#做自动化/测试卡滑动缺口？试试这篇2023优化的OpenCV方案

import cv2
import numpy as np
from typing import Optional, Tuple

class SlideCaptchaSolver:
    def __init__(self, debug: bool = False):
        self.debug = debug

    def solve(self, image_path: str) -> Optional[int]:
        """
        识别滑动验证码缺口的起始X坐标
        :param image_path: 验证码原图路径（必须是有缺口的完整图，不是缺口+背景分离的）
        :return: 缺口起始X坐标（像素），识别失败返回None
        """
        # 1. 读取图像并校验
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError(f"无法加载图像：{image_path}")
        img_h, img_w = image.shape[:2]

        # 2. 2023优化版预处理：偏暗/偏亮自适应增强+高斯模糊降噪
        processed = self._preprocess(image)

        # 3. 边缘检测+形态学闭运算填充轮廓缝隙
        edges = self._edge_detection(processed)

        # 4. 查找轮廓+按2023主流规则筛选
        contours, _ = cv2.findContours(edges, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
        targets = self._filter_contours(contours, img_w, img_h)

        if not targets:
            print("未找到符合条件的缺口！")
            return None

        # 取外接矩形面积最大的那个（最可能是缺口）
        target = sorted(targets, key=lambda x: x[2] * x[3], reverse=True)[0]
        x, _, w, _ = target

        if self.debug:
            self._debug_show(image, edges, target)

        return x + w // 2  # 通常返回缺口中心点X坐标，更贴合拖动需求

    def _preprocess(self, image: np.ndarray) -> np.ndarray:
        """预处理：自适应亮度增强+高斯模糊"""
        # 转LAB颜色空间（L通道是亮度，A/B是颜色，处理亮度不会影响颜色）
        lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
        l, a, b = cv2.split(lab)

        # 2023推荐参数：clipLimit=3.0（对比度别太高，避免产生噪点）
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
        l_enhanced = clahe.apply(l)

        # 合并LAB通道+转回BGR
        merged = cv2.merge([l_enhanced, a, b])
        bgr_enhanced = cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)

        # 高斯模糊：(5,5)核，刚好过滤高频噪点又保留边缘
        return cv2.GaussianBlur(bgr_enhanced, (5, 5), 0)

    def _edge_detection(self, image: np.ndarray) -> np.ndarray:
        """边缘检测+形态学闭运算填充缝隙"""
        # Canny参数：100低阈值，200高阈值（常规缺口通用）
        edges = cv2.Canny(image, 100, 200)

        # 形态学闭运算：(5,5)矩形核，填充缺口轮廓的小断裂
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
        return cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)

    def _filter_contours(
        self, contours: list, img_w: int, img_h: int
    ) -> list[Tuple[int, int, int, int]]:
        """按2023主流缺口规则筛选轮廓"""
        targets = []
        right_area_start = img_w * 0.5  # 只看右侧50%的区域

        for cnt in contours:
            # 计算外接矩形
            x, y, w, h = cv2.boundingRect(cnt)
            # 过滤左侧区域
            if x < right_area_start:
                continue
            # 过滤宽高比太奇怪的
            aspect_ratio = w / h
            if not (0.8 < aspect_ratio < 1.2):  # 现在主流缺口更接近正方形
                continue
            # 过滤尺寸太小/太大的
            if not (0.1 * img_w < w < 0.3 * img_w):
                continue
            # 过滤填充度太低/太高的（排除文字、实心块）
            area = cv2.contourArea(cnt)
            rect_area = w * h
            extent = area / rect_area
            if not (0.6 < extent < 0.9):
                continue

            targets.append((x, y, w, h))

        return targets

    def _debug_show(self, image: np.ndarray, edges: np.ndarray, target: Tuple[int, int, int, int]) -> None:
        """调试模式：展示原图、边缘图、带框目标图"""
        x, y, w, h = target
        # 在原图上画矩形+中心点
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.circle(image, (x + w // 2, y + h // 2), 5, (0, 0, 255), -1)
        # 展示
        cv2.imshow("Debug-Original", image)
        cv2.imshow("Debug-Edges", edges)
        cv2.waitKey(0)
        cv2.destroyAllWindows()


if __name__ == "__main__":
    # 测试
    solver = SlideCaptchaSolver(debug=True)
    result = solver.solve("test_captcha.png")
    print(f"缺口中心点X坐标：{result}")

# 在solve函数开头加
scale = 0.5  # 根据原图尺寸调整
if max(img_w, img_h) > 600:
    image = cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
    img_w, img_h = img_w * scale, img_h * scale  # 筛选参数也要同步缩放