0x00. 图片读、写和显示操作
安装好 OpenCV 之后，首先尝试加载一张最简单的图片并显示出来，代码示例：
第一种方式使用cv2.cv的LoadImage、ShowImage和SaveImage函数
import cv2.cv as cv# 读图片
image=cv.LoadImage('img/image.png', cv.CV_LOAD_IMAGE_COLOR)#Load the image
#Or just: image=cv.LoadImage('img/image.png')cv.NamedWindow('a_window', cv.CV_WINDOW_AUTOSIZE) #Facultative
cv.ShowImage('a_window', image) #Show the image# 写图片
cv.SaveImage("thumb.png", thumb)
cv.WaitKey(0) #Wait for user input and quit也可以直接使用cv2的imread、imwrite和imshow函数
import numpy as np
import cv2img = cv2.imread('messi5.jpg',0)
cv2.imshow('image',img)
k = cv2.waitKey(0)
if k == 27: # wait for ESC key to exitcv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exitcv2.imwrite('messigray.png',img)cv2.destroyAllWindows()imread函数还可以定义加载的mode，默认是以RGB模式处理图片：
import cv2
grayImage = cv2.imread('MyPic.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)
# 可选参数CV_LOAD_IMAGE_COLOR (BGR), CV_LOAD_IMAGE_GRAYSCALE (grayscale), CV_LOAD_IMAGE_UNCHANGED(neither)
cv2.imwrite('MyPicGray.png', grayImage)0x01. 获取图片属性
import cv2
img = cv2.imread('img/image.png')
print img.shape
# (640, 640, 3)
print img.size
# 1228800
print img.dtype
# uint8
# 在debug的时候，dtype很重要0x02. 输出文本
在处理图片的时候，我们经常会需要把一些信息直接以文字的形式输出在图片上，下面的代码将实现这个效果：
import cv2.cv as cvimage=cv.LoadImage('img/lena.jpg', cv.CV_LOAD_IMAGE_COLOR) #Load the imagefont = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 3, 8) #Creates a fonty = image.height / 2 # y position of the text
x = image.width / 4 # x position of the textcv.PutText(image,"Hello World !", (x,y),font, cv.RGB(255, 255, 255)) #Draw the textcv.ShowImage('Hello World', image) #Show the imagecv.WaitKey(0)cv2:
cv2.putText(frame, 'Hello World', (300,100), 0, 0.5, (0,0,255),2)0x03. 缩放图片
下面的例子将实现缩放图片并保存，这个在使用 OpenCV 做图像处理的时候都是很常用的操作：
import cv2.cv as cvim = cv.LoadImage("img/alkaline.jpg") #get the imagethumb = cv.CreateImage((im.width / 2, im.height / 2), 8, 3) #Create an image that is twice smaller than the originalcv.Resize(im, thumb) #resize the original image into thumb
#cv.PyrDown(im, thumb)cv.SaveImage("thumb.png", thumb) # save the thumb imagecv2:
import cv2
import numpy as np
img = cv2.imread('messi5.jpg')
res = cv2.resize(img,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)#ORheight, width = img.shape[:2]
res = cv2.resize(img,(2*width, 2*height), interpolation = cv2.INTER_CUBIC)0x04. 图像平移
import cv2
import numpy as npimg = cv2.imread('messi5.jpg',0)
rows,cols = img.shapeM = np.float32([[1,0,100],[0,1,50]])
dst = cv2.warpAffine(img,M,(cols,rows))cv2.imshow('img',dst)
cv2.waitKey(0)
cv2.destroyAllWindows()0x05. 图像旋转
img = cv2.imread('messi5.jpg',0)
rows,cols = img.shapeM = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)
dst = cv2.warpAffine(img,M,(cols,rows))0x06. 仿射变换
import cv2
import numpy as npimg = cv2.imread('mao.jpg')
rows,cols,ch = img.shapepts1 = np.float32([[50,50],[200,50],[50,200]])
pts2 = np.float32([[10,100],[200,50],[100,250]])M = cv2.getAffineTransform(pts1,pts2)dst = cv2.warpAffine(img,M,(cols,rows))cv2.imshow('image',dst)
cv2.waitKey(0)0x07. 图像颜色变换
实际使用过程中，我们经常也需要对一些图片的颜色空间做一些改变之类的：
c2.cv:
import cv2.cv as cv
im=cv.LoadImage('img/fruits.jpg',cv.CV_LOAD_IMAGE_COLOR)
res = cv.CreateImage(cv.GetSize(im), cv.CV_8UC2, 3) #cv.CV_32F, cv.IPL_DEPTH_16S, ...
cv.Convert(im, res)
cv.ShowImage("Converted",res)
res2 = cv.CreateImage(cv.GetSize(im), cv.CV_8UC2, 3)
cv.CvtColor(im, res2, cv.CV_RGB2BGR) # HLS, HSV, YCrCb, ....
cv.ShowImage("CvtColor", res2)
cv.WaitKey(0)-
cv.Convert()：将图片从一个颜色空间转到另一个颜色空间
-
cv.CvtColor(src, dst, code)：
cv2:
cv2.cvtColor(input_image, flag)函数实现图片颜色空间的转换，flag 参数决定变换类型。如 BGR->Gray flag 就可以设置为 cv2.COLOR_BGR2GRAY 。
一个简单的例子，下面的代码实现识别摄像视频中蓝色的部分：
import cv2
import numpy as npcap = cv2.VideoCapture(0)while(1):# 读取视频的每一帧_, frame = cap.read()# 将图片从 BGR 空间转换到 HSV 空间hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)# 定义在HSV空间中蓝色的范围lower_blue = np.array([110,50,50])upper_blue = np.array([130,255,255])# 根据以上定义的蓝色的阈值得到蓝色的部分mask = cv2.inRange(hsv, lower_blue, upper_blue)res = cv2.bitwise_and(frame,frame, mask= mask)cv2.imshow('frame',frame)cv2.imshow('mask',mask)cv2.imshow('res',res)k = cv2.waitKey(5) & 0xFFif k == 27:breakcv2.destroyAllWindows()以上的代码给出了视频中获取兴趣对象的基本思想。
0x08. 通道的拆分/合并处理
对于一张图片的 R、G、B 通道，我们可以很方便的使用 OpenCV 获取并分离或者合并：
(这是将图像灰度化处理的一种方式)
cv2.cv
import cv2.cv as cvorig = cv.LoadImage('img/fruits.jpg')
b = cv.CreateImage(cv.GetSize(orig), orig.depth, 1)
g = cv.CloneImage(b)
r = cv.CloneImage(b)
cv.Split(orig, b, g, r, None)merged = cv.CreateImage(cv.GetSize(orig), 8, 3)
cv.Merge(g, b, r, None, merged)cv.ShowImage("Image", orig)
cv.ShowImage("Blue", b)
cv.ShowImage("Green", g)
cv.ShowImage("Red", r)
cv.ShowImage("Merged", merged)cv.WaitKey(0)cv2
import cv2
img = cv2.imread('img/image.png')
b,g,r = cv2.split(img)
img = cv2.merge((b,g,r))0x09. 图片添加边距
cv2.copyMakeBorder函数
import cv2
import numpy as npBLUE = [255,0,0]img1 = cv2.imread('opencv_logo.png')replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP)
constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE)
