3.10【单张图片处理过程】

origin:

后来增加了draw.py 在同一个文件夹中 参考3.9博客

import glob
import json
import os
import os.path as osp
import numpy as np
import PIL.Image
import labelme
import sys

input_dir = './cat_test/' # 里面是含有json文件所处的文件夹
output_dir = 'data_dataset_voc' #output_dir 不能是一个已经存在的文件夹，要起一个新名字，让系统自己创建
labels = './cat_test/label.txt'

if osp.exists(output_dir):
    print('Output directory already exists:',output_dir)
    sys.exit(1)
os.makedirs(output_dir) # 生成output_dir = 'data_dataset_voc' 文件，且与
# Python 文件处于同一文件夹下即 f:/dataset3/labelme_practice_cat_test/

print(osp.join(output_dir,'JPEGImages'))

输出

data_dataset_voc\JPEGImages  # 这里的斜杠与我前面./不同也，
 不知道后面可不可以生成文件夹

origin

os.makedirs(osp.join(output_dir, 'JPEGImages'))
os.makedirs(osp.join(output_dir,'SegmentationClass'))
os.makedirs(osp.join(output_dir,'SegmentationClassPNG'))
os.makedirs(osp.join(output_dir,'SegmentationClassVisualization'))
os.makedirs(osp.join(output_dir,'SegmentationObject'))
os.makedirs(osp.join(output_dir,'SegmentationObjectPNG'))
os.makedirs(osp.join(output_dir,'SegmentationObjectVisualization'))

then 输出

class_names = [] # 用来储藏类别
class_name_to_id = {} # 利用字典来储藏id 以及相对应的 class_name
for i , line in enumerate(open(labels).readlines()):
    class_id = i - 1 # 我希望 __ignore__对应的是-1，开始，因为我希望类别从0---background 开始数起
    class_name = line.strip() # 获取类别名称
    class_name_to_id[class_name] = class_id
    if class_id == -1:  # 下面的代码是用来检错的 看-1是否与__ignore__对应， 0是否与__background__对应
        assert class_name == '__ignore__'
        continue  # 用来阻止 __ignore__ 写入class_names 列表中
    elif class_id == 0:
        assert class_name == '__background__'
    
    class_names.append(class_name)

输出

class_names = tuple(class_names)
class_names

输出

('__background__', 'cat')

out_class_names_file = osp.join(output_dir, 'class_names.txt') # 创建一个文件用来保存类名
##out_class_names_file 为'data_dataset_voc\\class_names.txt'
with open(out_class_names_file,'w') as f:
    f.writelines('\n'.join(class_names))

输出

import draw
colormap = draw.label_colormap()

==About json.load()==

Help on function load in module json:

load(fp, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
    Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
    a JSON document) to a Python object.
    
    ``object_hook`` is an optional function that will be called with the
    result of any object literal decode (a ``dict``). The return value of
    ``object_hook`` will be used instead of the ``dict``. This feature
    can be used to implement custom decoders (e.g. JSON-RPC class hinting).
    
    ``object_pairs_hook`` is an optional function that will be called with the
    result of any object literal decoded with an ordered list of pairs.  The
    return value of ``object_pairs_hook`` will be used instead of the ``dict``.
    This feature can be used to implement custom decoders.  If ``object_hook``
    is also defined, the ``object_pairs_hook`` takes priority.
    
    To use a custom ``JSONDecoder`` subclass, specify it with the ``cls``
    kwarg; otherwise ``JSONDecoder`` is used.

 PIL.Image.fromarray(obj, mode=None)
        Creates an image memory from an object exporting the array interface
        (using the buffer protocol).
        
        If **obj** is not contiguous, then the tobytes method is called
        and :py:func:`~PIL.Image.frombuffer` is used.
        ------------------------------------------
       | If you have an image in NumPy::
        
           from PIL import Image
          import numpy as np
          im = Image.open('hopper.jpg')
          a = np.asarray(im)
        
        Then this can be used to convert it to a Pillow image::
        
          im = Image.fromarray(a)
       ------------------------------------------------------ 
        :param obj: Object with array interface
        :param mode: Mode to use (will be determined from type if None)
          See: :ref:`concept-modes`.
        :returns: An image object.

 	|       ImageObject.save(self, fp, format=None, **params)
     |      Saves this image under the given filename.  If no format is
     |      specified, the format to use is determined from the filename
     |      extension, if possible.
     |      
     |      Keyword options can be used to provide additional instructions
     |      to the writer. If a writer doesn't recognise an option, it is
     |      silently ignored. The available options are described in the
     |      :doc:`image format documentation
     |      <../handbook/image-file-formats>` for each writer.
     |      
     |      You can use a file object instead of a filename. In this case,
     |      you must always specify the format. The file object must
     |      implement the ``seek``, ``tell``, and ``write``
     |      methods, and be opened in binary mode.
     |      
     |      :param fp: A filename (string), pathlib.Path object or file object.
     |      :param format: Optional format override.  If omitted, the
     |         format to use is determined from the filename extension.
     |         If a file object was used instead of a filename, this
     |         parameter should always be used.
     |      :param params: Extra parameters to the image writer.
     |      :returns: None
     |      :exception ValueError: If the output format could not be determined
     |         from the file name.  Use the format option to solve this.
     |      :exception IOError: If the file could not be written.  
    ----------- The file
     | ---------        may have been created, and may contain partial data

for label_file in glob.glob(osp.join(input_dir,'10.json')): # 原本join 的时候：./cat_test/10.json
    label_file = label_file.replace('\\','/')
    print('Generating dataset from:',label_file) # ./cat_test/10.json
    with open(label_file) as f:
        base = osp.splitext(osp.basename(label_file))[0]# 这里就只有文件名，连文件格式都没有了:10
        #创建文件每个图片的名称
        out_img_file = osp.join(output_dir,'JPEGImages', base+'.jpg') # data_dataset_voc\JPEGImages\10.jpg
        out_cls_file = osp.join(output_dir,'SegmentationClass', base+'.npy')
        out_clsp_file = osp.join(output_dir,'SegmentationClassPNG', base+'.png')
        out_clsv_file = osp.join(output_dir,'SegmentationClassVisualization', base+'.jpg',)
        
        out_ins_file = osp.join(output_dir,'SegmentationObject', base+'.npy')
        out_insp_file = osp.join(output_dir,'SegmentationObjectPNG', base+'.png')
        out_insv_file = osp.join(output_dir,'SegmentationObjectVisualization', base+'.jpg',)
        # 载入数据Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
        #a JSON document) to a Python object.
        data = json.load(f) # data 是一个字典型数据，里面的内容与json里面的内容一致
        
        img_file = osp.join(osp.dirname(label_file),data['imagePath']) #生成一个路径
        #label_file = './cat_test/10.json' --dirname--- './cat_test'----join ---'./cat_test\10.jpg' 
        img = np.asarray(PIL.Image.open(img_file))  #Convert the input to an array.
        PIL.Image.fromarray(img).save(out_img_file)# 在out_img_file 生成10.jpg
        # [np.ndarray]---fromarray----[PIL.Image object] ----save----[return none]Saves this image under the given filename
        #生成标签
        cls, ins = labelme.utils.shapes_to_label(
                    img_shape = img.shape,
                    shapes = data['shapes'],
                    label_name_to_value = class_name_to_id,
                    type = 'instance',) # 通过np.unique(cls)--0,1  np.unique(ins) --0,1
        ins[cls == -1] =0 # 是之前ignore 在ins内也为 background 的0
        
        # class label
        labelme.utils.lblsave(out_clsp_file,cls)#lblsave(filename, lbl) # 这一个在out_clsp_file文件夹中生成10.png
        np.save(out_cls_file,cls) # 在out_cls_file 文件夹中，生成10.npy
#         clsv = labelme.utils.draw_label(
#             label =cls,img =img, label_names=class_names,colormap = colormap) 
        # label矩阵，img矩阵，class_names = ('__background__', 'cat') ,colormap = imgviz.label_colormap()
        clsv = draw.draw_label(label =cls, img=img, label_names=class_names, colormap = colormap)
        PIL.Image.fromarray(clsv).save(out_clsv_file) # 在out_clsv_file 中生成10.jpg （带colormap）有 图注1 
        
        # instance label
        labelme.utils.lblsave(out_insp_file,ins)
        np.save(out_ins_file,ins)
        instance_ids = np.unique(ins)
        instance_names = [str(i) for i in range(max(instance_ids)+1)]
        insv = draw.draw_label(label=ins, img=img, label_names = instance_names)
        PIL.Image.fromarray(insv).save(out_insv_file) # 也是有图注的 0 1

输出