数学之路-python计算实战(19)-机器视觉-卷积滤波-白红宇

数学之路-python计算实战(19)-机器视觉-卷积滤波

阅读量：5995 次

发布时间：2019-06-20

本文共 2182 字，大约阅读时间需要 7 分钟。

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

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:

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 will 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 will 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 and process them individually. anchor – anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center. delta – optional value added to the filtered pixels before storing them in `dst`. borderType – pixel extrapolation method (see for details).

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 will 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 will 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 and process them individually.

anchor – anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center.

delta – optional value added to the filtered pixels before storing them in dst.

borderType – pixel extrapolation method (see for details).

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

# -*- coding: utf-8 -*-   #卷积滤波#code:myhaspl@myhaspl.comimport 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()