81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
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# -*- coding: utf-8 -*-
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# @Time : 2024/2/19 9:54
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# @Author : len
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# @File : correct_qr.py
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# @Software: PyCharm
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# @Comment :
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import cv2
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import numpy as np
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import scipy.spatial.distance as dist
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def correct_qr_code_skew(image_path):
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# 读取图像
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image_source = cv2.imread(image_path)
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gray = cv2.cvtColor(image_source, cv2.COLOR_BGR2GRAY)
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# 使用goodFeaturesToTrack来检测角点
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corners = cv2.goodFeaturesToTrack(gray, maxCorners=4, qualityLevel=0.01, minDistance=10)
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# 如果检测到角点
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if corners is not None and len(corners) >= 4:
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# 对角点数组进行整形
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corners = np.intp(corners)
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# 对角点进行排序
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rect = order_points(corners.reshape(4, 2))
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# 计算变换后图像的宽和高
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width = max(int(dist.euclidean(rect[2], rect[3])), int(dist.euclidean(rect[1], rect[0])))
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height = max(int(dist.euclidean(rect[1], rect[2])), int(dist.euclidean(rect[0], rect[3])))
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# 构造目标点，用于将图像映射到鸟瞰图
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dst = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]], dtype="float32")
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# 计算透视变换矩阵并应用
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M = cv2.getPerspectiveTransform(rect, dst)
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warp = cv2.warpPerspective(image_source, M, (width, height))
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# 使用QRCodeDetector来检测和解码二维码
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detector = cv2.QRCodeDetector()
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data, vertices_array, binary_qr_code = detector.detectAndDecode(warp)
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# 如果检测到二维码，则返回解码数据
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if vertices_array is not None:
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return data, warp
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else:
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print("未检测到二维码。")
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return None
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else:
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# 如果未检测到足够的角点，则打印错误信息
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print("未检测到足够的角点。")
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return None
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# 用于排序角点的函数
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def order_points(pts):
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# 初始化排序后的点
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rect = np.zeros((4, 2), dtype="float32")
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# 根据点的坐标总和来进行排序
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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# 计算点的差值，用于找到其余两个点
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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# 返回排序后的点
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return rect
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# 运行校正函数
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# 请确保传入正确的图像路径
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result = correct_qr_code_skew("./data/img.png")
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if result:
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decoded_data, corrected_image = result
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print("解码的数据:", decoded_data)
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