自学教程：python3 使用OpenCV计算滑块拼图验证码缺口位置(场景示例)

import cv2# 作者-上海悠悠 QQ交流群:717225969# blog地址 https://www.cnblogs.com/yoyoketang/def show(name):    '''展示圈出来的位置'''    cv2.imshow('Show', name)    cv2.waitKey(0)    cv2.destroyAllWindows()def _tran_canny(image):    """消除噪声"""    image = cv2.GaussianBlur(image, (3, 3), 0)    return cv2.Canny(image, 50, 150)def detect_displacement(img_slider_path, image_background_path):    """detect displacement"""    # # 参数0是灰度模式    image = cv2.imread(img_slider_path, 0)    template = cv2.imread(image_background_path, 0)    # 寻找最佳匹配    res = cv2.matchTemplate(_tran_canny(image), _tran_canny(template), cv2.TM_CCOEFF_NORMED)    # 最小值，最大值，并得到最小值, 最大值的索引    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)    top_left = max_loc[0]  # 横坐标    # 展示圈出来的区域    x, y = max_loc  # 获取x,y位置坐标    w, h = image.shape[::-1]  # 宽高    cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)    show(template)    return top_leftif __name__ == '__main__':    top_left = detect_displacement("target.png", "background.png")    print(top_left)

# 展示圈出来的区域  #  x, y = max_loc  # 获取x,y位置坐标  #  w, h = image.shape[::-1]  # 宽高  #  cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)  #  show(template)

#coding=utf-8import cv2import numpy as npfrom PIL import Image def get_element_slide_distance():  otemp = 'captcha2.png' oblk = 'captcha1.png' target = cv2.imread(otemp, 0) # 读取进行色度图片，转换为numpy中的数组类型数据 template = cv2.imread(oblk, 0) width, height = target.shape[::-1] # 获取缺口图数组的形状 -->缺口图的宽和高 temp = 'temp.jpg' # 将处理之后的图片另存 targ = 'targ.jpg' cv2.imwrite(temp, template) cv2.imwrite(targ, target) target = cv2.imread(targ) # 读取另存的滑块图 target = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY) # 进行色彩转换 # 去除白色部分 获取滑块正常大小 target = target[target.any(1)]  target = abs(255 - target) # 获取色差的绝对值 cv2.imwrite(targ, target) # 保存图片 target = cv2.imread(targ) # 读取滑块 template = cv2.imread(temp) # 读取背景图 result = cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED) # 比较两张图的重叠区域 top, left = np.unravel_index(result.argmax(), result.shape) # 获取图片的缺口位置 #缺口位置 print((left, top, left + width, top + height)) # 背景图中的图片缺口坐标位置  #调用PIL Image 做测试 image = Image.open("captcha1.png")  rectangle = (left + 3, top + 3, left + width - 3, top + height - 3) #去掉白色块的影响(上面去掉白色部分的功能并没有真的起作用) #切割 imagecrop = image.crop(rectangle) #保存切割的缺口 imagecrop.save("new_image.jpg")  return left+3 distance = get_element_slide_distance()# 滑动距离误差校正，滑动距离*图片在网页上显示的缩放比-滑块相对的初始位置distance = distance*(280/680) - 22

def generate_tracks1(XCoordinates): element = browser.find_element_by_xpath("//div[@class='secsdk-captcha-drag-icon sc-jKJlTe fsBatO']") ActionChains(browser).click_and_hold(on_element = element).perform() # # ActionChains(browser).move_by_offset(xoffset=0, yoffset=y - 445).perform() # # time.sleep(0.15) # print("第二步，拖动元素") distance = XCoordinates - 60 # 初速度 v = 0 # 单位时间为0.2s来统计轨迹，轨迹即0.2内的位移,越低看起来越丝滑！！ t = 0.08 # 位移/轨迹列表，列表内的一个元素代表0.2s的位移 tracks = [] # 当前的位移 current = 0 # 到达mid值开始减速 mid = distance * 5 / 8  distance += 10  # 先滑过一点，最后再反着滑动回来 # a = random.randint(1,3) while current < distance:  if current < mid:   # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细   a = random.randint(100, 200)  # 加速运动  else:   a = -random.randint(2, 10)  # 减速运动   # 初速度  v0 = v  # 0.2秒时间内的位移  s = v0 * t + 0.5 * a * (t ** 2)  # 当前的位置  current += s  # 添加到轨迹列表  tracks.append(round(s))   # 速度已经达到v,该速度作为下次的初速度  v = v0 + a * t  if current > distance:   break  # 反着滑动到大概准确位置 # for i in range(4): #     tracks.append(-random.randint(1, 3)) # for i in range(4): #    tracks.append(-random.randint(1,3)) random.shuffle(tracks) count = 0 for item in tracks:  print(item)  count += item  ActionChains(browser).move_by_offset(xoffset = item, yoffset = random.randint(-2, 2)).perform()  # ActionChains(browser).move_to_element_with_offset(to_element=element, xoffset=XCoordinates-18,yoffset=y - 445).perform() # time.sleep(2) # # 释放鼠标 print(count) ActionChains(browser).release(on_element = element).perform()