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

import cv2
import numpy as np

# 读取图像
image_path =r"E:\imgs\4.jpg"
image_bgr = cv2.imread(image_path)

# 全局变量
roi = []
drawing = False

def draw_rectangle(event, x, y, flags, param):
    global roi, drawing, image_bgr_copy

    if event == cv2.EVENT_LBUTTONDOWN:
        roi = [(x, y)]
        drawing = True

    elif event == cv2.EVENT_MOUSEMOVE:
        if drawing:
            image_bgr_copy = image_bgr.copy()
            cv2.rectangle(image_bgr_copy, roi[0], (x, y), (0, 0, 255), 2)
            cv2.imshow('Select ROI', image_bgr_copy)

    elif event == cv2.EVENT_LBUTTONUP:
        roi.append((x, y))
        drawing = False
        cv2.rectangle(image_bgr, roi[0], roi[1], (0, 0, 255), 2)
        cv2.imshow('Select ROI', image_bgr)

# 检查图像是否成功加载
if image_bgr is not None:
    image_bgr_copy = image_bgr.copy()
    cv2.imshow('Select ROI', image_bgr_copy)
    cv2.setMouseCallback('Select ROI', draw_rectangle)

    while True:
        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break

    cv2.destroyAllWindows()

    if len(roi) == 2:
        x1, y1 = roi[0]
        x2, y2 = roi[1]
        x, y, w, h = min(x1, x2), min(y1, y2), abs(x2 - x1), abs(y2 - y1)

        # 转换到 LAB 颜色空间
        image_lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
        cropped_lab = image_lab[y:y+h, x:x+w]

        # 计算裁剪区域的 LAB 颜色阈值下界和上界
        lower_red_threshold = np.min(cropped_lab.reshape(-1, 3), axis=0)
        upper_red_threshold = np.max(cropped_lab.reshape(-1, 3), axis=0)

        # 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]))

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