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"""
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模块作者：
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图像预处理：潘浩宇
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轮廓寻找与切分：戴晓齐
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代码优化/整合/打包：王昱博
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模块用途：
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对车牌图片进行预处理和切分，找出包含车牌号的部分
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"""
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import cv2
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class ImageCutter:
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@staticmethod
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# 图像去噪灰度处理，消除噪点
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def gray_guss(image):
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image = cv2.GaussianBlur(image, (3, 3), 0)
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gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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return gray_image
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@classmethod
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def ImagePreProcess(cls, image_path: str) -> tuple:
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# 复制一张图片，在复制图上进行图像操作，保留原图
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origin_image = cv2.imread(image_path)
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# 图像去噪灰度处理
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image = origin_image.copy()
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# x方向上的边缘检测（增强边缘信息）
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gray_image = cls.gray_guss(image)
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Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
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absX = cv2.convertScaleAbs(Sobel_x)
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image = absX
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# 图像阈值化操作——获得二值化图，将像素置为0或者255。将灰度转成黑白
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ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
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# 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作
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# 使用形状为（30，10）的矩形kernelX对图像进行偏X方向的闭运算，将图像进行X方向融合找出车牌区域。
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kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
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image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX, iterations=1)
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return origin_image, image
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@classmethod
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def CutPlateRect(cls, origin_image: cv2.Mat, image: cv2.Mat) -> cv2.Mat:
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# 去除细小的边缘
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# 腐蚀（erode）和膨胀（dilate）
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kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1))
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kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20))
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# x方向进行闭操作（抑制暗细节）
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image = cv2.dilate(image, kernelX)
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image = cv2.erode(image, kernelX)
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# y方向的开操作
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image = cv2.erode(image, kernelY)
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image = cv2.dilate(image, kernelY)
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# 中值滤波（去噪）将边缘平滑
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image = cv2.medianBlur(image, 21)
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# 获得轮廓 RETR_EXTERNAL矩形的外边缘
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contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 筛选
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for item in contours:
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rect = cv2.boundingRect(item)
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x = rect[0]
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y = rect[1]
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weight = rect[2]
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height = rect[3]
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# 根据轮廓的形状特点，确定车牌的轮廓位置并截取图像
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if (weight > (height * 3.5)) and (weight < (height * 4)): # 对长宽比例进行确定
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_image = origin_image[y:y + height, x:x + weight] # 对图片进行裁剪
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return cv2.Mat(_image)
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return origin_image
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