python – OpenCV：如何正确应用morphologyEx操作？

[weixin_38061608]

我遇到了有关topologyEx的内核大小的问题.我有一些验证码图像,我想对它们进行相同的操作,并获得相同的最终结果. 代码： image = cv2.imread("Image.jpg") gray = cv2.cvtColor(image , cv2.COLOR_BGR2GRAY) ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) k1 = np.ones((3,3)) k2 = np.ones((5,5)) bottom_image = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, k1) bottom_image = 255-bottom_image bottom_image = remove_boxes(bottom_image , True) ret,thresh2 = cv2.threshold(bottom_image,127,255,cv2.THRESH_BINARY_INV) opening = cv2.morphologyEx(thresh2, cv2.MORPH_OPEN, k1) #closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, k) # cv2.imshow('opening', opening) dilate = cv2.morphologyEx(opening, cv2.MORPH_DILATE, k2) dilate = cv2.bitwise_not(dilate) # cv2.imshow('dilation', dilate) bottom_image = cv2.morphologyEx(bottom_image, cv2.MORPH_CLOSE, k1) 完美的结果将是 输入： 输出： 但是当我将其应用于具有相同结构输出的其他图像时出现问题. 例1： 输入： 输出： 例2： 输入： 输出： 例3： 输入： 输出：

[weixin_38065414]

这个答案的灵感来自于这个excellent post. import numpy as np import cv2 if __name__ == '__main__': image = cv2.imread('image.png',cv2.IMREAD_UNCHANGED) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ret,binary = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY) binary = cv2.bitwise_not(binary) H = cv2.Sobel(binary, cv2.CV_8U, 0, 2) V = cv2.Sobel(binary, cv2.CV_8U, 2, 0) rows,cols = image.shape[:2] _,contours,_ = cv2.findContours(V, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: (x,y,w,h) = cv2.boundingRect(cnt) # rows/3 is the threshold for length of line if h > rows/3: cv2.drawContours(V, [cnt], -1, 255, -1) cv2.drawContours(binary, [cnt], -1, 255, -1) else: cv2.drawContours(V, [cnt], -1, 0, -1) _,contours,_ = cv2.findContours(H, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: (x,y,w,h) = cv2.boundingRect(cnt) # cols/3 is the threshold for length of line if w > cols/3: cv2.drawContours(H, [cnt], -1, 255, -1) cv2.drawContours(binary, [cnt], -1, 255, -1) else: cv2.drawContours(H, [cnt], -1, 0, -1) kernel = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(3,3)) H = cv2.morphologyEx(H, cv2.MORPH_DILATE, kernel,iterations = 3) V = cv2.morphologyEx(V, cv2.MORPH_DILATE, kernel, iterations = 3) cross = cv2.bitwise_and(H, V) _,contours,_ = cv2.findContours(cross,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) centroids = [] for cnt in contours: mom = cv2.moments(cnt) (x,y) = mom['m10']/mom['m00'], mom['m01']/mom['m00'] cv2.circle(image,(int(x),int(y)),4,(0,255,0),-1) centroids.append((x,y)) centroids.sort(key = lambda x: x[0], reverse = False) centroids.sort(key = lambda x: x[1], reverse = False) dx = int(centroids[1][0] - centroids[0][0]) centroids = np.array(centroids, dtype = np.float32) (x,y,w,h) = cv2.boundingRect(centroids) if x-dx > -5: x = max(x-dx,0) if h+dx <= rows+5: h = min(h+dx,rows) if w+dx <= cols+5: w = min(w+dx,cols) cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,0)) roi = binary[y:y+h,x:x+w] roi = cv2.morphologyEx(roi, cv2.MORPH_OPEN, kernel,iterations = 1) cv2.imshow('image', image) cv2.imshow('roi', roi) cv2.waitKey(0) cv2.destroyAllWindows()