# encoding:utf-8
import cv2 as cv
import numpy as np


# 對滑塊進行二值化處理
def handle_img1(image):
    kernel = np.ones((8, 8), np.uint8)  # 去滑塊的前景噪聲內(nèi)核
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    width, heigth = gray.shape
    for h in range(heigth):
        for w in range(width):
            if gray[w, h] == 0:
                gray[w, h] = 96
    # cv.imshow("gray", gray)
    binary = cv.inRange(gray, 96, 96)
    res = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel)  # 開運算去除白色噪點
    # cv.imshow("res", res)
    return res


# 模板匹配(用于尋找缺口有點誤差)
def template_match(img_target, img_template):
    tpl = handle_img1(img_template)  # 誤差來源就在于滑塊的背景圖為白色
    blurred = cv.GaussianBlur(img_target, (3, 3), 0)  # 目標圖高斯濾波
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    ret, target = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)  # 目標圖二值化
    # cv.imshow("template", tpl)
    # cv.imshow("target", target)
    method = cv.TM_CCOEFF_NORMED
    width, height = tpl.shape[:2]
    result = cv.matchTemplate(target, tpl, method)
    min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)
    left_up = max_loc
    right_down = (left_up[0] + height, left_up[1] + width)
    cv.rectangle(img_target, left_up, right_down, (0, 0, 255), 2)
    cv.imshow("res", img_target)


if __name__ == "__main__":
    img0 = cv.imread("./demo/3/hycdn_3.jpg")
    img1 = cv.imread("./demo/3/hycdn_3_2.png")
    template_match(img0, img1)
    cv.waitKey(0)
    cv.destroyAllWindows()

輪廓檢測識別缺口
基于輪廓檢測缺口的思路簡單很多，加上合理的條件識別率在95%以上，實現(xiàn)過程如下：

帶缺口圖高斯模糊去噪

用(200,400)的閾值做Canny邊緣檢測

尋找輪廓

對已有的輪廓做約束，比如輪廓的面積范圍，輪廓的周長范圍

多個匹配結(jié)果如下：

實現(xiàn)代碼如下：

# encoding:utf-8
import cv2 as cv


def get_pos(image):
    blurred = cv.GaussianBlur(image, (5, 5), 0)
    canny = cv.Canny(blurred, 200, 400)
    contours, hierarchy = cv.findContours(canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for i, contour in enumerate(contours):
        M = cv.moments(contour)
        if M["m00"] == 0:
            cx = cy = 0
        else:
            cx, cy = M["m10"] / M["m00"], M["m01"] / M["m00"]
        if 6000 < cv.contourArea(contour) < 8000 and 370 < cv.arcLength(contour, True) < 390:
            if cx < 400:
                continue
            x, y, w, h = cv.boundingRect(contour)  # 外接矩形
            cv.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv.imshow("image", image)
            return x
    return 0


if __name__ == "__main__":
    img0 = cv.imread("./demo/4/hycdn_4.jpg")
    get_pos(img0)
    cv.waitKey(0)
    cv.destroyAllWindows()

問題1
如何將滑塊圖的純白背景變?yōu)橥该鞅尘埃?/p>

問題2
使用Selenium和軌跡算法拖動滑塊時將滑塊拖出左側(cè)的范圍之外，軌跡算法是先加速后減速整體是向前移動的，按道理來說不可能往回走，但是模擬拖動的時候會出現(xiàn)滑塊向后拖動且拖出范圍的現(xiàn)象，這問題如何解決？

有知道上述問題如何解決的小伙伴，期待你的留言或評論?。?！