Python OpenCV 图像处理初级使用

# -*- coding: utf-8 -*-

"""

Created on Thu Apr 25 08:11:32 2019

@author: jiangshan

"""

import cv2

import numpy as np

dataSetDir = 'D:\\Workspace\\Spyder\\'

#filename = dataSetDir+'Mask_10228690_15.tif'

filename = dataSetDir+'Input_10228690_15.tiff'

# 加载彩图

img = cv2.imread(filename)

# 灰度图模式加载一副彩图

#img = cv2.imread(filename,0)

# 0 - 1 二值化

#Grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#ret, thresh = cv2.threshold(Grayimg, 0, 1,cv2.THRESH_BINARY)

# 显示图像

#cv2.imshow('image',img)

#cv2.waitKey(0) #cv2.waitKey() 是键盘绑定函数。如果没有键盘输入，返回值为-1，如果这个参数为0，将会无限期的等待键盘输入。

"""

cv2.destroyAllWindows() 删除任何建立的窗口。如果想删除特定的窗口可以使用cv2.destroyWindow()，在函数内输入想删除的窗口名。

"""

# 保存图像

cv2.imwrite('Input-test.png',img)

#cv2.imwrite('Target.png',thresh)

image = cv2.imread('Input-test.png',cv2.COLOR_RGB2GRAY)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

gaus = cv2.GaussianBlur(gray,(3,3),0)#高斯模糊预处理

edges = cv2.Canny(gaus, 50, 150, apertureSize=3)#Canny算子

minLineLength = 300

maxLineGap = 10

lines = cv2.HoughLinesP(edges,1.0,np.pi/180,10,minLineLength,maxLineGap)#HoughLinesP直线特征提取

line = lines[:,0,:]#提取为二维

for x1, y1, x2, y2 in line:

cv2.line(image, (x1, y1), (x2, y2), (0,0,255), 2)#绘线

cv2.imshow('Detected houghline', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

#HoughCircles圆形特征提取

circles1 = cv2.HoughCircles(edges,cv2.HOUGH_GRADIENT,1,

100,param1=100,param2=30,minRadius=200,maxRadius=300)

circles = circles1[0,:,:]#提取为二维

circles = np.uint16(np.around(circles))#四舍五入，取整

for i in circles[:]:

cv2.circle(image,(i[0],i[1]),i[2],(255,0,0),5)#画圆

cv2.circle(image,(i[0],i[1]),2,(255,0,255),10)#画圆心

cv2.imshow('Detected houghCircles', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

image = cv2.imread('Input-test.png')

image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)#将图像转化为灰度图像

cv2.imshow("Original",image)

cv2.waitKey()

#拉普拉斯边缘检测

lap = cv2.Laplacian(image,cv2.CV_64F)#拉普拉斯边缘检测

lap = np.uint8(np.absolute(lap))##对lap去绝对值

cv2.imshow("Laplacian",lap)

cv2.waitKey()

#Sobel边缘检测

sobelX = cv2.Sobel(image,cv2.CV_64F,1,0)#x方向的梯度

sobelY = cv2.Sobel(image,cv2.CV_64F,0,1)#y方向的梯度

sobelX = np.uint8(np.absolute(sobelX))#x方向梯度的绝对值

sobelY = np.uint8(np.absolute(sobelY))#y方向梯度的绝对值

sobelCombined = cv2.bitwise_or(sobelX,sobelY)#

cv2.imshow("Sobel X", sobelX)

cv2.waitKey()

cv2.imshow("Sobel Y", sobelY)

cv2.waitKey()

cv2.imshow("Sobel Combined", sobelCombined)

cv2.waitKey()

#Canny边缘检测

canny = cv2.Canny(image,30,150)

cv2.imshow("Canny",canny)

cv2.waitKey()

#Canny边缘提取

import cv2 as cv

def edge_demo(image):

blurred = cv.GaussianBlur(image, (3, 3), 0)

gray = cv.cvtColor(blurred, cv.COLOR_RGB2GRAY)

# xgrad = cv.Sobel(gray, cv.CV_16SC1, 1, 0) #x方向梯度

# ygrad = cv.Sobel(gray, cv.CV_16SC1, 0, 1) #y方向梯度

# edge_output = cv.Canny(xgrad, ygrad, 50, 150)

edge_output = cv.Canny(gray, 50, 150)

cv.imshow("Canny Edge", edge_output)

dst = cv.bitwise_and(image, image, mask= edge_output)

cv.imshow("Color Edge", dst)

src = cv.imread('Input-test.png')

cv.namedWindow('input_image', cv.WINDOW_NORMAL) #设置为WINDOW_NORMAL可以任意缩放

cv.imshow('input_image', src)

edge_demo(src)

cv.waitKey(0)

cv.destroyAllWindows()