How to use Python to extract contours from pictures

How to use Python to extract contours from pictures

Introduction:
In the fields of image processing and computer vision, contour extraction is a very important task. It can separate the target object in the image from the background and extract the shape edge information of the object. This article will introduce how to use the OpenCV library in Python to extract contours from images.

1. Install the OpenCV library
   Before you start, you need to make sure that the OpenCV library has been installed. You can use the pip command to install:

   pip install opencv-python

2. Import the necessary libraries
   First, we need to import the OpenCV library and numpy library. OpenCV is used for image processing and numpy is used for array operations.

   import cv2
   import numpy as np

3. Loading images
   Use OpenCV’s imread function to load images. The sample image is saved locally, and the path can be modified according to the actual situation.

   image = cv2.imread('path/to/image.jpg')

4. Convert to grayscale image
   Since contour extraction is usually performed on grayscale images, the color image needs to be converted into grayscale images. This can be achieved using OpenCV’s cvtColor function.

   gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

5. Binarization using thresholding
   Contour extraction is often achieved by binarizing the image. You can use OpenCV's threshold function to convert a grayscale image into a binary image.

   _, threshold = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

6. Contour extraction
   Use OpenCV’s findContours function to perform contour extraction on binary images. This function returns the extracted contours and hierarchy.

   contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

7. Drawing Contours
   You can use OpenCV’s drawContours function to draw the extracted contours on the original image.

   cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

8. Display the results
   Finally use OpenCV’s imshow function to display the results.

   cv2.imshow('Contours', image)
   cv2.waitKey(0)
   cv2.destroyAllWindows()

Full code example:

import cv2
import numpy as np

# 加载图片
image = cv2.imread('path/to/image.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 二值化处理
_, threshold = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 轮廓提取
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

# 显示结果
cv2.imshow('Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion:
This article introduces how to use the OpenCV library in Python to extract contours from images. By performing grayscale conversion and binarization on the image and using the findContours function to extract the contours, the extracted contours can finally be drawn on the original image. Contour extraction can be widely used in image processing, computer vision, target detection and other fields, and is a very practical technology.

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