滑块验证码，滑块和有缺口的背景
突破滑动验证码有缺口的背景内容
import cv2 as cv
import numpy as np
import osdef find_p(img):ret, thresh = cv.threshold(img, 127, 255, 0)img = cv.cvtColor(img, cv.COLOR_GRAY2BGR)contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)# print(len(contours))cnt = contours[0]x, y, w, h = cv.boundingRect(cnt)img = cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)# cv.imshow('7', img)return x, y, w, h, thresh[y: y + h, x: x + w]def tmp_match(img, tar):w, h = tar.shape[0:-1]# methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR',# 'cv.TM_CCORR_NORMED', 'cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED']method = cv.TM_CCOEFF_NORMED# Apply template Matchingres = cv.matchTemplate(img, tar, method)min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimumtop_left = max_locbottom_right = (top_left[0] + w, top_left[1] + h)cv.rectangle(img, top_left, bottom_right, 255, 2)# cv.imshow('5', img)return top_leftdef sub_img_op(pth, pth1):img = cv.imread(pth, -1)img_tar = cv.imread(pth1)sub_img = img[:, :, 3]img_o = cv.cvtColor(img, cv.COLOR_BGRA2BGR)# sub_img = cv.cvtColor(sub_img, cv.GRAY2BGR)# print(sub_img[50:70, :])x, y, w, h, mask = find_p(sub_img)white_img = np.ones([h, w, 3], dtype='uint8') * 255# print(x, y, w, h)img_tar_sub = img_tar[y: y + h, :, :]img_o_sub = img_o[y: y + h, x: x + w, :]res_img = cv.addWeighted(img_o_sub, 0.7, white_img, 0.3, 0)# res_img = img_o_subtop_left = tmp_match(img_tar_sub, res_img)print(top_left[0])# cv.imshow('1', img_o)cv.imshow('2', img_tar_sub)cv.imshow('3', res_img)cv.waitKey()cv.destroyAllWindows()return top_left[0]
