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Slight copy-error corrections (hopefully a step towards #46)
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@jakewilliami
jakewilliami committed Oct 30, 2020
1 parent f9b0719 commit 3e9be4a
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Showing 2 changed files with 47 additions and 27 deletions.
15 changes: 6 additions & 9 deletions src/AdaBoost.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -123,9 +123,6 @@ function learn(
classification_errors = Vector{Float64}(undef, length(feature_indices))

for t in 1:num_classifiers
# classification_errors = zeros(length(feature_indices))
#classification_errors = Matrix{Float64}(undef, length(feature_indices), 1)

# normalize the weights $w_{t,i}\gets \frac{w_{t,i}}{\sum_{j=1}^n w_{t,j}}$
weights .*= inv(sum(weights))

Expand All @@ -135,8 +132,7 @@ function learn(
labels[img_idx] !== votes[feature_indices[j], img_idx] ? weights[img_idx] : zero(Float64)
end
end
# classification_errors[:] .= [sum([labels[img_idx] !== votes[feature_indices[j], img_idx] ? weights[img_idx] : zero(Float64) for img_idx in 1:num_imgs]) for j in 1:length(feature_indices)]


# choose the classifier $h_t$ with the lowest error $\varepsilon_t$
best_error, min_error_idx = findmin(classification_errors)
best_feature_idx = feature_indices[min_error_idx]
Expand All @@ -146,6 +142,7 @@ function learn(
best_feature = features[best_feature_idx]
feature_weight = β(best_error)
best_feature.weight = feature_weight
# println(best_error)

# append selected features
classifiers = push!(classifiers, best_feature)
Expand Down Expand Up @@ -244,11 +241,11 @@ function create_features(

for feature in values(feature_types) # (feature_types are just tuples)
feature_start_width = max(min_feature_width, first(feature))
for feature_width in range(feature_start_width, stop=max_feature_width, step=first(feature))
for feature_width in feature_start_width:first(feature):(max_feature_width - 1)
feature_start_height = max(min_feature_height, last(feature))
for feature_height in range(feature_start_height, stop=max_feature_height, step=last(feature))
for x in 1:(img_width - feature_width)
for y in 1:(img_height - feature_height)
for feature_height in feature_start_height:last(feature):(max_feature_height - 1)
for x in 0:(img_width - feature_width)
for y in 0:(img_height - feature_height)
push!(features, HaarLikeObject(feature, (x, y), feature_width, feature_height, 0, 1))
push!(features, HaarLikeObject(feature, (x, y), feature_width, feature_height, 0, -1))
end # end for y
Expand Down
59 changes: 41 additions & 18 deletions src/HaarLikeFeature.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,16 @@ abstract type HaarFeatureAbstractType end
mutable struct HaarLikeObject{I <: Integer, F <: AbstractFloat}
Struct representing a Haar-like feature.
feature_type::Tuple{I, I}
position::Tuple{I, I}
top_left::Tuple{I, I}
bottom_right::Tuple{I, I}
width::I
height::I
threshold::I
polarity::I
weight::F
"""
mutable struct HaarLikeObject{I <: Integer, F <: AbstractFloat} <: HaarFeatureAbstractType
#parametric struct to store the ints and floats efficiently
Expand All @@ -32,6 +42,16 @@ mutable struct HaarLikeObject{I <: Integer, F <: AbstractFloat} <: HaarFeatureAb
weight::F
end # end structure

"""
HaarLikeObject(
feature_type::Tuple{Integer, Integer},
position::Tuple{Integer, Integer},
width::Integer,
height::Integer,
threshold::Integer,
polarity::Integer
) -> HaarLikeObject
"""
function HaarLikeObject(
feature_type::Tuple{Integer, Integer},
position::Tuple{Integer, Integer},
Expand Down Expand Up @@ -59,7 +79,7 @@ end
"""
get_score(feature::HaarLikeObject, int_img::Array) -> Tuple{Number, Number}
Get score for given integral image array.
Get score for given integral image array. This is the feature cascade.
# Arguments
Expand All @@ -70,7 +90,7 @@ Get score for given integral image array.
- `score::Number`: Score for given feature
"""
function get_score(feature::HaarLikeObject{I,F}, int_img::Array) where {I, F}
function get_score(feature::HaarLikeObject{I, F}, int_img::Array) where {I, F}
score = zero(I)
faceness = zero(I)
_2f = F(2)
Expand All @@ -79,36 +99,36 @@ function get_score(feature::HaarLikeObject{I,F}, int_img::Array) where {I, F}
_one_third = F(1.0 / 3.0)

if feature.feature_type == feature_types.two_vertical
_first = sum_region(int_img, feature.top_left, (first(feature.top_left) + feature.width, I(round(last(feature.top_left) + feature.height / 2))))
second = sum_region(int_img, (first(feature.top_left), I(round(last(feature.top_left) + feature.height / 2))), feature.bottom_right)
_first = sum_region(int_img, feature.top_left, (first(feature.top_left) + feature.width, round(I, last(feature.top_left) + feature.height / 2)))
second = sum_region(int_img, (first(feature.top_left), round(I, last(feature.top_left) + feature.height / 2)), feature.bottom_right)
score = _first - second
faceness = I(1)
elseif feature.feature_type == feature_types.two_horizontal
_first = sum_region(int_img, feature.top_left, (I(round(first(feature.top_left) + feature.width / 2)), last(feature.top_left) + feature.height))
second = sum_region(int_img, (I(round(first(feature.top_left) + feature.width / 2)), last(feature.top_left)), feature.bottom_right)
_first = sum_region(int_img, feature.top_left, (round(I, first(feature.top_left) + feature.width / 2), last(feature.top_left) + feature.height))
second = sum_region(int_img, (round(I, first(feature.top_left) + feature.width / 2), last(feature.top_left)), feature.bottom_right)
score = _first - second
faceness = I(2)
elseif feature.feature_type == feature_types.three_horizontal
_first = sum_region(int_img, feature.top_left, (I(round(first(feature.top_left) + feature.width / 3)), last(feature.top_left) + feature.height))
second = sum_region(int_img, (I(round(first(feature.top_left) + feature.width / 3)), last(feature.top_left)), (I(round(first(feature.top_left) + 2 * feature.width / 3)), last(feature.top_left) + feature.height))
third = sum_region(int_img, (I(round(first(feature.top_left) + 2 * feature.width / 3)), last(feature.top_left)), feature.bottom_right)
_first = sum_region(int_img, feature.top_left, (round(I, first(feature.top_left) + feature.width / 3), last(feature.top_left) + feature.height))
second = sum_region(int_img, (round(I, first(feature.top_left) + feature.width / 3), last(feature.top_left)), (round(I, first(feature.top_left) + 2 * feature.width / 3), last(feature.top_left) + feature.height))
third = sum_region(int_img, (round(I, first(feature.top_left) + 2 * feature.width / 3), last(feature.top_left)), feature.bottom_right)
score = _first - second + third
faceness = I(3)
elseif feature.feature_type == feature_types.three_vertical
_first = sum_region(int_img, feature.top_left, (first(feature.bottom_right), I(round(last(feature.top_left) + feature.height / 3))))
second = sum_region(int_img, (first(feature.top_left), I(round(last(feature.top_left) + feature.height / 3))), (first(feature.bottom_right), I(round(last(feature.top_left) + 2 * feature.height / 3))))
third = sum_region(int_img, (first(feature.top_left), I(round(last(feature.top_left) + 2 * feature.height / 3))), feature.bottom_right)
_first = sum_region(int_img, feature.top_left, (first(feature.bottom_right), round(I, last(feature.top_left) + feature.height / 3)))
second = sum_region(int_img, (first(feature.top_left), round(I, last(feature.top_left) + feature.height / 3)), (first(feature.bottom_right), round(I, last(feature.top_left) + 2 * feature.height / 3)))
third = sum_region(int_img, (first(feature.top_left), round(I, last(feature.top_left) + 2 * feature.height / 3)), feature.bottom_right)
score = _first - second + third
faceness = I(4)
elseif feature.feature_type == feature_types.four
# top left area
_first = sum_region(int_img, feature.top_left, (I(round(first(feature.top_left) + feature.width / 2)), I(round(last(feature.top_left) + feature.height / 2))))
_first = sum_region(int_img, feature.top_left, (round(I, first(feature.top_left) + feature.width / 2), round(I, last(feature.top_left) + feature.height / 2)))
# top right area
second = sum_region(int_img, (I(round(first(feature.top_left) + feature.width / 2)), last(feature.top_left)), (first(feature.bottom_right), I(round(last(feature.top_left) + feature.height / 2))))
second = sum_region(int_img, (round(I, first(feature.top_left) + feature.width / 2), last(feature.top_left)), (first(feature.bottom_right), round(I, last(feature.top_left) + feature.height / 2)))
# bottom left area
third = sum_region(int_img, (first(feature.top_left), I(round(last(feature.top_left) + feature.height / 2))), (I(round(first(feature.top_left) + feature.width / 2)), last(feature.bottom_right)))
third = sum_region(int_img, (first(feature.top_left), round(I, last(feature.top_left) + feature.height / 2)), (round(I, first(feature.top_left) + feature.width / 2), last(feature.bottom_right)))
# bottom right area
fourth = sum_region(int_img, (I(round(first(feature.top_left) + feature.width / 2)), I(round(last(feature.top_left) + feature.height / 2))), feature.bottom_right)
fourth = sum_region(int_img, (round(I, first(feature.top_left) + feature.width / 2), round(I, last(feature.top_left) + feature.height / 2)), feature.bottom_right)
score = _first - second - third + fourth
faceness = I(5)
end
Expand All @@ -133,6 +153,9 @@ Get vote of this feature for given integral image.
-1 otherwise
"""
function get_vote(feature::HaarLikeObject, int_img::AbstractArray)
score = first(get_score(feature, int_img)) # we only care about score here
return (feature.weight * score) < (feature.polarity * feature.threshold) ? one(Int8) : -one(Int8)
score = first(get_score(feature, int_img)) # we only care about score here, not faceness
# 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

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