基于支持向量机SVM的车祸识别，基于SVM的车祸识别（代码完整，数据齐全）

function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST) % % [gist, param] = LMgist(D, HOMEIMAGES, param); % [gist, param] = LMgist(filename, HOMEIMAGES, param); % [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST); % % For a set of images: % gist = LMgist(img, [], param); % % When calling LMgist with a fourth argument it will store the gists in a % new folder structure mirroring the folder structure of the images. Then, % when called again, if the gist files already exist, it will just read % them without recomputing them: % % [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST); % [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST); % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Modeling the shape of the scene: a holistic representation of the spatial envelope % Aude Oliva, Antonio Torralba % International Journal of Computer Vision, Vol. 42(3): 145-175, 2001. if nargin==4 precomputed = 1; % get list of folders and create non-existing ones %listoffolders = {D(:).annotation.folder}; %for i = 1:length(D); % f{i} = D(i).annotation.folder; %end %[categories,b,class] = unique(f); else precomputed = 0; HOMEGIST = ''; end % select type of input if isstruct(D) % [gist, param] = LMgist(D, HOMEIMAGES, param); Nscenes = length(D); typeD = 1; end if iscell(D) % [gist, param] = LMgist(filename, HOMEIMAGES, param); Nscenes = length(D); typeD = 2; end if isnumeric(D) % [gist, param] = LMgist(img, HOMEIMAGES, param); Nscenes = size(D,4); typeD = 3; if ~isfield(param, 'imageSize') param.imageSize = [size(D,1) size(D,2)]; end end param.boundaryExtension = 32; % number of pixels to pad if nargin<3 % Default parameters param.imageSize = 128; param.orientationsPerScale = [8 8 8 8]; param.numberBlocks = 4; param.fc_prefilt = 4; param.G = createGabor(param.orientationsPerScale, param.imageSize+2*param.boundaryExtension); else if ~isfield(param, 'G') param.G = createGabor(param.orientationsPerScale, param.imageSize+2*param.boundaryExtension); end end % Precompute filter transfert functions (only need to do this once, unless % image size is changes): Nfeatures = size(param.G,3)*param.numberBlocks^2; % Loop: Compute gist features for all scenes gist = zeros([Nscenes Nfeatures], 'single'); for n = 1:Nscenes g = []; todo = 1; % if gist has already been computed, just read the file if precomputed==1 filegist = fullfile(HOMEGIST, D(n).annotation.folder, [D(n).annotation.filename(1:end-4) '.mat']); if exist(filegist, 'file') load(filegist, 'g'); todo = 0; end end % otherwise compute gist if todo==1 if Nscenes>1 disp([n Nscenes]); end % load image try switch typeD case 1 img = LMimread(D, n, HOMEIMAGES); case 2 img = imread(fullfile(HOMEIMAGES, D{n})); case 3 img = D(:,:,:,n); end catch disp(D(n).annotation.folder) disp(D(n).annotation.filename) rethrow(lasterror) end % convert to gray scale img = single(mean(img,3)); % resize and crop image to make it square %img = imresizecrop(img, param.imageSize, 'bilinear'); img = imresize(img, param.imageSize, 'bilinear'); %jhhays % scale intensities to be in the range [0 255] img = img-min(img(:)); img = 255*img/max(img(:)); if Nscenes>1 imshow(uint8(img)) title(n) end % prefiltering: local contrast scaling output = prefilt(img, param.fc_prefilt); % get gist: g = gistGabor(output, param); % save gist if a HOMEGIST file is provided if precomputed mkdir(fullfile(HOMEGIST, D(n).annotation.folder)) save (filegist, 'g') end end gist(n,:) = g; drawnow end function output = prefilt(img, fc) % ima = prefilt(img, fc); % fc = 4 (default) % % Input images are double in the range [0, 255]; % You can also input a block of images [ncols nrows 3 Nimages] % % For color images, normalization is done by dividing by the local % luminance variance. if nargin == 1 fc = 4; % 4 cycles/image end w = 5; s1 = fc/sqrt(log(2)); % Pad images to reduce boundary artifacts img = log(img+1); img = padarray(img, [w w], 'symmetric'); [sn, sm, c, N] = size(img); n = max([sn sm]); n = n + mod(n,2); img = padarray(img, [n-sn n-sm], 'symmetric','post'); % Filter [fx, fy] = meshgrid(-n/2:n/2-1); gf = fftshift(exp(-(fx.^2+fy.^2)/(s1^2))); gf = repmat(gf, [1 1 c N]); % Whitening output = img - real(ifft2(fft2(img).*gf)); clear img % Local contrast normalization localstd = repmat(sqrt(abs(ifft2(fft2(mean(output,3).^2).*gf(:,:,1,:)))), [1 1 c 1]); output = output./(.2+localstd); % Crop output to have same size than the input output = output(w+1:sn-w, w+1:sm-w,:,:); function g = gistGabor(img, param) % % Input: % img = input image (it can be a block: [nrows, ncols, c, Nimages]) % param.w = number of windows (w*w) % param.G = precomputed transfer functions % % Output: % g: are the global features = [Nfeatures Nimages], % Nfeatures = w*w*Nfilters*c img = single(img); w = param.numberBlocks; G = param.G; be = param.boundaryExtension; if ndims(img)==2 c = 1; N = 1; [nrows ncols c] = size(img); end if ndims(img)==3 [nrows ncols c] = size(img); N = c; end if ndims(img)==4 [nrows ncols c N] = size(img); img = reshape(img, [nrows ncols c*N]); N = c*N; end [ny nx Nfilters] = size(G); W = w*w; g = zeros([W*Nfilters N]); % pad image img = padarray(img, [be be], 'symmetric'); img = single(fft2(img)); k=0; for n = 1:Nfilters ig = abs(ifft2(img.*repmat(G(:,:,n), [1 1 N]))); ig = ig(be+1:ny-be, be+1:nx-be, :); v = downN(ig, w); g(k+1:k+W,:) = reshape(v, [W N]); k = k + W; drawnow end if c == 3 % If the input was a color image, then reshape 'g' so that one column % is one images output: g = reshape(g, [size(g,1)*3 size(g,2)/3]); end function y=downN(x, N) % % averaging over non-overlapping square image blocks % % Input % x = [nrows ncols nchanels] % Output % y = [N N nchanels] nx = fix(linspace(0,size(x,1),N+1)); ny = fix(linspace(0,size(x,2),N+1)); y = zeros(N, N, size(x,3)); for xx=1:N for yy=1:N v=mean(mean(x(nx(xx)+1:nx(xx+1), ny(yy)+1:ny(yy+1),:),1),2); y(xx,yy,:)=v(:); end end function G = createGabor(or, n) % % G = createGabor(numberOfOrientationsPerScale, n); % % Precomputes filter transfer functions. All computations are done on the % Fourier domain. % % If you call this function without output arguments it will show the % tiling of the Fourier domain. % % Input % numberOfOrientationsPerScale = vector that contains the number of % orientations at each scale (from HF to BF) % n = imagesize = [nrows ncols] % % output % G = transfer functions for a jet of gabor filters Nscales = length(or); Nfilters = sum(or); if length(n) == 1 n = [n(1) n(1)]; end l=0; for i=1:Nscales for j=1:or(i) l=l+1; param(l,:)=[.35 .3/(1.85^(i-1)) 16*or(i)^2/32^2 pi/(or(i))*(j-1)]; end end % Frequencies: %[fx, fy] = meshgrid(-n/2:n/2-1); [fx, fy] = meshgrid(-n(2)/2:n(2)/2-1, -n(1)/2:n(1)/2-1); fr = fftshift(sqrt(fx.^2+fy.^2)); t = fftshift(angle(fx+sqrt(