# -*- coding: utf-8 -*-
# @File    : 识别QR颜色.py
# @Author  : len
# @Time    : 2024/1/14 20:46

import cv2
import numpy as np

def analyze_qrcode_color(image_path):
    # 读取图像
    image = cv2.imread(image_path)

    # 获取图像尺寸
    height, width, _ = image.shape

    # 计算中心四分之一区域的坐标
    x = width // 4
    y = height // 4
    w = width // 2
    h = height // 2

    # 提取二维码区域 (ROI)
    qrcode_roi = image[y:y+h, x:x+w]

    # 转换到 HSV 色彩空间
    hsv = cv2.cvtColor(qrcode_roi, cv2.COLOR_BGR2HSV)

    # 提高饱和度，以便非白色部分在阈值处理中更加明显
    h, s, v = cv2.split(hsv)
    s = cv2.add(s, 50)  # 增加饱和度

    # 应用阈值处理
    _, thresholded = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

    # 寻找阈值图像的轮廓
    contours, _ = cv2.findContours(thresholded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 在 ROI 上绘制轮廓
    # 注意：这里我们绘制的是彩色的轮廓，所以需要确保 qrcode_roi 是彩色的
    cv2.drawContours(qrcode_roi, contours, -1, (0, 255, 0), 2)


    # 显示二维码区域
    cv2.imshow("img", qrcode_roi)
    cv2.imshow("thresholded", thresholded)

    # 等待按键后关闭窗口
    cv2.waitKey(0)






    # 将ROI转换为HSV色彩空间
    hsv_roi = cv2.cvtColor(qrcode_roi, cv2.COLOR_BGR2HSV)

    # 计算ROI区域的平均颜色
    average_color_per_row = np.average(hsv_roi, axis=0)
    average_color = np.average(average_color_per_row, axis=0)
    average_color = np.uint8([[average_color]])

    # 将平均颜色转换回BGR色彩空间以便显示
    average_color_bgr = cv2.cvtColor(average_color, cv2.COLOR_HSV2BGR)
    average_color_bgr = average_color_bgr[0][0]



    # 创建一个显示平均颜色的小矩形
    color_image = np.zeros((50, 50, 3), np.uint8)
    color_image[:] = average_color_bgr

    # 显示平均颜色
    cv2.imshow("Average Color", color_image)

    # 等待按键后关闭窗口
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return average_color_bgr

# 使用示例
# image_path: 图像文件路径
image_path = r'D:\Code\Python\Embedded_game\QR\android_img\QR\9.jpg'

color = analyze_qrcode_color(image_path)
print("二维码的平均颜色为 (BGR):", color)