Mathematical Road-python Calculation (19)-Machine vision-convolution filter

Filter2d

Convolves an image with the kernel.

C + +:   void  filter2d ( inputarray  src , outputarray  DST , int  ddepth , Inputarray kernel , point  anchor =point ( -1,-1), double  delta =0, int  bordertype =border_default  )

Python:   Cv2. filter2d (SRC, ddepth, kernel [Dst [, anchor [, Delta [, Bordertype ] ] ] ] )→dst

C:   void  cvfilter2d ( Const cvarr*  src , cvarr*  DST , const cvmat*  Kernel, Cvpoint anchor =cvpoint ( -1,-1)   )

Python: cv. filter2d (src, DST, kernel, anchor= ( -1,-1) ) →none

Parameters:

src –input image. DST –output image of the same size and the same number of channels as src. ddepth– Desired depth of the destination image; If it is negative, it'll be the same as src.depth (); The following combinations of src.depth () and ddepth are supported: src.depth ()  = cv_8u ,  ddepth  = -1/cv_16s /cv_32f /cv_64f src.depth ()  = cv_16u /cv_16s ,  ddepth  = -1/cv_32f /cv_64f src.depth ()  = cv_32f ,  ddepth  = -1/cv_32f /cv_64f src.depth () = cv_64f, ddepth = -1/cv_64f When ddepth=-1, the output image would have the same depth as the source. kernel –convolution kernel (or rather a correlation kernel), a single-channel floating point matrix; If you want to apply different kernels to different channels, split the image into separate color planes using split () and process them individually. anchor –anchor of the kernel that indicates the relative position for a filtered point within the kernel; The anchor should lie within the kernel; Default value ( -1,-1) means that the anchor are at the kernel center. Delta –optional Value added to the filtered pixels before storing them in DST. Bordertype –pixel extrapolation method (seeborderinterpolate () for details).

The function applies a arbitrary linear filter to an image. In-place operation is supported. When the aperture was partially outside the image, the function interpolates outlier pixel values according to the Specifie d Border mode.

#-*-Coding:utf-8-*-   #卷积滤波 #code:[email protected]import cv2import numpy as npfn= "test2.jpg" Myimg=cv2.imread (FN) Img=cv2.cvtcolor (Myimg,cv2. Color_bgr2gray) Myh=np.array ([[[0,1,0],[1,-4,1],[0,1,0]]) jgimg=cv2.filter2d (IMG,-1,MYH) cv2.imshow (' src ', img) Cv2.imshow (' DST ', jgimg) Cv2.waitkey () cv2.destroyallwindows ()

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Mathematical Road-python Calculation (19)-Machine vision-convolution filter