import cv2
import numpy as np
import base64
from io import BytesIO
from PIL import Image

def base64_to_cv2(base64_str: str):
    """
    将 base64 字符串解码为 OpenCV 图像
    """
    image_data = base64.b64decode(base64_str)
    image = Image.open(BytesIO(image_data)).convert('RGB')
    return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)


def get_slide_offset_from_base64(bg_base64: str, slider_base64: str, debug=False) -> int:
    """
    从 Base64 图像中计算滑动距离
    """
    # 解码 base64 为图像
    bg_img = base64_to_cv2(bg_base64)
    slider_img = base64_to_cv2(slider_base64)

    # 转灰度图
    bg_gray = cv2.cvtColor(bg_img, cv2.COLOR_BGR2GRAY)
    slider_gray = cv2.cvtColor(slider_img, cv2.COLOR_BGR2GRAY)

    # 边缘检测
    bg_edge = cv2.Canny(bg_gray, 100, 200)
    slider_edge = cv2.Canny(slider_gray, 100, 200)

    # 模板匹配
    result = cv2.matchTemplate(bg_edge, slider_edge, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, max_loc = cv2.minMaxLoc(result)

    x, y = max_loc
    if debug:
        print(f"[DEBUG] 最大匹配值: {max_val:.4f} 位置: ({x}, {y})")
        h, w = slider_gray.shape
        vis = bg_img.copy()
        cv2.rectangle(vis, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.imshow("Match Result", vis)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    return x