contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

这行代码使用 OpenCV 的 `findContours` 函数从二值化图像中提取轮廓。它接受三个参数：

- `binary`：二值化的输入图像。
- `cv2.RETR_EXTERNAL`：表示只提取最外层轮廓。
- `cv2.CHAIN_APPROX_SIMPLE`：表示压缩水平、垂直和对角线方向上相邻的轮廓点，只保留端点。

函数的返回值是一个包含两个元素的元组：轮廓列表和轮廓的层次结构。其中，轮廓列表是一个包含每个轮廓点坐标的 numpy 数组的列表，而轮廓的层次结构是一个 numpy 数组，描述了每个轮廓之间的关系。

相关问题

import cv2import numpy as np# 读取图像image = cv2.imread('工件图像.jpg')# 转换为灰度图像gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# 使用二值化处理获取缺陷图像ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)# 使用形态学操作进行缺陷的去噪和填充kernel = np.ones((3,3), np.uint8)closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)# 获取缺陷轮廓contours, hierarchy = cv2.findContours(opening, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 遍历每一个缺陷，提取几何特征并绘制位置for i in range(len(contours)): # 获取面积和周长 area = cv2.contourArea(contours[i]) perimeter = cv2.arcLength(contours[i], True) # 获取质心 moments = cv2.moments(contours[i]) cx = int(moments['m10'] / moments['m00']) cy = int(moments['m01'] / moments['m00']) # 绘制位置和特征 cv2.drawContours(image, [contours[i]], -1, (0, 255, 0), 2) cv2.circle(image,(cx,cy), 5, (0,0,255), -1) cv2.putText(image, "Area: {:.2f}".format(area), (cx+10, cy-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 2) cv2.putText(image, "Perimeter: {:.2f}".format(perimeter), (cx+10, cy+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 2)# 显示结果cv2.imshow('Defect Detection', image)cv2.waitKey(0)# 保存结果cv2.imwrite('Defect Detection.jpg', image)

这是Python中的两个库，cv2是OpenCV库，用于图像和视频处理，包括读取、写入、显示、处理、分析等。numpy是一个数学库，用于处理数字和数组。一般情况下，当我们读取图像时，使用cv2，而对图片进行处理时，使用numpy。

contours, _ = cv.findContours(binary1, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

This line of code uses the OpenCV library to find contours in a binary image.

- The first argument, "binary1", is the input image. It should be a binary image with white objects on a black background.
- The second argument, "cv.RETR_TREE", specifies the retrieval mode for contours. This mode retrieves all of the contours and reconstructs a full hierarchy of nested contours.
- The third argument, "cv.CHAIN_APPROX_SIMPLE", specifies the contour approximation method. This method compresses horizontal, vertical, and diagonal segments and leaves only their end points. For example, a rectangle contour would be represented by only 4 points instead of a series of connected lines.

The function returns two values:
- "contours" is a list of all the contours found in the image, each represented as a list of points.
- The second value is not assigned to anything, so it is discarded. It represents the hierarchy of contours, which is useful for understanding how contours are nested within each other.