# coding=utf-8
import os
import sys
import cv2
import numpy as np

# 将项目根目录添加到 sys.path
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
if project_root not in sys.path:
    sys.path.append(project_root)

from Apps.TeLaiDian import Kit

def detect_price_areas_cv(image_path):
    """
    专门用于详情页识别价格点击区域的测试函数
    """
    img = Kit.read_image(image_path)
    if img is None:
        print("错误: 无法读取图片")
        return []
    
    h, w = img.shape[:2]
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # 1. 转换为 HSV 空间，方便寻找橘红色价格
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # 橘红色的 HSV 范围 (大致)
    lower_orange = np.array([0, 150, 150])
    upper_orange = np.array([20, 255, 255])
    mask = cv2.inRange(hsv, lower_orange, upper_orange)
    
    # 2. 对掩码进行膨胀，连接数字
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 20))
    dilated = cv2.dilate(mask, kernel)
    
    # 3. 寻找轮廓
    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    detected_areas = []
    
    for cnt in contours:
        x, y, cw, ch = cv2.boundingRect(cnt)
        
        # 橘红色价格通常在屏幕上半部，且宽度适中
        if 150 < y < h * 0.6 and cw > 100:
            # 我们找到了价格数字。用户说要点击的是这个区域。
            # 我们可以适当扩大这个区域，或者直接取其中心。
            padding = 20
            detected_areas.append([
                max(0, x - padding), 
                max(0, y - padding), 
                min(w, x + cw + padding), 
                min(h, y + ch + padding)
            ])

    # 按 X 轴排序，找到最左边的价格
    detected_areas.sort(key=lambda b: b[0])
    return detected_areas

def run_test(image_path):
    print(f"分析图片: {image_path}")
    
    # 获取所有可能的区域
    bboxes = detect_price_areas_cv(image_path)
    print(f"检测到 {len(bboxes)} 个潜在区域:")
    for i, box in enumerate(bboxes):
        print(f"  区域 {i}: {box} (w={box[2]-box[0]}, h={box[3]-box[1]})")
    
    # 生成 _vl.jpg (仅框)
    vl_path = image_path.replace(".jpg", "_vl.jpg")
    img_vl = Kit.read_image(image_path)
    for i, box in enumerate(bboxes):
        cv2.rectangle(img_vl, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
        cv2.putText(img_vl, str(i), (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
    Kit.save_image(vl_path, img_vl)
    print(f"已生成标注图: {vl_path}")

    # 生成 _flag.jpg (框 + 中心点)
    flag_path = image_path.replace(".jpg", "_flag.jpg")
    img_flag = Kit.read_image(image_path)
    for i, box in enumerate(bboxes):
        cv2.rectangle(img_flag, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
        center_x = (box[0] + box[2]) // 2
        center_y = (box[1] + box[3]) // 2
        cv2.circle(img_flag, (center_x, center_y), 10, (0, 0, 255), -1)
        cv2.putText(img_flag, f"P{i}", (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
    Kit.save_image(flag_path, img_flag)
    print(f"已生成人工核对图: {flag_path}")

if __name__ == "__main__":
    test_image = r"d:\dsWork\aiData\Output\Screenshot_20260114_075758.jpg"
    if os.path.exists(test_image):
        run_test(test_image)
    else:
        print(f"错误: 找不到测试图片 {test_image}")