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Detailed explanation of the basic operation methods of Python-OpenCV_python
Detailed explanation of the basic operation methods of Python-OpenCV_python
The editor below will share with you a detailed explanation of the basic operation methods of Python-OpenCV. It has a good reference value and I hope it will be helpful to everyone. Let’s follow the editor to take a look
Basic properties
cv2.imread (file name, properties) Read in the image
Attribute: Specify how the image is read from the file
cv2.IMREAD_COLOR: Read in color images, default parameters, Opencv reads color images in BGR mode! ! ! Note
cv2.IMREAD_GRAYSCALE: Read in grayscale images.
cv2.imshow(window name, image file) Display image
Can create multiple windows
cv2.waitKey() keyboard binding function
The function waits for a specific number of milliseconds to see if there is input from the keyboard.
cv2.namedWindow(window name, attribute) Create a window
Attribute: Specify window size mode
cv2.WINDOW_AUTOSIZE: Automatically based on image size Create size
cv2.WINDOW_NORMAL: The window size can be adjusted
cv2.destoryAllWindows(window name) Delete any created window
Code example:
import cv2 img=cv2.imread('test.py',cv2.IMREAD_COLOR) cv2.namedWindow('image',cv2.WINDOW_NORMAL) cv2.imshow('image',img) cv2.waitKey(0) cv2.destoryAllWindows()
cv2.imwrite(save image name, need to save image) Save image
Code example:
import cv2 img=cv2.imread('test.png',0) cv2.imshow('image',img) k=cv2.waitKey(0) if k==27: #等待 ESC 键 cv2.destoryAllWindows() elif k==ord('s') #等待 's' 键来保存和退出 cv2.imwrite('messigray.png',img) cv2.destoryAllWindows()
Some operations on images
0x01. Get image attributes
import cv2 img=img.imread('test.png') print img.shape #(768,1024,3) print img.size #2359296 768*1024*3 print img.dtype #uint8
0x02. Output text
When processing pictures, output some information directly in the form of text. On the picture
cv2.putText(picture name, text, coordinates, text color)
##0x03. Zoom picture
Implement scaling and saving of images, a common operation when using OpenCV. cv2.resize() supports a variety of interpolation algorithms. By default, cv2.INTER_LINEAR is used. The most suitable one for reducing is cv2.INTER_AREA. The most suitable one for enlarging is cv2.INTER_CUBIC or cv2.INTER_LINEAR.res=cv2.resize(image,(2*width,2*height),interpolation=cv2.INTER_CUBIC)
res=cv2.resize(image,None,fx=2,fy=2,interpolation=cv2.INTER_CUBIC)
0x04 is set later. The image translation is
cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]])
For example, pan the picture (100,50)
import cv2 img=cv2.imread('test.png',1) rows,cols,channel=img.shape M=np.float32([[1,0,100],[0,1,50]]) dst=cv2.warpAffine(img,M,(cols,rows)) cv2.imshow('img',dst) cv2.waitKey(0) cv2.destoryALLWindows()
0x05. Image rotation
In OpenCV, you first need to construct a rotation matrix, which is obtained through cv2.getRotationMatrix2D.import cv2 img=cv2.imread('test.png',0) rows,cols=img.shape #第一个参数为旋转中心，第二个为旋转角度，第三个为旋转后的缩放因子 M=cv2.getRotationMatrix2D((cols/2,rows/2),45,0.6) #第三个参数为图像的尺寸中心 dst=cv2.warpAffine(img,M,(2*cols,2*rows)) cv2.imshow('img',dst) cv2.waitKey(0) cv2.destoryALLWindows()
0x06. Affine transformation
In affine transformation, the original image All parallel lines in are equally parallel in the resulting image. To create the offset matrix, you need to find three points in the original image and their positions in the output image. Then OpenCV provides cv2.getAffineTransform to create a 2*3 matrix, and finally passes the matrix to the function cv2.warpAffine.import cv2 import matplotlib.pyplot as plt import numpy as np img=cv2.imread('test.png') rows,cols,ch=img.shape pts1=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)) plt.subplot(121),plt.imshow(img),plt.title('Input') plt.subplot(122),plt.imshow(dst),plt.title('Output') plt.show()
##0x07. Perspective transformation Perspective transformation requires a 3*3 transformation matrix. Make sure the straight line is still straight before and after the transformation. Constructing this matrix requires finding 4 points in the input image and their corresponding positions in the output image. Any three of these four points cannot be collinear. Transformation matrix OpenCV provides cv2.getPerspectiveTransform() construction. Then pass the matrix into the function cv2.warpPerspective.
import cv2 import numpy as np import matplotlib.pyplot as plt img=cv2.imread('test.png') rows,cols,ch=img.shape pts1=np.float32([[56,65],[368,52],[28,387],[389,390]]) pts2=np.float32([[0,0],[300,0],[0,300],[300,300]]) M=cv2.getPerspectiveTransform(pts1,pts2) dst=cv2.warpPerspective(img,M,(300,300)) plt.subplot(121),plt.imshow(img),plt.title('Input') plt.subplot(122),plt.imshow(dst),plt.title('Output') plt.show()
Sometimes it is necessary to operate on a specific area of an image, and the ROI is obtained using Numpy index.
import cv2
import numpy as np
import matplotlib.pyplot as plt
image=cv2.imread('test.png')
rows,cols,ch=image.shape
tall=image[0:100,300:700]
image[0:100,600:1000]=tallall
cv2.imshow("image",image)
cv2.waitKey(0)
cv2.destoryALLWindows()
##0x10. Channel split/merge processing
Sometimes it is necessary to operate the three BGR channels separately. At this time, the BGR needs to be split into a single channel. At the same time, sometimes it is necessary to merge independent channel images into a BGR image.
Use OpenCV library function versionimport cv2
import numpy as np
import matplotlib.pyplot as plt
image=cv2.imread('pitt1.jpg')
rows,cols,ch=image.shape
#拆分通道，cv2.split()是一个比较耗时的操作。只有需要时使用，尽量Numpy
b,g,r=cv2.split(image)
print b.shape
#(768,1024)
#合并通道
image=cv2.merge(b,g,r)
The above is the detailed content of Detailed explanation of the basic operation methods of Python-OpenCV_python. For more information, please follow other related articles on the PHP Chinese website!import cv2
import numpy as np
import matplotlib.pyplot as plt
image=cv2.imread('pitt1.jpg')
rows,cols,ch=image.shape
#直接获取
b=img[:,:,0]
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