97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/2/18 11:06
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# @Author : len
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# @File : 矫正车牌.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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# 读取图像
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image_path = './img/img_2.png'
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image = cv2.imread(image_path)
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# 检查图像是否正确加载
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if image is None:
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raise ValueError("未能加载图像文件。")
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# 转换为灰度图
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 应用自适应阈值来突出车牌
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
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# 查找轮廓
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contours, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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# 车牌候选的轮廓
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candidates = []
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for cnt in contours:
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rect = cv2.minAreaRect(cnt)
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box = cv2.boxPoints(rect)
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box = np.intp(box)
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(x, y), (w, h), angle = rect
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if w > 0 and h > 0 and (w * h) > 1000: # 面积要大于1000，过滤掉太小的轮廓
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aspect_ratio = max(w, h) / min(w, h)
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if 2 < aspect_ratio < 5: # 车牌的宽高比大约在2到5之间
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candidates.append((box, angle))
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# 对每个候选轮廓进行透视变换
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for box, angle in candidates:
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# 调整角度以使车牌水平
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if angle < -45:
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angle += 90
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# 旋转原始图像，使车牌水平
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center = (int(image.shape[1] / 2), int(image.shape[0] / 2))
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rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
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rotated = cv2.warpAffine(image, rotation_matrix, (image.shape[1], image.shape[0]))
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# 获取旋转后的灰度图像并重新寻找边缘
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rotated_gray = cv2.cvtColor(rotated, cv2.COLOR_BGR2GRAY)
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rotated_thresh = cv2.adaptiveThreshold(rotated_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
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rotated_contours, _ = cv2.findContours(rotated_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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# 重新定位车牌
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rotated_candidates = []
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for rotated_cnt in rotated_contours:
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rotated_rect = cv2.minAreaRect(rotated_cnt)
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rotated_box = cv2.boxPoints(rotated_rect)
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rotated_box = np.intp(rotated_box)
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(rx, ry), (rw, rh), rangle = rotated_rect
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if rw > 0 and rh > 0 and (rw * rh) > 1000:
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aspect_ratio_rotated = max(rw, rh) / min(rw, rh)
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if 2 < aspect_ratio_rotated < 5:
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rotated_candidates.append(rotated_box)
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# 对第一个候选框应用透视变换
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if rotated_candidates:
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box = rotated_candidates[0]
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width = int(rotated_rect[1][0])
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height = int(rotated_rect[1][1])
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# 确保宽度大于高度
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if width < height:
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width, height = height, width
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# 定义透视变换的目标坐标
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dst_pts = np.array([[0, height - 1],
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[0, 0],
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[width - 1, 0],
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[width - 1, height - 1]], dtype="float32")
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src_pts = np.array(box, dtype="float32")
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# 计算透视变换矩阵
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M = cv2.getPerspectiveTransform(src_pts, dst_pts)
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# 应用透视变换
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warped = cv2.warpPerspective(rotated, M, (width, height))
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break
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# 显示结果
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cv2.imshow("Original Image", image)
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cv2.imshow("Rotated and Corrected Plate", warped)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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