Mathematical Road-python Computing (21)-Machine vision-Laplace linear filtering

Laplace linear filtering,. Edge Detection

Laplacian

Calculates the Laplacian of an image.

C + +: void Laplacian (inputarray src, outputarray DST, int ddepth,  int ksize=1, double scale =1, double delta=0, int bordertype=border_default )

Python:   Cv2. Laplacian (SRC, ddepth [Dst [, ksize [, scale [, Delta [, Bordertype ] ] ] ] ] )→dst

C:   void  cvlaplace ( const cvarr*  src , cvarr*  DST , int  aperture_size =3  )

Python: cv. Laplace (src, DST, aperturesize=3 ) →none

Parameters:

src –source image. DST –destination image of the same size and the same number of channels as src . ddepth –desired Depth of the destination image. ksize –aperture size used to compute the second-derivative filters. See Getderivkernels () for details. The size must be positive and odd. Scale –optional scale factor for the computed Laplacian values. By default, no scaling is applied. See Getderivkernels () for details. Delta –optional Delta value, added to the results prior to storing them in DST . Bordertype –pixel extrapolation method. SeeBorderinterpolate () for details.

The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:

This is do when ksize > 1 .  When ksize = = 1 , the Laplacian is computed by filtering the image with the following Aperture

Laplace

Laplacian transformation of the computed image

void Cvlaplace (const cvarr* SRC, cvarr* dst, int aperture_size=3);

Src

Enter an image.

Dst

The output image.

Aperture_size

the kernel size (as defined in Cvsobel).

The function Cvlaplace computes the Laplacian transformation of the input image by first calculating the second-order X-and Y-differential with the Sobel operator, and then summing:

The aperture_size=1 gives the fastest results, which is equivalent to making a convolution of images such as the following cores:

the whole content of this blog is original, if reproduced please indicate the sourcehttp://blog.csdn.net/myhaspl/

#-*-Coding:utf-8-*-   #线性锐化滤波, Laplace image transform #code:[email protected]import cv2fn= "test6.jpg" Myimg=cv2.imread (FN) img= Cv2.cvtcolor (Myimg,cv2. Color_bgr2gray) Jgimg=cv2. Laplacian (img,-1) cv2.imshow (' src ', img) cv2.imshow (' DST ', jgimg) Cv2.waitkey () cv2.destroyallwindows ()

the whole content of this blog is original, if reproduced please indicate the sourcehttp://blog.csdn.net/myhaspl/

Mathematical Road-python Computing (21)-Machine vision-Laplace linear filtering