德玛西亚99
这是改编自这篇博文的一种方法将图像转换为灰度获得二值图像的 Otsu 阈值计算欧几里得距离变换执行连通分量分析应用分水岭遍历标签值并提取对象这是结果在遍历每个轮廓时，您可以累积总面积1388903.5import cv2import numpy as npfrom skimage.feature import peak_local_maxfrom skimage.morphology import watershedfrom scipy import ndimage# Load in image, convert to gray scale, and Otsu's thresholdimage = cv2.imread('1.jpg')gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]# Compute Euclidean distance from every binary pixel# to the nearest zero pixel then find peaksdistance_map = ndimage.distance_transform_edt(thresh)local_max = peak_local_max(distance_map, indices=False, min_distance=20, labels=thresh)# Perform connected component analysis then apply Watershedmarkers = ndimage.label(local_max, structure=np.ones((3, 3)))[0]labels = watershed(-distance_map, markers, mask=thresh)# Iterate through unique labelstotal_area = 0for label in np.unique(labels): if label == 0: continue # Create a mask mask = np.zeros(gray.shape, dtype="uint8") mask[labels == label] = 255 # Find contours and determine contour area cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if len(cnts) == 2 else cnts[1] c = max(cnts, key=cv2.contourArea) area = cv2.contourArea(c) total_area += area cv2.drawContours(image, [c], -1, (36,255,12), 4)print(total_area)cv2.imshow('image', image)cv2.waitKey()