# -*- coding:utf-8 -*-
# @Author len
# @Create 2023/11/21 21:16

import cv2
import numpy as np

# 读取图像
image_path = 'xdl/1.png'  # 请替换为您图像的实际路径
image_bgr = cv2.imread(image_path)

# 检查图像是否成功加载
if image_bgr is not None:
    # 将图像从 BGR 颜色空间转换到 LAB 颜色空间
    image_lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)

    # 定义红框区域的坐标 (x, y, width, height)
    # 请用实际的红框坐标替换
    x, y, w, h = 280, 340, 60, 60  # 替换为红框的实际坐标

    # 裁剪 LAB 图像以获取红框内的区域
    cropped_lab = image_lab[y:y+h, x:x+w]

    # 计算裁剪区域的平均 LAB 值
    avg_lab = np.mean(cropped_lab.reshape(-1, 3), axis=0)

    # 定义阈值范围
    threshold_range = 10
    lower_red_threshold = np.clip(avg_lab - threshold_range, 0, 255)
    upper_red_threshold = np.clip(avg_lab + threshold_range, 0, 255)

    # 输出红色 LAB 颜色阈值
    lower = lower_red_threshold.astype(int)
    upper = upper_red_threshold.astype(int)
    print('红色的 LAB 颜色阈值下界:', lower)
    print('红色的 LAB 颜色阈值上界:', upper)
    print("{}, {}, {}, {}, {}, {}".format(lower[0], upper[0], lower[1], upper[1], lower[2], upper[2]))

    # 在原图像上绘制红框
    cv2.rectangle(image_bgr, (x, y), (x + w, y + h), (0, 0, 255), 2)

    # 显示图像
    cv2.imshow('Image with Red Rectangle', image_bgr)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    # 保存处理后的图像
    cv2.imwrite("image_with_red_rectangle.jpg", image_bgr)
else:
    print("图像无法加载。")