Count number of Object using Python-OpenCV Last Updated : 03 Apr, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report In this article, we will use image processing to count the number of Objects using OpenCV in Python.Google Colab link: https://colab.research.google.com/drive/10lVjcFhdy5LVJxtSoz18WywM92FQAOSV?usp=sharingModule neededOpenCv: OpenCv is an open-source library that is useful for computer vision applications such as image processing, video processing, facial recognition, and detection, etc.Numpy: Numpy is a python package for scientific computing. It is a popular math library for Machine Learning. The main Object of Numpy is a multidimensional array.Matplotlib: Matplotlib is a Python library used for data visualization and graphical plotting of the data.Image Used:.Stepwise implementationStep 1: Import required libraries. Python # Import libraries import cv2 import numpy as np import matplotlib.pyplot as plt Step 2: We will read the image by using "cv2.imread(image-name)" command & then convert this image into grayscale image using "cv2.cvtColor(image-name, cv2.COLOR_BGR2GRAY)" command. Python image = cv2.imread('coins.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) plt.imshow(gray, cmap='gray') Output:Step 3: For counting, we have to detect the edges but before detecting the edges we have to make the image blur to avoid the noises. Use "cv2.GaussianBlur(image-name, Kernal size, std. deviation)". Python blur = cv2.GaussianBlur(gray, (11, 11), 0) plt.imshow(blur, cmap='gray') Output: Step 4: Now we will detect edges using a canny algorithm, 2nd & 3rd parameters in cv2.canny() function are threshold values. a value between 30 & 150 are consider as an edge for this image. Python canny = cv2.Canny(blur, 30, 150, 3) plt.imshow(canny, cmap='gray') Output:Step 5: We can see that edges are not connected. We need to connect the edges, have to make more thiker & visible. Python dilated = cv2.dilate(canny, (1, 1), iterations=0) plt.imshow(dilated, cmap='gray') Output:Step 6: Now we have to calculate the contour in the image & convert the image into RGB from BGR & then draw the contours. Python (cnt, hierarchy) = cv2.findContours( dilated.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) cv2.drawContours(rgb, cnt, -1, (0, 255, 0), 2) plt.imshow(rgb) Output:Step 7: Printing the result Python print("coins in the image : ", len(cnt)) Output:coins in the image: 5Below is the complete implementation: Python # Import libraries import cv2 import numpy as np import matplotlib.pyplot as plt image = cv2.imread('coins.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray, (11, 11), 0) canny = cv2.Canny(blur, 30, 150, 3) dilated = cv2.dilate(canny, (1, 1), iterations=0) (cnt, hierarchy) = cv2.findContours( dilated.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) cv2.drawContours(rgb, cnt, -1, (0, 255, 0), 2) print("coins in the image : ", len(cnt)) Output: coins in the image : 5Get the complete notebook link: click here. Comment More infoAdvertise with us Next Article Count number of Faces using Python - OpenCV A asmitapatidar2023 Follow Improve Article Tags : Computer Vision AI-ML-DS OpenCV Similar Reads 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read Classification ProjectsWine Quality Prediction - Machine LearningHere we will predict the quality of wine on the basis of given features. We use the wine quality dataset available on Internet for free. This dataset has the fundamental features which are responsible for affecting the quality of the wine. 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