# -*- coding: utf-8 -*-
# @Time    : 2024/3/12 21:03
# @Author  : len
# @File    : QR矫正.py
# @Software: PyCharm 
# @Comment :
import cv2
import numpy as np
from matplotlib import pyplot as plt


# 读取原图
srcImagePath = r"./android_img/test/img_1.png"
srcImage = cv2.imread(srcImagePath)
srcImage = cv2.resize(srcImage, (500, 500))  # 调整图像大小

# 调整亮度和对比度
alpha = 1.8  # 对比度因子
beta = -30  # 亮度因子
contrastImage = cv2.convertScaleAbs(srcImage, alpha=alpha, beta=beta)

# 转换为灰度图
grayImage = cv2.cvtColor(contrastImage, cv2.COLOR_BGR2GRAY)

# 双边滤波
filterImage = cv2.bilateralFilter(grayImage, 13, 26, 6)

# 二值化
_, binaryImage = cv2.threshold(filterImage, 210, 255, cv2.THRESH_BINARY_INV)

# 腐蚀
erodeImage = cv2.erode(binaryImage, None, iterations=2)

# 膨胀
dilateImage = cv2.dilate(erodeImage, None, iterations=19)

# Canny边缘检测
cannyImage = cv2.Canny(dilateImage, 10, 100)

# Hough变换检测直线
lines = cv2.HoughLines(cannyImage, 5, np.pi/180, 100)

# 直线简化逻辑（这里简化为展示所有检测到的直线）
if lines is not None:
    for line in lines:
        rho, theta = line[0]
        a = np.cos(theta)
        b = np.sin(theta)
        x0 = a * rho
        y0 = b * rho
        x1 = int(x0 + 1000 * (-b))
        y1 = int(y0 + 1000 * (a))
        x2 = int(x0 - 1000 * (-b))
        y2 = int(y0 - 1000 * (a))
        cv2.line(srcImage, (x1, y1), (x2, y2), (0, 0, 255), 2)

plt.imshow(cv2.cvtColor(srcImage, cv2.COLOR_BGR2RGB))
plt.title('Detected Lines')
plt.show()