Python OpenCV 过点击式和滑动式图形验证码的校验

{
    "data": {
        "errorCode": 0,
        "errorMsg": "",
        "bg": "https://example.com/ENZg076503962.jpg",
        "front": "https://example.com/ENZg076503963.png",
        "token": "eyJhbGci...",
        "width": 765,
        "height": 396
    }
}

{
    "m": "[{"x":395,"y":256},{"x":89,"y":273},{"x":670,"y":140}]",
    "token": "eyJhbGci..."
}

rectangle_list = [[9, 9, 75, 75], [109, 9, 175, 75], [209, 9, 275, 75]]  
for idx, rectangle in enumerate(rectangle_list, start=1):  
    cropped_image = white_front[rectangle[1]:rectangle[3], rectangle[0]:rectangle[2]]

gray_bg = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)
gray_front = cv2.cvtColor(front, cv2.COLOR_BGR2GRAY)

res = cv2.matchTemplate(gray_front, gray_bg, cv2.TM_CCOEFF_NORMED)  
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
cv2.putText(bg, str(idx), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (7, 249, 151), 2)
show(bg)

gray_bg = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)  
_, strong_contrast_bg = cv2.threshold(gray_bg, 250, 255, cv2.THRESH_BINARY)

gray_bg = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)  
_, strong_contrast_bg = cv2.threshold(gray_bg, 250, 255, cv2.THRESH_BINARY)  
  
res = cv2.matchTemplate(  
    strong_contrast_bg,  
    cv2.bitwise_not(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)),  
    cv2.TM_CCOEFF_NORMED  
)  
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)  
  
x, y = max_loc  
x, y = x + 20, y + 20

import logging  
  
import cv2  
import numpy as np

def ProcessCaptcha(bg_path: str, front_path: str):  
    result = []  
  
    """  
    加载图像  
    背景为jpg格式的普通图像 像素为 765x396
    目标为png格式的包含透明通道图像 像素为 300x84    
    """    
    bg = cv2.imread(bg_path)  
    front = cv2.imread(front_path, cv2.IMREAD_UNCHANGED)  
  
    """  
    由于目标图为透明黑色图片，直接加载会导致图像全黑，  
    为了避免全黑情况，创建与图像尺寸相同的白色背景图像，再提取图像的透明度通道，将透明部分的像素值设置为白色
    这样加载完后的图像就变成了白底黑色目标的图像  
    """    
    white_front = np.ones_like(front) * 255  
    alpha_channel = front[:, :, 3]  
    white_front[:, :, 0:3] = cv2.bitwise_and(  
        white_front[:, :, 0:3],  
        white_front[:, :, 0:3],  
        mask=cv2.bitwise_not(alpha_channel)  
    )  
  
    """  
    为了按序点击，需要提取目标区域的矩形方块  
    由于目标图像较为规律有序，于是计算出3个目标图像像素坐标，直接按像素截取  
    """
    rectangle_list = [[9, 9, 75, 75], [109, 9, 175, 75], [209, 9, 275, 75]]  
    for rectangle in rectangle_list:  
        cropped_image = white_front[rectangle[1]:rectangle[3], rectangle[0]:rectangle[2]]  
  
        """  
        将背景图片转换为黑白两色的图片，只保留RGB(250-255)的图像  
        如此能保留绝大部分目标图像轮廓
        将目标图像转换为黑色背景白色轮廓  
        如此便与背景的颜色逻辑一致  
        """
        gray_bg = cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY)  
        _, strong_contrast_bg = cv2.threshold(gray_bg, 250, 255, cv2.THRESH_BINARY)  
        strong_contrast_front = cv2.bitwise_not(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY))  
  
        res = cv2.matchTemplate(  
            strong_contrast_bg,  
            strong_contrast_front,  
            cv2.TM_CCOEFF_NORMED  
        )  
        """  
        使用 TM_CCOEFF_NORMED 算法匹配到最佳  
        由于此时的 X,Y 坐标为左上角坐标，需要加20进行偏移处理，获取到点击坐标  
        """
        x, y = cv2.minMaxLoc(res)[3]
        result.append((x + 20, y + 20))  
  
    logging.info(f"Done process: "+str(result).replace('n', ' '))  
    return result

def process(bg_path: str, front_path: str) -> None:  
  
    # flags0是灰度模式  
    image = cv2.imread(front_path, 0)  
    template = cv2.imread(bg_path, 0)  
    template = cv2.resize(template, (340, 191), interpolation=cv2.INTER_CUBIC)  
  
    # 寻找最佳匹配  
    res = cv2.matchTemplate(_tran_canny(image), _tran_canny(template), cv2.TM_CCOEFF_NORMED)  
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)  
  
    x, y = max_loc  
    w, h = image.shape[::-1]  
    cv2.rectangle(template, (x, y), (x + w, y + h), (7, 249, 151), 2)  
    show(template)