import os
import time
import cv2
import numpy as np
from PIL import Image


# 工具类
class AiTools(object):

    @classmethod
    def find_image_in_image(
            cls,
            smallImageUrl,
            bigImageUrl,
            match_threshold=0.90,
            consecutive_required=3,
            scales=None
    ):

        if scales is None:
            scales = [0.5, 0.75, 1.0, 1.25, 1.5]

        template = cv2.imread(smallImageUrl, cv2.IMREAD_COLOR)
        # if template is None:
        #     raise Exception(f"❌ 无法读取模板 '{smallImageUrl}'")

        template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)

        cap = cv2.imread(bigImageUrl, cv2.IMREAD_COLOR)
        # if not cap.isOpened():
        #     print(f"❌ 无法打开视频流: {bigImageUrl}")
        #     return None

        detected_consecutive_frames = 0

        print("🚀 正在检测爱心图标...")
        while True:
            print("死了")
            ret, frame = cap.read()
            if not ret or frame is None:
                time.sleep(0.01)
                continue
            print("哈哈哈")
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            current_frame_has_match = False
            best_match_val = 0
            best_match_loc = None
            best_match_w_h = None
            print("aaaaaaaaaaaa")
            for scale in scales:
                resized_template = cv2.resize(template_gray, (0, 0), fx=scale, fy=scale)
                th, tw = resized_template.shape[:2]

                if th > frame_gray.shape[0] or tw > frame_gray.shape[1]:
                    continue

                result = cv2.matchTemplate(frame_gray, resized_template, cv2.TM_CCOEFF_NORMED)
                _, max_val, _, max_loc = cv2.minMaxLoc(result)

                if max_val > best_match_val:
                    best_match_val = max_val
                    best_match_loc = max_loc
                    best_match_w_h = (tw, th)
                if max_val >= match_threshold:
                    current_frame_has_match = True
                    print("break 了")
                    break
            print("bbbbbbbbbbbbbbbbbbbbbb")
            if current_frame_has_match:
                print("111111")
                detected_consecutive_frames += 1
                last_detection_info = (best_match_loc, best_match_w_h, best_match_val)
            else:
                print("2222222")
                detected_consecutive_frames = 0
                last_detection_info = None

            if detected_consecutive_frames >= consecutive_required and last_detection_info:
                print("333333333")
                top_left, (w, h), match_val = last_detection_info
                center_x = top_left[0] + w // 2
                center_y = top_left[1] + h // 2

                print(f"🎯 成功识别爱心图标: 中心坐标=({center_x}, {center_y}), 匹配度={match_val:.4f}")
                return center_y, center_y
            else:
                return -1, -1
        cap.release()
        print("释放了")
        return -1, -1



    @classmethod
    def imagePath(cls, name):
        current_file_path = os.path.abspath(__file__)
        # 获取当前文件所在的目录（即script目录）
        current_dir = os.path.dirname(current_file_path)
        # 由于script目录位于项目根目录下一级，因此需要向上一级目录移动两次
        project_root = os.path.abspath(os.path.join(current_dir, '..'))
        # 构建资源文件的完整路径，向上两级目录，然后进入 resources 目录
        resource_path = os.path.abspath(os.path.join(project_root, 'resources', name + ".png")).replace('/', '\\\\')
        return resource_path