cannyhough.zip

import cv2 import numpy as np class Canny: def __init__(self, Guassian_kernal_size, img, HT_high_threshold, HT_low_threshold): ''' :param Guassian_kernal_size: 高斯滤波器尺寸 :param img: 输入的图片，在算法过程中改变 :param HT_high_threshold: 滞后阈值法中的高阈值 :param HT_low_threshold: 滞后阈值法中的低阈值 ''' self.Guassian_kernal_size = Guassian_kernal_size self.img = img self.y, self.x = img.shape[0:2] self.angle = np.zeros([self.y, self.x]) self.img_origin = None self.x_kernal = np.array([[-1, 1]]) self.y_kernal = np.array([[-1], [1]]) self.HT_high_threshold = HT_high_threshold self.HT_low_threshold = HT_low_threshold def Get_gradient_img(self): ''' 计算梯度图和梯度方向矩阵。 :return: 生成的梯度图 ''' print('Get_gradient_img') new_img_x = np.zeros([self.y, self.x], dtype=np.float) new_img_y = np.zeros([self.y, self.x], dtype=np.float) for i in range(0, self.x): for j in range(0, self.y): if j == 0: new_img_y[j][i] = 1 else: new_img_y[j][i] = np.sum(np.array([[self.img[j - 1][i]], [self.img[j][i]]]) * self.y_kernal) if i == 0: new_img_x[j][i] = 1 else: new_img_x[j][i] = np.sum(np.array([self.img[j][i - 1], self.img[j][i]]) * self.x_kernal) gradient_img, self.angle = cv2.cartToPolar(new_img_x, new_img_y) # 返回幅值和相位 self.angle = np.tan(self.angle) self.img = gradient_img.astype(np.uint8) return self.img def Non_maximum_suppression(self): ''' 对生成的梯度图进行非极大化抑制，将tan值的大小与正负结合，确定离散中梯度的方向。 :return: 生成的非极大化抑制结果图 ''' print('Non_maximum_suppression') result = np.zeros([self.y, self.x]) for i in range(1, self.y - 1): for j in range(1, self.x - 1): if abs(self.img[i][j]) <= 4: result[i][j] = 0 continue elif abs(self.angle[i][j]) > 1: gradient2 = self.img[i - 1][j] gradient4 = self.img[i + 1][j] # g1 g2 # C # g4 g3 if self.angle[i][j] > 0: gradient1 = self.img[i - 1][j - 1] gradient3 = self.img[i + 1][j + 1] # g2 g1 # C # g3 g4 else: gradient1 = self.img[i - 1][j + 1] gradient3 = self.img[i + 1][j - 1] else: gradient2 = self.img[i][j - 1] gradient4 = self.img[i][j + 1] if self.angle[i][j] > 0: gradient1 = self.img[i - 1][j - 1] gradient3 = self.img[i + 1][j + 1] else: gradient3 = self.img[i - 1][j + 1] gradient1 = self.img[i + 1][j - 1] temp1 = abs(self.angle[i][j]) * gradient1 + (1 - abs(self.angle[i][j])) * gradient2 temp2 = abs(self.angle[i][j]) * gradient3 + (1 - abs(self.angle[i][j])) * gradient4 if self.img[i][j] >= temp1 and self.img[i][j] >= temp2: result[i][j] = self.img[i][j] else: result[i][j] = 0 self.img = result return self.img def Hysteresis_thresholding(self): ''' 对生成的非极大化抑制结果图进行滞后阈值法，用强边延伸弱边，这里的延伸方向为梯度的垂直方向， 将比低阈值大比高阈值小的点置为高阈值大小，方向在离散点上的确定与非极大化抑制相似。 :return: 滞后阈值法结果图 ''' print('Hysteresis_thresholding') for i in range(1, self.y - 1): for j in range(1, self.x - 1): if self.img[i][j] >= self.HT_high_threshold: if abs(self.angle[i][j]) < 1: if self.img_origin[i - 1][j] > self.HT_low_threshold: self.img[i - 1][j] = self.HT_high_threshold if self.img_origin[i + 1][j] > self.HT_low_threshold: self.img[i + 1][j] = self.HT_high_threshold if self.angle[i][j] < 0: if self.img_origin[i - 1][j - 1] > self.HT_low_threshold: self.img[i - 1][j - 1] = self.HT_high_threshold if self.img_origin[i + 1][j + 1] > self.HT_low_threshold: self.img[i + 1][j + 1] = self.HT_high_threshold else: if self.img_origin[i - 1][j + 1] > self.HT_low_threshold: self.img[i - 1][j + 1] = self.HT_high_threshold if self.img_origin[i + 1][j - 1] > self.HT_low_threshold: self.img[i + 1][j - 1] = self.HT_high_threshold else: if self.img_origin[i][j - 1] > self.HT_low_threshold: self.img[i][j - 1] = self.HT_high_threshold if self.img_origin[i][j + 1] > self.HT_low_threshold: self.img[i][j + 1] = self.HT_high_threshold if self.angle[i][j] < 0: if self.img_origin[i - 1][j - 1] > self.HT_low_threshold: self.img[i - 1][j - 1] = self.HT_high_threshold if self.img_origin[i + 1][j + 1] > self.HT_low_threshold: self.img[i + 1][j + 1] = self.HT_high_threshold # g3 # g2 C g4 # g1 else: if self.img_origin[i - 1][j + 1] > self.HT_low_threshold: self.img[i + 1][j - 1] = self.HT_high_threshold if self.img_origin[i + 1][j - 1] > self.HT_low_threshold: self.img[i + 1][j - 1] = self.HT_high_threshold return self.img def canny_algorithm(self): ''' 按照顺序和步骤调用以上所有成员函数。 :return: Canny 算法的结果 ''' self.img = cv2.GaussianBlur(self.img, (self.Guassian_kernal_size, self.Guassian_kernal_size), 0) self.Get_gradient_img() self.img_origin = self.img.copy() self.Non_maximum_suppression() self.Hysteresis_thresholding() return self.img