fhog.m的压缩文件

function H = fhog( I, binSize, nOrients, clip, crop ) % Efficiently compute Felzenszwalb's HOG (FHOG) features. % % A fast implementation of the HOG variant used by Felzenszwalb et al. % in their work on discriminatively trained deformable part models. % http://www.cs.berkeley.edu/~rbg/latent/index.html % Gives nearly identical results to features.cc in code release version 5 % but runs 4x faster (over 125 fps on VGA color images). % % The computed HOG features are 3*nOrients+5 dimensional. There are % 2*nOrients contrast sensitive orientation channels, nOrients contrast % insensitive orientation channels, 4 texture channels and 1 all zeros % channel (used as a 'truncation' feature). Using the standard value of % nOrients=9 gives a 32 dimensional feature vector at each cell. This % variant of HOG, refered to as FHOG, has been shown to achieve superior % performance to the original HOG features. For details please refer to % work by Felzenszwalb et al. (see link above). % % This function is essentially a wrapper for calls to gradientMag() % and gradientHist(). Specifically, it is equivalent to the following: % [M,O] = gradientMag( I,0,0,0,1 ); softBin = -1; useHog = 2; % H = gradientHist(M,O,binSize,nOrients,softBin,useHog,clip); % See gradientHist() for more general usage. % % This code requires SSE2 to compile and run (most modern Intel and AMD % processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2. % % USAGE % H = fhog( 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 % % OUTPUTS % H - [h/binSize w/binSize nOrients*3+5] computed hog features % % EXAMPLE % I=imResample(single(imread('peppers.png'))/255,[480 640]); % tic, for i=1:100, H=fhog(I,8,9); end; disp(100/toc) % >125 fps % figure(1); im(I); V=hogDraw(H,25,1); figure(2); im(V) % % EXAMPLE % % comparison to features.cc (requires DPM code release version 5) % I=imResample(single(imread('peppers.png'))/255,[480 640]); Id=double(I); % tic, for i=1:100, H1=features(Id,8); end; disp(100/toc) % tic, for i=1:100, H2=fhog(I,8,9,.2,1); end; disp(100/toc) % figure(1); montage2(H1); figure(2); montage2(H2); % D=abs(H1-H2); mean(D(:)) % % See also hog, hogDraw, gradientHist % % Piotr's Image&Video Toolbox Version 3.23 % Copyright 2013 Piotr Dollar. [pdollar-at-caltech.edu] % Please email me if you find bugs, or have suggestions or questions! % Licensed under the Simplified BSD License [see external/bsd.txt] %Note: modified to be more self-contained if( nargin<2 ), binSize=8; end if( nargin<3 ), nOrients=9; end if( nargin<4 ), clip=.2; end if( nargin<5 ), crop=0; end softBin = -1; useHog = 2; b = binSize; [M,O]=gradientMex('gradientMag',I,0,1); H = gradientMex('gradientHist',M,O,binSize,nOrients,softBin,useHog,clip); if( crop ), e=mod(size(I),b)<b/2; H=H(2:end-e(1),2:end-e(2),:); end end