Added more specific zero and one types (addresses #26)

src/AdaBoost.jl

@@ -153,7 +153,7 @@ function learn(
             f_idx = feature_indices[j]
             # classifier error is the sum of image weights where the classifier is right
             # ε = sum(map(img_idx -> labels[img_idx] ≠ votes[img_idx, f_idx] ? weights[img_idx] : zero(Integer), 1:num_imgs))
-            ε = sum([labels[img_idx] ≠ votes[img_idx, f_idx] ? weights[img_idx] : zero(Integer) for img_idx in 1:num_imgs])
+            ε = sum([labels[img_idx] ≠ votes[img_idx, f_idx] ? weights[img_idx] : zero(Float64) for img_idx in 1:num_imgs])
 
             classification_errors[j] = ε
         end

src/HaarLikeFeature.jl

@@ -118,5 +118,5 @@ Get vote of this feature for given integral image.
 function get_vote(feature::HaarLikeObject, int_img::AbstractArray)
     score = get_score(feature, int_img)[1] # we only care about score here
 
-    return (feature.weight * score) < (feature.polarity * feature.threshold) ? one(Integer) : -one(Integer)
+    return (feature.weight * score) < (feature.polarity * feature.threshold) ? one(Int8) : -one(Int8)
 end

src/IntegralImage.jl

@@ -70,13 +70,13 @@ getindex(A::IntegralArray, ids::Tuple...) = getindex(A, ids[1]...)
 =#
 function sum_region(
 	integral_image_arr::AbstractArray,
-	top_left::Tuple{Int64,Int64},
-	bottom_right::Tuple{Int64,Int64}
+	top_left::Tuple{Integer,Integer},
+	bottom_right::Tuple{Integer,Integer}
 )
 	sum = integral_image_arr[bottom_right[2], bottom_right[1]]
-    sum -= top_left[1] > 1 ? integral_image_arr[bottom_right[2], top_left[1] - 1] : zero(Integer)
-    sum -= top_left[2] > 1 ? integral_image_arr[top_left[2] - 1, bottom_right[1]] : zero(Integer)
-    sum += top_left[2] > 1 && top_left[1] > 1 ? integral_image_arr[top_left[2] - 1, top_left[1] - 1] : zero(Integer)
+    sum -= top_left[1] > 1 ? integral_image_arr[bottom_right[2], top_left[1] - 1] : zero(Int64)
+    sum -= top_left[2] > 1 ? integral_image_arr[top_left[2] - 1, bottom_right[1]] : zero(Int64)
+    sum += top_left[2] > 1 && top_left[1] > 1 ? integral_image_arr[top_left[2] - 1, top_left[1] - 1] : zero(Int64)
 
     return sum
 end

src/Utils.jl

@@ -165,7 +165,7 @@ function ensemble_vote(int_img::AbstractArray, classifiers::AbstractArray)
     # weightedSum = sum([c[2] for c in classifiers])
     # return evidence >= (weightedSum / 2) ? 1 : -1
 
-    return sum([get_vote(c, int_img) for c in classifiers]) >= 0 ? one(Integer) : zero(Integer)
+    return sum([get_vote(c, int_img) for c in classifiers]) >= 0 ? one(Int8) : zero(Int8)
 end
 
 #=