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import math
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import cv2
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import numpy as np
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import struct
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import matplotlib.pyplot as plt
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from PyQt5.QtWidgets import QMessageBox
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class EdgeMenu:
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def __init__(self, pic_path):
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self.pic_path = pic_path
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self.pic = cv2.imread(self.pic_path, 0).astype(np.uint8) #灰度模式读取
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self.cpic = cv2.imread(self.pic_path, 1).astype(np.uint8)
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def ShowPic(self):
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cv2.imshow('当前图片', self.pic)
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cv2.waitKey(0) # 0一直显示，直到有键盘输入。也可以是其他数字.
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def PicStrength(self):
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self.pic = self.pic.astype('float')
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row, column = self.pic.shape
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gradient = np.zeros((row, column))
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for x in range(row - 1):
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for y in range(column - 1):
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gx = abs(self.pic[x + 1, y] - self.pic[x, y])
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gy = abs(self.pic[x, y + 1] - self.pic[x, y])
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gradient[x, y] = gx + gy
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# 3. 对图像进行增强，增强后的图像变量名为sharp
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sharp = self.pic + gradient
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sharp = np.where(sharp > 255, 255, sharp)
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sharp = np.where(sharp < 0, 0, sharp)
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gradient = gradient.astype('uint8')
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sharp = sharp.astype('uint8')
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', sharp)
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cv2.waitKey(0)
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def EdgeRoberts(self):
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kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
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kernely = np.array([[0, -1], [1, 0]], dtype=int)
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x = cv2.filter2D(self.pic, cv2.CV_16S, kernelx)
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y = cv2.filter2D(self.pic, cv2.CV_16S, kernely)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', Roberts)
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cv2.waitKey(0)
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def EdgeSobel(self):
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x = cv2.Sobel(self.pic, cv2.CV_16S, 1, 0)
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y = cv2.Sobel(self.pic, cv2.CV_16S, 0, 1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', Sobel)
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cv2.waitKey(0)
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def EdgeLaplacian(self, k_size):
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grayImage = cv2.GaussianBlur(self.pic, (5, 5), 0, 0)
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# 3. 拉普拉斯算法
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if k_size == '1':
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize=1)
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elif k_size == '3':
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize=3)
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elif k_size == '5':
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize=5)
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elif k_size == '7':
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize=7)
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else:
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return
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# 4. 数据格式转换
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Laplacian = cv2.convertScaleAbs(dst)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', Laplacian)
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cv2.waitKey(0)
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def EdgeLoG(self):
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img = cv2.imread(self.pic_path)
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# 2. 边缘扩充处理图像
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image = cv2.copyMakeBorder(grayImage, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
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image = cv2.GaussianBlur(image, (3, 3), 0, 0)
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# 3. 使用Numpy定义LoG算子
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m1 = np.array(
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[[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]])
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rows = image.shape[0]
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cols = image.shape[1]
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image1 = np.zeros(image.shape)
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# 4. 卷积运算
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for k in range(0, 2):
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for i in range(2, rows - 2):
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for j in range(2, cols - 2):
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image1[i, j] = np.sum((m1 * image[i - 2:i + 3, j - 2:j + 3, k]))
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image1 = cv2.convertScaleAbs(image1)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', image1)
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cv2.waitKey(0)
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def EdgeCanny(self):
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self.picColor = cv2.imread(self.pic_path, 1).astype(np.uint8) #彩色模式读取
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# 2. 灰度转换
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blur = cv2.cvtColor(self.picColor, cv2.COLOR_BGR2GRAY)
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# 3. 求x，y方向的Sobel算子
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gradx = cv2.Sobel(blur, cv2.CV_16SC1, 1, 0)
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grady = cv2.Sobel(blur, cv2.CV_16SC1, 0, 1)
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# 4. 使用Canny函数处理图像，x,y分别是3求出来的梯度，低阈值50，高阈值150
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edge_output = cv2.Canny(gradx, grady, 50, 150)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', edge_output)
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cv2.waitKey(0)
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def HoughDetect(self, threshold, color):
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img = cv2.GaussianBlur(self.cpic, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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lines = cv2.HoughLines(edges, 1, np.pi / 2, threshold)
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result = img.copy()
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if lines is None:
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', result)
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return
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for i_line in lines:
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for line in i_line:
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rho = line[0]
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theta = line[1]
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if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
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pt1 = (int(rho / np.cos(theta)), 0)
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pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
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if color == 'red':
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cv2.line(result, pt1, pt2, (0, 0, 255))
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if color == 'white':
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cv2.line(result, pt1, pt2, (255, 255, 255))
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if color == 'black':
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cv2.line(result, pt1, pt2, (0, 0, 0))
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if color == 'green':
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cv2.line(result, pt1, pt2, (0, 255, 0))
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if color == 'blue':
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cv2.line(result, pt1, pt2, (0, 255, 0))
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else:
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pt1 = (0, int(rho / np.sin(theta)))
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pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
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if color == 'red':
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cv2.line(result, pt1, pt2, (0, 0, 255), 1)
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if color == 'white':
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cv2.line(result, pt1, pt2, (255, 255, 255), 1)
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if color == 'black':
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cv2.line(result, pt1, pt2, (0, 0, 0), 1)
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if color == 'green':
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cv2.line(result, pt1, pt2, (0, 255, 0), 1)
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if color == 'blue':
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cv2.line(result, pt1, pt2, (0, 255, 0), 1)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', result)
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cv2.waitKey(0)
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def HoughDetectP(self, threshold, MinLineLength, MaxLineGap, color):
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img = cv2.GaussianBlur(self.cpic, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold, minLineLength = MinLineLength,maxLineGap = MaxLineGap)
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result_P = img.copy()
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if linesP is None:
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', result_P)
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return
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for i_P in linesP:
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for x1, y1, x2, y2 in i_P:
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if color == 'red':
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 0, 255), 3)
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if color == 'white':
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cv2.line(result_P, (x1, y1), (x2, y2), (255, 255, 255), 3)
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if color == 'black':
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 0, 0), 3)
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if color == 'green':
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
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if color == 'blue':
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cv2.line(result_P, (x1, y1), (x2, y2), (255, 0, 0), 3)
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cv2.imwrite('img/EdgeImgs/EdgePic.jpg', result_P)
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cv2.waitKey(0) |
