视觉/图像重磅干货，第一时间送达
导 读
本文主要介绍基于OpenCV的两种不同方法实现粘连大米分割计数，并给详细步骤和代码。
背景介绍
测试图如下，图中有个别米粒相互粘连，本文主要演示如何使用OpenCV用两种不同方法将其分割并计数。
方法一：基于分水岭算法
基于分水岭算法分割步骤如下：
【1】高斯滤波 + 二值化 + 开运算
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)gray = cv2.GaussianBlur(gray,(5,5),0)ret, binary= cv2.threshold(gray, 115, 255, cv2.THRESH_BINARY)kernel = np.ones((5, 5), np.uint8)binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=1)cv2.imshow('thres', binary)
【2】距离变换 + 提取前景
dist = cv2.distanceTransform(binary, cv2.DIST_L2, 3)dist_out = cv2.normalize(dist, 0, 1.0, cv2.NORM_MINMAX)cv2.imshow('distance-Transform', dist_out * 100)ret, surface = cv2.threshold(dist_out, 0.35*dist_out.max(), 255, cv2.THRESH_BINARY)cv2.imshow('surface', surface)sure_fg = np.uint8(surface)# 转成8位整型cv2.imshow('Sure foreground', sure_fg)
【3】标记位置区域
# 未知区域标记为0markers[unknown == 255] = 0kernel = np.ones((5, 5), np.uint8)binary = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel, iterations=1)unknown = binary - sure_fgcv2.imshow('unknown',unknown)
【4】分水岭算法分割
markers = cv2.watershed(img, markers=markers)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(markers)
【5】轮廓查找和标记
contours,hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt in contours:M = cv2.moments(cnt)cx = int(M['m10']/M['m00'])cx = int(M['m10']/M['m00'])cy = int(M['m01']/M['m00'])cv2.drawContours(img,contours,-1,colors[rd.randint(0,5)],2)cv2.drawMarker(img, (cx,cy),(0,255,0),1,8,2)
方法二：轮廓凸包缺陷方法
基于轮廓凸包缺陷分割步骤如下：
【1】高斯滤波 + 二值化 + 开运算
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)gray = cv2.GaussianBlur(gray,(5,5),0)ret, binary= cv2.threshold(gray, 115, 255, cv2.THRESH_BINARY)kernel = np.ones((5, 5), np.uint8)binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=1)cv2.imshow('thres', binary)
【2】轮廓遍历 + 筛选轮廓含有凸包缺陷的轮廓
contours,hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt in contours:hull = cv2.convexHull(cnt,returnPoints=False)#默认returnPoints=Truedefects = cv2.convexityDefects(cnt,hull)#print defectspt_list = []if defects is not None:flag = Falsefor i in range(0,defects.shape[0]):s,e,f,d = defects[i,0]if d > 4500:flag = True
【3】将距离d最大的两个凸包缺陷点连起来，将二值图中对应的粘连区域分割开，红色圆标注为分割开的部分
if len(pt_list) > 0:cv2.line(binary,pt_list[0],pt_list[1],0,2)cv2.imshow('binary2',binary)
【4】重新查找轮廓并标记结果
contours,hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt in contours:try:M = cv2.moments(cnt)cx = int(M['m10']/M['m00'])cx = int(M['m10']/M['m00'])cy = int(M['m01']/M['m00'])cv2.drawContours(img,cnt,-1,colors[rd.randint(0,5)],2)cv2.drawMarker(img, (cx,cy),(0,0,255),1,8,2)except:pass
—THE END—
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