW!~e=aa3&+8 zPSCmY3SP?N{MWU6ObN@(W#VtM9V2gR0vG zmL!pmjVTdrSXP?UaEOF~pxx-x>4dLL-g7Dy$AB~&m z#8UtF-xe+7F|3mwn{zl$RLme$*Lf}FM*DgC5=dthdyTWruPI}8bG*$ltG&0lW(uD? z*j9_BC!;Aj_eny#(Rjld6JswjZf { + const handleResize = () => { + if (window.innerWidth < 768) { + setSidebarOpen(false) + } else { + setSidebarOpen(true) + } + } + + // Set initial state + handleResize() + + window.addEventListener('resize', handleResize) + return () => window.removeEventListener('resize', handleResize) + }, []) + + const renderContent = () => { + switch (activeTab) { + case 'remove-bg': + return + default: + return + } + } + + return ( +
+ {/* Expand Button - Shows when sidebar is closed */} + {!sidebarOpen && ( + + )} + + setSidebarOpen(!sidebarOpen)} + /> + +
+
+ {renderContent()} +
+
+
+
+ ) +} + +export default App + diff --git a/apps/web/frontend/src/api.js b/apps/web/frontend/src/api.js new file mode 100644 index 0000000..dcaccb2 --- /dev/null +++ b/apps/web/frontend/src/api.js @@ -0,0 +1,45 @@ +import axios from 'axios'; + +const API_BASE_URL = import.meta.env.VITE_API_URL || '/api'; + +export const api = axios.create({ + baseURL: API_BASE_URL, + headers: { + 'Content-Type': 'multipart/form-data', + }, +}); + +export const removeBackground = async (file, options = {}) => { + const formData = new FormData(); + formData.append('file', file); + formData.append('format', options.format || 'png'); + formData.append('quality', options.quality || 95); + + if (options.apiKey) { + formData.append('api_key', options.apiKey); + } + + const response = await api.post('/remove-background', formData, { + responseType: 'blob', + }); + + return response.data; +}; + +export const healthCheck = async () => { + const response = await api.get('/health'); + return response.data; +}; + +export const getUsage = async (apiKey) => { + const response = await api.get(`/usage?api_key=${apiKey}`); + return response.data; +}; + + + + + + + + diff --git a/apps/web/frontend/src/components/ThemeSwitcher.jsx b/apps/web/frontend/src/components/ThemeSwitcher.jsx new file mode 100644 index 0000000..e8900fc --- /dev/null +++ b/apps/web/frontend/src/components/ThemeSwitcher.jsx @@ -0,0 +1,68 @@ +import { useEffect, useState } from 'react'; + +export function ThemeSwitcher() { + const [mounted, setMounted] = useState(false); + const [isDark, setIsDark] = useState(false); + + useEffect(() => { + setMounted(true); + + // Check if user has a saved preference + const savedTheme = localStorage.getItem('theme'); + const prefersDark = window.matchMedia('(prefers-color-scheme: dark)').matches; + + const shouldBeDark = savedTheme === 'dark' || (!savedTheme && prefersDark); + setIsDark(shouldBeDark); + + if (shouldBeDark) { + document.documentElement.classList.add('dark'); + } else { + document.documentElement.classList.remove('dark'); + } + }, []); + + const toggleTheme = () => { + const newIsDark = !isDark; + setIsDark(newIsDark); + + if (newIsDark) { + document.documentElement.classList.add('dark'); + localStorage.setItem('theme', 'dark'); + } else { + document.documentElement.classList.remove('dark'); + localStorage.setItem('theme', 'light'); + } + }; + + if (!mounted) return null; + + return ( + + ); +} + +export default ThemeSwitcher; + + + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/ErrorMessage.jsx b/apps/web/frontend/src/components/demo/ErrorMessage.jsx new file mode 100644 index 0000000..5183748 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ErrorMessage.jsx @@ -0,0 +1,24 @@ +import React from 'react' + +/** + * ErrorMessage component for displaying error messages + * @param {Object} props + * @param {string} props.error - Error message to display + */ +export const ErrorMessage = ({ error }) => { + if (!error) return null + + return ( +
+

โŒ {error}

+
+ ) +} + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/Footer.jsx b/apps/web/frontend/src/components/demo/Footer.jsx new file mode 100644 index 0000000..6a225ed --- /dev/null +++ b/apps/web/frontend/src/components/demo/Footer.jsx @@ -0,0 +1,14 @@ +import React from 'react' + +/** + * Footer component for the application + */ +export const Footer = () => { + return ( + + ) +} diff --git a/apps/web/frontend/src/components/demo/Header.jsx b/apps/web/frontend/src/components/demo/Header.jsx new file mode 100644 index 0000000..91042fb --- /dev/null +++ b/apps/web/frontend/src/components/demo/Header.jsx @@ -0,0 +1,33 @@ +import React from 'react' + +/** + * Header component for the application + */ +export const Header = () => { + return ( +
+ withoutbg logo +

+ Background Remover +

+

+ brought to you by{' '} + + withoutbg.com + +

+ +
+ ) +} + + diff --git a/apps/web/frontend/src/components/demo/ImageComparisonCard.jsx b/apps/web/frontend/src/components/demo/ImageComparisonCard.jsx new file mode 100644 index 0000000..5d8d664 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ImageComparisonCard.jsx @@ -0,0 +1,61 @@ +import React from 'react' +import { PrimaryButton } from '../ui/PrimaryButton' + +/** + * ImageComparisonCard component for displaying original vs processed image + * @param {Object} props + * @param {Object} props.result - Result object with original, result, name, status, and error + * @param {Function} props.onDownload - Callback when download button is clicked + */ +export const ImageComparisonCard = ({ result, onDownload }) => { + return ( +
+ {result.status === 'success' ? ( + <> +
+
+

Original

+ Original +
+
+

Without Background

+
+ Result +
+
+
+
+

{result.name}

+ onDownload(result.result, result.name)} + className="py-2 px-4" + > + Download + +
+ + ) : ( +
+

{result.name}

+

Error: {result.error}

+
+ )} +
+ ) +} + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/ImagePreview.jsx b/apps/web/frontend/src/components/demo/ImagePreview.jsx new file mode 100644 index 0000000..92073a0 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ImagePreview.jsx @@ -0,0 +1,72 @@ +import React from 'react' +import { PrimaryButton } from '../ui/PrimaryButton' +import { SecondaryButton } from '../ui/SecondaryButton' + +/** + * ImagePreview component for displaying uploaded images before processing + * @param {Object} props + * @param {Array} props.files - Array of file objects with preview URLs + * @param {'single' | 'batch'} props.mode - Processing mode + * @param {Function} props.onProcess - Callback when process button is clicked + * @param {Function} props.onCancel - Callback when cancel button is clicked + * @param {boolean} props.processing - Whether processing is in progress + */ +export const ImagePreview = ({ files, mode, onProcess, onCancel, processing }) => { + return ( +
+

+ {mode === 'single' ? 'Image Preview' : `Selected Images (${files.length})`} +

+ +
+ {files.map((file, index) => ( +
+ {file.name} +
+

{file.name}

+
+
+ ))} +
+ +
+ + {processing ? ( + + + + + + Processing... + + ) : ( + 'โœจ Remove Background' + )} + + + Cancel + +
+
+ ) +} + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/ImageUploadZone.jsx b/apps/web/frontend/src/components/demo/ImageUploadZone.jsx new file mode 100644 index 0000000..c4f5380 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ImageUploadZone.jsx @@ -0,0 +1,43 @@ +import React from 'react' +import { Upload } from 'lucide-react' + +/** + * ImageUploadZone component for drag-and-drop file upload + * @param {Object} props + * @param {Object} props.getRootProps - react-dropzone root props + * @param {Object} props.getInputProps - react-dropzone input props + * @param {boolean} props.isDragActive - Whether drag is active + */ +export const ImageUploadZone = ({ getRootProps, getInputProps, isDragActive }) => { + return ( +
+
+ +
+ +
+ {isDragActive ? ( +

Drop your images here...

+ ) : ( + <> +

+ Drag & drop images here +

+

+ or click to select files (max 10) +

+

+ Supported formats: PNG, JPEG, WebP +

+ + )} +
+
+ ) +} diff --git a/apps/web/frontend/src/components/demo/ProcessingModeDescription.jsx b/apps/web/frontend/src/components/demo/ProcessingModeDescription.jsx new file mode 100644 index 0000000..2ccab57 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ProcessingModeDescription.jsx @@ -0,0 +1,35 @@ +import React from 'react' + +/** + * ProcessingModeDescription component displays information about the selected processing mode + * @param {Object} props + * @param {'local' | 'api'} props.processingType - Current processing type + */ +export const ProcessingModeDescription = ({ processingType }) => { + return ( +
+ {processingType === 'local' ? ( +

+ Process images directly in your browser using open-source Focus AI models. + No API key needed, completely free, and your images never leave your device. +

+ ) : ( +

+ Use professional-grade cloud processing with superior quality. + Requires an API key from{' '} + + withoutbg.com + +

+ )} +
+ ) +} + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/ProcessingResults.jsx b/apps/web/frontend/src/components/demo/ProcessingResults.jsx new file mode 100644 index 0000000..c6a5db4 --- /dev/null +++ b/apps/web/frontend/src/components/demo/ProcessingResults.jsx @@ -0,0 +1,59 @@ +import React from 'react' +import { PrimaryButton } from '../ui/PrimaryButton' +import { SecondaryButton } from '../ui/SecondaryButton' +import { ImageComparisonCard } from './ImageComparisonCard' + +/** + * ProcessingResults component for displaying processed images + * @param {Object} props + * @param {Array} props.results - Array of result objects + * @param {'single' | 'batch'} props.mode - Processing mode + * @param {Function} props.onDownloadAll - Callback when download all button is clicked + * @param {Function} props.onReset - Callback when process more button is clicked + * @param {Function} props.onDownloadImage - Callback for downloading individual images + */ +export const ProcessingResults = ({ results, mode, onDownloadAll, onReset, onDownloadImage }) => { + return ( +
+
+

+ Results +

+
+ {mode === 'batch' && ( + + โฌ‡๏ธ Download All + + )} + + Process More + +
+
+ +
+ {results.map((result, index) => ( + + ))} +
+
+ ) +} + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/ProcessingTypeTabs.jsx b/apps/web/frontend/src/components/demo/ProcessingTypeTabs.jsx new file mode 100644 index 0000000..d1aa76a --- /dev/null +++ b/apps/web/frontend/src/components/demo/ProcessingTypeTabs.jsx @@ -0,0 +1,42 @@ +import React from 'react' + +/** + * ProcessingTypeTabs component for switching between local and API processing modes + * @param {Object} props + * @param {'local' | 'api'} props.processingType - Current processing type + * @param {Function} props.onProcessingTypeChange - Callback when processing type changes + */ +export const ProcessingTypeTabs = ({ processingType, onProcessingTypeChange }) => { + return ( +
+ + +
+ ) +} + + diff --git a/apps/web/frontend/src/components/demo/README.md b/apps/web/frontend/src/components/demo/README.md new file mode 100644 index 0000000..c4c99c6 --- /dev/null +++ b/apps/web/frontend/src/components/demo/README.md @@ -0,0 +1,132 @@ +# Demo Components + +This directory contains modularized, reusable components for the background removal demo interface. + +## Components Overview + +### Layout Components + +- **`Header`** - Application header with title and description +- **`Footer`** - Application footer with links and credits + +### Processing Configuration + +- **`ProcessingTypeTabs`** - Tabs for switching between local and API processing modes +- **`ProcessingModeDescription`** - Information about the selected processing mode +- **`SettingsPanel`** - Configuration panel for processing mode, output format, and API key + +### File Upload & Display + +- **`ImageUploadZone`** - Drag-and-drop file upload area using react-dropzone +- **`ImagePreview`** - Preview of uploaded images before processing +- **`ErrorMessage`** - Error message display component + +### Results Display + +- **`ProcessingResults`** - Container for displaying processed image results +- **`ImageComparisonCard`** - Individual card showing original vs processed image comparison + +## Usage Example + +```jsx +import { + Header, + Footer, + ProcessingTypeTabs, + SettingsPanel, + ImageUploadZone, + ImagePreview, + ProcessingResults +} from './components/demo' + +// Use the components in your app +function MyApp() { + return ( +
+
+ + {/* ... other components */} +
+
+ ) +} +``` + +## Benefits of Modularization + +1. **Reusability** - Components can be used across different pages or applications +2. **Maintainability** - Easier to update and fix bugs in isolated components +3. **Testability** - Each component can be tested independently +4. **Readability** - Smaller, focused components are easier to understand +5. **Separation of Concerns** - Each component has a single, well-defined responsibility + +## Component Props + +### ProcessingTypeTabs +- `processingType`: `'local' | 'api'` - Current processing type +- `onProcessingTypeChange`: `(type) => void` - Callback when type changes + +### SettingsPanel +- `mode`: `'single' | 'batch'` - Processing mode +- `onModeChange`: `(mode) => void` - Callback when mode changes +- `format`: `string` - Output format (png, jpg, webp) +- `onFormatChange`: `(format) => void` - Callback when format changes +- `processingType`: `'local' | 'api'` - Processing type +- `apiKey`: `string` - API key value +- `onApiKeyChange`: `(key) => void` - Callback when API key changes + +### ImageUploadZone +- `getRootProps`: `object` - react-dropzone root props +- `getInputProps`: `object` - react-dropzone input props +- `isDragActive`: `boolean` - Whether drag is active +- `mode`: `'single' | 'batch'` - Processing mode + +### ImagePreview +- `files`: `Array<{file, preview, name}>` - Array of file objects +- `mode`: `'single' | 'batch'` - Processing mode +- `onProcess`: `() => void` - Callback when process button is clicked +- `onCancel`: `() => void` - Callback when cancel button is clicked +- `processing`: `boolean` - Whether processing is in progress + +### ProcessingResults +- `results`: `Array<{original, result, name, status, error}>` - Array of result objects +- `mode`: `'single' | 'batch'` - Processing mode +- `onDownloadAll`: `() => void` - Callback for download all button +- `onReset`: `() => void` - Callback for process more button +- `onDownloadImage`: `(url, filename) => void` - Callback for downloading images + +### ImageComparisonCard +- `result`: `{original, result, name, status, error}` - Result object +- `onDownload`: `(url, filename) => void` - Callback for download button + +### ErrorMessage +- `error`: `string | null` - Error message to display + +## File Structure + +``` +demo/ +โ”œโ”€โ”€ Header.jsx # Application header +โ”œโ”€โ”€ Footer.jsx # Application footer +โ”œโ”€โ”€ ProcessingTypeTabs.jsx # Local/API mode tabs +โ”œโ”€โ”€ ProcessingModeDescription.jsx # Mode description text +โ”œโ”€โ”€ SettingsPanel.jsx # Settings configuration +โ”œโ”€โ”€ ImageUploadZone.jsx # Drag-and-drop upload +โ”œโ”€โ”€ ErrorMessage.jsx # Error display +โ”œโ”€โ”€ ImagePreview.jsx # Preview before processing +โ”œโ”€โ”€ ImageComparisonCard.jsx # Individual result card +โ”œโ”€โ”€ ProcessingResults.jsx # Results container +โ”œโ”€โ”€ index.js # Component exports +โ””โ”€โ”€ README.md # This file +``` + + + + + + + + diff --git a/apps/web/frontend/src/components/demo/RemoveBackground.jsx b/apps/web/frontend/src/components/demo/RemoveBackground.jsx new file mode 100644 index 0000000..70ac10c --- /dev/null +++ b/apps/web/frontend/src/components/demo/RemoveBackground.jsx @@ -0,0 +1,466 @@ +import React, { useState, useCallback, useEffect, useRef } from 'react' +import { useDropzone } from 'react-dropzone' +import { removeBackground } from '../../api' +import { + ProcessingTypeTabs, + ProcessingModeDescription, + SettingsPanel, + ImageUploadZone, + ErrorMessage +} from './' +import { PrimaryButton } from '../ui/PrimaryButton' +import { SecondaryButton } from '../ui/SecondaryButton' +import { Hourglass, CheckCircle2, AlertCircle, Download, Star } from 'lucide-react' + +const delay = (ms) => new Promise(resolve => setTimeout(resolve, ms)) + +const statusCopy = { + pending: 'Queued', + processing: 'Processing', + success: 'Completed', + error: 'Failed' +} + +const statusBadgeClasses = { + pending: 'bg-purple-100 text-purple-700 dark:bg-purple-900/40 dark:text-purple-200', + processing: 'bg-blue-100 text-blue-700 dark:bg-blue-900/40 dark:text-blue-200', + success: 'bg-emerald-100 text-emerald-700 dark:bg-emerald-900/30 dark:text-emerald-200', + error: 'bg-red-100 text-red-700 dark:bg-red-900/40 dark:text-red-200' +} + +/** + * RemoveBackground component - handles image background removal + */ +export const RemoveBackground = () => { + const [uploads, setUploads] = useState([]) + const [processingType, setProcessingType] = useState('local') + const [apiKey, setApiKey] = useState('') + const [error, setError] = useState(null) + const [queueRunning, setQueueRunning] = useState(false) + const [starCount, setStarCount] = useState(null) + + const uploadsRef = useRef(uploads) + useEffect(() => { + uploadsRef.current = uploads + }, [uploads]) + + const revokeObjectUrls = useCallback((items) => { + items.forEach(item => { + if (item.preview) { + URL.revokeObjectURL(item.preview) + } + if (item.resultUrl) { + URL.revokeObjectURL(item.resultUrl) + } + }) + }, []) + + useEffect(() => { + return () => { + revokeObjectUrls(uploadsRef.current) + } + }, [revokeObjectUrls]) + + // Fetch GitHub star count + useEffect(() => { + const CACHE_KEY = 'withoutbg_star_count' + const CACHE_TIMESTAMP_KEY = 'withoutbg_star_count_timestamp' + const CACHE_DURATION = 1000 * 60 * 60 // 1 hour + + const fetchStarCount = async () => { + try { + const response = await fetch('https://api.github.com/repos/withoutbg/withoutbg') + if (response.ok) { + const data = await response.json() + const count = data.stargazers_count + setStarCount(count) + localStorage.setItem(CACHE_KEY, count.toString()) + localStorage.setItem(CACHE_TIMESTAMP_KEY, Date.now().toString()) + } + } catch (err) { + // Silently fail if we can't fetch the star count + console.error('Failed to fetch star count:', err) + } + } + + // Check cache first + const cachedCount = localStorage.getItem(CACHE_KEY) + const cachedTimestamp = localStorage.getItem(CACHE_TIMESTAMP_KEY) + + if (cachedCount && cachedTimestamp) { + const age = Date.now() - parseInt(cachedTimestamp, 10) + if (age < CACHE_DURATION) { + setStarCount(parseInt(cachedCount, 10)) + return + } + } + + // Fetch fresh data if cache is missing or expired + void fetchStarCount() + }, []) + + const onDrop = useCallback((acceptedFiles) => { + setError(null) + const imageFiles = acceptedFiles.filter(file => file.type.startsWith('image/')) + + if (imageFiles.length === 0) { + setError('Please upload valid image files') + return + } + + if (imageFiles.length > 10) { + setError('Maximum 10 images allowed') + return + } + + revokeObjectUrls(uploadsRef.current) + + const timestamp = Date.now() + const mapped = imageFiles.map((file, index) => ({ + id: `${timestamp}-${index}-${file.name}`, + file, + preview: URL.createObjectURL(file), + name: file.name, + status: 'pending', + resultUrl: null, + error: null + })) + + uploadsRef.current = mapped + setUploads(mapped) + }, [revokeObjectUrls]) + + const { getRootProps, getInputProps, isDragActive } = useDropzone({ + onDrop, + accept: { + 'image/*': ['.png', '.jpg', '.jpeg', '.webp'] + }, + multiple: true + }) + + const updateUpload = useCallback((index, updater) => { + setUploads(prev => { + if (!prev[index]) { + return prev + } + + const updated = [...prev] + const current = updated[index] + const nextValue = typeof updater === 'function' ? updater(current) : { ...current, ...updater } + + if (nextValue.resultUrl && current.resultUrl && nextValue.resultUrl !== current.resultUrl) { + URL.revokeObjectURL(current.resultUrl) + } + + updated[index] = nextValue + uploadsRef.current = updated + return updated + }) + }, []) + + const processQueue = useCallback(async () => { + if (queueRunning) { + return + } + + if (processingType === 'api' && !apiKey) { + setError('API key is required for API processing') + return + } + + setQueueRunning(true) + + try { + while (true) { + const nextIndex = uploadsRef.current.findIndex(item => item.status === 'pending') + if (nextIndex === -1) { + break + } + + const current = uploadsRef.current[nextIndex] + if (!current) { + break + } + + setError(null) + updateUpload(nextIndex, (item) => ({ ...item, status: 'processing', error: null })) + + try { + const blob = await removeBackground(current.file, { + format: 'png', + apiKey: processingType === 'api' ? apiKey : undefined + }) + + const resultUrl = URL.createObjectURL(blob) + updateUpload(nextIndex, (item) => ({ ...item, status: 'success', resultUrl, error: null })) + } catch (err) { + const message = err?.response?.data?.detail || err?.message || 'Failed to process image' + updateUpload(nextIndex, (item) => ({ ...item, status: 'error', error: message })) + setError(message) + } + + if (processingType === 'api') { + await delay(3000) + } + } + } finally { + setQueueRunning(false) + } + }, [apiKey, processingType, queueRunning, updateUpload]) + + useEffect(() => { + if (uploads.length === 0) { + return + } + + const hasPending = uploads.some(item => item.status === 'pending') + if (hasPending) { + if (processingType === 'api' && !apiKey) { + setError('API key is required for API processing') + return + } + void processQueue() + } + }, [uploads, processQueue, apiKey, processingType]) + + const downloadImage = useCallback((resultUrl, filename) => { + const link = document.createElement('a') + link.href = resultUrl + link.download = `withoutbg-${filename}` + document.body.appendChild(link) + link.click() + document.body.removeChild(link) + }, []) + + const downloadAll = useCallback(() => { + uploads.forEach((item, index) => { + if (item.status === 'success' && item.resultUrl) { + setTimeout(() => { + downloadImage(item.resultUrl, item.name) + }, index * 150) + } + }) + }, [downloadImage, uploads]) + + const reset = useCallback(() => { + revokeObjectUrls(uploadsRef.current) + uploadsRef.current = [] + setUploads([]) + setError(null) + setQueueRunning(false) + }, [revokeObjectUrls]) + + const handleStarClick = useCallback(() => { + // Clear cache when user clicks to star + localStorage.removeItem('withoutbg_star_count') + localStorage.removeItem('withoutbg_star_count_timestamp') + }, []) + + const handleProcessingTypeChange = (type) => { + setProcessingType(type) + if (type === 'local') { + setApiKey('') + } + reset() + } + + const hasResults = uploads.some(item => item.status === 'success') + const processingCount = uploads.filter(item => item.status === 'processing').length + const pendingCount = uploads.filter(item => item.status === 'pending').length + const successCount = uploads.filter(item => item.status === 'success').length + const errorCount = uploads.filter(item => item.status === 'error').length + const allProcessed = uploads.length > 0 && processingCount === 0 && pendingCount === 0 + + const summaryParts = [] + if (processingCount) summaryParts.push(`${processingCount} processing`) + if (pendingCount) summaryParts.push(`${pendingCount} queued`) + if (successCount) summaryParts.push(`${successCount} completed`) + if (errorCount) summaryParts.push(`${errorCount} failed`) + const progressSummary = summaryParts.join(' โ€ข ') + + return ( +
+ + +
+ + + + + {uploads.length === 0 && ( + + )} + + + + {uploads.length > 0 && ( +
+
+
+

+ {progressSummary || 'Preparing images...'} +

+

+ {processingType === 'api' + ? 'API mode adds a 3s pause between requests to stay within rate limits.' + : 'Images are processed one by one by the local backend.'} +

+
+
+ {hasResults && ( + + + Download All + + )} + + Reset + +
+
+ +
+ {uploads.map((item, index) => ( +
+
+

+ {item.name} +

+ + {statusCopy[item.status]} + +
+ +
+
+
+ {/* Original Panel */} +
+

Original

+
+
+ {item.name} + {item.status === 'pending' && ( +
+ + In queue +
+ )} +
+
+
+ + {/* Result Panel */} +
+

Result

+
+
+ {item.status === 'processing' && ( + <> +
+
+
+
Processing
+
Hang tight โœจ
+
+ + )} + {item.status === 'success' && ( + <> + {`${item.name} +
+ +
+ + )} +
+
+
+
+
+ + {item.status === 'success' && ( +
+ downloadImage(item.resultUrl, item.name)} + className="px-6 py-2 flex items-center gap-2" + > + + Download + +
+ )} + + {item.status === 'error' && ( +
+ +
+

Processing failed

+

{item.error}

+
+
+ )} +
+
+ ))} +
+ + {allProcessed && successCount > 0 && ( +
+
+
+

+ Love the results? +

+

+ Star withoutbg on GitHub to support the project! +

+
+ + + Star on GitHub{starCount !== null && ` (${starCount.toLocaleString()})`} + +
+
+ )} +
+ )} +
+
+ ) +} + diff --git a/apps/web/frontend/src/components/demo/SettingsPanel.jsx b/apps/web/frontend/src/components/demo/SettingsPanel.jsx new file mode 100644 index 0000000..5297cdb --- /dev/null +++ b/apps/web/frontend/src/components/demo/SettingsPanel.jsx @@ -0,0 +1,114 @@ +import React, { useEffect, useState } from 'react' +import { Key } from 'lucide-react' + +const STORAGE_KEY = 'withoutbg_api_key' + +/** + * SettingsPanel component for API key configuration + * @param {Object} props + * @param {'local' | 'api'} props.processingType - Processing type + * @param {string} props.apiKey - API key value + * @param {Function} props.onApiKeyChange - Callback when API key changes + */ +export const SettingsPanel = ({ + processingType, + apiKey, + onApiKeyChange +}) => { + const [storageUpdateTrigger, setStorageUpdateTrigger] = useState(0) + + // Load stored API key whenever switching to API mode and field is empty + useEffect(() => { + if (processingType === 'api') { + const storedKey = localStorage.getItem(STORAGE_KEY) + // Load from storage if there's a stored key and current field is empty + if (storedKey && !apiKey) { + onApiKeyChange(storedKey) + } + } + }, [processingType]) // Only depend on processingType to reload when switching tabs + + // Only show settings panel when in API mode + if (processingType !== 'api') { + return null + } + + // Check if current key is stored in localStorage + // Using storageUpdateTrigger to force re-render when storage changes + const storedKey = localStorage.getItem(STORAGE_KEY) + const isStored = storedKey === apiKey && apiKey !== '' && storageUpdateTrigger >= 0 + + const handleRemember = () => { + localStorage.setItem(STORAGE_KEY, apiKey) + // Force a re-render by incrementing the trigger + setStorageUpdateTrigger(prev => prev + 1) + } + + const handleRemove = () => { + localStorage.removeItem(STORAGE_KEY) + onApiKeyChange('') + // Force a re-render + setStorageUpdateTrigger(prev => prev + 1) + } + + // Show Remember button when: user has entered a key AND it's not stored + const showRememberButton = apiKey && !isStored + // Show Remove button when: key is stored + const showRemoveButton = isStored + + return ( + <> +
+
+ + onApiKeyChange(e.target.value)} + placeholder="sk_..." + className="w-full py-2 px-4 bg-white dark:bg-gray-700 text-gray-900 dark:text-white rounded-lg border border-gray-300 dark:border-gray-600 focus:border-purple-500 focus:ring-2 focus:ring-purple-500 focus:outline-none placeholder-gray-400 dark:placeholder-gray-500" + required + /> + + {/* Remember and Remove buttons */} + {(showRememberButton || showRemoveButton) && ( +
+ {showRememberButton && ( + + )} + {showRemoveButton && ( + + )} +
+ )} +
+ + {!apiKey && ( +
+

+ + API key is required for API processing. Get yours at{' '} + + withoutbg.com + + +

+
+ )} +
+ + ) +} + diff --git a/apps/web/frontend/src/components/demo/Sidebar.jsx b/apps/web/frontend/src/components/demo/Sidebar.jsx new file mode 100644 index 0000000..5aaa323 --- /dev/null +++ b/apps/web/frontend/src/components/demo/Sidebar.jsx @@ -0,0 +1,135 @@ +import React, { useEffect } from 'react' +import { Header } from './Header' +import { ThemeSwitcher } from '../ThemeSwitcher' + +/** + * Sidebar component with navigation + */ +export const Sidebar = ({ activeTab, onTabChange, isOpen, onToggle }) => { + const navItems = [ + { id: 'remove-bg', label: 'Remove Background', enabled: true }, + { id: 'generate-sticker', label: 'Generate Sticker', enabled: false }, + { id: 'shadow-generator', label: 'Shadow Generator', enabled: false } + ] + + // Handle click on navigation items to close sidebar on mobile + const handleNavClick = (itemId) => { + onTabChange(itemId) + // Close sidebar on mobile after selection + if (window.innerWidth < 768) { + onToggle() + } + } + + // Close sidebar when clicking outside on mobile + useEffect(() => { + if (!isOpen || window.innerWidth >= 768) return + + const handleClickOutside = (e) => { + const sidebar = document.getElementById('sidebar') + const toggleButton = document.getElementById('sidebar-toggle') + if (sidebar && !sidebar.contains(e.target) && toggleButton && !toggleButton.contains(e.target)) { + onToggle() + } + } + + document.addEventListener('mousedown', handleClickOutside) + return () => document.removeEventListener('mousedown', handleClickOutside) + }, [isOpen, onToggle]) + + return ( + <> + {/* Overlay for mobile */} + {isOpen && ( +
+ )} + + {/* Sidebar */} + + + ) +} + diff --git a/apps/web/frontend/src/components/demo/index.js b/apps/web/frontend/src/components/demo/index.js new file mode 100644 index 0000000..ad6f2eb --- /dev/null +++ b/apps/web/frontend/src/components/demo/index.js @@ -0,0 +1,13 @@ +// Demo components for background removal interface +export { Header } from './Header' +export { Footer } from './Footer' +export { ProcessingTypeTabs } from './ProcessingTypeTabs' +export { ProcessingModeDescription } from './ProcessingModeDescription' +export { SettingsPanel } from './SettingsPanel' +export { ImageUploadZone } from './ImageUploadZone' +export { ErrorMessage } from './ErrorMessage' +export { ImagePreview } from './ImagePreview' +export { ImageComparisonCard } from './ImageComparisonCard' +export { ProcessingResults } from './ProcessingResults' +export { Sidebar } from './Sidebar' +export { RemoveBackground } from './RemoveBackground' diff --git a/apps/web/frontend/src/components/ui/PrimaryButton.jsx b/apps/web/frontend/src/components/ui/PrimaryButton.jsx new file mode 100644 index 0000000..affd61c --- /dev/null +++ b/apps/web/frontend/src/components/ui/PrimaryButton.jsx @@ -0,0 +1,42 @@ +/** + * PrimaryButton Component + * + * A versatile primary button component that supports both link and click functionality. + * Can be used as a navigation link or as an action button. + */ +export const PrimaryButton = (props) => { + const baseClasses = "inline-block px-4 py-2 rounded-lg text-sm font-medium bg-purple-600 hover:bg-purple-700 disabled:bg-purple-400 disabled:cursor-not-allowed transition-all duration-200 text-white dark:bg-purple-600 dark:hover:bg-purple-700 dark:text-white shadow-sm hover:shadow-md text-center"; + + // If href prop is provided, render as anchor tag + if (props.href) { + const { href, children, className = "", onClick, target, rel } = props; + return ( + + {children} + + ); + } + + // Otherwise render as button + const { onClick, children, className = "", disabled, type = "button", ...buttonProps } = props; + return ( + + ); +}; + +export default PrimaryButton; + diff --git a/apps/web/frontend/src/components/ui/SecondaryButton.jsx b/apps/web/frontend/src/components/ui/SecondaryButton.jsx new file mode 100644 index 0000000..a53193a --- /dev/null +++ b/apps/web/frontend/src/components/ui/SecondaryButton.jsx @@ -0,0 +1,42 @@ +/** + * SecondaryButton Component + * + * A versatile secondary button component that supports both link and click functionality. + * Can be used as a navigation link or as an action button. + */ +export const SecondaryButton = (props) => { + const baseClasses = "inline-block px-4 py-2 rounded-lg text-sm font-medium border border-gray-200 text-gray-700 hover:border-gray-300 hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed transition-colors duration-75 bg-white dark:border-gray-600 dark:text-gray-300 dark:hover:border-gray-500 dark:hover:bg-gray-800/30 dark:bg-gray-800 text-center"; + + // If href prop is provided, render as anchor tag + if (props.href) { + const { href, children, className = "", onClick, target, rel } = props; + return ( + + {children} + + ); + } + + // Otherwise render as button + const { onClick, children, className = "", disabled, type = "button", ...buttonProps } = props; + return ( + + ); +}; + +export default SecondaryButton; + diff --git a/apps/web/frontend/src/index.css b/apps/web/frontend/src/index.css new file mode 100644 index 0000000..730107d --- /dev/null +++ b/apps/web/frontend/src/index.css @@ -0,0 +1,84 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + --background: #ffffff; + --foreground: #171717; + + /* Primary Purple Colors */ + --primary: #6C2BD9; + --primary-hover: #5A23B8; + --primary-light: #A97BFF; + --primary-light-hover: #9460E8; + --primary-text: #ffffff; + --primary-text-dark: #000000; + + font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; + line-height: 1.5; + font-weight: 400; + + font-synthesis: none; + text-rendering: optimizeLegidity; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.dark { + --background: #0a0a0a; + --foreground: #ededed; + + /* Dark mode primary colors */ + --primary: #A97BFF; + --primary-hover: #9460E8; + --primary-text: #000000; +} + +body { + margin: 0; + display: flex; + min-width: 320px; + min-height: 100vh; + color: var(--foreground); + background: var(--background); + transition: background-color 0.3s ease, color 0.3s ease; +} + +#root { + width: 100%; +} + +/* Checkerboard pattern for transparent backgrounds */ +.checkerboard { + background-image: + linear-gradient(45deg, rgba(0, 0, 0, 0.12) 25%, transparent 25%), + linear-gradient(-45deg, rgba(0, 0, 0, 0.12) 25%, transparent 25%), + linear-gradient(45deg, transparent 75%, rgba(0, 0, 0, 0.12) 75%), + linear-gradient(-45deg, transparent 75%, rgba(0, 0, 0, 0.12) 75%); + background-size: 20px 20px; + background-position: 0 0, 0 10px, 10px -10px, -10px 0px; +} + +/* Dark mode checkerboard */ +.dark .checkerboard { + background-image: + linear-gradient(45deg, rgba(255, 255, 255, 0.25) 25%, transparent 25%), + linear-gradient(-45deg, rgba(255, 255, 255, 0.25) 25%, transparent 25%), + linear-gradient(45deg, transparent 75%, rgba(255, 255, 255, 0.25) 75%), + linear-gradient(-45deg, transparent 75%, rgba(255, 255, 255, 0.25) 75%); +} + +/* Better text selection colors */ +::selection { + background-color: rgba(147, 51, 234, 0.2); + color: inherit; +} + +.dark ::selection { + background-color: rgba(196, 181, 253, 0.3); +} + +/* Smooth scrolling for anchor links */ +html { + scroll-behavior: smooth; +} \ No newline at end of file diff --git a/apps/web/frontend/src/main.jsx b/apps/web/frontend/src/main.jsx new file mode 100644 index 0000000..54b39dd --- /dev/null +++ b/apps/web/frontend/src/main.jsx @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.jsx' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')).render( + + + , +) diff --git a/apps/web/frontend/tailwind.config.js b/apps/web/frontend/tailwind.config.js new file mode 100644 index 0000000..3f5e1b6 --- /dev/null +++ b/apps/web/frontend/tailwind.config.js @@ -0,0 +1,42 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + darkMode: 'class', + theme: { + extend: { + colors: { + primary: { + DEFAULT: '#6C2BD9', + hover: '#5A23B8', + light: '#A97BFF', + 'light-hover': '#9460E8', + text: '#ffffff', + 'text-dark': '#000000', + }, + }, + fontFamily: { + sans: ['Inter', 'system-ui', 'Avenir', 'Helvetica', 'Arial', 'sans-serif'], + }, + animation: { + 'fade-in-up': 'fade-in-up 0.4s ease-out', + }, + keyframes: { + 'fade-in-up': { + '0%': { + opacity: '0', + transform: 'translateY(20px)', + }, + '100%': { + opacity: '1', + transform: 'translateY(0)', + }, + }, + }, + }, + }, + plugins: [], +} + diff --git a/apps/web/frontend/vite.config.js b/apps/web/frontend/vite.config.js new file mode 100644 index 0000000..0a295c8 --- /dev/null +++ b/apps/web/frontend/vite.config.js @@ -0,0 +1,31 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vitejs.dev/config/ +export default defineConfig({ + plugins: [react()], + server: { + host: '0.0.0.0', + port: 3000, + proxy: { + '/api': { + target: 'http://localhost:8000', + changeOrigin: true, + }, + }, + }, + build: { + outDir: 'dist', + sourcemap: false, + }, +}) + + + + + + + + + + diff --git a/apps/web/nginx/nginx.conf b/apps/web/nginx/nginx.conf new file mode 100644 index 0000000..64a54a1 --- /dev/null +++ b/apps/web/nginx/nginx.conf @@ -0,0 +1,96 @@ +user nginx; +worker_processes auto; +error_log /var/log/nginx/error.log notice; +pid /var/run/nginx.pid; + +events { + worker_connections 1024; +} + +http { + include /etc/nginx/mime.types; + default_type application/octet-stream; + + log_format main '$remote_addr - $remote_user [$time_local] "$request" ' + '$status $body_bytes_sent "$http_referer" ' + '"$http_user_agent" "$http_x_forwarded_for"'; + + access_log /var/log/nginx/access.log main; + + sendfile on; + tcp_nopush on; + tcp_nodelay on; + keepalive_timeout 65; + types_hash_max_size 2048; + + # Gzip compression + gzip on; + gzip_vary on; + gzip_proxied any; + gzip_comp_level 6; + gzip_types text/plain text/css text/xml text/javascript + application/json application/javascript application/xml+rss + application/rss+xml font/truetype font/opentype + application/vnd.ms-fontobject image/svg+xml; + + # Server configuration + server { + listen 80; + server_name localhost; + + # Max upload size for images + client_max_body_size 50M; + + # Root directory for React static files + root /usr/share/nginx/html; + index index.html; + + # Serve static files + location / { + try_files $uri $uri/ /index.html; + add_header Cache-Control "public, max-age=3600"; + } + + # Proxy API requests to FastAPI backend + location /api/ { + proxy_pass http://backend:8000/api/; + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection 'upgrade'; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + proxy_cache_bypass $http_upgrade; + + # Timeouts for long-running requests + proxy_connect_timeout 300s; + proxy_send_timeout 300s; + proxy_read_timeout 300s; + } + + # Health check endpoint + location /health { + access_log off; + return 200 "healthy\n"; + add_header Content-Type text/plain; + } + + # Error pages + error_page 404 /index.html; + error_page 500 502 503 504 /50x.html; + location = /50x.html { + root /usr/share/nginx/html; + } + } +} + + + + + + + + + + diff --git a/src/withoutbg/py.typed b/docs/.gitkeep similarity index 100% rename from src/withoutbg/py.typed rename to docs/.gitkeep diff --git a/examples/basic_usage.py b/examples/basic_usage.py deleted file mode 100644 index ae9ab14..0000000 --- a/examples/basic_usage.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -"""Basic usage examples for withoutbg.""" - -import withoutbg -from PIL import Image - - -def basic_local_processing(): - """Example: Basic background removal with local Snap model.""" - print("๐ŸŸข Example: Local processing with Snap model") - - # Remove background using local model (free) - result = withoutbg.remove_background("input.jpg") - result.save("output_open_source.png") - - print("โœ… Background removed and saved as output_open_source.png") - print("๐ŸŽฏ Want best quality? Try withoutbg.com") - - -def api_processing(): - """Example: Background removal with Studio API.""" - print("\n๐Ÿ”ด Example: Cloud processing with Studio API") - - # Set your API key (get one at withoutbg.com) - API_KEY = "sk_your_api_key_here" - - try: - # Remove background using Studio API (best quality) - result = withoutbg.remove_background("input.jpg", api_key=API_KEY) - result.save("output_studio.png") - - print("โœ… Background removed with Studio API") - print("๐ŸŽฏ Best quality processing") - - except withoutbg.APIError as e: - print(f"โŒ API Error: {e}") - print("๐Ÿ’ก Get your API key at https://withoutbg.com") - - -def batch_processing(): - """Example: Process multiple images at once.""" - print("\n๐Ÿ“ฆ Example: Batch processing") - - # List of images to process - image_files = ["photo1.jpg", "photo2.jpg", "photo3.jpg"] - - # Process all images and save to directory - results = withoutbg.remove_background_batch( - image_files, - output_dir="batch_results/" - ) - - print(f"โœ… Processed {len(results)} images") - print("๐Ÿ“ Results saved in batch_results/ directory") - - -def e_commerce_example(): - """Example: E-commerce product photos.""" - print("\n๐Ÿ›๏ธ Example: E-commerce product processing") - - from pathlib import Path - - # Process entire product catalog - product_dir = Path("products/") - if product_dir.exists(): - product_images = list(product_dir.glob("*.jpg")) - - # Use API for best quality in production - results = withoutbg.remove_background_batch( - product_images, - output_dir="catalog-withoutbg/", - api_key="sk_your_key" # Replace with your key - ) - - print(f"โœ… Processed {len(results)} product images") - else: - print("๐Ÿ“ Create a 'products/' directory with images to try this example") - - -def custom_background_example(): - """Example: Add custom background after removal.""" - print("\n๐ŸŽจ Example: Custom background replacement") - - # Remove background - foreground = withoutbg.remove_background("portrait.jpg") - - # Load custom background - background = Image.open("gradient_background.jpg") - - # Resize background to match foreground - background = background.resize(foreground.size, Image.Resampling.LANCZOS) - - # Composite the images - if foreground.mode == 'RGBA': - background.paste(foreground, (0, 0), foreground) # Use alpha as mask - else: - background.paste(foreground, (0, 0)) - - background.save("custom_background_result.jpg") - print("โœ… Custom background applied and saved") - - -def quality_comparison(): - """Example: Compare different model tiers.""" - print("\n๐Ÿ† Example: Quality comparison") - - input_image = "test_image.jpg" - - # Snap model (local, free) - print("Processing with Snap model...") - open_source_result = withoutbg.remove_background(input_image) - open_source_result.save("comparison_open_source.png") - - # Studio model (API, best quality) - print("Processing with Studio API...") - try: - studio_result = withoutbg.remove_background( - input_image, - api_key="sk_your_key" - ) - studio_result.save("comparison_studio.png") - - print("โœ… Comparison complete!") - print("๐Ÿ“Š Compare results:") - print(" - comparison_open_source.png (Snap model)") - print(" - comparison_studio.png (Studio API)") - - except withoutbg.APIError: - print("โš ๏ธ Studio API requires valid API key") - - -if __name__ == "__main__": - print("๐ŸŽญ withoutbg Examples") - print("=" * 50) - - # Run examples (comment out as needed) - basic_local_processing() - # api_processing() # Uncomment and add your API key - # batch_processing() - # e_commerce_example() - # custom_background_example() - # quality_comparison() - - print("\n๐ŸŽฏ Get best quality results at https://withoutbg.com") \ No newline at end of file diff --git a/examples/custom_backgrounds.py b/examples/custom_backgrounds.py deleted file mode 100644 index 3e77e2e..0000000 --- a/examples/custom_backgrounds.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -"""Custom background replacement examples.""" - -import withoutbg -from PIL import Image, ImageDraw, ImageFilter -import numpy as np -from pathlib import Path - - -def solid_color_background(): - """Replace background with solid color.""" - print("๐ŸŽจ Example: Solid color background") - - # Remove original background - foreground = withoutbg.remove_background("portrait.jpg") - - # Create solid color backgrounds - colors = [ - (255, 255, 255), # White - (0, 0, 0), # Black - (70, 130, 180), # Steel Blue - (255, 182, 193), # Light Pink - (144, 238, 144), # Light Green - ] - - for i, color in enumerate(colors): - # Create background - background = Image.new('RGB', foreground.size, color) - - # Composite - if foreground.mode == 'RGBA': - background.paste(foreground, (0, 0), foreground) - else: - background.paste(foreground, (0, 0)) - - # Save - color_name = ['white', 'black', 'blue', 'pink', 'green'][i] - background.save(f"portrait_{color_name}_bg.jpg", quality=95) - - print("โœ… Solid color backgrounds created") - - -def gradient_background(): - """Create gradient backgrounds.""" - print("\n๐ŸŒˆ Example: Gradient backgrounds") - - foreground = withoutbg.remove_background("portrait.jpg") - width, height = foreground.size - - # Create different gradients - gradients = [ - # Vertical blue to purple - {"start": (0, 100, 255), "end": (128, 0, 255), "direction": "vertical"}, - # Horizontal orange to pink - {"start": (255, 165, 0), "end": (255, 20, 147), "direction": "horizontal"}, - # Diagonal green to blue - {"start": (0, 255, 127), "end": (0, 191, 255), "direction": "diagonal"}, - ] - - for i, grad in enumerate(gradients): - # Create gradient - background = create_gradient(width, height, grad["start"], grad["end"], grad["direction"]) - - # Composite - if foreground.mode == 'RGBA': - background.paste(foreground, (0, 0), foreground) - else: - background.paste(foreground, (0, 0)) - - # Save - background.save(f"portrait_gradient_{i+1}.jpg", quality=95) - - print("โœ… Gradient backgrounds created") - - -def create_gradient(width, height, start_color, end_color, direction): - """Create a gradient image.""" - background = Image.new('RGB', (width, height)) - draw = ImageDraw.Draw(background) - - if direction == "vertical": - for y in range(height): - ratio = y / height - r = int(start_color[0] * (1 - ratio) + end_color[0] * ratio) - g = int(start_color[1] * (1 - ratio) + end_color[1] * ratio) - b = int(start_color[2] * (1 - ratio) + end_color[2] * ratio) - draw.line([(0, y), (width, y)], fill=(r, g, b)) - - elif direction == "horizontal": - for x in range(width): - ratio = x / width - r = int(start_color[0] * (1 - ratio) + end_color[0] * ratio) - g = int(start_color[1] * (1 - ratio) + end_color[1] * ratio) - b = int(start_color[2] * (1 - ratio) + end_color[2] * ratio) - draw.line([(x, 0), (x, height)], fill=(r, g, b)) - - return background - - -def scenic_background(): - """Replace with scenic/nature backgrounds.""" - print("\n๐Ÿž๏ธ Example: Scenic backgrounds") - - foreground = withoutbg.remove_background("portrait.jpg") - - # List of scenic backgrounds (you'd have these images) - scenic_files = [ - "beach_sunset.jpg", - "mountain_landscape.jpg", - "city_skyline.jpg", - "forest_path.jpg" - ] - - for scenic_file in scenic_files: - if Path(scenic_file).exists(): - # Load scenic background - scenic_bg = Image.open(scenic_file) - - # Resize to match foreground - scenic_bg = scenic_bg.resize(foreground.size, Image.Resampling.LANCZOS) - - # Optional: Blur background slightly for depth effect - scenic_bg = scenic_bg.filter(ImageFilter.GaussianBlur(radius=1.5)) - - # Composite - if foreground.mode == 'RGBA': - scenic_bg.paste(foreground, (0, 0), foreground) - else: - scenic_bg.paste(foreground, (0, 0)) - - # Save - output_name = f"portrait_{Path(scenic_file).stem}.jpg" - scenic_bg.save(output_name, quality=95) - - print(f"โœ… Created {output_name}") - else: - print(f"โš ๏ธ Scenic background not found: {scenic_file}") - - -def professional_studio_background(): - """Create professional studio-style backgrounds.""" - print("\n๐Ÿ“ธ Example: Professional studio backgrounds") - - foreground = withoutbg.remove_background("portrait.jpg") - width, height = foreground.size - - # Studio background 1: Soft gray with vignette - studio_bg1 = Image.new('RGB', (width, height), (240, 240, 240)) - - # Add subtle vignette effect - mask = Image.new('L', (width, height), 0) - draw = ImageDraw.Draw(mask) - - # Create radial gradient for vignette - center_x, center_y = width // 2, height // 2 - max_radius = min(width, height) // 2 - - for radius in range(max_radius, 0, -5): - opacity = int(255 * (1 - radius / max_radius) * 0.3) - draw.ellipse([ - center_x - radius, center_y - radius, - center_x + radius, center_y + radius - ], fill=opacity) - - # Apply vignette - vignette = Image.new('RGB', (width, height), (200, 200, 200)) - studio_bg1 = Image.composite(vignette, studio_bg1, mask) - - # Composite with foreground - if foreground.mode == 'RGBA': - studio_bg1.paste(foreground, (0, 0), foreground) - - studio_bg1.save("portrait_studio_gray.jpg", quality=95) - - # Studio background 2: Classic white with shadow - studio_bg2 = Image.new('RGB', (width, height), (255, 255, 255)) - - # Add subtle shadow effect - shadow = Image.new('RGBA', (width, height), (0, 0, 0, 0)) - shadow_draw = ImageDraw.Draw(shadow) - - # Create drop shadow (simplified) - if foreground.mode == 'RGBA': - # Create shadow from alpha channel - alpha = foreground.split()[-1] - shadow_offset = 5 - shadow_img = Image.new('RGBA', (width, height), (0, 0, 0, 0)) - shadow_img.paste((50, 50, 50, 100), (shadow_offset, shadow_offset), alpha) - shadow_img = shadow_img.filter(ImageFilter.GaussianBlur(radius=3)) - - # Composite shadow, then foreground - studio_bg2 = Image.alpha_composite( - studio_bg2.convert('RGBA'), - shadow_img - ).convert('RGB') - studio_bg2.paste(foreground, (0, 0), foreground) - else: - studio_bg2.paste(foreground, (0, 0)) - - studio_bg2.save("portrait_studio_white.jpg", quality=95) - - print("โœ… Professional studio backgrounds created") - - -def social_media_templates(): - """Create social media ready images with backgrounds.""" - print("\n๐Ÿ“ฑ Example: Social media templates") - - foreground = withoutbg.remove_background("portrait.jpg") - - # Instagram square format - instagram_size = (1080, 1080) - instagram_bg = Image.new('RGB', instagram_size, (255, 182, 193)) # Light pink - - # Center the foreground - fg_ratio = min(800 / foreground.width, 800 / foreground.height) - new_fg_size = (int(foreground.width * fg_ratio), int(foreground.height * fg_ratio)) - foreground_resized = foreground.resize(new_fg_size, Image.Resampling.LANCZOS) - - paste_x = (instagram_size[0] - new_fg_size[0]) // 2 - paste_y = (instagram_size[1] - new_fg_size[1]) // 2 - - if foreground_resized.mode == 'RGBA': - instagram_bg.paste(foreground_resized, (paste_x, paste_y), foreground_resized) - else: - instagram_bg.paste(foreground_resized, (paste_x, paste_y)) - - instagram_bg.save("portrait_instagram.jpg", quality=95) - - # LinkedIn banner format - linkedin_size = (1584, 396) - linkedin_bg = Image.new('RGB', linkedin_size, (0, 119, 181)) # LinkedIn blue - - # Position portrait on left side - fg_ratio = min(350 / foreground.width, 350 / foreground.height) - new_fg_size = (int(foreground.width * fg_ratio), int(foreground.height * fg_ratio)) - foreground_resized = foreground.resize(new_fg_size, Image.Resampling.LANCZOS) - - paste_x = 50 - paste_y = (linkedin_size[1] - new_fg_size[1]) // 2 - - if foreground_resized.mode == 'RGBA': - linkedin_bg.paste(foreground_resized, (paste_x, paste_y), foreground_resized) - else: - linkedin_bg.paste(foreground_resized, (paste_x, paste_y)) - - linkedin_bg.save("portrait_linkedin_banner.jpg", quality=95) - - print("โœ… Social media templates created") - print(" - portrait_instagram.jpg (1080x1080)") - print(" - portrait_linkedin_banner.jpg (1584x396)") - - -if __name__ == "__main__": - print("๐ŸŽจ Custom Background Examples") - print("=" * 50) - - # Check if input image exists - if not Path("portrait.jpg").exists(): - print("โŒ Please add a 'portrait.jpg' image to try these examples") - print("๐Ÿ’ก Any portrait photo will work!") - exit(1) - - try: - # Run all examples - solid_color_background() - gradient_background() - scenic_background() - professional_studio_background() - social_media_templates() - - print("\nโœ… All background examples complete!") - print("๐ŸŽฏ Perfect for:") - print(" - E-commerce product photos") - print(" - Professional headshots") - print(" - Social media content") - print(" - Marketing materials") - - except Exception as e: - print(f"โŒ Error: {e}") - print("๐Ÿ’ก Make sure you have a 'portrait.jpg' file in this directory") - - print("\n๐ŸŽฏ Get best quality processing at https://withoutbg.com") \ No newline at end of file diff --git a/examples/e_commerce_batch.py b/examples/e_commerce_batch.py deleted file mode 100644 index 8533f11..0000000 --- a/examples/e_commerce_batch.py +++ /dev/null @@ -1,230 +0,0 @@ -#!/usr/bin/env python3 -"""E-commerce batch processing example.""" - -import withoutbg -from pathlib import Path -import time -from PIL import Image -import json - - -def process_product_catalog(): - """Process an entire product catalog for e-commerce.""" - print("๐Ÿ›๏ธ E-commerce Product Catalog Processing") - print("=" * 50) - - # Configuration - input_dir = Path("product_catalog") - output_dir = Path("catalog-withoutbg") - api_key = "sk_your_api_key_here" # Replace with your key - - # Create directories if they don't exist - output_dir.mkdir(exist_ok=True) - - # Find all product images - image_extensions = ['.jpg', '.jpeg', '.png', '.webp'] - product_images = [] - - for ext in image_extensions: - product_images.extend(input_dir.glob(f"*{ext}")) - product_images.extend(input_dir.glob(f"*{ext.upper()}")) - - if not product_images: - print(f"โŒ No images found in {input_dir}") - print("๐Ÿ’ก Create the directory and add some product photos to try this example") - return - - print(f"๐Ÿ“ฆ Found {len(product_images)} product images") - - # Process with timing - start_time = time.time() - - try: - # Option 1: Use API for best quality (recommended for production) - print("๐Ÿ”ด Processing with Studio API (best quality)...") - results = withoutbg.remove_background_batch( - product_images, - output_dir=output_dir, - api_key=api_key - ) - - processing_method = "Studio API" - - except withoutbg.APIError as e: - print(f"โš ๏ธ API Error: {e}") - print("๐ŸŸข Falling back to local Snap model...") - - # Option 2: Fallback to local processing - results = withoutbg.remove_background_batch( - product_images, - output_dir=output_dir - ) - - processing_method = "Local Snap" - - end_time = time.time() - total_time = end_time - start_time - - # Generate report - print("\n๐Ÿ“Š Processing Report") - print("-" * 30) - print(f"Images processed: {len(results)}") - print(f"Processing method: {processing_method}") - print(f"Total time: {total_time:.2f} seconds") - print(f"Average per image: {total_time/len(results):.2f} seconds") - print(f"Output directory: {output_dir}") - - # Save processing report - report = { - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - "images_processed": len(results), - "processing_method": processing_method, - "total_time_seconds": round(total_time, 2), - "average_time_per_image": round(total_time/len(results), 2), - "input_directory": str(input_dir), - "output_directory": str(output_dir) - } - - with open(output_dir / "processing_report.json", "w") as f: - json.dump(report, f, indent=2) - - print(f"๐Ÿ“„ Report saved: {output_dir}/processing_report.json") - - -def optimize_for_web(): - """Optimize processed images for web use.""" - print("\n๐ŸŒ Web Optimization") - print("-" * 30) - - output_dir = Path("catalog-withoutbg") - web_dir = Path("catalog_web") - web_dir.mkdir(exist_ok=True) - - # Find processed images - processed_images = list(output_dir.glob("*.png")) - - if not processed_images: - print("โŒ No processed images found. Run process_product_catalog() first.") - return - - print(f"๐Ÿ”ง Optimizing {len(processed_images)} images for web...") - - for img_path in processed_images: - try: - # Load image - img = Image.open(img_path) - - # Create different sizes for responsive design - sizes = { - "thumbnail": (150, 150), - "small": (300, 300), - "medium": (600, 600), - "large": (1200, 1200) - } - - stem = img_path.stem - - for size_name, dimensions in sizes.items(): - # Resize maintaining aspect ratio - img_copy = img.copy() - img_copy.thumbnail(dimensions, Image.Resampling.LANCZOS) - - # Save as WebP for better compression - webp_path = web_dir / f"{stem}_{size_name}.webp" - img_copy.save(webp_path, "WebP", quality=85, method=6) - - # Also save PNG for compatibility - png_path = web_dir / f"{stem}_{size_name}.png" - img_copy.save(png_path, "PNG", optimize=True) - - except Exception as e: - print(f"โš ๏ธ Failed to process {img_path}: {e}") - - print(f"โœ… Web-optimized images saved to {web_dir}") - - -def generate_html_preview(): - """Generate HTML preview of processed products.""" - print("\n๐Ÿ“„ Generating HTML Preview") - print("-" * 30) - - web_dir = Path("catalog_web") - thumbnail_images = list(web_dir.glob("*_thumbnail.webp")) - - if not thumbnail_images: - print("โŒ No thumbnail images found. Run optimize_for_web() first.") - return - - html_content = """ - - - - - - Product Catalog - Background Removed - - - -
-

๐Ÿ›๏ธ Product Catalog

-

Backgrounds removed with withoutbg

-
AI-Powered
-
- -
-""" - - for img_path in sorted(thumbnail_images): - product_name = img_path.stem.replace("_thumbnail", "").replace("_", " ").title() - - html_content += f""" -
- {product_name} -

{product_name}

-
No Background
-
-""" - - html_content += """ -
- -
-

Processed with withoutbg

-

๐ŸŽฏ Get best quality results with Studio API

-
- - -""" - - html_path = web_dir / "index.html" - with open(html_path, "w", encoding="utf-8") as f: - f.write(html_content) - - print(f"โœ… HTML preview generated: {html_path}") - print(f"๐ŸŒ Open {html_path} in your browser to view the catalog") - - -if __name__ == "__main__": - print("๐Ÿ›๏ธ E-commerce Batch Processing Example") - print("=" * 50) - - # Step 1: Process product catalog - process_product_catalog() - - # Step 2: Optimize for web - optimize_for_web() - - # Step 3: Generate HTML preview - generate_html_preview() - - print("\nโœ… Complete! Your product catalog is ready for e-commerce.") - print("๐ŸŽฏ Get best quality processing at https://withoutbg.com") \ No newline at end of file diff --git a/examples/images/example1.png b/examples/images/example1.png deleted file mode 100644 index 998eb52936820fc1be106efc0e7222d113cf8bf9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 428887 zcmX_nby!qi)b1ck4+w~KiGoPO&@BQYCDPI&3?W?u4BZXVT@pHUcQeG$-95n24MTbT z-S4~iJkNU8+Uu-+p7Y1rXYci{_k?{`kq6>Y;Q;^upu)!wUjP8iT>t<>8VBp2XNxOn z|DSpamR6Pq04ihf?@gZl8#9=G{Gtp1crXJ1zJUP1?LU|Azez6K0KlFx03ebA0Fc{f zG^vUGy8$uNQ7~6l2E6-c;{cvwPyzmX0>(d)z@YyBwj9P=0OtSoKLG$jECEmdH%8^3 z{_pzuPyUR!o*7W z4U)&|8T8(M`95@QZQT+-Z3dFQBa%Kolk){gZ!PW899<88t-kK+vITs2&T6H7y6S} ziEU)7eZn&IBT;K8?C@t%lnXK*87}kJc|)YZ&Ei+rR z-4Euds8ITfo_PY@NF#?72@<@FXLG}G5b{j#{d)qNGHJKc$dB}MwY9apVjyOj+-713 zEoCXR&i&K!@8mz!$S3{~#t7%c_Q=4jUW3i47NnEV#u{mh%$v{si?Qy0h&%Im?SR~V zra}WL;`%|}NX|YM7R2t&)9c^70z94kUzv+o2DhekQd834J~fdsQStI{|2__Jm?-Du zShJ4}i}reR=dxln62UfCvcA6+RC^+Cdm1-7VKe?Dxn;+5%zF0^>E*9QJna!U*W4N9 zjkCnIUKFOPo*$WNKa)Nw*ZIav9hueZK;01XY$V+Cwr}}*OvadyVl(Xs27~!rz=WzEzNm*1b3M{`WR2!u! zOhPs{H@%PvS{*tu0k>eva;JmV{T+?~qsKPjv@pv}OlSH~SE;U6!;m zsQ51O45!O27hNYR+TshLZkZ?x59XLd`*mf~$wGSN^B8n7y|S4g*F`>S3#eJBfa88r zRnnlasOT6kSsPJk269>4o;}%l3*PtIN+?8D>lwXPK5s_1AtoOz(j?gzA5K0^di2*E zZ8NRM&M`SP*^e=Ol;l#Lj9diI%UjmS82%>`@SSycp3>gtBE?XP#^jN`<)M;UN$}$ z=I-*ik+h`~FWiunUte5jjiTs}l}%$38N%#wVUhJBasXvJ7weR~RQ zLS7n1>3<-ggq1k!^>^wPDBYJlxcexFZVT3 znTZD|Pk@c{gD{{vS5x-){hdDgkH_C=R@|6(_g~WCG1*1@@ zygYK)vD{JDQ`)^ngnx*+=w07B?|IVXLo6*TOU8)D$5eHJ)!lr*jslvXsXHf|f;a1Z z;EKNH(X(L95zGcEvmo0OM+?Ns`g+61I{fbAppee6lU2s5e{n+DXP-fYW_UYIzOMwZFNTXo=t@21_*KJw-Yo z`i_?K{L3VdUC{6HGU``x@3_YN|}K#`bEccCMzIT>cyr zLaj1HeSae)3>*_q516cexjDSnZ@=sFJ0$m6yGD(5h~E{aj(@Kecr|P{8M|*M_q+@U z^O0mxGS3Jk!hCY@*z2?QCsmJz%Z=v17jTbQ6`~+J>CST^g6xDMZx`hhjTr{q`OwWxao*jQbEV7mxVa1OVwJQan z6ZuX%p+i*Ewy!>M#bH@w%l2V`7P03$G169ZOFru8~2kLW2fYKB}Hyadl=V0F^Je7E#|LwarE3`ncK06-wAU`s+yI zXSmr&UU8kaME6SgU!w2;-S4#n%ZN&4$dxD#HkU#|Nu6u*P2Oj{C-B)K%6flEbvGgq zz;@0SgF&Ff4?r$MItoa$Kk~=Qfc%kP#HiU|X0%MYZL_ffZ^+CbnkyOe(?a0bC&Gf< zN5e(=rP5fZdSU6|BB$=C*Q~=$j(&n@4Eth~P)Jtd`v9Q^j2PqK)u4*)$)+b#J=d(?6Y_K8G-{ zQ=!UegzVR(hJIbc0_btZ0Twxat4_d_>so~a?XNLk1rD;^T{5|;cBoK?yqiw9$nS9t zJ?ka69SYs$Js>o3?(Sw5oFZ(U&Y;Ng*+y`{!ZX4mU>ZI@`|FTS*9V~<0B zhNdG>Ec!OCbMAoYIy={o0*Ph(4AaLb>3L<4;L{qZ z_FMVOZ_64^&eDWM1e-}pOmtq~clNh|j+FC#v3)6{75&sXF54cK4wbj*4Adi9F+T-* z@;~4d(U{C3cvZ@Ri9z5r)1;*f9)bmbOC74wL;UUE&LeRA&*z-M6}-<3Po$kpy@C+B z&L>g4*3l~R@(txtowFij4<9LAn)L21te&EgZ-Mhu)h$~yPwFFfj|rMlrqQ@`v4WEy zLHlJ4K|Z<{Suk8^6v(-w$H!t;?8|vtgV*&SFW6AgCod$vj(eDzYqudR9g5JKHpUE& z?CO{KaM}49$Vjj^9u3|NJ|O9SW0>t(Ez$^X1|Gax`x z`s3v6p{StU3OhbNzH00Q;8F@RTLL(DJm6iY=aj+jDC{wdYw9!AEZ4xdql^7j)k9dF z4)*T1a}b$I2B)8!rwXUnsD#Q&a}CyUHW1j5)$pl{gKAw%hSu{-`n6#-UIvFinP+*Z z$0NG!nRPQQ(1kP`0A|U@Hm)XI;T%cB2r6G+CO|c7yFOU}kqR6KvzEf)awQ>U_ZpHu z$Igck%85~~?iRh91#UHaSi?ALl~3O7MY(5ldI!hrO9KjE6NOOd4hP^KHYD}Koq4$K z@k{nHH&cNTbqL7xGl1iF6oemkiiA1S1wF3pjM*2CBMMge07(o@q~M@|eEk1cs--POv>-uL?@ zADe7^8zYKQiN*fYTQuIfPE1)O_YYmN+9?%orUh3nXjtY{^%%t&qJ5iWRzZt+M0kS%C>?R&`tBVmM3{F zCMk-?cQzF1;hcdpsfB)bQOKS znR;67eGqv$v60ZE^7_}ZLYKW@fXAhaSs)}J9gp@FBiYSmt|jDafZe9t=Ov~93R3ZF zW0~Zt{9)+eE9#^Pge zoW@9McmRtTxg=)vY0@%3l{?Q3vVs!fZ(RhX2$a6aXwH1_Om5E3klV$c96jdTT7-yw zT=M3AxUXY$NWKo}IsE4?kARA%9NsUU-gTycU5}P15tG3F9kK^Lb_0YOmrN?wF`|pfw`d zxb?=h`q#vyDmO>LQ(NvdbH}glpUV^Nym&dz47KG!+-qUp->|Uowt%_>dJzv>ge%3_ zsi=AGrkx!-p7-tj81|;@4n_U}OU%{lBRVlr4r2=qiiyH&QECBi%xCw1@ah-{lr23f z@S*a2u4@nxp7Y{FQZfdrll^Afyssq8Mx9Onm2dLEdlW9ATfosYo3`ILpuoxUz29^~ zCzJCFOnQ%o`sIQnu|Y+9BtBCa_1N`SFwrfQ=h$qjxRHU0uIEsmsZO88M^`Ia2DMce zVKSl?a2kI@`UPH`?hL(%8(AMtrtKV!Ym8*pNO63EW_Dbla?_wuzF_j&&H+@}?)&FD zEV)}0RaFaC?+Ia8z8FZzj2~mXR)v4}?5G;^othwE|H|JU_!UVaaBjB3_S_L|++xw; 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b/packages/python/README.md @@ -0,0 +1,78 @@ +# withoutbg - Python SDK + +AI-powered background removal with local and cloud options. + +[![PyPI](https://img.shields.io/pypi/v/withoutbg.svg)](https://pypi.org/project/withoutbg/) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) + +## Installation + +```bash +pip install withoutbg +``` + +## Quick Start + +```python +from withoutbg import remove_background + +# Remove background (local processing) +result = remove_background("input.jpg") +result.save("output.png") + +# Use cloud API for best quality +result = remove_background("input.jpg", api_key="sk_your_key") +result.save("output.png") +``` + +## CLI Usage + +```bash +# Process single image +withoutbg photo.jpg + +# Batch processing +withoutbg photos/ --batch --output-dir results/ + +# Use cloud API +withoutbg photo.jpg --api-key sk_your_key +``` + +## Features + +- โœจ Local processing with Focus v1.0.0 model (free) +- ๐Ÿš€ Cloud API for best quality results +- ๐Ÿ“ฆ Batch processing support +- ๐ŸŽฏ Python API and CLI +- ๐Ÿ”ง Flexible output formats (PNG, JPEG, WebP) + +## Documentation + +For complete documentation, see the [main project README](../../README.md). + +## Development + +```bash +# Install in development mode +pip install -e ".[dev]" + +# Run tests +pytest + +# Type checking +mypy src/ + +# Format code +black src/ tests/ +ruff check src/ tests/ +``` + +## License + +Apache License 2.0 - see [LICENSE](../../LICENSE) + + + + + + diff --git a/pyproject.toml b/packages/python/pyproject.toml similarity index 100% rename from pyproject.toml rename to packages/python/pyproject.toml diff --git a/src/withoutbg/__init__.py b/packages/python/src/withoutbg/__init__.py similarity index 100% rename from src/withoutbg/__init__.py rename to packages/python/src/withoutbg/__init__.py diff --git a/src/withoutbg/__version__.py b/packages/python/src/withoutbg/__version__.py similarity index 100% rename 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instructions, API endpoints, and development guidelines. - Included sample images demonstrating the background removal capabilities of the AI model. - Established a clear project structure and deployment instructions for Docker. --- apps/web/README.md | 95 ++++++++++ apps/web/backend/README.md | 176 ++++++++++++++++++ sample-results/open-source-focus/example1.png | Bin 0 -> 428887 bytes sample-results/open-source-focus/example2.png | Bin 0 -> 399128 bytes sample-results/open-source-focus/example3.png | Bin 0 -> 391964 bytes sample-results/open-source-focus/example4.png | Bin 0 -> 480693 bytes sample-results/open-source-focus/example5.png | Bin 0 -> 614322 bytes sample-results/open-source-focus/example6.png | Bin 0 -> 553964 bytes sample-results/open-source-focus/example7.png | Bin 0 -> 218663 bytes 9 files changed, 271 insertions(+) create mode 100644 apps/web/README.md create mode 100644 apps/web/backend/README.md create mode 100644 sample-results/open-source-focus/example1.png create mode 100644 sample-results/open-source-focus/example2.png create mode 100644 sample-results/open-source-focus/example3.png create mode 100644 sample-results/open-source-focus/example4.png create mode 100644 sample-results/open-source-focus/example5.png create mode 100644 sample-results/open-source-focus/example6.png create mode 100644 sample-results/open-source-focus/example7.png diff --git a/apps/web/README.md b/apps/web/README.md new file mode 100644 index 0000000..012ae1d --- /dev/null +++ b/apps/web/README.md @@ -0,0 +1,95 @@ +# withoutbg Web Application + +Modern web interface for background removal with local AI processing. + +## Architecture + +- **Backend**: FastAPI (Python 3.12) +- **Frontend**: React 18 + Vite + Tailwind CSS +- **Server**: Nginx reverse proxy +- **Models**: Focus v1.0.0 (local processing) + +## Quick Start + +From the **repository root**: + +```bash +# Development mode (hot-reload) +docker-compose -f apps/web/docker-compose.yml up + +# Access the application +open http://localhost:3000 +``` + +## Development + +### Backend + +```bash +cd apps/web/backend + +# Install core package (from repository root) +pip install -e ../../packages/python + +# Install backend dependencies +pip install -e . + +# Run development server +uvicorn app.main:app --reload +``` + +### Frontend + +```bash +cd apps/web/frontend + +# Install dependencies +npm install + +# Run development server +npm run dev +``` + +## Production Deployment + +Build and run with Docker: + +```bash +# From repository root +docker build -f apps/web/backend/Dockerfile -t withoutbg-backend . +docker build -f apps/web/frontend/Dockerfile -t withoutbg-frontend . + +# Run containers +docker run -p 8000:8000 withoutbg-backend +docker run -p 80:80 withoutbg-frontend +``` + +## API Documentation + +When running, visit: +- Backend API docs: http://localhost:8000/docs +- Health check: http://localhost:8000/api/health + +## Structure + +``` +apps/web/ +โ”œโ”€โ”€ backend/ # FastAPI application +โ”‚ โ”œโ”€โ”€ app/ +โ”‚ โ”‚ โ””โ”€โ”€ main.py +โ”‚ โ”œโ”€โ”€ Dockerfile +โ”‚ โ””โ”€โ”€ pyproject.toml +โ”œโ”€โ”€ frontend/ # React application +โ”‚ โ”œโ”€โ”€ src/ +โ”‚ โ”œโ”€โ”€ Dockerfile +โ”‚ โ””โ”€โ”€ package.json +โ”œโ”€โ”€ nginx/ # Nginx configuration +โ”‚ โ””โ”€โ”€ nginx.conf +โ””โ”€โ”€ docker-compose.yml +``` + + + + + + diff --git a/apps/web/backend/README.md b/apps/web/backend/README.md new file mode 100644 index 0000000..a6b16ed --- /dev/null +++ b/apps/web/backend/README.md @@ -0,0 +1,176 @@ +# withoutbg Backend + +FastAPI-based REST API for AI-powered background removal. + +## Features + +- ๐Ÿš€ High-performance async API with FastAPI +- ๐Ÿค– Built-in Focus v1.0.0 AI model +- ๐Ÿ”Œ Optional cloud API integration +- ๐Ÿ“Š Health checks and monitoring +- ๐Ÿ”„ Single and batch processing +- ๐Ÿ“ Interactive API documentation (Swagger/OpenAPI) + +## Development + +### Prerequisites + +- Python 3.12 +- uv (recommended) or pip + +### Setup + +```bash +# Install dependencies with uv +uv pip install -r requirements.txt + +# Or with pip +pip install -r requirements.txt + +# Run the server +python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 +``` + +### Environment Variables + +Create a `.env` file: + +```bash +PORT=8000 +PYTHONUNBUFFERED=1 +WITHOUTBG_API_KEY=sk_your_api_key_here # Optional +``` + +## Project Structure + +``` +backend/ +โ”œโ”€โ”€ app/ +โ”‚ โ”œโ”€โ”€ __init__.py +โ”‚ โ””โ”€โ”€ main.py # FastAPI application +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ”œโ”€โ”€ pyproject.toml # Package configuration +โ””โ”€โ”€ README.md +``` + +## API Endpoints + +### Health Check +``` +GET /api/health +``` + +Response: +```json +{ + "status": "healthy", + "version": "1.0.0", + "service": "withoutbg-api" +} +``` + +### Remove Background (Single) +``` +POST /api/remove-background +Content-Type: multipart/form-data +``` + +Parameters: +- `file`: Image file (required) +- `format`: Output format - png/jpg/webp (default: png) +- `quality`: JPEG quality 1-100 (default: 95) +- `api_key`: Optional API key for cloud processing + +Returns: Processed image file + +### Remove Background (Batch) +``` +POST /api/batch-remove-background +Content-Type: multipart/form-data +``` + +Parameters: +- `files[]`: Multiple image files (max 10) +- `format`: Output format - png/jpg/webp (default: png) +- `quality`: JPEG quality 1-100 (default: 95) +- `api_key`: Optional API key for cloud processing + +Returns: JSON with base64-encoded images + +### Get Usage +``` +GET /api/usage?api_key=sk_your_key +``` + +Returns: API usage statistics + +## Interactive Documentation + +When the server is running, visit: +- **Swagger UI**: http://localhost:8000/docs +- **ReDoc**: http://localhost:8000/redoc + +## Technology Stack + +- **FastAPI**: Modern Python web framework +- **Uvicorn**: ASGI server +- **Pillow**: Image processing +- **python-multipart**: File upload handling +- **withoutbg**: Core background removal library + +## Docker + +Build and run with Docker: + +```bash +# Build +docker build -f ../Dockerfile.backend -t withoutbg-backend .. + +# Run +docker run -p 8000:8000 -v $(pwd)/../checkpoints:/app/checkpoints withoutbg-backend +``` + +## Testing + +```bash +# Test health endpoint +curl http://localhost:8000/api/health + +# Test background removal +curl -X POST http://localhost:8000/api/remove-background \ + -F "file=@test.jpg" \ + -F "format=png" \ + --output result.png +``` + +## Performance + +- Async/await for non-blocking I/O +- Efficient memory management +- Supports concurrent requests +- Health checks for monitoring + +## Error Handling + +The API returns appropriate HTTP status codes: +- `200`: Success +- `400`: Bad request (invalid input) +- `500`: Internal server error + +Error responses include detailed messages: +```json +{ + "detail": "File must be an image" +} +``` + +## Contributing + +1. Make changes to the code +2. Test with `uvicorn app.main:app --reload` +3. Ensure all endpoints work correctly +4. 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z2YkTf$BQGDYu7>42Rq{&ak38ZVckVe;!%3zqz|8-w!3iVN6wv(mj#c;=F*C%4_JT4vWD={N5L=mW5EdD zK0Zz#ti*>l@#+;f&G_d-wwri9)XKOy4hJ$mn2rAE!=~B~!;(X+z7G!%)8QfF^c5?{ z8ea}%+fQwtadJ3k92Db-qs^fU&V1;j&wl-_jYB^C<0ESXCm*z>pEzm{Pkitkr8Ul% zgJ9S}nh&wz%>U@X&dc~y@a+$5p^U>nf5_c7otL!%j9sE`eI+2^B$HM@V3TCA2?n)w-KkYmN<#uDkDyFZQ-zuIMJxr-{X`g zk6-%|=Lk2#F5%vaC7xr9xyS1fCs<=djMpu!{yojXws6KA;R5#i=F{H)6SHUib%XzZ z&lm+S1xkTZ;8QD5chEoe^i}eIhXVC~vcF>34lmewdDX@{=Xoti`UyeFW!c%Jn1k`3TOM6I36U qwHmF(jkBrVQlJzl1xkUn3j6{-deOWqTFc-70000 Date: Wed, 5 Nov 2025 00:03:29 +0100 Subject: [PATCH 22/73] Enhance model configuration and documentation for local usage - Updated Docker and Docker Compose configurations to set environment variables for local model paths, allowing the application to use pre-existing models instead of downloading from HuggingFace. - Modified the backend README to include instructions for setting model path environment variables, emphasizing offline deployment benefits. - Improved the models.py file to check for local model paths before attempting to download from HuggingFace, enhancing flexibility and performance. --- apps/web/backend/Dockerfile | 6 +++ apps/web/backend/README.md | 11 +++++ apps/web/docker-compose.yml | 9 +++- packages/python/README.md | 19 ++++++++ packages/python/src/withoutbg/models.py | 61 +++++++++++++++++++++++-- 5 files changed, 100 insertions(+), 6 deletions(-) diff --git a/apps/web/backend/Dockerfile b/apps/web/backend/Dockerfile index 1545ed1..de27081 100644 --- a/apps/web/backend/Dockerfile +++ b/apps/web/backend/Dockerfile @@ -47,6 +47,12 @@ COPY models/checkpoints/ ./checkpoints/ # Set environment variables ENV PYTHONUNBUFFERED=1 +# Set model paths to use local files instead of downloading from HuggingFace +ENV WITHOUTBG_DEPTH_MODEL_PATH=/app/checkpoints/depth_anything_v2_vits_slim.onnx +ENV WITHOUTBG_ISNET_MODEL_PATH=/app/checkpoints/isnet.onnx +ENV WITHOUTBG_MATTING_MODEL_PATH=/app/checkpoints/focus_matting_1.0.0.onnx +ENV WITHOUTBG_REFINER_MODEL_PATH=/app/checkpoints/focus_refiner_1.0.0.onnx + # Expose port EXPOSE 8000 diff --git a/apps/web/backend/README.md b/apps/web/backend/README.md index a6b16ed..1f2279f 100644 --- a/apps/web/backend/README.md +++ b/apps/web/backend/README.md @@ -39,8 +39,19 @@ Create a `.env` file: PORT=8000 PYTHONUNBUFFERED=1 WITHOUTBG_API_KEY=sk_your_api_key_here # Optional + +# Model paths (optional - if not set, models will be downloaded from HuggingFace) +WITHOUTBG_DEPTH_MODEL_PATH=/path/to/depth_anything_v2_vits_slim.onnx +WITHOUTBG_ISNET_MODEL_PATH=/path/to/isnet.onnx +WITHOUTBG_MATTING_MODEL_PATH=/path/to/focus_matting_1.0.0.onnx +WITHOUTBG_REFINER_MODEL_PATH=/path/to/focus_refiner_1.0.0.onnx ``` +**Model Path Configuration:** +- If environment variables are set, the application will use local model files instead of downloading from HuggingFace +- This is useful for offline deployments or when you want to use specific model versions +- In Docker, these are automatically set to use the models copied into the container at `/app/checkpoints/` + ## Project Structure ``` diff --git a/apps/web/docker-compose.yml b/apps/web/docker-compose.yml index 32900cf..c83efcb 100644 --- a/apps/web/docker-compose.yml +++ b/apps/web/docker-compose.yml @@ -14,9 +14,14 @@ services: - PYTHONUNBUFFERED=1 - PORT=8000 - RELOAD=true + # Use local model files instead of downloading from HuggingFace + - WITHOUTBG_DEPTH_MODEL_PATH=/app/checkpoints/depth_anything_v2_vits_slim.onnx + - WITHOUTBG_ISNET_MODEL_PATH=/app/checkpoints/isnet.onnx + - WITHOUTBG_MATTING_MODEL_PATH=/app/checkpoints/focus_matting_1.0.0.onnx + - WITHOUTBG_REFINER_MODEL_PATH=/app/checkpoints/focus_refiner_1.0.0.onnx volumes: - # Mount source code for hot-reload - - ../../packages/python/src:/usr/local/lib/python3.13/site-packages/withoutbg:ro + # Mount source code for hot-reload (correct path for Python 3.12 venv) + - ../../packages/python/src/withoutbg:/app/apps/web/backend/.venv/lib/python3.12/site-packages/withoutbg:ro - ./backend:/app/backend:ro - ../../models/checkpoints:/app/checkpoints:ro working_dir: /app diff --git a/packages/python/README.md b/packages/python/README.md index f3ea668..717755b 100644 --- a/packages/python/README.md +++ b/packages/python/README.md @@ -46,6 +46,25 @@ withoutbg photo.jpg --api-key sk_your_key - ๐ŸŽฏ Python API and CLI - ๐Ÿ”ง Flexible output formats (PNG, JPEG, WebP) +## Configuration + +### Model Path Environment Variables + +By default, models are downloaded from HuggingFace Hub. You can override this by setting environment variables to use local model files: + +```bash +export WITHOUTBG_DEPTH_MODEL_PATH=/path/to/depth_anything_v2_vits_slim.onnx +export WITHOUTBG_ISNET_MODEL_PATH=/path/to/isnet.onnx +export WITHOUTBG_MATTING_MODEL_PATH=/path/to/focus_matting_1.0.0.onnx +export WITHOUTBG_REFINER_MODEL_PATH=/path/to/focus_refiner_1.0.0.onnx +``` + +This is useful for: +- Offline environments +- CI/CD pipelines +- Custom model versions +- Faster startup times (no download needed) + ## Documentation For complete documentation, see the [main project README](../../README.md). diff --git a/packages/python/src/withoutbg/models.py b/packages/python/src/withoutbg/models.py index 92db547..82d241f 100644 --- a/packages/python/src/withoutbg/models.py +++ b/packages/python/src/withoutbg/models.py @@ -1,6 +1,7 @@ """Local model implementations.""" import io +import os from pathlib import Path from typing import Any, Optional, Union @@ -120,21 +121,73 @@ def __init__( self._load_models() def _get_default_depth_model_path(self) -> Path: - """Get path to Depth Anything V2 model from Hugging Face.""" + """Get path to Depth Anything V2 model from environment variable or Hugging Face. + + Checks WITHOUTBG_DEPTH_MODEL_PATH environment variable first. + If not set, downloads from Hugging Face. + """ + env_path = os.getenv("WITHOUTBG_DEPTH_MODEL_PATH") + if env_path: + path = Path(env_path) + if path.exists(): + return path + else: + raise ModelNotFoundError( + f"Depth model not found at path specified in WITHOUTBG_DEPTH_MODEL_PATH: {env_path}" + ) return self._download_from_hf( "depth_anything_v2_vits_slim.onnx", "Depth Anything V2 model" ) def _get_default_isnet_model_path(self) -> Path: - """Get path to ISNet segmentation model from Hugging Face.""" + """Get path to ISNet segmentation model from environment variable or Hugging Face. + + Checks WITHOUTBG_ISNET_MODEL_PATH environment variable first. + If not set, downloads from Hugging Face. + """ + env_path = os.getenv("WITHOUTBG_ISNET_MODEL_PATH") + if env_path: + path = Path(env_path) + if path.exists(): + return path + else: + raise ModelNotFoundError( + f"ISNet model not found at path specified in WITHOUTBG_ISNET_MODEL_PATH: {env_path}" + ) return self._download_from_hf("isnet.onnx", "ISNet segmentation model") def _get_default_matting_model_path(self) -> Path: - """Get path to Matting model from Hugging Face.""" + """Get path to Matting model from environment variable or Hugging Face. + + Checks WITHOUTBG_MATTING_MODEL_PATH environment variable first. + If not set, downloads from Hugging Face. + """ + env_path = os.getenv("WITHOUTBG_MATTING_MODEL_PATH") + if env_path: + path = Path(env_path) + if path.exists(): + return path + else: + raise ModelNotFoundError( + f"Matting model not found at path specified in WITHOUTBG_MATTING_MODEL_PATH: {env_path}" + ) return self._download_from_hf("focus_matting_1.0.0.onnx", "Focus matting model") def _get_default_refiner_model_path(self) -> Path: - """Get path to Refiner model from Hugging Face.""" + """Get path to Refiner model from environment variable or Hugging Face. + + Checks WITHOUTBG_REFINER_MODEL_PATH environment variable first. + If not set, downloads from Hugging Face. + """ + env_path = os.getenv("WITHOUTBG_REFINER_MODEL_PATH") + if env_path: + path = Path(env_path) + if path.exists(): + return path + else: + raise ModelNotFoundError( + f"Refiner model not found at path specified in WITHOUTBG_REFINER_MODEL_PATH: {env_path}" + ) return self._download_from_hf("focus_refiner_1.0.0.onnx", "Focus refiner model") def _download_from_hf(self, filename: str, model_name: str) -> Path: From caf3346b74fd34cef87e8f7340ec47c5fcee0aa4 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Wed, 5 Nov 2025 00:09:39 +0100 Subject: [PATCH 23/73] Update documentation to recommend using 'uv' for installation and development - Enhanced installation instructions across multiple README files to prioritize 'uv' as the recommended package manager. - Added guidance on downloading 'uv' and its usage for setting up the development environment. - Updated commands for installing dependencies in development mode to reflect the new recommendations. - Improved clarity in package import instructions within the backend code. --- CONTRIBUTING.md | 18 +++++++++++++++--- README.md | 17 ++++++++++++----- STRUCTURE.md | 9 +++++++-- apps/web/README.md | 9 ++++----- apps/web/backend/README.md | 6 ++++-- apps/web/backend/app/main.py | 2 +- packages/python/README.md | 11 ++++++++++- 7 files changed, 53 insertions(+), 19 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 1ec354d..0657b6b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -19,9 +19,17 @@ We welcome contributions to withoutbg! This document provides guidelines for con 2. **Set Up Development Environment** ```bash + # Install uv (if you don't have it yet) + # Download from: https://astral.sh/uv + + # Install dependencies (using uv - recommended) + uv sync --extra dev + + # Or with pip (create venv first) python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -e ".[dev]" + pre-commit install ``` @@ -203,11 +211,15 @@ pytest -v git clone https://github.com/withoutbg/withoutbg.git cd withoutbg -# Create virtual environment +# Install uv (if you don't have it yet) +# Download from: https://astral.sh/uv + +# Install package in development mode (using uv - recommended) +uv sync --extra dev + +# Or with pip (create venv first) python -m venv venv source venv/bin/activate - -# Install package in development mode pip install -e ".[dev]" # Install pre-commit hooks diff --git a/README.md b/README.md index 65e31d5..9c93975 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,10 @@ Remove backgrounds from images instantly with AI. Choose between local processin ### Python SDK ```bash -# Install +# Install (using uv - recommended) +uv add withoutbg + +# Or with pip pip install withoutbg # Use in Python @@ -24,6 +27,8 @@ result.save("output.png") withoutbg image.jpg ``` +> **Don't have `uv` yet?** Download it at [astral.sh/uv](https://astral.sh/uv) - it's a fast, modern Python package installer. + ### Web Application ```bash @@ -63,7 +68,7 @@ withoutbg/ #### ๐Ÿ“š [Python SDK](packages/python/) Core library for background removal. Published to PyPI. -- **Install**: `pip install withoutbg` +- **Install**: `uv add withoutbg` (or `pip install withoutbg`) - **Use**: Python API + CLI - **Models**: Focus v1.0.0 (local), Cloud API @@ -104,7 +109,10 @@ Plugins for popular creative tools. ```bash cd packages/python -# Install in development mode +# Install in development mode (using uv - recommended) +uv sync --extra dev + +# Or with pip pip install -e ".[dev]" # Run tests @@ -123,8 +131,7 @@ docker-compose -f apps/web/docker-compose.yml up # Or run components separately cd apps/web/backend -pip install -e ../../packages/python -pip install -e . +uv sync uvicorn app.main:app --reload cd apps/web/frontend diff --git a/STRUCTURE.md b/STRUCTURE.md index 5124566..9b62ca7 100644 --- a/STRUCTURE.md +++ b/STRUCTURE.md @@ -76,14 +76,19 @@ The **core Python SDK** - independently versioned and published to PyPI. **Installation:** ```bash -# From PyPI (end users) +# From PyPI (end users) - using uv (recommended) +uv add withoutbg + +# Or with pip pip install withoutbg # From source (development) cd packages/python -pip install -e ".[dev]" +uv sync --extra dev # or: pip install -e ".[dev]" ``` +> **Don't have `uv` yet?** Download it at [astral.sh/uv](https://astral.sh/uv) - it's a fast, modern Python package installer. + **Publishing:** ```bash cd packages/python diff --git a/apps/web/README.md b/apps/web/README.md index 012ae1d..6f81331 100644 --- a/apps/web/README.md +++ b/apps/web/README.md @@ -28,16 +28,15 @@ open http://localhost:3000 ```bash cd apps/web/backend -# Install core package (from repository root) -pip install -e ../../packages/python - -# Install backend dependencies -pip install -e . +# Install dependencies (using uv - recommended) +uv sync # Run development server uvicorn app.main:app --reload ``` +> **Don't have `uv` yet?** Download it at [astral.sh/uv](https://astral.sh/uv) - it's a fast, modern Python package installer. + ### Frontend ```bash diff --git a/apps/web/backend/README.md b/apps/web/backend/README.md index 1f2279f..0f7fdeb 100644 --- a/apps/web/backend/README.md +++ b/apps/web/backend/README.md @@ -18,11 +18,13 @@ FastAPI-based REST API for AI-powered background removal. - Python 3.12 - uv (recommended) or pip +> **Don't have `uv` yet?** Download it at [astral.sh/uv](https://astral.sh/uv) - it's a fast, modern Python package installer. + ### Setup ```bash -# Install dependencies with uv -uv pip install -r requirements.txt +# Install dependencies with uv (recommended) +uv sync # Or with pip pip install -r requirements.txt diff --git a/apps/web/backend/app/main.py b/apps/web/backend/app/main.py index f7a8662..a6a8a87 100644 --- a/apps/web/backend/app/main.py +++ b/apps/web/backend/app/main.py @@ -12,7 +12,7 @@ from PIL import Image import uvicorn -# Import withoutbg package (install via: pip install -e ../../../packages/python) +# Import withoutbg package (install via: uv sync or pip install -e ../../../packages/python) from withoutbg import remove_background, __version__ from withoutbg.exceptions import WithoutBGError from withoutbg.api import StudioAPI diff --git a/packages/python/README.md b/packages/python/README.md index 717755b..46b7b9b 100644 --- a/packages/python/README.md +++ b/packages/python/README.md @@ -8,9 +8,15 @@ AI-powered background removal with local and cloud options. ## Installation ```bash +# Using uv (recommended) +uv add withoutbg + +# Or with pip pip install withoutbg ``` +> **Don't have `uv` yet?** Download it at [astral.sh/uv](https://astral.sh/uv) - it's a fast, modern Python package installer. + ## Quick Start ```python @@ -72,7 +78,10 @@ For complete documentation, see the [main project README](../../README.md). ## Development ```bash -# Install in development mode +# Install in development mode (using uv - recommended) +uv sync --extra dev + +# Or with pip pip install -e ".[dev]" # Run tests From 4772d3a70003ab5b51f70bda1ff34f1e6af3de38 Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Wed, 5 Nov 2025 13:26:50 +0100 Subject: [PATCH 24/73] Enhance Sidebar component with a new collapse button for desktop view - Added a collapse button for the sidebar that appears when the sidebar is open, improving user experience on desktop. - Removed the previous collapse button implementation to streamline the design and functionality. --- .../frontend/src/components/demo/Sidebar.jsx | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/apps/web/frontend/src/components/demo/Sidebar.jsx b/apps/web/frontend/src/components/demo/Sidebar.jsx index 5aaa323..4ed8a7c 100644 --- a/apps/web/frontend/src/components/demo/Sidebar.jsx +++ b/apps/web/frontend/src/components/demo/Sidebar.jsx @@ -47,6 +47,20 @@ export const Sidebar = ({ activeTab, onTabChange, isOpen, onToggle }) => { /> )} + {/* Collapse button for desktop - positioned on the border, outside sidebar */} + {isOpen && ( + + )} + {/* Sidebar */}

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zj)*+LM%6~^DSnd*X@ym(=4zC}1!$q3p|U*?LEZZPD$g&8c|eJv(N+&QIqo3fav0TxFTLD#<0OF>WgaFu zDkwm3#R{u!>d*NaQa9Tczd?#|0L$pwzuuuy${vGn?U+qLQ%$0hf`T)W?>>UZ14MR4 zQR7%yU`6A+S5NAtv=_U>nh&%9XFPevKE%<%eJ&ynHSq{8Nmvo6(-y35CVSKj)Z0ig z#o9kc5Tcl1H%H!3XL5%>=FZ7Z z{{YQ9B3zJ&G>5D|QiM2^jHfn17*rq>3K@eF>5ZKC>lCoCw!OljYPcXPRshapE)|S9 z(T38zLH5cPj%yK4JAte}SHz`TlvHTheD#>qfzYj3MqMq@q+Oef;LOrM^Axu9u?_~0 zL!Yz*5E*?Nln#QP7>yM&ns0NrKpb7?<(5}eYORI*^#-6WJ=(Oux{Qg#nu6MReIaFA zBWCM}HbUqrH3wT9OH1Q}iEj3cd9?dM)KyNHa~{iUd75Nk$&8FLcbw-EpX3iRo&Nxu LcMvK7b3gytZdoO& literal 0 HcmV?d00001 From a8d643111b76f2660de062b22b6d744c9c1ce6bf Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Fri, 12 Sep 2025 21:27:21 +0200 Subject: [PATCH 14/73] Update README to simplify API references and remove model tier details - Removed specific model tier information and replaced "Studio" with "API" for clarity. - Updated examples to reflect the changes in API usage terminology. - Streamlined the commercial licensing section for better readability. --- README.md | 26 +++++--------------------- 1 file changed, 5 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index eb41734..ebb4585 100644 --- a/README.md +++ b/README.md @@ -36,13 +36,6 @@ See the power of AI background removal in action: *Perfect edge detection, hair details, and transparent backgrounds* -## ๐Ÿ“ฆ Model Tiers - -| Tier | Quality | Usage | License | -|------|---------|-------|---------| -| ๐ŸŸข **Snap** | Good | Local/Free | Apache 2.0 | -| ๐ŸŸก **Focus** | Better | Commercial | Licensed | -| ๐Ÿ”ด **Studio** | Best | Cloud API | SaaS credits | ## ๐Ÿ’ป Python API @@ -53,7 +46,7 @@ from withoutbg import remove_background result = remove_background("input.jpg") result.save("output.png") -# Cloud processing with Studio API (best quality) +# Cloud processing with API (best quality) result = remove_background("input.jpg", api_key="sk_your_key") # Batch processing @@ -76,7 +69,7 @@ withoutbg photo.jpg --output result.png withoutbg photo.jpg --format webp --quality 90 ``` -### Cloud API (Studio) +### Cloud API ```bash # Set API key via environment export WITHOUTBG_API_KEY="sk_your_api_key" @@ -130,7 +123,7 @@ product_images = Path("products").glob("*.jpg") results = withoutbg.remove_background_batch( list(product_images), output_dir="catalog-withoutbg/", - api_key="sk_your_key" # Use Studio for best quality + api_key="sk_your_key" # Use for best quality ) ``` @@ -196,23 +189,14 @@ print(usage) ## ๐Ÿค Commercial Licensing -### Focus License -- โœ… Dockerized model for easy deployment -- โœ… Multiple runtime options: ONNX, TensorRT, AWS Inferentia -- โœ… Self-hosted deployment -- โœ… Better quality than Snap -- โœ… Priority support -- โœ… No API dependencies - -[Get Focus License โ†’](https://withoutbg.com/focus) -### Studio API (Pay-per-use) +### API (Pay-per-use) - โœ… Best quality processing - โœ… Best quality results - โœ… 99.9% uptime SLA - โœ… Scalable infrastructure -[Try Studio API โ†’](https://withoutbg.com/remove-background) +[Try API โ†’](https://withoutbg.com/remove-background) ## ๐Ÿ“š Documentation From a54e1deb0f9392f5b3866c145516b6a5f106d03e Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Fri, 12 Sep 2025 21:30:12 +0200 Subject: [PATCH 15/73] Refactor README to enhance clarity and remove outdated sections --- README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index ebb4585..2685750 100644 --- a/README.md +++ b/README.md @@ -187,7 +187,7 @@ usage = api.get_usage() print(usage) ``` -## ๐Ÿค Commercial Licensing +## Commercial ### API (Pay-per-use) @@ -201,9 +201,6 @@ print(usage) ## ๐Ÿ“š Documentation - **[API Reference](https://withoutbg.com/documentation)** - Complete API documentation -- **[Model Guide]()** - Choosing the right model -- **[Integration Examples]()** - Real-world usage patterns -- **[Quality Benchmarks]()** - Quality metrics and comparisons ## ๐Ÿ› Issues & Support @@ -215,7 +212,6 @@ print(usage) Find our models on Hugging Face: - **[withoutbg/snap](https://huggingface.co/withoutbg/snap)** - Open source Snap model -- **[Demo Widget](https://huggingface.co/spaces/withoutbg/demo)** - Try online ## ๐Ÿ“„ License From 33211794ffc0474720ba097362b1a7f1fbc2403a Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Sun, 21 Sep 2025 22:14:57 +0200 Subject: [PATCH 16/73] Refactor model references and update functionality for background removal - Changed model references from "Snap" to "Open Source" throughout the codebase. - Updated README to reflect the new model name and its usage. - Enhanced the remove_background function to include a progress callback for better user feedback. - Incremented version number to 0.2.0 to signify the updates. - Adjusted tests to accommodate the new model naming and functionality. --- README.md | 2 +- examples/basic_usage.py | 10 +- src/withoutbg/__init__.py | 4 +- src/withoutbg/__version__.py | 2 +- src/withoutbg/api.py | 25 +- src/withoutbg/cli.py | 39 ++- src/withoutbg/core.py | 30 +- src/withoutbg/models.py | 261 ++++++++++++++---- tests/fixtures/mock_responses.py | 4 +- .../integration/test_real_image_processing.py | 56 ++-- tests/performance/test_benchmarks.py | 40 +-- tests/performance/test_memory_usage.py | 26 +- tests/test_api.py | 2 +- tests/test_cli.py | 43 +-- tests/test_cli_e2e.py | 46 +-- 15 files changed, 400 insertions(+), 190 deletions(-) diff --git a/README.md b/README.md index 2685750..38389e8 100644 --- a/README.md +++ b/README.md @@ -211,7 +211,7 @@ print(usage) ## ๐Ÿค— Hugging Face Find our models on Hugging Face: -- **[withoutbg/snap](https://huggingface.co/withoutbg/snap)** - Open source Snap model +- **[withoutbg/focus](https://huggingface.co/withoutbg/open_source)** - Our best open source model ## ๐Ÿ“„ License diff --git a/examples/basic_usage.py b/examples/basic_usage.py index ff9355c..ae9ab14 100644 --- a/examples/basic_usage.py +++ b/examples/basic_usage.py @@ -11,9 +11,9 @@ def basic_local_processing(): # Remove background using local model (free) result = withoutbg.remove_background("input.jpg") - result.save("output_snap.png") + result.save("output_open_source.png") - print("โœ… Background removed and saved as output_snap.png") + print("โœ… Background removed and saved as output_open_source.png") print("๐ŸŽฏ Want best quality? Try withoutbg.com") @@ -108,8 +108,8 @@ def quality_comparison(): # Snap model (local, free) print("Processing with Snap model...") - snap_result = withoutbg.remove_background(input_image) - snap_result.save("comparison_snap.png") + open_source_result = withoutbg.remove_background(input_image) + open_source_result.save("comparison_open_source.png") # Studio model (API, best quality) print("Processing with Studio API...") @@ -122,7 +122,7 @@ def quality_comparison(): print("โœ… Comparison complete!") print("๐Ÿ“Š Compare results:") - print(" - comparison_snap.png (Snap model)") + print(" - comparison_open_source.png (Snap model)") print(" - comparison_studio.png (Studio API)") except withoutbg.APIError: diff --git a/src/withoutbg/__init__.py b/src/withoutbg/__init__.py index e086f87..aa290f1 100644 --- a/src/withoutbg/__init__.py +++ b/src/withoutbg/__init__.py @@ -4,12 +4,12 @@ from .api import StudioAPI from .core import remove_background, remove_background_batch from .exceptions import APIError, ModelNotFoundError, WithoutBGError -from .models import SnapModel +from .models import OpenSourceModel __all__ = [ "remove_background", "remove_background_batch", - "SnapModel", + "OpenSourceModel", "StudioAPI", "WithoutBGError", "ModelNotFoundError", diff --git a/src/withoutbg/__version__.py b/src/withoutbg/__version__.py index 15b62ef..f7449fe 100644 --- a/src/withoutbg/__version__.py +++ b/src/withoutbg/__version__.py @@ -1,3 +1,3 @@ """Version information for withoutbg.""" -__version__ = "0.1.0" +__version__ = "0.2.0" diff --git a/src/withoutbg/api.py b/src/withoutbg/api.py index c1759c8..4f14d6f 100644 --- a/src/withoutbg/api.py +++ b/src/withoutbg/api.py @@ -115,12 +115,13 @@ def _apply_alpha_channel( return rgba_image def remove_background( - self, input_image: Union[str, Path, Image.Image, bytes], **kwargs: Any + self, input_image: Union[str, Path, Image.Image, bytes], progress_callback: Optional[callable] = None, **kwargs: Any ) -> Image.Image: """Remove background using Studio API. Args: input_image: Input image + progress_callback: Optional callback function for progress updates (progress) **kwargs: Additional API parameters Returns: @@ -133,6 +134,9 @@ def remove_background( raise APIError("API key required for Studio service") try: + if progress_callback: + progress_callback(0.1) + # Store original image for local alpha application if isinstance(input_image, (str, Path)): with Image.open(input_image) as img: @@ -146,15 +150,21 @@ def remove_background( raise APIError(f"Unsupported image type: {type(input_image)}") # Resize image for API transmission to optimize latency + if progress_callback: + progress_callback(0.2) api_image, original_size = self._resize_for_api(original_image) # Encode resized image + if progress_callback: + progress_callback(0.3) encoded_image = self._encode_image(api_image) # Prepare request for base64 endpoint payload = {"image_base64": encoded_image} # Make API request to base64 endpoint + if progress_callback: + progress_callback(0.5) response = self.session.post( f"{self.base_url}/v1.0/alpha-channel-base64", json=payload, timeout=30 ) @@ -180,6 +190,8 @@ def remove_background( raise APIError(f"API request failed: {error_msg}") # Decode response + if progress_callback: + progress_callback(0.7) result_data = response.json() if "alpha_base64" not in result_data: @@ -189,6 +201,8 @@ def remove_background( ) # Decode alpha channel + if progress_callback: + progress_callback(0.8) alpha_image = self._decode_image(result_data["alpha_base64"]) # Resize alpha channel back to original dimensions if needed @@ -198,7 +212,14 @@ def remove_background( ) # Apply alpha channel to original image locally - return self._apply_alpha_channel(original_image, alpha_image) + if progress_callback: + progress_callback(0.9) + result = self._apply_alpha_channel(original_image, alpha_image) + + if progress_callback: + progress_callback(1.0) + + return result except requests.RequestException as e: raise APIError(f"Network error: {str(e)}") from e diff --git a/src/withoutbg/cli.py b/src/withoutbg/cli.py index 5958c52..9fae16b 100644 --- a/src/withoutbg/cli.py +++ b/src/withoutbg/cli.py @@ -26,13 +26,13 @@ help="API key for Studio service (or set WITHOUTBG_API_KEY env var)", ) @click.option( - "--use-api", is_flag=True, help="Use Studio API instead of local Snap model" + "--use-api", is_flag=True, help="Use API instead of local Open Source model" ) @click.option( "--model", - default="snap", - type=click.Choice(["snap", "studio"]), - help="Model to use (snap=local, studio=API)", + default="opensource", + type=click.Choice(["opensource", "api"]), + help="Model to use (opensource=local, api=API)", ) @click.option( "--batch", @@ -69,10 +69,10 @@ def main( Examples: - # Process single image with local Snap model + # Process single image with local Open Source model withoutbg image.jpg - # Use Studio API for best quality processing + # Use API for best quality processing withoutbg image.jpg --use-api --api-key sk_... # Process all images in directory @@ -84,20 +84,20 @@ def main( try: # Determine if using API - using_api = use_api or api_key or model == "studio" + using_api = use_api or api_key or model == "api" if using_api and not api_key: - click.echo("Error: API key required when using Studio service", err=True) + click.echo("Error: API key required when using API service", err=True) click.echo( "Set WITHOUTBG_API_KEY environment variable or use --api-key", err=True ) sys.exit(1) # Configure model - actual_model = "studio" if using_api else "snap" + actual_model = "api" if using_api else "opensource" if verbose: - mode = "Studio API" if using_api else "Local Snap model" + mode = "API" if using_api else "Local Open Source model" click.echo(f"Using {mode} for processing...") # Process images @@ -147,11 +147,22 @@ def _process_single( click.echo(f"Processing: {input_path}") click.echo(f"Output: {output_path}") - # Remove background + # Remove background with progress tracking bar: ProgressBar - with click.progressbar(length=1, label="Removing background") as bar: - result = remove_background(input_path, api_key=api_key, model_name=model) - bar.update(1) + with click.progressbar(length=100, label="Removing background") as bar: + def progress_callback(progress: float) -> None: + """Update progress bar with progress information.""" + # Update to the target progress position + target_pos = int(progress * 100) + if target_pos > bar.pos: + bar.update(target_pos - bar.pos) + + result = remove_background( + input_path, + api_key=api_key, + model_name=model, + progress_callback=progress_callback + ) # Save result save_kwargs: dict[str, Any] = {} diff --git a/src/withoutbg/core.py b/src/withoutbg/core.py index 36143e7..e728b45 100644 --- a/src/withoutbg/core.py +++ b/src/withoutbg/core.py @@ -7,14 +7,15 @@ from .api import StudioAPI from .exceptions import WithoutBGError -from .models import SnapModel +from .models import OpenSourceModel def remove_background( input_image: Union[str, Path, Image.Image, bytes], output_path: Optional[Union[str, Path]] = None, api_key: Optional[str] = None, - model_name: str = "snap", + model_name: str = "opensource", + progress_callback: Optional[callable] = None, **kwargs: Any, ) -> Image.Image: """Remove background from an image. @@ -22,32 +23,33 @@ def remove_background( Args: input_image: Input image as file path, PIL Image, or bytes output_path: Optional path to save the result - api_key: API key for Studio service (uses local Snap model if None) - model_name: Model to use ("snap" for local, "studio" for API) + api_key: API key for Studio service (uses local Open Source model if None) + model_name: Model to use ("opensource" for local, "api" for API) + progress_callback: Optional callback function for progress updates (progress) **kwargs: Additional arguments passed to the model/API Returns: PIL Image with background removed Examples: - >>> # Local processing with Snap model + >>> # Local processing with Open Source model >>> result = remove_background("input.jpg") - >>> # Cloud processing with Studio API + >>> # Cloud processing with API >>> result = remove_background("input.jpg", api_key="sk_...") >>> # Save result directly >>> remove_background("input.jpg", output_path="output.png") """ try: - if api_key or model_name == "studio": - # Use Studio API + if api_key or model_name == "api": + # Use API api = StudioAPI(api_key) - result = api.remove_background(input_image, **kwargs) + result = api.remove_background(input_image, progress_callback=progress_callback, **kwargs) else: - # Use local Snap model - model = SnapModel() - result = model.remove_background(input_image, **kwargs) + # Use local Open Source model + model = OpenSourceModel() + result = model.remove_background(input_image, progress_callback=progress_callback, **kwargs) if output_path: result.save(output_path) @@ -62,7 +64,7 @@ def remove_background_batch( input_images: list[Union[str, Path, Image.Image, bytes]], output_dir: Optional[Union[str, Path]] = None, api_key: Optional[str] = None, - model_name: str = "snap", + model_name: str = "opensource", **kwargs: Any, ) -> list[Image.Image]: """Remove background from multiple images. @@ -70,7 +72,7 @@ def remove_background_batch( Args: input_images: List of input images output_dir: Directory to save results (optional) - api_key: API key for Studio service + api_key: API key for API service model_name: Model to use **kwargs: Additional arguments diff --git a/src/withoutbg/models.py b/src/withoutbg/models.py index ad2669c..f4fec7d 100644 --- a/src/withoutbg/models.py +++ b/src/withoutbg/models.py @@ -12,24 +12,29 @@ from .exceptions import ModelNotFoundError, WithoutBGError -class SnapModel: - """Local ONNX-based background removal model (Snap tier).""" +class OpenSourceModel: + """Local ONNX-based background removal model (Open Source tier).""" def __init__( self, depth_model_path: Optional[Union[str, Path]] = None, + isnet_model_path: Optional[Union[str, Path]] = None, matting_model_path: Optional[Union[str, Path]] = None, refiner_model_path: Optional[Union[str, Path]] = None, ): - """Initialize the Snap model with 3-stage pipeline. + """Initialize the Open Source model with 4-stage pipeline. Args: depth_model_path: Path to Depth Anything V2 ONNX model. If None, downloads from HF. + isnet_model_path: Path to ISNet segmentation ONNX model. If None, downloads from HF. matting_model_path: Path to Matting ONNX model. If None, downloads from HF. refiner_model_path: Path to Refiner ONNX model. If None, downloads from HF. """ self.depth_model_path = depth_model_path or self._get_default_depth_model_path() + self.isnet_model_path = ( + isnet_model_path or self._get_default_isnet_model_path() + ) self.matting_model_path = ( matting_model_path or self._get_default_matting_model_path() ) @@ -37,7 +42,9 @@ def __init__( refiner_model_path or self._get_default_refiner_model_path() ) + self.depth_session = None + self.isnet_session = None self.matting_session = None self.refiner_session = None @@ -49,13 +56,17 @@ def _get_default_depth_model_path(self) -> Path: "depth_anything_v2_vits_slim.onnx", "Depth Anything V2 model" ) + def _get_default_isnet_model_path(self) -> Path: + """Get path to ISNet segmentation model from Hugging Face.""" + return self._download_from_hf("isnet.onnx", "ISNet segmentation model") + def _get_default_matting_model_path(self) -> Path: """Get path to Matting model from Hugging Face.""" - return self._download_from_hf("snap_matting_0.1.0.onnx", "Snap matting model") + return self._download_from_hf("focus_matting_0.1.0.onnx", "Focus matting model") def _get_default_refiner_model_path(self) -> Path: """Get path to Refiner model from Hugging Face.""" - return self._download_from_hf("snap_refiner_0.1.0.onnx", "Snap refiner model") + return self._download_from_hf("focus_refiner_0.1.0.onnx", "Focus refiner model") def _download_from_hf(self, filename: str, model_name: str) -> Path: """Download model from Hugging Face Hub with caching. @@ -75,7 +86,7 @@ def _download_from_hf(self, filename: str, model_name: str) -> Path: # First try to get from cache try: model_path = hf_hub_download( - repo_id="withoutbg/snap", + repo_id="withoutbg/focus", filename=filename, cache_dir=None, # Use default cache local_files_only=True, # Only check cache first @@ -85,7 +96,7 @@ def _download_from_hf(self, filename: str, model_name: str) -> Path: # If not in cache, download it print(f"Downloading {model_name} from Hugging Face...") model_path = hf_hub_download( - repo_id="withoutbg/snap", + repo_id="withoutbg/focus", filename=filename, cache_dir=None, # Use default cache local_files_only=False, @@ -97,11 +108,11 @@ def _download_from_hf(self, filename: str, model_name: str) -> Path: raise ModelNotFoundError( f"Failed to download {model_name} from Hugging Face: {str(e)}\n" f"You can manually download models from: " - f"https://huggingface.co/withoutbg/snap" + f"https://huggingface.co/withoutbg/focus" ) from e def _load_models(self) -> None: - """Load all three ONNX models.""" + """Load all four ONNX models.""" try: # Configure ONNX Runtime for optimal performance providers = ["CPUExecutionProvider"] @@ -116,6 +127,11 @@ def _load_models(self) -> None: str(self.depth_model_path), providers=providers ) + # Load ISNet segmentation model + self.isnet_session = ort.InferenceSession( + str(self.isnet_model_path), providers=providers + ) + # Load Matting model self.matting_session = ort.InferenceSession( str(self.matting_model_path), providers=providers @@ -186,6 +202,78 @@ def _prepare_image(self, img: np.ndarray) -> np.ndarray: img = np.ascontiguousarray(img, dtype=np.float32) return img + def transform_for_isnet(self, im: np.ndarray) -> np.ndarray: + """Transform image for ISNet model preprocessing. + + Args: + im: Input image as numpy array (H, W, C) with values in [0, 255] + + Returns: + Preprocessed image ready for ISNet model inference + """ + # Convert numpy array to PIL Image for resizing + pil_image = Image.fromarray(im.astype(np.uint8)) + + # Resize to 1024x1024 using PIL + pil_image = pil_image.resize((1024, 1024), Image.Resampling.LANCZOS) + + # Convert back to numpy array + im = np.array(pil_image, dtype=np.float32) + + # Normalize to [0, 1] + im = im / 255.0 + + # Apply ISNet-specific normalization + mean = np.array([0.5, 0.5, 0.5]) + std = np.array([1.0, 1.0, 1.0]) + im = (im - mean) / std + + # Convert to channel-first format (C, H, W) + im = np.transpose(im, (2, 0, 1)) + + # Convert to float32 + im = im.astype(np.float32) + + # Add batch dimension (1, C, H, W) + im = np.expand_dims(im, axis=0) + + return im + + def _isnet_stage(self, image: Image.Image) -> np.ndarray: + """ + ISNet segmentation stage for background removal. + + Parameters: + - image (PIL.Image.Image): The input RGB PIL Image. + + Returns: + - np.ndarray: Alpha mask from ISNet segmentation (H, W) with values in [0, 1]. + """ + # Convert PIL image to numpy array + img_array = np.array(image) + + # Transform for ISNet + processed_img = self.transform_for_isnet(img_array) + + # Run inference using ISNet + assert self.isnet_session is not None, "ISNet model not loaded" + + # Get the actual input name from the model + input_name = self.isnet_session.get_inputs()[0].name + ort_inputs = {input_name: processed_img} # type: ignore[unreachable] + ort_outs = self.isnet_session.run(None, ort_inputs) + alpha_output = ort_outs[0] + + # Process output: remove batch dimension + alpha_output = alpha_output.squeeze(0) + if len(alpha_output.shape) == 3: + alpha_output = alpha_output[0] + + # Keep values in [0, 1] range for further processing + alpha_output = np.clip(alpha_output, 0, 1).astype(np.float32) + + return alpha_output + def _preprocess_for_depth( self, image: Image.Image, @@ -279,21 +367,26 @@ def _estimate_depth( return depth_image def _matting_stage( - self, rgb_image: Image.Image, depth_image: Image.Image + self, rgb_image: Image.Image, depth_image: Image.Image, isnet_mask: np.ndarray ) -> Image.Image: """ - Stage 2: Matting using RGBD input (RGB + inverse depth concatenated). + Stage 2: Matting using RGBD + ISNet mask input (RGB + inverse depth + ISNet mask concatenated). Parameters: - rgb_image (PIL.Image.Image): The original RGB image. - depth_image (PIL.Image.Image): The inverse depth map from stage 1. + - isnet_mask (np.ndarray): The ISNet segmentation mask from stage 1.5. Returns: - PIL.Image.Image: Alpha channel (A1) as grayscale PIL Image. """ - # Resize both images to 256x256 for matting model + # Resize all inputs to 256x256 for matting model rgb_resized = rgb_image.resize((256, 256), Image.Resampling.LANCZOS) depth_resized = depth_image.resize((256, 256), Image.Resampling.LANCZOS) + + # Resize ISNet mask to 256x256 using PIL + isnet_mask_pil = Image.fromarray((isnet_mask * 255).astype(np.uint8), mode='L') + isnet_mask_resized = np.array(isnet_mask_pil.resize((256, 256), Image.Resampling.LANCZOS), dtype=np.float32) / 255.0 # Convert to numpy arrays and normalize to [0, 1] rgb_array = np.array(rgb_resized, dtype=np.float32) / 255.0 @@ -303,19 +396,27 @@ def _matting_stage( if len(depth_array.shape) == 3: depth_array = depth_array[:, :, 0] - # Concatenate RGB and depth to create RGBD (4-channel input) - rgbd_array = np.concatenate( - [rgb_array, np.expand_dims(depth_array, axis=2)], axis=2 + # Concatenate RGB + depth + ISNet mask to create 5-channel input + rgbd_mask_array = np.concatenate( + [ + rgb_array, + np.expand_dims(depth_array, axis=2), + np.expand_dims(isnet_mask_resized, axis=2), + ], + axis=2, ) # Prepare for model: transpose to CHW format and add batch dimension - rgbd_tensor = np.transpose(rgbd_array, (2, 0, 1)) - rgbd_tensor = np.expand_dims(rgbd_tensor, axis=0) - rgbd_tensor = np.ascontiguousarray(rgbd_tensor, dtype=np.float32) + rgbd_mask_tensor = np.transpose(rgbd_mask_array, (2, 0, 1)) + rgbd_mask_tensor = np.expand_dims(rgbd_mask_tensor, axis=0) + rgbd_mask_tensor = np.ascontiguousarray(rgbd_mask_tensor, dtype=np.float32) # Run inference through matting model assert self.matting_session is not None, "Matting model not loaded" - ort_inputs = {"rgbd_input": rgbd_tensor} # type: ignore[unreachable] + + # Get the actual input name from the model + input_name = self.matting_session.get_inputs()[0].name + ort_inputs = {input_name: rgbd_mask_tensor} # type: ignore[unreachable] ort_outs = self.matting_session.run(None, ort_inputs) alpha_output = ort_outs[0] @@ -332,15 +433,33 @@ def _matting_stage( return alpha_image + def _calculate_refiner_size(self, original_size: tuple[int, int]) -> tuple[int, int]: + """Calculate optimal size for refiner model (max 1024px on bigger side).""" + width, height = original_size + max_size = 1024 + + # If both dimensions are already <= 1024, no resizing needed + if width <= max_size and height <= max_size: + return original_size + + # Calculate scale factor to make the bigger side = 1024 + scale = max_size / max(width, height) + new_width = int(width * scale) + new_height = int(height * scale) + + return (new_width, new_height) + def _refiner_stage( - self, rgb_image: Image.Image, depth_image: Image.Image, alpha1: Image.Image + self, rgb_image: Image.Image, alpha1: Image.Image ) -> Image.Image: """ - Stage 3: Refine alpha channel using RGB + depth + alpha concatenated input. + Stage 3: Refine alpha channel using RGB + alpha concatenated input. + + Optimizes performance by resizing large images to max 1024px on the bigger side + for inference, then upscaling the result back to original resolution. Parameters: - rgb_image (PIL.Image.Image): The original RGB image. - - depth_image (PIL.Image.Image): The depth map from stage 1. - alpha1 (PIL.Image.Image): The alpha channel from matting stage. Returns: @@ -348,42 +467,49 @@ def _refiner_stage( """ # Get original image size original_size = rgb_image.size + + # Calculate optimal size for refiner model (max 1024px on bigger side) + refiner_size = self._calculate_refiner_size(original_size) + + # Resize RGB image for refiner model if needed + if refiner_size != original_size: + rgb_resized = rgb_image.resize(refiner_size, Image.Resampling.LANCZOS) + else: + rgb_resized = rgb_image - # Scale RGB image to [0, 1] without resizing - rgb_array = np.array(rgb_image, dtype=np.float32) / 255.0 + # Scale RGB image to [0, 1] + rgb_array = np.array(rgb_resized, dtype=np.float32) / 255.0 - # Resize depth and alpha to match RGB image size - depth_resized = depth_image.resize(original_size, Image.Resampling.LANCZOS) - alpha_resized = alpha1.resize(original_size, Image.Resampling.LANCZOS) + # Resize alpha to match the refiner input size + alpha_resized = alpha1.resize(refiner_size, Image.Resampling.LANCZOS) - # Convert to arrays and scale to [0, 1] - depth_array = np.array(depth_resized, dtype=np.float32) / 255.0 + # Convert to array and scale to [0, 1] alpha_array = np.array(alpha_resized, dtype=np.float32) / 255.0 - # Ensure depth and alpha are single channel - if len(depth_array.shape) == 3: - depth_array = depth_array[:, :, 0] + # Ensure alpha is single channel if len(alpha_array.shape) == 3: alpha_array = alpha_array[:, :, 0] - # Concatenate RGB + depth + alpha to create 5-channel input - rgba_depth_array = np.concatenate( + # Concatenate RGB + alpha to create 4-channel input + rgba_array = np.concatenate( [ rgb_array, - np.expand_dims(depth_array, axis=2), np.expand_dims(alpha_array, axis=2), ], axis=2, ) # Prepare for model: transpose to CHW format and add batch dimension - input_tensor = np.transpose(rgba_depth_array, (2, 0, 1)) + input_tensor = np.transpose(rgba_array, (2, 0, 1)) input_tensor = np.expand_dims(input_tensor, axis=0) input_tensor = np.ascontiguousarray(input_tensor, dtype=np.float32) # Run inference through refiner model assert self.refiner_session is not None, "Refiner model not loaded" - ort_inputs = {"rgbd_alpha_input": input_tensor} # type: ignore[unreachable] + + # Get the actual input name from the model + input_name = self.refiner_session.get_inputs()[0].name + ort_inputs = {input_name: input_tensor} # type: ignore[unreachable] ort_outs = self.refiner_session.run(None, ort_inputs) alpha_output = ort_outs[0] @@ -397,38 +523,79 @@ def _refiner_stage( # Convert to PIL Image refined_alpha = Image.fromarray(alpha_output, mode="L") + + # Resize refined alpha back to original resolution if it was downscaled + if refiner_size != original_size: + refined_alpha = refined_alpha.resize(original_size, Image.Resampling.LANCZOS) return refined_alpha - def estimate_alpha(self, image: Image.Image) -> Image.Image: + def estimate_alpha(self, image: Image.Image, progress_callback: Optional[callable] = None) -> Image.Image: """ - Full 3-stage pipeline: Depth Anything V2 -> Matting -> Refiner. + Full 4-stage pipeline: Depth Anything V2 -> ISNet -> Matting -> Refiner. Parameters: - image (PIL.Image.Image): Input RGB image. + - progress_callback: Optional callback function for progress updates (progress) Returns: - PIL.Image.Image: Final refined alpha channel. """ + if progress_callback: + progress_callback(0.0) + # Stage 1: Depth estimation + if progress_callback: + progress_callback(0.2) depth_map = self._estimate_depth(image) - # Stage 2: Matting (RGBD -> A1) - alpha1 = self._matting_stage(image, depth_map) + # Stage 1.5: ISNet segmentation + if progress_callback: + progress_callback(0.6) + isnet_mask = self._isnet_stage(image) - # Stage 3: Refiner (RGB + depth + alpha -> A2) - alpha2 = self._refiner_stage(image, depth_map, alpha1) + # Stage 2: Matting (RGB + Depth + ISNet mask -> A1) + if progress_callback: + progress_callback(0.7) + alpha1 = self._matting_stage(image, depth_map, isnet_mask) + + # Stage 3: Refiner (RGB + alpha -> A2) + if progress_callback: + progress_callback(0.8) + alpha2 = self._refiner_stage(image, alpha1) + + if progress_callback: + progress_callback(1.0) return alpha2 + def estimate_alpha_isnet(self, image: Image.Image) -> Image.Image: + """ + Single-stage ISNet-based alpha estimation. + + Parameters: + - image (PIL.Image.Image): Input RGB image. + + Returns: + - PIL.Image.Image: Alpha channel from ISNet segmentation. + """ + isnet_mask = self._isnet_stage(image) + + # Convert numpy array to PIL Image + alpha_output = np.clip(isnet_mask * 255.0, 0, 255).astype(np.uint8) + alpha_image = Image.fromarray(alpha_output, mode="L") + + return alpha_image + def remove_background( - self, input_image: Union[str, Path, Image.Image, bytes], **kwargs: Any + self, input_image: Union[str, Path, Image.Image, bytes], progress_callback: Optional[callable] = None, **kwargs: Any ) -> Image.Image: - """Remove background from image using local Snap model. + """Remove background from image using local Open Source model. Args: input_image: Input image - **kwargs: Additional arguments (unused for Snap model) + progress_callback: Optional callback function for progress updates (progress) + **kwargs: Additional arguments (unused for Open Source model) Returns: PIL Image with background removed @@ -453,7 +620,7 @@ def remove_background( try: # Run 3-stage pipeline to get final alpha channel - alpha_channel = self.estimate_alpha(image) + alpha_channel = self.estimate_alpha(image, progress_callback=progress_callback) # Resize alpha to original image size alpha_resized = alpha_channel.resize( diff --git a/tests/fixtures/mock_responses.py b/tests/fixtures/mock_responses.py index 53ecc29..ce499e6 100644 --- a/tests/fixtures/mock_responses.py +++ b/tests/fixtures/mock_responses.py @@ -84,10 +84,10 @@ def get_mock_api_responses(): "models_info": { "status_code": 200, "json": { - "models": ["snap", "studio", "premium"], + "models": ["open_source", "studio", "premium"], "default": "studio", "features": { - "snap": ["basic_removal"], + "open_source": ["basic_removal"], "studio": ["advanced_removal", "edge_refinement"], "premium": ["ultra_quality", "batch_processing"], }, diff --git a/tests/integration/test_real_image_processing.py b/tests/integration/test_real_image_processing.py index 335ef5b..86422dc 100644 --- a/tests/integration/test_real_image_processing.py +++ b/tests/integration/test_real_image_processing.py @@ -9,7 +9,7 @@ from withoutbg.core import remove_background from withoutbg.exceptions import ModelNotFoundError, WithoutBGError -from withoutbg.models import SnapModel +from withoutbg.models import OpenSourceModel def assert_alpha_channel_valid(result_image: Image.Image) -> None: @@ -102,10 +102,10 @@ def assert_images_similar( @pytest.fixture(scope="session") -def real_snap_model() -> Optional[SnapModel]: +def real_opensource_model() -> Optional[OpenSourceModel]: """Load actual ONNX models if available, skip if not.""" try: - model = SnapModel() + model = OpenSourceModel() # Verify models actually loaded assert model.depth_session is not None, "Depth model not loaded" assert model.matting_session is not None, "Matting model not loaded" @@ -125,14 +125,14 @@ class TestRealImageProcessing: """Integration tests using actual ONNX models and real images.""" def test_ice_cream_processing( - self, real_snap_model, test_images_dir, expected_outputs_dir + self, real_opensource_model, test_images_dir, expected_outputs_dir ): """Test processing the ice cream image with real models.""" input_path = test_images_dir / "test-ice-cream.png" assert input_path.exists(), f"Test image not found: {input_path}" # Process with real model - result = real_snap_model.remove_background(input_path) + result = real_opensource_model.remove_background(input_path) # Validate result properties assert_alpha_channel_valid(result) @@ -156,27 +156,27 @@ def test_core_api_with_real_model(self, test_images_dir): original = Image.open(input_path) assert result.size == original.size - def test_different_input_formats(self, real_snap_model, test_images_dir): + def test_different_input_formats(self, real_opensource_model, test_images_dir): """Test processing with different input formats.""" input_path = test_images_dir / "test-ice-cream.png" original_image = Image.open(input_path) # Test with PIL Image - result_pil = real_snap_model.remove_background(original_image) + result_pil = real_opensource_model.remove_background(original_image) assert_alpha_channel_valid(result_pil) # Test with file path (string) - result_path = real_snap_model.remove_background(str(input_path)) + result_path = real_opensource_model.remove_background(str(input_path)) assert_alpha_channel_valid(result_path) # Test with Path object - result_pathobj = real_snap_model.remove_background(input_path) + result_pathobj = real_opensource_model.remove_background(input_path) assert_alpha_channel_valid(result_pathobj) # Test with bytes with open(input_path, "rb") as f: image_bytes = f.read() - result_bytes = real_snap_model.remove_background(image_bytes) + result_bytes = real_opensource_model.remove_background(image_bytes) assert_alpha_channel_valid(result_bytes) # All results should have same dimensions @@ -196,7 +196,7 @@ def test_different_input_formats(self, real_snap_model, test_images_dir): ], ) def test_different_image_formats( - self, real_snap_model, sample_test_image, format_name, pil_format + self, real_opensource_model, sample_test_image, format_name, pil_format ): """Test processing various image formats.""" # Convert test image to RGB for JPEG compatibility @@ -213,7 +213,7 @@ def test_different_image_formats( buffer.seek(0) # Process the formatted image - result = real_snap_model.remove_background(buffer.getvalue()) + result = real_opensource_model.remove_background(buffer.getvalue()) assert_alpha_channel_valid(result) @pytest.mark.parametrize( @@ -225,7 +225,7 @@ def test_different_image_formats( (1024, 768), # Large landscape ], ) - def test_different_resolutions(self, real_snap_model, size): + def test_different_resolutions(self, real_opensource_model, size): """Test processing different image sizes.""" # Create test image with specific size test_img = Image.new("RGB", size, color=(128, 64, 192)) @@ -239,30 +239,30 @@ def test_different_resolutions(self, real_snap_model, size): img_array[:, :, 0] = gradient[None, :] # Horizontal gradient in red channel test_img = Image.fromarray(img_array) - result = real_snap_model.remove_background(test_img) + result = real_opensource_model.remove_background(test_img) # Should preserve original dimensions assert result.size == size assert_alpha_channel_valid(result) - def test_processing_consistency(self, real_snap_model, test_images_dir): + def test_processing_consistency(self, real_opensource_model, test_images_dir): """Test that multiple runs produce consistent results.""" input_path = test_images_dir / "test-ice-cream.png" # Process same image multiple times - result1 = real_snap_model.remove_background(input_path) - result2 = real_snap_model.remove_background(input_path) + result1 = real_opensource_model.remove_background(input_path) + result2 = real_opensource_model.remove_background(input_path) # Results should be identical (deterministic) - perfect IoU iou = calculate_alpha_iou(result1, result2) assert iou == 1.0, f"Processing not deterministic: IoU={iou:.6f} < 1.0" - def test_pipeline_stages_integration(self, real_snap_model, test_images_dir): + def test_pipeline_stages_integration(self, real_opensource_model, test_images_dir): """Test that the 3-stage pipeline produces reasonable outputs.""" input_path = test_images_dir / "test-ice-cream.png" original = Image.open(input_path) - result = real_snap_model.remove_background(original) + result = real_opensource_model.remove_background(original) # Validate the result has expected characteristics for ice cream image result_array = np.array(result) @@ -280,14 +280,14 @@ def test_pipeline_stages_integration(self, real_snap_model, test_images_dir): assert very_opaque > 100, "Not enough foreground preservation" def test_alpha_iou_metric( - self, real_snap_model, test_images_dir, expected_outputs_dir + self, real_opensource_model, test_images_dir, expected_outputs_dir ): """Test alpha IoU metric calculation and interpretation.""" input_path = test_images_dir / "test-ice-cream.png" expected_path = expected_outputs_dir / "test-ice-cream.png" # Process image - result = real_snap_model.remove_background(input_path) + result = real_opensource_model.remove_background(input_path) if expected_path.exists(): expected = Image.open(expected_path) @@ -314,15 +314,15 @@ def test_alpha_iou_metric( print(f"Union pixels: {union}") print(f"IoU = {intersection}/{union} = {intersection/union:.4f}") - def test_error_handling_with_real_model(self, real_snap_model): + def test_error_handling_with_real_model(self, real_opensource_model): """Test error handling with real models.""" # Test with invalid input with pytest.raises((WithoutBGError, ValueError, TypeError)): - real_snap_model.remove_background(None) + real_opensource_model.remove_background(None) # Test with invalid file path with pytest.raises((FileNotFoundError, WithoutBGError)): - real_snap_model.remove_background("/nonexistent/file.png") + real_opensource_model.remove_background("/nonexistent/file.png") @pytest.mark.performance @@ -331,7 +331,7 @@ class TestRealProcessingPerformance: """Performance tests for real image processing.""" def test_processing_speed_benchmark( - self, real_snap_model, test_images_dir, performance_tracker + self, real_opensource_model, test_images_dir, performance_tracker ): """Benchmark processing speed with real models.""" input_path = test_images_dir / "test-ice-cream.png" @@ -339,7 +339,7 @@ def test_processing_speed_benchmark( import time start_time = time.time() - result = real_snap_model.remove_background(input_path) + result = real_opensource_model.remove_background(input_path) processing_time = time.time() - start_time # Record performance metric @@ -355,7 +355,7 @@ def test_processing_speed_benchmark( message=f"Processing too slow: {processing_time:.2f}s", ) - def test_memory_usage_real_processing(self, real_snap_model, test_images_dir): + def test_memory_usage_real_processing(self, real_opensource_model, test_images_dir): """Test memory usage during real processing.""" import os @@ -365,7 +365,7 @@ def test_memory_usage_real_processing(self, real_snap_model, test_images_dir): memory_before = process.memory_info().rss / 1024 / 1024 # MB input_path = test_images_dir / "test-ice-cream.png" - result = real_snap_model.remove_background(input_path) + result = real_opensource_model.remove_background(input_path) memory_after = process.memory_info().rss / 1024 / 1024 # MB memory_increase = memory_after - memory_before diff --git a/tests/performance/test_benchmarks.py b/tests/performance/test_benchmarks.py index edcbd5b..02f5bd1 100644 --- a/tests/performance/test_benchmarks.py +++ b/tests/performance/test_benchmarks.py @@ -9,7 +9,7 @@ from PIL import Image from withoutbg.core import remove_background, remove_background_batch -from withoutbg.models import SnapModel +from withoutbg.models import OpenSourceModel @pytest.fixture @@ -54,8 +54,8 @@ def test_small_image_processing_time(self, benchmark_images, mock_onnx_setup): """Benchmark processing time for small images.""" image = benchmark_images["256x256"] - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (256, 256), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -78,8 +78,8 @@ def test_medium_image_processing_time(self, benchmark_images, mock_onnx_setup): """Benchmark processing time for medium images.""" image = benchmark_images["512x512"] - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (512, 512), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -101,8 +101,8 @@ def test_large_image_processing_time(self, benchmark_images, mock_onnx_setup): """Benchmark processing time for large images.""" image = benchmark_images["1024x768"] - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (1024, 768), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -124,8 +124,8 @@ def test_xl_image_processing_time(self, benchmark_images, mock_onnx_setup): """Benchmark processing time for extra large images.""" image = benchmark_images["2048x1536"] - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (2048, 1536), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -182,7 +182,7 @@ def test_model_initialization_time(self): # Measure initialization time start_time = time.time() - model = SnapModel() + model = OpenSourceModel() end_time = time.time() init_time = end_time - start_time @@ -196,7 +196,7 @@ def test_model_initialization_time(self): def test_preprocessing_performance(self, benchmark_images, mock_onnx_setup): """Benchmark preprocessing performance.""" image = benchmark_images["1024x768"] - model = SnapModel() + model = OpenSourceModel() # Measure preprocessing time start_time = time.time() @@ -214,7 +214,7 @@ def test_preprocessing_performance(self, benchmark_images, mock_onnx_setup): def test_depth_estimation_performance(self, benchmark_images, mock_onnx_setup): """Benchmark depth estimation performance.""" image = benchmark_images["512x512"] - model = SnapModel() + model = OpenSourceModel() # Measure depth estimation time start_time = time.time() @@ -240,8 +240,8 @@ def test_concurrent_processing_performance(self, mock_onnx_setup): processing_times = [] for image in test_images: - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (256, 256), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -322,8 +322,8 @@ def test_memory_scaling_with_image_size(self, mock_onnx_setup): test_image = Image.new("RGB", size, color=(100, 150, 200)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", size, color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -393,8 +393,8 @@ def test_repeated_processing_stability(self, mock_onnx_setup): # Process same image multiple times for _i in range(10): - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (512, 384), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -433,8 +433,8 @@ def test_baseline_performance_regression(self, mock_onnx_setup): """Test against baseline performance to catch regressions.""" test_image = Image.new("RGB", (512, 512), color=(128, 64, 192)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (512, 512), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha diff --git a/tests/performance/test_memory_usage.py b/tests/performance/test_memory_usage.py index 93280ee..ee665c0 100644 --- a/tests/performance/test_memory_usage.py +++ b/tests/performance/test_memory_usage.py @@ -11,7 +11,7 @@ from PIL import Image from withoutbg.core import remove_background, remove_background_batch -from withoutbg.models import SnapModel +from withoutbg.models import OpenSourceModel def get_memory_usage(): @@ -49,8 +49,8 @@ def test_single_image_memory_usage(self, mock_onnx_setup): test_image = Image.new("RGB", (1024, 768), color=(128, 64, 192)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (1024, 768), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -90,8 +90,8 @@ def test_large_image_memory_usage(self, mock_onnx_setup): # Create large image large_image = Image.new("RGB", (2048, 1536), color=(100, 150, 200)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (2048, 1536), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -173,8 +173,8 @@ def test_sequential_processing_memory_accumulation(self, mock_onnx_setup): for i in range(5): test_image = Image.new("RGB", (512, 384), color=(i * 50, i * 30, i * 20)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (512, 384), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -215,7 +215,7 @@ def test_model_initialization_memory_usage(self): mock_session.return_value = Mock() # Initialize model - model = SnapModel() + model = OpenSourceModel() post_init_memory = get_memory_usage() memory_increase = post_init_memory - initial_memory @@ -238,7 +238,7 @@ def test_preprocessing_memory_efficiency(self, mock_onnx_setup): initial_memory = get_memory_usage() test_image = Image.new("RGB", (1024, 768), color=(128, 64, 192)) - model = SnapModel() + model = OpenSourceModel() # Test preprocessing preprocessed = model._preprocess_for_depth(test_image, 518, 518, 14) @@ -267,8 +267,8 @@ def test_memory_leak_detection(self, mock_onnx_setup): for _iteration in range(10): test_image = Image.new("RGB", (256, 256), color=(100, 150, 200)) - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: - with patch("withoutbg.models.SnapModel._refiner_stage") as mock_refiner: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._refiner_stage") as mock_refiner: mock_alpha = Image.new("L", (256, 256), color=128) mock_matting.return_value = mock_alpha mock_refiner.return_value = mock_alpha @@ -383,9 +383,9 @@ def test_temporary_file_memory_usage(self, mock_onnx_setup): test_image.save(tmp_file.name, "JPEG") try: - with patch("withoutbg.models.SnapModel._matting_stage") as mock_matting: + with patch("withoutbg.models.OpenSourceModel._matting_stage") as mock_matting: with patch( - "withoutbg.models.SnapModel._refiner_stage" + "withoutbg.models.OpenSourceModel._refiner_stage" ) as mock_refiner: mock_alpha = Image.new("L", (512, 384), color=128) mock_matting.return_value = mock_alpha diff --git a/tests/test_api.py b/tests/test_api.py index 1c68337..737eb2c 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -300,7 +300,7 @@ def test_get_models_success(self, mock_get, api_client): mock_response = Mock() mock_response.status_code = 200 mock_response.json.return_value = { - "models": ["snap", "studio", "premium"], + "models": ["open_source", "studio", "premium"], "default": "studio", } mock_response.raise_for_status.return_value = None diff --git a/tests/test_cli.py b/tests/test_cli.py index 828d404..5e49812 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -63,7 +63,7 @@ def test_model_choice_validation(self): """Test model option accepts only valid choices.""" with tempfile.NamedTemporaryFile(suffix=".jpg") as temp_file: # Valid choice - result = self.runner.invoke(main, [temp_file.name, "--model", "snap"]) + result = self.runner.invoke(main, [temp_file.name, "--model", "opensource"]) # Should fail because of processing, not validation assert "Invalid value" not in result.output @@ -174,8 +174,8 @@ def test_images_directory(self, temp_dir): return images_dir @patch("src.withoutbg.cli.remove_background") - def test_single_image_processing_snap_model(self, mock_remove_bg, test_image_file): - """Test processing single image with Snap model.""" + def test_single_image_processing_open_source_model(self, mock_remove_bg, test_image_file): + """Test processing single image with local opensource model.""" # Mock successful processing result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) mock_remove_bg.return_value = result_image @@ -183,9 +183,12 @@ def test_single_image_processing_snap_model(self, mock_remove_bg, test_image_fil result = self.runner.invoke(main, [str(test_image_file)]) assert result.exit_code == 0 - mock_remove_bg.assert_called_once_with( - test_image_file, api_key=None, model_name="snap" - ) + mock_remove_bg.assert_called_once() + call_args = mock_remove_bg.call_args + assert call_args[0][0] == test_image_file + assert call_args[1]["api_key"] is None + assert call_args[1]["model_name"] == "opensource" + assert "progress_callback" in call_args[1] @patch("src.withoutbg.cli.remove_background") def test_single_image_processing_with_output_path( @@ -205,22 +208,25 @@ def test_single_image_processing_with_output_path( @patch("src.withoutbg.cli.remove_background") def test_single_image_processing_studio_api(self, mock_remove_bg, test_image_file): - """Test processing with Studio API.""" + """Test processing with API model.""" result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) mock_remove_bg.return_value = result_image result = self.runner.invoke( - main, [str(test_image_file), "--model", "studio", "--api-key", "test_key"] + main, [str(test_image_file), "--model", "api", "--api-key", "test_key"] ) assert result.exit_code == 0 - mock_remove_bg.assert_called_once_with( - test_image_file, api_key="test_key", model_name="studio" - ) + mock_remove_bg.assert_called_once() + call_args = mock_remove_bg.call_args + assert call_args[0][0] == test_image_file + assert call_args[1]["api_key"] == "test_key" + assert call_args[1]["model_name"] == "api" + assert "progress_callback" in call_args[1] @patch("src.withoutbg.cli.remove_background") def test_use_api_flag_forces_studio_model(self, mock_remove_bg, test_image_file): - """Test that --use-api flag forces Studio model.""" + """Test that --use-api flag forces API model.""" result_image = Image.new("RGBA", (256, 256), color=(255, 0, 0, 128)) mock_remove_bg.return_value = result_image @@ -229,9 +235,12 @@ def test_use_api_flag_forces_studio_model(self, mock_remove_bg, test_image_file) ) assert result.exit_code == 0 - mock_remove_bg.assert_called_once_with( - test_image_file, api_key="test_key", model_name="studio" - ) + mock_remove_bg.assert_called_once() + call_args = mock_remove_bg.call_args + assert call_args[0][0] == test_image_file + assert call_args[1]["api_key"] == "test_key" + assert call_args[1]["model_name"] == "api" + assert "progress_callback" in call_args[1] @patch("src.withoutbg.cli.remove_background") def test_batch_processing_directory( @@ -326,7 +335,7 @@ def test_verbose_output(self, mock_remove_bg, test_image_file): result = self.runner.invoke(main, [str(test_image_file), "--verbose"]) assert result.exit_code == 0 - assert "Using Local Snap model" in result.output + assert "Using Local Open Source model for processing" in result.output assert "Processing:" in result.output assert "โœ… Processing complete!" in result.output @@ -342,7 +351,7 @@ def test_verbose_output_with_api(self, mock_remove_bg, test_image_file): ) assert result.exit_code == 0 - assert "Using Studio API" in result.output + assert "Using API for processing" in result.output def test_output_filename_generation(self, test_image_file, temp_dir): """Test automatic output filename generation.""" diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py index 6a1e769..105735b 100644 --- a/tests/test_cli_e2e.py +++ b/tests/test_cli_e2e.py @@ -25,12 +25,12 @@ def temp_output_dir(self, temp_dir): return output_dir @pytest.mark.real_processing - def test_e2e_single_image_snap_model(self, real_test_image_path, temp_output_dir): + def test_e2e_single_image_open_source_model(self, real_test_image_path, temp_output_dir): """Test end-to-end processing of single image with Snap model.""" if not real_test_image_path.exists(): pytest.skip("Real test image not available") - output_path = temp_output_dir / "output_snap.png" + output_path = temp_output_dir / "output_open_source.png" result = self.runner.invoke( main, [str(real_test_image_path), "--output", str(output_path), "--verbose"] @@ -330,51 +330,51 @@ def test_e2e_model_comparison(self, real_test_image_path, temp_output_dir): if not real_test_image_path.exists(): pytest.skip("Real test image not available") - # Test Snap model - snap_output = temp_output_dir / "snap_result.png" - result_snap = self.runner.invoke( + # Test opensource model + opensource_output = temp_output_dir / "opensource_result.png" + result_opensource = self.runner.invoke( main, [ str(real_test_image_path), "--output", - str(snap_output), + str(opensource_output), "--model", - "snap", + "opensource", ], ) - assert result_snap.exit_code == 0, "Snap model processing failed" - assert snap_output.exists(), "Snap model output not created" + assert result_opensource.exit_code == 0, "Opensource model processing failed" + assert opensource_output.exists(), "Opensource model output not created" - # Test Studio API if available + # Test API model if available api_key = os.getenv("WITHOUTBG_API_KEY") if api_key: - studio_output = temp_output_dir / "studio_result.png" - result_studio = self.runner.invoke( + api_output = temp_output_dir / "api_result.png" + result_api = self.runner.invoke( main, [ str(real_test_image_path), "--output", - str(studio_output), + str(api_output), "--model", - "studio", + "api", "--api-key", api_key, ], ) - assert result_studio.exit_code == 0, "Studio API processing failed" - assert studio_output.exists(), "Studio API output not created" + assert result_api.exit_code == 0, "API processing failed" + assert api_output.exists(), "API output not created" # Compare basic properties - snap_image = Image.open(snap_output) - studio_image = Image.open(studio_output) + opensource_image = Image.open(opensource_output) + api_image = Image.open(api_output) assert ( - snap_image.size == studio_image.size + opensource_image.size == api_image.size ), "Both models should produce same size output" assert ( - snap_image.mode == studio_image.mode == "RGBA" + opensource_image.mode == api_image.mode == "RGBA" ), "Both should produce RGBA output" @@ -484,7 +484,7 @@ def setup_method(self): @pytest.mark.performance @pytest.mark.real_processing - def test_e2e_processing_time_snap_model( + def test_e2e_processing_time_open_source_model( self, real_test_image_path, temp_output_dir, performance_tracker ): """Test processing time for Snap model.""" @@ -503,7 +503,7 @@ def test_e2e_processing_time_snap_model( processing_time = end_time - start_time performance_tracker.record_metric( - "snap_processing_time", processing_time, "seconds" + "open_source_processing_time", processing_time, "seconds" ) assert result.exit_code == 0 @@ -512,7 +512,7 @@ def test_e2e_processing_time_snap_model( # Snap model should process reasonably quickly # This is generous since it includes model download time on first run performance_tracker.assert_performance( - "snap_processing_time", + "open_source_processing_time", 30.0, # 30 seconds max "Snap model processing took too long", ) From 6a8b94df78292f6b45cb7901834e81ed5d84a96b Mon Sep 17 00:00:00 2001 From: Imran on Mac Date: Sun, 28 Sep 2025 21:40:26 +0200 Subject: [PATCH 17/73] Update README and third-party licenses for Focus v1.0.0 release - Introduced Focus v1.0.0 model with improved edge detail, hair handling, and generalization as the default for local processing. - Added visual examples demonstrating the model's capabilities in challenging scenarios. - Updated third-party licenses to include ISNet for segmentation. - Removed outdated image examples and added new ones to showcase the latest model's performance. --- README.md | 91 +++++++++++++++++++++++++++---- THIRD_PARTY_LICENSES.md | 22 ++++++++ examples/images/dog-mugshot.jpg | Bin 172736 -> 0 bytes examples/images/example1.png | Bin 0 -> 428887 bytes examples/images/example2.png | Bin 0 -> 399128 bytes examples/images/example3.png | Bin 0 -> 391964 bytes examples/images/example4.png | Bin 0 -> 480693 bytes examples/images/example5.png | Bin 0 -> 614322 bytes examples/images/example6.png | Bin 0 -> 553964 bytes examples/images/example7.png | Bin 0 -> 218663 bytes examples/images/feather.jpg | Bin 80256 -> 0 bytes examples/images/pizza-parody.jpg | Bin 86660 -> 0 bytes src/withoutbg/models.py | 4 +- 13 files changed, 103 insertions(+), 14 deletions(-) delete mode 100644 examples/images/dog-mugshot.jpg create mode 100644 examples/images/example1.png create mode 100644 examples/images/example2.png create mode 100644 examples/images/example3.png create mode 100644 examples/images/example4.png create mode 100644 examples/images/example5.png create mode 100644 examples/images/example6.png create mode 100644 examples/images/example7.png delete mode 100644 examples/images/feather.jpg delete mode 100644 examples/images/pizza-parody.jpg diff --git a/README.md b/README.md index 38389e8..1eae3df 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,16 @@ Remove backgrounds from images instantly with AI. Choose between local processing (free) or cloud API (best quality). +## ๐Ÿ†• Latest Update: Focus v1.0.0 + +We've just released our most advanced open source model yet! **Focus v1.0.0** delivers significantly improved quality over the previous Snap v0.1.0 model: + +- โœ… **Significantly better edge detail** - Crisp, clean edges on complex objects +- โœ… **Superior hair/fur handling** - Natural-looking results on fine details +- โœ… **Better generalization** - Works great on diverse image types + +*The Focus model is now the default for all local processing.* + ## ๐Ÿš€ Quick Start ```bash @@ -24,17 +34,33 @@ withoutbg image.jpg --api-key sk_your_api_key ## โœจ Visual Examples -See the power of AI background removal in action: +See how our latest **Focus v1.0.0** model handles challenging objects and scenes with superior quality compared to previous versions: + +### ๐ŸŽฏ Challenging Scenarios +*These examples showcase Focus v1.0.0's advanced capabilities in edge cases that previous models struggled with* -![Woman with confetti - before and after](examples/images/woman-golden-hour.jpg) +![Portrait 1](examples/images/example1.png) +*Complex hair details and fine edges* -![Dog portrait - before and after](examples/images/dog-mugshot.jpg) +![Portrait 2](examples/images/example2.png) +*Mixed lighting and subtle shadows* -![Pizza product - before and after](examples/images/pizza-parody.jpg) +![Dandelion](examples/images/example3.png) +*Ultra-fine details and transparency* -![Feather detail - before and after](examples/images/feather.jpg) +![Spider Web](examples/images/example4.png) +*Intricate patterns and semi-transparent elements* -*Perfect edge detection, hair details, and transparent backgrounds* +![Cat](examples/images/example5.png) +*Fur texture and natural lighting* + +![Mesh Texture](examples/images/example6.png) +*Complex geometric patterns* + +![Knitted Object](examples/images/example7.png) +*Textured materials and depth variations* + +> **๐Ÿ’ก Pro Tip**: Focus v1.0.0 excels at preserving fine details like hair, fur, and transparent objects that were challenging for previous models. ## ๐Ÿ’ป Python API @@ -42,7 +68,7 @@ See the power of AI background removal in action: ```python from withoutbg import remove_background -# Local processing with Snap model (free) +# Local processing with Focus model (free) result = remove_background("input.jpg") result.save("output.png") @@ -175,6 +201,26 @@ ruff check src/ tests/ mypy src/ ``` +## ๐Ÿ”„ Active Development + +This project is actively maintained with regular updates and improvements: + +- **Latest Release**: Focus v1.0.0 (Sep 2025) +- **Development Cycle**: Monthly releases with new features and model improvements +- **Community Driven**: Open to contributions and feature requests +- **Performance Focus**: Continuous optimization for speed and quality + +### Recent Improvements +- ๐Ÿš€ **Focus Model**: Complete rewrite with 4-stage pipeline +- ๐ŸŽฏ **Quality**: Significant improvements in edge detection and fine details +- ๐Ÿ”ง **API**: Enhanced error handling and progress callbacks +- ๐Ÿ“ฆ **Packaging**: Better dependency management and installation + +### Upcoming Features +- ๐Ÿณ **Dockerized Application**: Easy deployment with intuitive web UI +- ๐Ÿค– **Model Retraining**: Continuous improvement with newly generated data + + ## ๐Ÿ“Š Usage Analytics Track your API usage: @@ -187,11 +233,9 @@ usage = api.get_usage() print(usage) ``` -## Commercial - +## ๐Ÿ’ผ Commercial ### API (Pay-per-use) -- โœ… Best quality processing - โœ… Best quality results - โœ… 99.9% uptime SLA - โœ… Scalable infrastructure @@ -210,9 +254,31 @@ print(usage) ## ๐Ÿค— Hugging Face -Find our models on Hugging Face: -- **[withoutbg/focus](https://huggingface.co/withoutbg/open_source)** - Our best open source model +### ๐Ÿš€ Latest Model: Focus v1.0.0 +**[withoutbg/focus](https://huggingface.co/withoutbg/focus)** - Our most advanced open source model +- โญ **Recommended**: Best quality and performance +- ๐ŸŽฏ **4-stage pipeline**: Depth โ†’ ISNet โ†’ Matting โ†’ Refiner + +### ๐Ÿ“š Model Archive +**[withoutbg/snap](https://huggingface.co/withoutbg/snap)** - Previous generation model (v0.1.0) +- ๐Ÿ“– **Legacy**: Maintained for compatibility +- ๐Ÿ”„ **Migration**: Easy upgrade path to Focus v1.0.0 + +### ๐Ÿ† Why Choose Focus? +- **Superior Quality**: 30-40% better edge detection +- **Active Development**: Regular updates and improvements +- **Community Support**: Active discussions and contributions + +## โญ Star This Repository + +If you find this project useful, please consider giving it a star! Your support helps us: + +- ๐Ÿš€ **Accelerate development** of new features and models +- ๐Ÿ› **Improve quality** through community feedback and testing +- ๐Ÿ“ˆ **Grow the ecosystem** with better documentation and examples +- ๐ŸŽฏ **Focus on innovation** rather than marketing +[![GitHub stars](https://img.shields.io/github/stars/withoutbg/withoutbg?style=social)](https://github.com/withoutbg/withoutbg) ## ๐Ÿ“„ License @@ -220,6 +286,7 @@ This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENS ### Third-Party Components - **Depth Anything**: Apache 2.0 License +- **ISNet**: Apache 2.0 License - [Highly Accurate Dichotomous Image Segmentation](https://github.com/xuebinqin/DIS) See [THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md) for complete attribution. diff --git a/THIRD_PARTY_LICENSES.md b/THIRD_PARTY_LICENSES.md index ad0f0dd..b3f8669 100644 --- a/THIRD_PARTY_LICENSES.md +++ b/THIRD_PARTY_LICENSES.md @@ -24,6 +24,28 @@ See the License for the specific language governing permissions and limitations under the License. ``` +## ISNet (Highly Accurate Dichotomous Image Segmentation) + +**Repository**: https://github.com/xuebinqin/DIS +**License**: Apache License 2.0 +**Usage**: Used as part of the background removal pipeline for segmentation + +``` +Copyright (c) 2022 Xuebin Qin, Hang Dai, Xiaobin Hu, Deng-Ping Fan, Ling Shao, Luc Van Gool + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +``` + ## Python Dependencies The following Python packages are used by this project. 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