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pdollar committed Oct 30, 2014
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2 changes: 1 addition & 1 deletion README.md
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http://vision.ucsd.edu/~pdollar/toolbox/doc/
http://vision.ucsd.edu/~pdollar/toolbox/doc/
76 changes: 38 additions & 38 deletions channels/Contents.m
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% CHANNELS
% See also
%
% Fast channel feature computation code based on the papers:
% [1] P. Dollár, Z. Tu, P. Perona and S. Belongie
% "Integral Channel Features", BMVC 2009.
% [2] P. Dollár, S. Belongie and P. Perona
% "The Fastest Pedestrian Detector in the West," BMVC 2010.
% [3] P. Dollár, R. Appel and W. Kienzle
% "Crosstalk Cascades for Frame-Rate Pedestrian Detection," ECCV 2012.
% [4] P. Dollár, R. Appel, S. Belongie and P. Perona
% "Fast Feature Pyramids for Object Detection", PAMI 2014.
% Please cite a subset of the above papers if you end up using the code.
% The PAMI 2014 paper has the most thorough and up to date descriptions.
% Code written and maintained by Piotr Dollar and Ron Appel.
%
% Channels:
% chnsCompute - Compute channel features at a single scale given an input image.
% chnsPyramid - Compute channel feature pyramid given an input image.
% chnsScaling - Compute lambdas for channel power law scaling.
%
% Constant time image smoothing:
% convBox - Extremely fast 2D image convolution with a box filter.
% convMax - Extremely fast 2D image convolution with a max filter.
% convTri - Extremely fast 2D image convolution with a triangle filter.
%
% Gradients and gradient histograms:
% gradient2 - Compute numerical gradients along x and y directions.
% gradientHist - Compute oriented gradient histograms.
% gradientMag - Compute gradient magnitude and orientation at each image location.
% hog - Efficiently compute histogram of oriented gradient (HOG) features.
% hogDraw - Create visualization of hog descriptor.
% fhog - Efficiently compute Felzenszwalb's HOG (FHOG) features.
%
% Miscellaneous:
% imPad - Pad an image along its four boundaries.
% imResample - Fast bilinear image downsampling/upsampling.
% rgbConvert - Convert RGB image to other color spaces (highly optimized).
% CHANNELS
% See also
%
% Fast channel feature computation code based on the papers:
% [1] P. Dollár, Z. Tu, P. Perona and S. Belongie
% "Integral Channel Features", BMVC 2009.
% [2] P. Dollár, S. Belongie and P. Perona
% "The Fastest Pedestrian Detector in the West," BMVC 2010.
% [3] P. Dollár, R. Appel and W. Kienzle
% "Crosstalk Cascades for Frame-Rate Pedestrian Detection," ECCV 2012.
% [4] P. Dollár, R. Appel, S. Belongie and P. Perona
% "Fast Feature Pyramids for Object Detection", PAMI 2014.
% Please cite a subset of the above papers if you end up using the code.
% The PAMI 2014 paper has the most thorough and up to date descriptions.
% Code written and maintained by Piotr Dollar and Ron Appel.
%
% Channels:
% chnsCompute - Compute channel features at a single scale given an input image.
% chnsPyramid - Compute channel feature pyramid given an input image.
% chnsScaling - Compute lambdas for channel power law scaling.
%
% Constant time image smoothing:
% convBox - Extremely fast 2D image convolution with a box filter.
% convMax - Extremely fast 2D image convolution with a max filter.
% convTri - Extremely fast 2D image convolution with a triangle filter.
%
% Gradients and gradient histograms:
% gradient2 - Compute numerical gradients along x and y directions.
% gradientHist - Compute oriented gradient histograms.
% gradientMag - Compute gradient magnitude and orientation at each image location.
% hog - Efficiently compute histogram of oriented gradient (HOG) features.
% hogDraw - Create visualization of hog descriptor.
% fhog - Efficiently compute Felzenszwalb's HOG (FHOG) features.
%
% Miscellaneous:
% imPad - Pad an image along its four boundaries.
% imResample - Fast bilinear image downsampling/upsampling.
% rgbConvert - Convert RGB image to other color spaces (highly optimized).
374 changes: 187 additions & 187 deletions channels/chnsCompute.m
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408 changes: 204 additions & 204 deletions channels/chnsPyramid.m
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224 changes: 112 additions & 112 deletions channels/chnsScaling.m
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function [lambdas,as,scales,fs] = chnsScaling( pChns, Is, show )
% Compute lambdas for channel power law scaling.
%
% For a broad family of features, including gradient histograms and all
% channel types tested, the feature responses computed at a single scale
% can be used to approximate feature responses at nearby scales. The
% approximation is accurate at least within an entire scale octave. For
% details and to understand why this unexpected result holds, please see:
% P. Dollár, R. Appel, S. Belongie and P. Perona
% "Fast Feature Pyramids for Object Detection", PAMI 2014.
%
% This function computes channels at multiple image scales and plots the
% resulting power law scaling. The purpose of this function is two-fold:
% (1) compute lambdas for fast approximate channel computation for use in
% chnsPyramid() and (2) provide a visualization of the power law channel
% scaling described in the BMVC2010 paper.
%
% chnsScaling() takes two main inputs: the parameters for computing image
% channels (pChns), and an image or set of images (Is). The images are
% cropped to the dimension of the smallest image for simplicity of
% computing the lambdas (and fairly high resolution images are best). The
% computed lambdas will depend on the channel parameters (e.g. how much
% smoothing is performed), but given enough images (>1000) the computed
% lambdas should not depend on the exact images used.
%
% USAGE
% [lambdas,as,scales,fs] = chnsScaling( pChns, Is, [show] )
%
% INPUTS
% pChns - parameters for creating channels (see chnsCompute.m)
% Is - [nImages x 1] cell array of images (nImages may be 1)
% show - [1] figure in which to display results
%
% OUTPUTS
% lambdas - [nTypes x 1] computed lambdas
% as - [nTypes x 1] computed y-intercepts
% scales - [nScales x 1] vector of actual scales used
% fs - [nImages x nScales x nTypes] array of feature means
%
% EXAMPLE
% sDir = 'data/Inria/train/neg/';
% Is = fevalImages( @(x) {x}, {}, sDir, 'I', 'png', 0, 200 );
% p = chnsCompute(); lambdas = chnsScaling( p, Is, 1 );
%
% See also chnsCompute, chnsPyramid, fevalImages
%
% Piotr's Computer Vision Matlab Toolbox Version 3.25
% Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]

% get additional input arguments
if(nargin<3 || isempty(show)), show=1; end

% construct pPyramid (don't pad, concat or appoximate)
pPyramid=chnsPyramid(); pPyramid.pChns=pChns; pPyramid.concat=0;
pPyramid.pad=[0 0]; pPyramid.nApprox=0; pPyramid.smooth=0;
pPyramid.minDs(:)=max(8,pChns.shrink*4);

% crop all images to smallest image size
ds=[inf inf]; nImages=numel(Is);
for i=1:nImages, ds=min(ds,[size(Is{i},1) size(Is{i},2)]); end
ds=round(ds/pChns.shrink)*pChns.shrink;
for i=1:nImages, Is{i}=Is{i}(1:ds(1),1:ds(2),:); end

% compute fs [nImages x nScales x nTypes] array of feature means
P=chnsPyramid(Is{1},pPyramid); scales=P.scales'; info=P.info;
nScales=P.nScales; nTypes=P.nTypes; fs=zeros(nImages,nScales,nTypes);
parfor i=1:nImages, P=chnsPyramid(Is{i},pPyramid); for j=1:nScales
for k=1:nTypes, fs(i,j,k)=mean(P.data{j,k}(:)); end; end; end

% remove fs with fs(:,1,:) having small values
kp=max(fs(:,1,:)); kp=fs(:,1,:)>kp(ones(1,nImages),1,:)/50;
kp=min(kp,[],3); fs=fs(kp,:,:); nImages=size(fs,1);

% compute ratios, intercepts and lambdas using least squares
scales1=scales(2:end); nScales=nScales-1; O=ones(nScales,1);
rs=fs(:,2:end,:)./fs(:,O,:); mus=permute(mean(rs,1),[2 3 1]);
out=[O -log2(scales1)]\log2(mus); as=2.^out(1,:); lambdas=out(2,:);
if(0), lambdas=-log2(scales1)\log2(mus); as(:)=1; end
if(show==0), return; end

% compute predicted means and errors for display purposes
musp=as(O,:).*scales1(:,ones(1,nTypes)).^-lambdas(O,:);
errsFit=mean(abs(musp-mus)); stds=permute(std(rs,0,1),[2 3 1]);

% plot results
if(show<0), show=-show; clear=0; else clear=1; end
figureResized(.75,show); if(clear), clf; end
lp={'LineWidth',2}; tp={'FontSize',12};
for k=1:nTypes
% plot ratios
subplot(2,nTypes,k); set(gca,tp{:});
for i=round(linspace(1,nImages,20))
loglog(1./scales1,rs(i,:,k),'Color',[1 1 1]*.8); hold on; end
h0=loglog(1./scales1,mus(:,k),'go',lp{:});
h1=loglog(1./scales1,musp(:,k),'b-',lp{:});
title(sprintf('%s\n\\lambda = %.03f, error = %.2e',...
info(k).name,lambdas(k),errsFit(k)));
legend([h0 h1],{'real','fit'},'location','ne');
xlabel('log2(scale)'); ylabel('\mu (ratio)'); axis tight;
ax=axis; ax(1)=1; ax(3)=min(.9,ax(3)); ax(4)=max(2,ax(4)); axis(ax);
set(gca,'ytick',[.5 1 1.4 2 3 4],'YMinorTick','off');
set(gca,'xtick',2.^(-10:.5:10),'XTickLabel',10:-.5:-10);
% plot variances
subplot(2,nTypes,k+nTypes); set(gca,tp{:});
semilogx(1./scales1,stds(:,k),'go',lp{:}); hold on;
xlabel('log2(scale)'); ylabel('\sigma (ratio)'); axis tight;
ax=axis; ax(1)=1; ax(3)=0; ax(4)=max(.5,ax(4)); axis(ax);
set(gca,'xtick',2.^(-10:.5:10),'XTickLabel',10:-.5:-10);
end

end
function [lambdas,as,scales,fs] = chnsScaling( pChns, Is, show )
% Compute lambdas for channel power law scaling.
%
% For a broad family of features, including gradient histograms and all
% channel types tested, the feature responses computed at a single scale
% can be used to approximate feature responses at nearby scales. The
% approximation is accurate at least within an entire scale octave. For
% details and to understand why this unexpected result holds, please see:
% P. Dollár, R. Appel, S. Belongie and P. Perona
% "Fast Feature Pyramids for Object Detection", PAMI 2014.
%
% This function computes channels at multiple image scales and plots the
% resulting power law scaling. The purpose of this function is two-fold:
% (1) compute lambdas for fast approximate channel computation for use in
% chnsPyramid() and (2) provide a visualization of the power law channel
% scaling described in the BMVC2010 paper.
%
% chnsScaling() takes two main inputs: the parameters for computing image
% channels (pChns), and an image or set of images (Is). The images are
% cropped to the dimension of the smallest image for simplicity of
% computing the lambdas (and fairly high resolution images are best). The
% computed lambdas will depend on the channel parameters (e.g. how much
% smoothing is performed), but given enough images (>1000) the computed
% lambdas should not depend on the exact images used.
%
% USAGE
% [lambdas,as,scales,fs] = chnsScaling( pChns, Is, [show] )
%
% INPUTS
% pChns - parameters for creating channels (see chnsCompute.m)
% Is - [nImages x 1] cell array of images (nImages may be 1)
% show - [1] figure in which to display results
%
% OUTPUTS
% lambdas - [nTypes x 1] computed lambdas
% as - [nTypes x 1] computed y-intercepts
% scales - [nScales x 1] vector of actual scales used
% fs - [nImages x nScales x nTypes] array of feature means
%
% EXAMPLE
% sDir = 'data/Inria/train/neg/';
% Is = fevalImages( @(x) {x}, {}, sDir, 'I', 'png', 0, 200 );
% p = chnsCompute(); lambdas = chnsScaling( p, Is, 1 );
%
% See also chnsCompute, chnsPyramid, fevalImages
%
% Piotr's Computer Vision Matlab Toolbox Version 3.25
% Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]

% get additional input arguments
if(nargin<3 || isempty(show)), show=1; end

% construct pPyramid (don't pad, concat or appoximate)
pPyramid=chnsPyramid(); pPyramid.pChns=pChns; pPyramid.concat=0;
pPyramid.pad=[0 0]; pPyramid.nApprox=0; pPyramid.smooth=0;
pPyramid.minDs(:)=max(8,pChns.shrink*4);

% crop all images to smallest image size
ds=[inf inf]; nImages=numel(Is);
for i=1:nImages, ds=min(ds,[size(Is{i},1) size(Is{i},2)]); end
ds=round(ds/pChns.shrink)*pChns.shrink;
for i=1:nImages, Is{i}=Is{i}(1:ds(1),1:ds(2),:); end

% compute fs [nImages x nScales x nTypes] array of feature means
P=chnsPyramid(Is{1},pPyramid); scales=P.scales'; info=P.info;
nScales=P.nScales; nTypes=P.nTypes; fs=zeros(nImages,nScales,nTypes);
parfor i=1:nImages, P=chnsPyramid(Is{i},pPyramid); for j=1:nScales
for k=1:nTypes, fs(i,j,k)=mean(P.data{j,k}(:)); end; end; end

% remove fs with fs(:,1,:) having small values
kp=max(fs(:,1,:)); kp=fs(:,1,:)>kp(ones(1,nImages),1,:)/50;
kp=min(kp,[],3); fs=fs(kp,:,:); nImages=size(fs,1);

% compute ratios, intercepts and lambdas using least squares
scales1=scales(2:end); nScales=nScales-1; O=ones(nScales,1);
rs=fs(:,2:end,:)./fs(:,O,:); mus=permute(mean(rs,1),[2 3 1]);
out=[O -log2(scales1)]\log2(mus); as=2.^out(1,:); lambdas=out(2,:);
if(0), lambdas=-log2(scales1)\log2(mus); as(:)=1; end
if(show==0), return; end

% compute predicted means and errors for display purposes
musp=as(O,:).*scales1(:,ones(1,nTypes)).^-lambdas(O,:);
errsFit=mean(abs(musp-mus)); stds=permute(std(rs,0,1),[2 3 1]);

% plot results
if(show<0), show=-show; clear=0; else clear=1; end
figureResized(.75,show); if(clear), clf; end
lp={'LineWidth',2}; tp={'FontSize',12};
for k=1:nTypes
% plot ratios
subplot(2,nTypes,k); set(gca,tp{:});
for i=round(linspace(1,nImages,20))
loglog(1./scales1,rs(i,:,k),'Color',[1 1 1]*.8); hold on; end
h0=loglog(1./scales1,mus(:,k),'go',lp{:});
h1=loglog(1./scales1,musp(:,k),'b-',lp{:});
title(sprintf('%s\n\\lambda = %.03f, error = %.2e',...
info(k).name,lambdas(k),errsFit(k)));
legend([h0 h1],{'real','fit'},'location','ne');
xlabel('log2(scale)'); ylabel('\mu (ratio)'); axis tight;
ax=axis; ax(1)=1; ax(3)=min(.9,ax(3)); ax(4)=max(2,ax(4)); axis(ax);
set(gca,'ytick',[.5 1 1.4 2 3 4],'YMinorTick','off');
set(gca,'xtick',2.^(-10:.5:10),'XTickLabel',10:-.5:-10);
% plot variances
subplot(2,nTypes,k+nTypes); set(gca,tp{:});
semilogx(1./scales1,stds(:,k),'go',lp{:}); hold on;
xlabel('log2(scale)'); ylabel('\sigma (ratio)'); axis tight;
ax=axis; ax(1)=1; ax(3)=0; ax(4)=max(.5,ax(4)); axis(ax);
set(gca,'xtick',2.^(-10:.5:10),'XTickLabel',10:-.5:-10);
end

end
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