OpenCV轮廓提取并计算图片中某一封闭区域的面积
最近遇到一个问题，如果需要计算区域内的面积，比如说要计算下图中类似三角形区域内的面积 之前的做法是用Canny算子提取边缘，再用HoughLines拟合直线，然后求出交点坐标并计算三角形面积，其中，边缘提取后的图像如下图所示： 我们可以很明显的看出这不是一个标准的三角形，所以如果想要更精确的获得三角形，就需要对图片进行轮廓提取，然后计算轮廓内区域的面积。这里给出代码： {% codeblock %} import cv2 import numpy as np # Input image img = cv2.imread('cut.jpeg', cv2.IMREAD_GRAYSCALE) # Needed due to JPG artifacts _, temp = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY) # Dilate to better detect contours temp = cv2.dilate(temp, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))) # Find largest contour _, cnts, _ = cv2.findContours(temp, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) largestCnt = [] for cnt in cnts: if len(cnt) > len(largestCnt): largestCnt = cnt # Determine center of area of largest contour M = cv2.moments(largestCnt) x = int(M["m10"] / M["m00"]) y = int(M["m01"] / M["m00"]) # Initiale mask for flood filling width, height = temp.shape mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255 mask[1:width, 1:height] = 0 # Generate intermediate image, draw largest contour, flood filled temp = np.zeros(temp.shape, np.uint8) temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED) _, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255) temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))) # Count pixels in desired region area = cv2.countNonZero(temp) # Put result on original image img = cv2.putText(img, str(area), (x, y), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, 255) cv2.imshow('Input', img) cv2.imshow('Temp image', temp) cv2.waitKey(0) {% endcodeblock %} ...