opencv识别微信登录验证滑动块位置

# 读取截图
screenshot = cv2.imread('screenshot.jpg')
# 筛选出符合颜色区间的区域
inRange = cv2.inRange(screenshot, np.array([90, 90, 90]), np.array([115, 115, 115]))
# 从图中找出所有的轮廓
_, cnts, _ = cv2.findContours(inRange.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 对所有轮廓做排序，排序依据是每个轮廓包含的点的数量
cnts.sort(key=len, reverse=True)
# 取前两个轮廓(有些图片目标位置不一定是第一个轮廓)
for cnt in cnts[0: 2]:
  xSum = 0
  xCounter = 0
  for position in cnt:
    xCounter += 1
    xSum += position[0][0]
  # 算出所有点的X坐标平均值，并在此基础上做一个60像素的偏移，这个偏移可以根据自己手机进行调整
  x = int(xSum / xCounter - 60)
  # 在截图上画一条红线，表示识别的x坐标位置
  cv2.line(screenshot, (x, 0), (x, 500), (0, 0, 255), 5)
cv2.imshow("screenshot", screenshot)
cv2.waitKey(0)

# 读取滑块图片，并给其加上相同的灰黑色透明前景色，再进行灰化
block = cv2.imread('block.jpg')
blockCopy = block.copy()
w, h = block.shape[:-1]
cv2.rectangle(blockCopy, (0, 0), (w, h), (47, 47, 47), -1)
cv2.addWeighted(blockCopy, 0.7, block, 0.3, 0, block)
block = cv2.cvtColor(block, cv2.COLOR_RGB2GRAY)
# 读取验证码图片，并灰化
captcha = cv2.imread('captcha.jpg')
captchaGray = cv2.cvtColor(captcha, cv2.COLOR_RGB2GRAY)
# 寻找captcha中匹配block的位置
res = cv2.matchTemplate(captchaGray, block, cv2.TM_SQDIFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 在最符合的画一个矩形
cv2.rectangle(captcha, min_loc, (min_loc[0] + w, min_loc[1] + h), (0, 0, 255), -1)
cv2.imshow('block', block)
cv2.imshow("captcha", captcha)
cv2.waitKey(0);