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import os
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import cv2
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import numpy as np
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import sys
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def robs():
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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cap.release()
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if ok:
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img = src
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# 1. 灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 2. Roberts算子
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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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# 3. 卷积操作
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x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
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y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
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# 4. 数据格式转换
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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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print(src.shape)
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#单通道转为三通道
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Roberts=cv2.cvtColor(Roberts, cv2.COLOR_GRAY2RGB)
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img = np.hstack([src, Roberts])
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cv2.imshow("(roberts)resourse and result", img)
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# 等待关闭
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cv2.waitKey(0)
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def sob():
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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cap.release()
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if ok:
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img = src
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########## Begin ##########
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# 1. 灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 2. 求Sobel 算子
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x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0) # 对x求一阶导
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y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1) # 对y求一阶导
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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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########## End ##########
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Sobel = cv2.cvtColor(Sobel, cv2.COLOR_GRAY2RGB)
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img = np.hstack([src, Sobel])
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cv2.imshow("resourse and result", img)
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# 等待关闭
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cv2.waitKey(0)
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def lap():
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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cap.release()
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if ok:
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img = src
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########## Begin ##########
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# 1. 灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 2. 高斯滤波
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grayImage= cv2.GaussianBlur(grayImage, (5, 5), 0)
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# 3. 拉普拉斯算法
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize=3)
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# 4. 数据格式转换
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Laplacian = cv2.convertScaleAbs(dst)
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Laplacian = cv2.cvtColor(Laplacian, cv2.COLOR_GRAY2RGB)
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########## End ##########
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img = np.hstack([src, Laplacian])
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cv2.imshow("resourse and result", img)
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# 等待关闭
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cv2.waitKey(0)
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def _log():
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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cap.release()
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if ok:
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img = src
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########## Begin ##########
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# 1. 灰度转换
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 2. 边缘扩充处理图像并使用高斯滤波处理该图像
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image = cv2.copyMakeBorder(img,2,2,2,2,borderType=cv2.BORDER_REPLICATE)
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image = cv2.copyMakeBorder(img,2,2,2,2,borderType=cv2.BORDER_REPLICATE)
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image = cv2.GaussianBlur(image,(3,3),0)
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# 3. 使用Numpy定义LoG算子
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m1=np.array([[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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image1=np.zeros(image.shape,dtype='float')
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# 4. 卷积运算
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# 为了使卷积对每个像素都进行运算，原图像的边缘像素要对准模板的中心。
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# 由于图像边缘扩大了2像素，因此要从位置2到行(列)-2
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for i in range(2,image.shape[0]-2):
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for j in range(2,image.shape[1]-2):
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image1[i,j]=np.sum(m1*image[i-2:i+3,j-2:j+3,1])
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# 5. 数据格式转换
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image1 = cv2.convertScaleAbs(image1)
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image1 = cv2.resize(image1, (img.shape[1], img.shape[0]))
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img = np.hstack([src, image1])
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cv2.imshow("resourse and result", img)
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# 等待关闭
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cv2.waitKey(0)
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def main():
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print("1.Roberts算子 2.Sobel算子 3.Laplacian算子 4.log边缘算子")
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myinput = input()
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if myinput == '1':
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robs()
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elif myinput == '2':
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sob()
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elif myinput == '3':
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lap()
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elif myinput == '4':
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_log()
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else:
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print("wrong input!")
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