Changed vote construction to non-allocating array using view (addresses

#26)

examples/basic.jl

@@ -23,7 +23,9 @@ println("...done")
 
 function main(;
     smart_choose_feats::Bool=false,
-    alt::Bool=false
+    alt::Bool=false,
+	scale::Bool=false,
+	scale_to::Tuple=(200, 200)
 )
     include("constants.jl")
 
@@ -38,7 +40,7 @@ function main(;
         # For performance reasons restricting feature size
         notify_user("Selecting best feature width and height...")
 
-        max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_training_path, neg_training_path; scale = false, scale_to = (577, 577))
+        max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_training_path, neg_training_path; scale = scale, scale_to = scale_to)
 
         println("...done.  Maximum feature width selected is $max_feature_width pixels; minimum feature width is $min_feature_width; maximum feature height is $max_feature_height pixels; minimum feature height is $min_feature_height.\n")
     else
@@ -49,7 +51,7 @@ function main(;
     end
 
     # classifiers are haar like features
-    classifiers = FD.learn(pos_training_path, neg_training_path, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width; scale = false, scale_to = (577, 577))
+    classifiers = FD.learn(pos_training_path, neg_training_path, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width; scale = scale, scale_to = scale_to)
 
     FD.notify_user("Testing selected classifiers...")
 	num_faces = length(filtered_ls(pos_testing_path))
@@ -74,4 +76,4 @@ function main(;
     @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
 end
 
-@time main(smart_choose_feats=true, alt=false)
+@time main(smart_choose_feats=true, alt=false, scale=true, scale_to=(19, 19))

src/AdaBoost.jl

@@ -129,12 +129,10 @@ function learn(
     #     next!(p)
     # end
     Base.Threads.@threads for image_file in image_files
-        println("about to load images")
         ii_img = load_image(image_file, scale=scale, scale_to=scale_to)
         num_processed += 1
-        println("putting votes into array")
-        votes[num_processed, :] .= map(f -> get_vote(f, ii_img), features)
-        # map!(f -> get_vote(f, ii_img), view(votes, num_processed, :), features)
+        # votes[num_processed, :] .= map(f -> get_vote(f, ii_img), features)
+        map!(f -> get_vote(f, ii_img), view(votes, num_processed, :), features)
 
         # increment progress bar
         next!(p)
@@ -146,7 +144,7 @@ function learn(
     classifiers = []
     p = Progress(num_classifiers, 1)
     Base.Threads.@threads for t in 1:num_classifiers # previously, zerosarray
-        classification_errors = spzeros(length(feature_indices))
+        classification_errors = zeros(length(feature_indices))
         # normalize the weights $w_{t,i}\gets \frac{w_{t,i}}{\sum_{j=1}^n w_{t,j}}$
         weights = float(weights) / sum(weights)