Reduce number of color space conversions

source/button-matching/createButtonMosaic.m

@@ -2,7 +2,7 @@
 % button_history, similarity_threshold, min_dominant_radius, 
 % unique_button_limit, scale, AA
 
-function [mosaic, corrected] = createButtonMosaic(circles, image, S)
+function [mosaic, corrected] = createButtonMosaic(circles, image_lab, S)
 
     search_settings.num_matches = S.button_history;
     search_settings.similarity_threshold = S.similarity_threshold;
@@ -11,7 +11,7 @@
 
     clear findMatchingButtons;
     if(search_settings.use_subset)
-        createLimitedDatabase(image, S.unique_button_limit);
+        createLimitedDatabase(image_lab, S.unique_button_limit);
     end
 
     if nargin < 3 || ~isPowerOfTwo(S.scale)
@@ -22,7 +22,7 @@
         S.AA = 1;
     end
 
-    dims = size(image, 1:2);
+    dims = size(image_lab, 1:2);
     mosaic = zeros([dims * S.AA * S.scale, 3]);
 
     produce_corrected = nargout > 1;

source/buttonMosaic.m

@@ -3,26 +3,26 @@
     addpath(genpath('.'));
     warning('off','images:bwfilt:tie');
 
-    image = im2double(image);
+    image_lab = rgb2lab(im2double(image));
 
     if nargin < 3
-        [circle_packing_settings, mosaic_settings] = estSettings(image);
+        [circle_packing_settings, mosaic_settings] = estSettings(image_lab);
     end
 
-    label_image = segmentImage(image, circle_packing_settings);
-    circles = createPackedCircles(image, label_image, circle_packing_settings);
+    label_image = segmentImage(image_lab, circle_packing_settings);
+    circles = createPackedCircles(image_lab, label_image, circle_packing_settings);
 
     if nargout > 1
-        [mosaic, corrected] = createButtonMosaic(circles, image, mosaic_settings);
+        [mosaic, corrected] = createButtonMosaic(circles, image_lab, mosaic_settings);
     else
-        mosaic = createButtonMosaic(circles, image, mosaic_settings);
+        mosaic = createButtonMosaic(circles, image_lab, mosaic_settings);
     end
 end
 
-function [circle_packing_settings, mosaic_settings] = estSettings(image)
-    width = size(image, 2);
+function [circle_packing_settings, mosaic_settings] = estSettings(image_lab)
+    width = size(image_lab, 2);
     est_factor = width/1024;
-    [num_clusters, low_unique] = findNumClusters(image);
+    [num_clusters, low_unique] = findNumClusters(image_lab);
 
     disp(['Number of clusters: ' num2str(num_clusters)])
 

source/circle-packing/createPackedCircles.m

@@ -1,7 +1,7 @@
 % S requires the fields:
 % min_radius, max_radius, radius_reduction_start
 
-function circles = createPackedCircles(image, label_image, S)
+function circles = createPackedCircles(image_lab, label_image, S)
     num_regions = max(label_image(:));
 
     circles = [];
@@ -29,10 +29,10 @@
             BB(2) = clamp(BB(2) - 1, 1, h);
 
             small_mask = mask(BB(2):BB(2)+BB(4)-1, BB(1):BB(1)+BB(3)-1);
-            small_image = image(BB(2):BB(2)+BB(4)-1, BB(1):BB(1)+BB(3)-1, :);
+            small_image_lab = image_lab(BB(2):BB(2)+BB(4)-1, BB(1):BB(1)+BB(3)-1, :);
             offset = [BB(1) - 1, BB(2) - 1];
         else
-            small_image = image;
+            small_image_lab = image_lab;
             small_mask = remaining;
             offset = [0, 0];
         end
@@ -61,7 +61,7 @@
 
             radius = floor(radius);
 
-            [small_mask, circles] = createCircle(small_image, circles, centroid, radius, small_mask, offset);
+            [small_mask, circles] = createCircle(small_image_lab, circles, centroid, radius, small_mask, offset);
         end
 
         if(i <= (num_regions - 1))
@@ -85,7 +85,7 @@
     close(f)
 end
 
-function [L, circles] = createCircle(image, circles, centroid, radius, L, offset)
+function [L, circles] = createCircle(image_lab, circles, centroid, radius, L, offset)
 
     new_circle = struct;
 
@@ -102,15 +102,15 @@
     region(circle) = false;
     L(x_range, y_range) = region;
 
-    rgb_region = image(x_range, y_range, :);
+    lab_region = image_lab(x_range, y_range, :);
 
-    new_circle.dominant_colors = kDominantColors(rgb_region, circle, 3, 20000, 3);
+    new_circle.dominant_colors = kDominantColors(lab_region, circle, 3, 20000, 3);
     new_circle.radius = radius;
     new_circle.position = offset + centroid;
 
-    colors = reshape(rgb_region, [], 3);
-    colors = colors(circle(:), :);
-    new_circle.mean_color_lab = mean(rgb2lab(colors));
+    colors_lab = reshape(lab_region, [], 3);
+    colors_lab = colors_lab(circle(:), :);
+    new_circle.mean_color_lab = mean(colors_lab);
 
     circles = [circles new_circle];
 end

source/circle-packing/findNumClusters.m

@@ -1,8 +1,8 @@
-function [num_clusters, low_unique] = findNumClusters(image)
+function [num_clusters, low_unique] = findNumClusters(image_lab)
 
     max_clusters = 16;
 
-    num_unique_colors = size(unique(reshape(im2uint8(image), [], 3), 'rows'), 1);
+    num_unique_colors = size(unique(reshape(im2uint8(lab2rgb(image_lab)), [], 3), 'rows'), 1);
 
     low_unique = false;
     if num_unique_colors <= max_clusters
@@ -11,22 +11,20 @@
         return;
     end
 
-    num_colors = prod(size(image, 1:2));
+    num_colors = prod(size(image_lab, 1:2));
     max_colors = 256*256;
 
     if(num_colors > max_colors)
-        image = imresize(image, sqrt(max_colors/num_colors), 'bilinear');
+        image_lab = imresize(image_lab, sqrt(max_colors/num_colors), 'bilinear');
     end
-
-    image = rgb2lab(image);
 
-    image_vec = reshape(image, [], 3);
+    colors_lab = reshape(image_lab, [], 3);
 
     kmeans_func = @(X,K)(kmeans(X, K, 'MaxIter', 10000, ...
                                       'Options', statset('UseParallel',1), ...
                                       'Replicates', 3));
 
-    evaluation = evalclusters(image_vec, kmeans_func, 'CalinskiHarabasz', 'KList', 1:max_clusters);
+    evaluation = evalclusters(colors_lab, kmeans_func, 'CalinskiHarabasz', 'KList', 1:max_clusters);
 
     num_clusters = evaluation.OptimalK;
 end

source/circle-packing/segmentImage.m

@@ -2,9 +2,9 @@
 % smooth_est_scale, num_clusters, label_min_area, 
 % label_close_radius, min_radius
 
-function label_image = segmentImage(image, S)
+function label_image = segmentImage(image_lab, S)
 
-    lab_image = rgb2lab(smoothColor(image, S.smooth_est_scale));
+    lab_image = smoothColor(image_lab, S.smooth_est_scale);
     colors_lab = reshape(lab_image, [], 3);
 
     rng(1);
@@ -14,7 +14,7 @@
                             'Options', statset('UseParallel',1), ...
                             'Replicates', 5);
 
-    label_image = reshape(label_vec, size(image, 1:2));
+    label_image = reshape(label_vec, size(image_lab, 1:2));
 
     for i = 1:max(label_image(:))
         mask = label_image == i;

source/circle-packing/smoothColor.m

@@ -1,14 +1,19 @@
-function image = smoothColor(image, scale)
+function image_lab = smoothColor(image_lab, scale)
 
     if scale <= 0 || scale > 1
         return;
     end
+
+    image = lab2rgb(image_lab);
 
     f = waitbar(0, 'Smoothing image colors');
     for i = 1:3
         image(:,:,i) = diffuseWithEst(image(:,:,i), scale);
         waitbar(i/3, f, 'Smoothing image colors');
     end
+
+    image_lab = rgb2lab(image);
+
     close(f)
 end
 

source/database/createButtonDatabase.m

@@ -120,16 +120,16 @@ function saveImage(image, alpha, prefix, folder)
     db_info_filename = strcat(folder, '\buttons.mat');
 
     mask = alpha > 128;
-    image = im2double(image);
+    image_lab = rgb2lab(im2double(image));
 
-    entry.dominant_colors = kDominantColors(image, mask, 3, 20000, 5);
+    entry.dominant_colors = kDominantColors(image_lab, mask, 3, Inf, 5);
 
-    colors = reshape(image, [], 3);
-    colors = colors(mask(:), :);
-    entry.mean_color_lab = mean(rgb2lab(colors));
+    colors_lab = reshape(image_lab, [], 3);
+    colors_lab = colors_lab(mask(:), :);
+    entry.mean_color_lab = mean(colors_lab);
 
     entry.filename = filename;
-    entry.diameter = size(image, 1);
+    entry.diameter = size(image_lab, 1);
 
     if ~isfile(db_info_filename)
         data = entry;

source/database/createLimitedDatabase.m

@@ -1,5 +1,5 @@
-function createLimitedDatabase(image, num_buttons)
-    colors_lab = reshape(rgb2lab(image), [], 3);
+function createLimitedDatabase(image_lab, num_buttons)
+    colors_lab = reshape(image_lab, [], 3);
 
     [label_vec, ~] = kmeans(colors_lab, num_buttons, ...
                             'MaxIter', 10000);
@@ -8,8 +8,8 @@ function createLimitedDatabase(image, num_buttons)
 
     buttons_subset = struct;
     for i = 1:max(label_vec)
-        mask = reshape(label_vec == i, size(image, 1:2));
-        dominant_colors = kDominantColors(image, mask, 3, Inf, 5);
+        mask = reshape(label_vec == i, size(image_lab, 1:2));
+        dominant_colors = kDominantColors(image_lab, mask, 3, Inf, 5);
         buttons_subset.data(i) = findBestButton(dominant_colors);
     end
     data = buttons_subset.data;

source/database/recomputeColorInfo.m

@@ -5,13 +5,13 @@ function recomputeColorInfo()
         entry = buttons.data(i);
         [image, ~, alpha] = imread(sprintf('../buttons/%s', entry.filename));
         mask = alpha > 128;
-        image = im2double(image);
+        image_lab = rgb2lab(im2double(image));
 
-        entry.dominant_colors = kDominantColors(image, mask, 3, Inf, 5);
+        entry.dominant_colors = kDominantColors(image_lab, mask, 3, Inf, 5);
 
-        colors = reshape(image, [], 3);
-        colors = colors(mask(:), :);
-        entry.mean_color_lab = mean(rgb2lab(colors));
+        colors_lab = reshape(image_lab, [], 3);
+        colors_lab = colors_lab(mask(:), :);
+        entry.mean_color_lab = mean(colors_lab);
 
         buttons.data(i) = entry;
 

source/lib/scielab/deltaLab.m

@@ -13,7 +13,7 @@
 %   delta E, using the numbers in k94 as the k parameter (see
 %   deltaE94). If k94 is only one number, use the default k=[1 1 1].
 %
-% Functions called: xyz2lab, deltaE94.
+% Functions called: xyz2lab_cie, deltaE94.
 %
 % Xuemei Zhang 10/26/95
 % Last modified 4/15/98
@@ -34,8 +34,8 @@
   xyz2 = reshape(xyz2, prod(m)/3, 3);
 end
 
-lab1 = xyz2lab(xyz1, whitepoint, exp);
-lab2 = xyz2lab(xyz2, whitepoint, exp);
+lab1 = xyz2lab_cie(xyz1, whitepoint, exp);
+lab2 = xyz2lab_cie(xyz2, whitepoint, exp);
 
 if (nargin>4)
   if (length(k94)<3)

source/lib/scielab/scielab.m

@@ -38,7 +38,7 @@
 % Functions called: changeColorSpace, cmatrix,
 %                   getPlanes, pad4conv, resize, deltaLab,
 %                   separableFilters, separableConv.
-%                   (implicit: gauss, gauss2, sumGauss, xyz2lab).
+%                   (implicit: gauss, gauss2, sumGauss, xyz2lab_cie).
 %
 % Xuemei Zhang  1/28/96
 % Last Modified 4/15/98

source/lib/scielab/xyz2lab.m → source/lib/scielab/xyz2lab_cie.m

@@ -1,5 +1,5 @@
-function lab = xyz2lab(xyz, whitepoint, exp)
-% xyz2lab(xyz,whitepoint,exp)
+function lab = xyz2lab_cie(xyz, whitepoint, exp)
+% xyz2lab_cie(xyz,whitepoint,exp)
 %
 % Converts colors in XYZ coordinates into CIEL*a*b* coordinates.
 %

source/util/kDominantColors.m

@@ -1,4 +1,4 @@
-function result = kDominantColors(image, mask, k, max_colors, replicates, should_plot)
+function result = kDominantColors(image_lab, mask, k, max_colors, replicates, should_plot)
 
     if nargin < 6
         should_plot = false;
@@ -17,29 +17,33 @@
     % It does however become trivial to determine dominant colors in such
     % cases.
 
-    color_samples = reshape(imresize(image, resize_scale, 'nearest'), [], 3);
-    color_samples = color_samples(mask(:), :);
-    unique_colors = uniquetol(color_samples, 1e-3, 'ByRows', true);
+    colors_lab = reshape(imresize(image_lab, resize_scale, 'nearest'), [], 3);
+    colors_lab = colors_lab(mask(:), :);
+    unique_colors = uniquetol(colors_lab, 1e-3, 'ByRows', true);
     num_unique_colors = size(unique_colors, 1);
 
     if(num_unique_colors <= k)
         result.dominance = zeros(1, k);
         result.colors_lab = zeros(k, 3);
         for i = 1:num_unique_colors
             c = unique_colors(i, :);
-            result.dominance(i) = sum(sum(abs(color_samples - c), 2) <= 1e-3);
-            result.colors_lab(i, :) = rgb2lab(c);
+            result.dominance(i) = sum(sum(abs(colors_lab - c), 2) <= 1e-3);
+            result.colors_lab(i, :) = c;
         end
         result.dominance = result.dominance / sum(result.dominance);
 
         [~,idx] = sort(result.dominance, 'descend');
         result.dominance = result.dominance(idx);
         result.colors_lab = result.colors_lab(idx, :);
     else
-        interpolated_color_samples = reshape(imresize(image, resize_scale, 'bilinear'), [], 3);
-        interpolated_color_samples = interpolated_color_samples(mask(:), :);
 
-        colors_lab = rgb2lab(interpolated_color_samples);
+        if resize_scale ~= 1
+            image_lab = imresize(image_lab, resize_scale, 'bilinear');
+        end
+
+        colors_lab = reshape(image_lab, [], 3);
+        colors_lab = colors_lab(mask(:), :);
+
         [L, dominant_colors] = kmeans(colors_lab, k, 'MaxIter', 10000, ...
                                       'Options', statset('UseParallel',1), ...
                                       'Replicates', replicates);