自己调了一套，比较好用，做一下记录

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import numpy as np
import cv2
debug=False # 调试的时候打开
def generate_distance(slice_content, bg_content):
    if debug:
        with open('slice.png', 'wb') as f:
            f.write(slice_content)
            f.close()
        with open('bg.png', 'wb') as f:
            f.write(bg_content)
            f.close()

    """
    :param bg_url: 背景图地址
    :param slice_url: 滑块图地址
    :return: distance
    :rtype: Integer
    """
    slice_image = np.asarray(bytearray(slice_content), dtype=np.uint8)
    slice_image = cv2.imdecode(slice_image, 1) 
    slice_image = cv2.Canny(slice_image, 22, 22) # 不准的话就调这几个参数，慢慢调

    bg_image = np.asarray(bytearray(bg_content), dtype=np.uint8)
    bg_image = cv2.imdecode(bg_image, 1)
    bg_image = cv2.pyrMeanShiftFiltering(bg_image, 20, 11)# 不准的话就调这几个参数，慢慢调
    bg_image = cv2.Canny(bg_image, 20, 20)# 不准的话就调这几个参数，慢慢调

    bg_image = cv2.cvtColor(bg_image, cv2.COLOR_GRAY2RGB)
    slice_image = cv2.cvtColor(slice_image, cv2.COLOR_GRAY2RGB)

    result = cv2.matchTemplate(bg_image, slice_image, cv2.TM_CCOEFF_NORMED)

    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
    if debug:
        th, tw = slice_image.shape[:2]
        tl = max_loc  # 左上角点的坐标
        br = (tl[0] + tw, tl[1] + th)  # 右下角点的坐标
        cv2.rectangle(bg_image, tl, br, (0, 0, 255), 2)  # 绘制矩形
        cv2.imwrite('out.jpg', bg_image)  # 保存在本地

    return [max_loc[0]]

另外可以把成功的图片做一下缓存记录，很多时候能利用上
我的方式：

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import redis
import hashlib
R = redis.Redis(host='127.0.0.1', port=6379, db=0,, decode_responses=True)

def md5(s):
    m = hashlib.md5()
    m.update(s.encode())
    return m.hexdigest()
    
if not R.exists(f'平台名字_{md5(str(bg_content))}'):
	代码省略...（处理图片并判断）
	R.set(f'平台名字_{md5(str(bg_content))}',value) # md5 自己实现 value是识别的结果 bg_content 是背景图的二进制流
else:
	value = R.get(f'平台名字_{md5(str(bg_content))}')

补充一个网上比较好用的

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import cv2
import numpy as np
import requests
def generate_distance(slice_url, bg_url):
    """
    :param bg_url: 背景图地址
    :param slice_url: 滑块图地址
    :return: distance
    :rtype: Integer
    """
    slice_image = np.asarray(bytearray(requests.get(slice_url).content), dtype=np.uint8)
    slice_image = cv2.imdecode(slice_image, 1)
    slice_image = cv2.Canny(slice_image, 255, 255)

    bg_image = np.asarray(bytearray(requests.get(bg_url).content), dtype=np.uint8)
    bg_image = cv2.imdecode(bg_image, 1)
    bg_image = cv2.pyrMeanShiftFiltering(bg_image, 5, 50)
    bg_image = cv2.Canny(bg_image, 255, 255)

    result = cv2.matchTemplate(bg_image, slice_image, cv2.TM_CCOEFF_NORMED)

    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

    return max_loc[0]