Rectify faceness measure (addresses #56)

This was previously incorrectly calculated.  The results for this are looking much more promising now.  The method of calculation is more in-line with what was originally proposed:
> As for your question about adapting the Viola-Jones face detector to return a measure of face-likeness - I think one simple thing you could use is the number of features computed before terminating from the cascade.  Face patches always make it to the end of the cascade, i.e. all Haar-like features are computed.  Non-faces usually result in early termination from the cascade, and the earlier, the less face-like the patch is (roughly speaking).  This certainly would not be a perfect measure, but I'm confident that there is a positive correlation between the total number of features computed in the cascade and the "face-likeness".

src/HaarLikeFeature.jl

@@ -131,7 +131,7 @@ Get vote of this feature for given integral image.
 
 # Arguments
 
-- `feature::HaarLikeObject`: given Haar-like feature (parameterised replacement of Python's `self`)
+- `feature::HaarLikeObject`: given Haar-like feature
 - `int_img::IntegralArray`: Integral image array
 
 # Returns
@@ -142,8 +142,5 @@ Get vote of this feature for given integral image.
 """
 function get_vote(feature::HaarLikeObject{I, F}, int_img::IntegralArray{T, N}) where {I, F, T, N}
     score = get_score(feature, int_img)
-    # return (feature.weight * score) < (feature.polarity * feature.threshold) ? one(Int8) : -one(Int8)
-    # return feature.weight * (score < feature.polarity * feature.threshold ? one(Int8) : -one(Int8))
     return score < feature.polarity * feature.threshold ? feature.weight : -feature.weight
-    # self.weight * (1 if score < self.polarity * self.threshold else -1)
 end

src/Utils.jl

@@ -123,6 +123,26 @@ function determine_feature_size(
 
 end
 
+function _ensemble_vote(int_img::IntegralArray{T, N}, classifiers::Vector{HaarLikeObject}) where {T, N}
+    classifiers = sort(classifiers, by = c -> c.weight, rev = true)
+    vote = 0
+    summed_vote = sum(get_vote(c, int_img) for c in classifiers) ≥ zero(Int8) ? one(Int8) : zero(Int8)
+    faceness = 0
+    for c in classifiers
+        vote = get_vote(c, int_img)
+        if vote < 0
+            # then no face is found using this classifier
+            # we reject this face
+            return summed_vote, faceness
+            return zero(Int8), faceness
+        end
+        # otherwise, keep looking
+        faceness += 1
+    end
+    return summed_vote, faceness
+    return one(Int8), faceness
+end
+
 @doc raw"""
     ensemble_vote(int_img::IntegralArray, classifiers::AbstractArray) -> Integer
 
@@ -149,8 +169,8 @@ h(x) = \begin{cases}
     1       ⟺ sum of classifier votes > 0
     0       otherwise
 """
-ensemble_vote(int_img::IntegralArray{T, N}, classifiers::Vector{HaarLikeObject}) where {T, N} =
-    sum(get_vote(c, int_img) for c in classifiers) ≥ zero(Int8) ? one(Int8) : zero(Int8)
+ensemble_vote(int_img::IntegralArray{T, N}, classifiers::Vector{HaarLikeObject}) where {T, N} = 
+    first(_ensemble_vote(int_img, classifiers))
 
 """
     ensemble_vote_all(images::Vector{String}, classifiers::Vector{HaarLikeObject}) -> Vector{Int8}
@@ -204,6 +224,8 @@ function get_faceness(feature::HaarLikeObject{I, F}, int_img::IntegralArray{T, N
     score, faceness = get_score(feature, int_img)
     return (feature.weight * score) < (feature.polarity * feature.threshold) ? faceness : zero(T)
 end
+get_faceness(classifiers::Vector{HaarLikeObject}, int_img::IntegralArray{T, N}) where {T, N} = 
+    last(_ensemble_vote(int_img, classifiers))
 
 #=
     reconstruct(classifiers::Vector, img_size::Tuple) -> AbstractArray