一个很有用的图像处理工具箱

转自蝴蝶也可以飞过沧海

gradientMag发现了一个很有用的图像处理工具箱:http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html。
下载下来工具箱，将其解压，通过下面两行代码将此工具箱的文件放到MATLAB的可查询路径：

>>  addpath(genpath('D:\piotr_toolbox\toolbox')); >> savepath;

这个工具箱一共包含七大部分：

channels Robust image features, including HOG, for fast object detection.classify Fast clustering, random ferns, RBF functions, PCA, etc.detector Aggregate Channel Features (ACF) object detection code.filters Routines for filtering images.images Routines for manipulating and displaying images.matlab General Matlab functions that should have been a part of Matlab.videos Routines for annotating and displaying videos.

（1）channel里的hog的使用：
注意：由于要用到部分C++程序，所以我们需要编译一下：
在MATLAB命令窗口输入：toolboxCompile;
用到的是：

H = hog( I, binSize, nOrients, clip, crop ) INPUTS%  I        - [hxw] color or grayscale input image (must have type single)%  binSize  - [8] spatial bin size%  nOrients - [9] number of orientation bins%  clip     - [.2] value at which to clip histogram bins%  crop     - [0] if true crop boundaries（对边界特别处理）%% OUTPUTS%  H        - [h/binSize w/binSize nOrients*4] computed hog features

调用方法：

>> I=imResample(single(imread('peppers.png')),[480 640])/255; 读取图像，统一图像大小，类型为Single>>H=hog(I,8,9); 返回的是60*80*36的多维矩阵（考虑边界的8个像素） >>e=hog(I,8,9,0.2,1);返回的是58*78*36的多维矩阵（考虑边界的8个像素）

（2）channel里的hogDraw的使用：
用到的是： V = hogDraw( H, w, fhog )
作用是画出hog的示意图。

INPUTS%  H          - [m n oBin*4] computed hog features%  w          - [15] width for each glyph%  fhog       - [0] if true draw features returned by fhog%% OUTPUTS%  V          - [m*w n*w] visualization of hog features

调用方法:

>> V=hogDraw(e,25);>> im(V);

(3)channel里的gradientMag的使用：
用到的是：[M,O] = gradientMag( I, channel, normRad, normConst, full )
作用是求出每个像素的梯度以及幅值。

% INPUTS%  I          - [hxwxk] input k channel single image%  channel    - [0] if>0 color channel to use for gradient computation%  normRad    - [0] normalization radius (no normalization if 0)%  normConst  - [.005] normalization constant%  full       - [0] if true compute angles in [0,2*pi) else in [0,pi)%% OUTPUTS%  M          - [hxw] gradient magnitude at each location%  O          - [hxw] approximate gradient orientation modulo PI调用：>>  a=rgbConvert(imread('peppers.png'),'gray');>> [M,O]=gradientMag(a);

（4）channel里的gradientHist的使用：
作用是求每一个patch的梯度统计。
用到的是：H = gradientHist( M, O, varargin )

% INPUTS%  M        - [hxw] gradient magnitude at each location (see gradientMag.m)%  O        - [hxw] gradient orientation in range defined by param flag%  binSize  - [8] spatial bin size%  nOrients - [9] number of orientation bins%  softBin  - [1] set soft binning (odd: spatial=soft, >=0: orient=soft)%  useHog   - [0] 1: compute HOG (see hog.m), 2: compute FHOG (see fhog.m)%  clipHog  - [.2] value at which to clip hog histogram bins%  full     - [false] if true expects angles in [0,2*pi) else in [0,pi)%% OUTPUTS%  H        - [w/binSize x h/binSize x nOrients] gradient histograms调用： H1=gradientHist(M,O,2,6,0);

（5）channel里的J = rgbConvert( I, colorSpace, useSingle )的使用：
作用是实现不同的图像空间的相互转换。

% INPUTS%  I          - [hxwx3] input rgb image (uint8 or single/double in [0,1])%  colorSpace - ['luv'] other choices include: 'gray', 'hsv', 'rgb', 'orig'%  useSingle  - [true] determines output type (faster if useSingle)%% OUTPUTS%  J          - [hxwx3] single or double output image (normalized to [0,1])

使用方法：

>>  I = imread('peppers.png');>> J = rgbConvert( I, 'luv' );>> J2=rgbConvert( I, 'hsv' );

（6）image里的varargout = montage2( IS, prm )的使用：
作用是显示各个通道的图像。
使用方法：

>> figure(2),montage2(J);