update function names again

esvm_apply_and_show_exemplars.m

@@ -1,7 +1,9 @@
-function esvm_apply_and_show_exemplars(imageset, models, M)
+function esvm_apply_and_show_exemplars(imageset, models, M, params)
 % We apply the ensemble of Exemplar-SVMs represented by [models,M] onto
 % the sequence of images [imageset], and display images without
-% saving anything
+% saving anything.  Takes as input optional [params], which default
+% to the default ones.
+%
 % Copyright (C) 2011-12 by Tomasz Malisiewicz
 % All rights reserved.
 % 
@@ -36,7 +38,10 @@ function esvm_apply_and_show_exemplars(imageset, models, M)
   end
 end
 
-params = esvm_get_default_params;
+if ~exist('params','var')
+  params = esvm_get_default_params;
+end
+
 params.calibration_propagate_onto_raw = 1;
 for i = 1:length(imageset)
   %Get local detections

esvm_demo_apply.m

@@ -12,6 +12,13 @@
 
 %% Download and load pre-trained VOC2007 bus models
 [models, M, test_set] = esvm_download_models(cls);
+params = esvm_get_default_params;
+
+%We can speed things (2x) up by turning of detections on image flips
+%params.detect_add_flip = 0;
+
+%We can also speed things up by taking a subset of models
+%[models,M] = esvm_subset_of_models(models,M,1:100);
 
 %If VOC is locally installed, let models point to local images, not URLs
 local_dir = '/Users/tomasz/projects/pascal/VOCdevkit/';
@@ -21,19 +28,21 @@
 
 %% Load one image, and apply bus detector
 I1 = imread('000858.jpg');
-esvm_apply_and_show_exemplars(I1, models, M);
+esvm_apply_and_show_exemplars(I1, models, M, params);
 
 %% Create an array of images, and apply bus detector
 I2 = imread('009021.jpg');
 I3 = imread('009704.jpg');
 Iarray = {I2, I3};
-esvm_apply_and_show_exemplars(Iarray, models, M);
+esvm_apply_and_show_exemplars(Iarray, models, M, params);
 
 %% Create URL-based set of images, and apply bus detector
+
 %Show image filepaths
 cat(1,test_set{1:5})
+
 %Apply detector
-esvm_apply_and_show_exemplars(test_set(1:5), models, M);
+esvm_apply_and_show_exemplars(test_set(1:5), models, M, params);
 
 %% Set image path directory, and apply bus detector
 Idirectory = '/v2/SUN/Images/b/bus_depot/outdoor/';
@@ -45,7 +54,7 @@
   MAXDIM = 500;
   Ilist = cellfun2(@(x)(@()imresize_max(convert_to_I(x),MAXDIM)), ...
                    Ilist);
-  esvm_apply_and_show_exemplars(Ilist, models, M);
+  esvm_apply_and_show_exemplars(Ilist, models, M, params);
 else
   fprintf(1,'Note: not applying because %s is not a directory\n',...
           Idirectory);

esvm_demo_train_synthetic.m

@@ -12,38 +12,44 @@
 addpath(genpath(pwd))
 
 %% Create a synthetic dataset of circles on a random background
-Npos = 100;
-Nneg = 100;
+Npos = 20;
+Nneg = 20;
 [pos_set,neg_set] = esvm_generate_dataset(Npos,Nneg);
 
+models_name = 'circle';
+
 %% Set exemplar-initialization parameters
 params = esvm_get_default_params;
 params.init_params.sbin = 4;
 params.init_params.MAXDIM = 6;
 params.model_type = 'exemplar';
 params.dataset_params.display = 1;
+
 %if localdir is commented out, no local saving happens
-%params.dataset_params.localdir = '/nfs/baikal/tmalisie/synthetic/';
+params.dataset_params.localdir = '/nfs/baikal/tmalisie/synthetic/';
 
 %%Initialize exemplar stream
 stream_params.stream_set_name = 'trainval';
-stream_params.stream_max_ex = 20;
+stream_params.stream_max_ex = 1;
 stream_params.must_have_seg = 0;
 stream_params.must_have_seg_string = '';
 stream_params.model_type = 'exemplar'; %must be scene or exemplar
-%assign pos_set as variable
+%assign pos_set as variable, because we need it for visualization
 stream_params.pos_set = pos_set;
+stream_params.cls = models_name;
 
 %% Get the positive stream
 e_stream_set = esvm_get_pascal_stream(stream_params, ...
                                       params.dataset_params);
 
+% break it up into a set of held out negatives, and the ones used
+% for mining
 val_neg_set = neg_set((Nneg/2+1):end);
 neg_set = neg_set(1:((Nneg/2)));
 
 %% Initialize Exemplars
 initial_models = esvm_initialize_exemplars(e_stream_set, params, ...
-                                           'initial');
+                                           models_name);
 
 %% Set exemplar-svm training parameters
 train_params = params;
@@ -52,38 +58,38 @@
 train_params.detect_exemplar_nms_os_threshold = 1.0; 
 train_params.detect_max_windows_per_exemplar = 100;
 
-train_params.CACHE_FILE = 1;
 %% Perform Exemplar-SVM training
 [models] = esvm_train_exemplars(initial_models, ...
                                 neg_set, train_params);
 
+%% Create validation set from positives and extra negatives
 val_params = params;
 val_params.detect_exemplar_nms_os_threshold = 0.5;
 val_params.gt_function = @esvm_load_gt_function;
 val_set = cat(1, pos_set(:), val_neg_set(:));
-val_params.val_set = val_set;
 val_set_name = 'valset';
 
 %% Apply trained exemplars on validation set
 val_grid = esvm_detect_imageset(val_set, models, val_params, val_set_name);
 
 %% Perform Platt calibration and M-matrix estimation
-M = esvm_perform_calibration(val_grid, models, val_params);
+M = esvm_perform_calibration(val_grid, val_set, models, val_params);
 
 %% Define test-set
+Ntest = 20;
 test_params = params;
 test_params.detect_exemplar_nms_os_threshold = 0.5;
-Ntest = 200;
 [stream_test] = esvm_generate_dataset(Ntest);
 test_set = cellfun2(@(x)x.I, stream_test);
+test_set_name = 'testset';
 
 %% Apply on test set
-test_grid = esvm_detect_imageset(test_set, models, test_params);
+test_grid = esvm_detect_imageset(test_set, models, test_params, test_set_name);
 
 %% Apply calibration matrix to test-set results
 test_struct = esvm_pool_exemplar_dets(test_grid, models, M, test_params);
 
 %% Show top detections
 maxk = 20;
 allbbs = esvm_show_top_dets(test_struct, test_grid, test_set, models, ...
-                       params,  maxk);
+                       params, maxk, test_set_name);

esvm_detect.m

@@ -254,30 +254,31 @@
 sizes2 = cellfun(@(x)x.model.hg_size(2),models);
 
 S = [max(sizes1(:)) max(sizes2(:))];
-templates = zeros(S(1),S(2),features,length(models));
-templates_x = zeros(S(1),S(2),features,length(models));
-template_masks = zeros(S(1),S(2),features,length(models));
+fsize = params.init_params.features();
+templates = zeros(S(1),S(2),fsize,length(models));
+templates_x = zeros(S(1),S(2),fsize,length(models));
+template_masks = zeros(S(1),S(2),fsize,length(models));
 
 for i = 1:length(models)
-  t = zeros(S(1),S(2),features);
+  t = zeros(S(1),S(2),fsize);
   t(1:models{i}.model.hg_size(1),1:models{i}.model.hg_size(2),:) = ...
       models{i}.model.w;
 
   templates(:,:,:,i) = t;
-  template_masks(:,:,:,i) = repmat(double(sum(t.^2,3)>0),[1 1 features]);
+  template_masks(:,:,:,i) = repmat(double(sum(t.^2,3)>0),[1 1 fsize]);
 
   if (~isempty(params.nnmode)) || ...
         (isfield(params,'wtype') && ...
          strcmp(params.wtype,'dfun')==1)
-    x = zeros(S(1),S(2),features);
+    x = zeros(S(1),S(2),fsize);
     x(1:models{i}.model.hg_size(1),1:models{i}.model.hg_size(2),:) = ...
         reshape(models{i}.model.x(:,1),models{i}.model.hg_size);
     templates_x(:,:,:,i) = x;
 
   end
 end
 
-%maskmat = repmat(template_masks,[1 1 1 features]);
+%maskmat = repmat(template_masks,[1 1 1 fsize]);
 %maskmat = permute(maskmat,[1 2 4 3]);
 %templates_x  = templates_x .* maskmat;
 
@@ -288,7 +289,7 @@
 pyr_N = cellfun(@(x)prod([size(x,1) size(x,2)]-S+1),t.hog);
 sumN = sum(pyr_N);
 
-X = zeros(S(1)*S(2)*features,sumN);
+X = zeros(S(1)*S(2)*fsize,sumN);
 offsets = cell(length(t.hog), 1);
 uus = cell(length(t.hog),1);
 vvs = cell(length(t.hog),1);
@@ -298,7 +299,7 @@
   s = size(t.hog{i});
   NW = s(1)*s(2);
   ppp = reshape(1:NW,s(1),s(2));
-  curf = reshape(t.hog{i},[],features);
+  curf = reshape(t.hog{i},[],fsize);
   b = im2col(ppp,[S(1) S(2)]);
 
   offsets{i} = b(1,:);
@@ -317,7 +318,7 @@
 uus = cat(2,uus{:});
 vvs = cat(2,vvs{:});
 
-% m.model.w = zeros(S(1),S(2),features);
+% m.model.w = zeros(S(1),S(2),fsize);
 % m.model.b = 0;
 % temp_params = params;
 % temp_params.detect_save_features = 1;

esvm_initialize_exemplars.m

@@ -150,7 +150,6 @@
     allfiles{i} = m;
   end
 
-
   % %Print the bounding box overlap between the initial window and
   % %the final window
   % finalos = getosmatrix_bb(m.gt_box, m.model.bb(1,:));
@@ -162,7 +161,7 @@
 
   %Show the initialized exemplars
   if params.dataset_params.display == 1
-    show_exemplar_frames({m}, 1, params.dataset_params);
+    esvm_show_exemplar_frames({m}, 1, params.dataset_params);
     drawnow
     snapnow;
   end

esvm_perform_calibration.m

@@ -1,4 +1,4 @@
-function M = esvm_perform_calibration(grid, models, params, CACHE_FILES)
+function M = esvm_perform_calibration(grid, val_set, models, params)
 % 1. Perform LABOO calibration procedure and 2. Learn a combination
 % matrix M which multiplexes the detection results (by compiling
 % co-occurrence statistics on true positives) 
@@ -10,20 +10,14 @@
 % available under the terms of the MIT license (see COPYING file).
 % Project homepage: https://github.com/quantombone/exemplarsvm
 
-if isfield(params,'CACHE_BETAS') && params.CACHE_BETAS==1%~exist('CACHE_FILES','var')
-  CACHE_FILES = 1;
-else
-  CACHE_FILES = 0;
-end
-
 %% Perform calibration
-betas = esvm_perform_platt_calibration(grid, models, ...
-                                       params, CACHE_FILES);
+betas = esvm_perform_platt_calibration(grid, val_set, models, ...
+                                       params);
 
+%% Estimate the co-occurrence matrix M
 if ~(isfield(params,'SKIP_M') && params.SKIP_M==1)
-  %% Estimate the co-occurrence matrix M
-  [M] = esvm_estimate_M(grid, models, params, CACHE_FILES);
-
+  M = esvm_estimate_M(grid, models, params);
 end
 
+%concatenate results
 M.betas = betas;

esvm_show_top_dets.m

@@ -1,13 +1,11 @@
 function allbbs = esvm_show_top_dets(test_struct, grid, ...
                                      test_set, models, params, ...
                                      maxk, set_name)
-
-
 % Show maxk top detections for [models] where [grid] is the set of
 % detections from the set [test_set], [test_struct] contains final
-% boxes after calibration and is obtained from applying calibration
-% If [set_name] is present, then results are saved based on naming
-% convention into www/ subfolder
+% boxes after pooling and calibration. If [dataset_params.localdir] is
+% present, then results are saved based on naming convention into a
+% "www" subfolder. maxk is the number of top detections we show
 %
 % NOTE: this function requires some cleanup, but is functional
 %
@@ -20,13 +18,13 @@
 
 allbbs = [];
 
-if exist('set_name','var') && length(set_name)>0
+if length(params.dataset_params.localdir) > 0
   CACHE_FILES = 1;
 else
   CACHE_FILES = 0;
-  set_name = '';
 end
 
+
 %Default exemplar-inpainting show mode
 %SHOW_MODE = 1;
 
@@ -111,18 +109,20 @@
     wwwdir = sprintf('%s/www/%s.%s%s/',params.dataset_params.localdir,...
                      set_name, ...
                      models{1}.models_name,test_struct.calib_string);
-    if ~exist(wwwdir,'dir') && exist('CACHE_FILES','var') && (CACHE_FILES == 1)
+    if ~exist(wwwdir,'dir') && (CACHE_FILES == 1)
       mkdir(wwwdir);
     end
 
-    filer = sprintf('%s/%05d%s',wwwdir,k,suffix);
+    filer = sprintf('%s/%05d%s.png',wwwdir,k,suffix);
     filerlock = [filer '.lock'];
-    if 0 %fileexists(filer) || (mymkdir_dist(filerlock) == 0)
+
+    if CACHE_FILES && (fileexists(filer) || (mymkdir_dist(filerlock) == 0))
       counter = counter + 1;
+      fprintf(1,'Already showed detection # %d, score=%.3f\n', k, bbs(bb(counter),end));
       continue
     end
 
-    fprintf(1,'Showing top detection %d\n', k);
+    fprintf(1,'Showing detection # %d, score=%.3f\n', k, bbs(bb(counter),end));
     allbbs(k,:) = bbs(bb(counter),:);
 
     curb = bb(counter);
@@ -235,9 +235,9 @@
     drawnow
     snapnow
 
-    if exist('CACHE_FILES','var') && CACHE_FILES == 1
+    if CACHE_FILES == 1
       %print(gcf,'-depsc2',filer);
-      print(gcf,'-dpng',[filer '.png']);
+      print(gcf,'-dpng',filer);
       %finalfile = strrep(filer,'.eps','.pdf');
       %unix(sprintf('zsh "ps2pdf -dEPSCrop -dPDFSETTINGS=/prepress %s %s"',...
       %             filer,finalfile));
@@ -246,7 +246,7 @@
       %  unix(sprintf('rm %s',filer));
       %end
 
-      if exist(filerlock,'dir')
+      if CACHE_FILES && exist(filerlock,'dir')
         rmdir(filerlock);
       end
     end