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diff --git a/ml/dlib/dlib/image_processing/shape_predictor.h b/ml/dlib/dlib/image_processing/shape_predictor.h
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-// Copyright (C) 2014 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-#ifndef DLIB_SHAPE_PREDICToR_H_
-#define DLIB_SHAPE_PREDICToR_H_
-
-#include "shape_predictor_abstract.h"
-#include "full_object_detection.h"
-#include "../algs.h"
-#include "../matrix.h"
-#include "../geometry.h"
-#include "../pixel.h"
-#include "../statistics.h"
-#include <utility>
-
-namespace dlib
-{
-
-// ----------------------------------------------------------------------------------------
-
- namespace impl
- {
- struct split_feature
- {
- unsigned long idx1;
- unsigned long idx2;
- float thresh;
-
- friend inline void serialize (const split_feature& item, std::ostream& out)
- {
- dlib::serialize(item.idx1, out);
- dlib::serialize(item.idx2, out);
- dlib::serialize(item.thresh, out);
- }
- friend inline void deserialize (split_feature& item, std::istream& in)
- {
- dlib::deserialize(item.idx1, in);
- dlib::deserialize(item.idx2, in);
- dlib::deserialize(item.thresh, in);
- }
- };
-
-
- // a tree is just a std::vector<impl::split_feature>. We use this function to navigate the
- // tree nodes
- inline unsigned long left_child (unsigned long idx) { return 2*idx + 1; }
- /*!
- ensures
- - returns the index of the left child of the binary tree node idx
- !*/
- inline unsigned long right_child (unsigned long idx) { return 2*idx + 2; }
- /*!
- ensures
- - returns the index of the left child of the binary tree node idx
- !*/
-
- struct regression_tree
- {
- std::vector<split_feature> splits;
- std::vector<matrix<float,0,1> > leaf_values;
-
- unsigned long num_leaves() const { return leaf_values.size(); }
-
- inline const matrix<float,0,1>& operator()(
- const std::vector<float>& feature_pixel_values,
- unsigned long& i
- ) const
- /*!
- requires
- - All the index values in splits are less than feature_pixel_values.size()
- - leaf_values.size() is a power of 2.
- (i.e. we require a tree with all the levels fully filled out.
- - leaf_values.size() == splits.size()+1
- (i.e. there needs to be the right number of leaves given the number of splits in the tree)
- ensures
- - runs through the tree and returns the vector at the leaf we end up in.
- - #i == the selected leaf node index.
- !*/
- {
- i = 0;
- while (i < splits.size())
- {
- if ((float)feature_pixel_values[splits[i].idx1] - (float)feature_pixel_values[splits[i].idx2] > splits[i].thresh)
- i = left_child(i);
- else
- i = right_child(i);
- }
- i = i - splits.size();
- return leaf_values[i];
- }
-
- friend void serialize (const regression_tree& item, std::ostream& out)
- {
- dlib::serialize(item.splits, out);
- dlib::serialize(item.leaf_values, out);
- }
- friend void deserialize (regression_tree& item, std::istream& in)
- {
- dlib::deserialize(item.splits, in);
- dlib::deserialize(item.leaf_values, in);
- }
- };
-
- // ------------------------------------------------------------------------------------
-
- inline vector<float,2> location (
- const matrix<float,0,1>& shape,
- unsigned long idx
- )
- /*!
- requires
- - idx < shape.size()/2
- - shape.size()%2 == 0
- ensures
- - returns the idx-th point from the shape vector.
- !*/
- {
- return vector<float,2>(shape(idx*2), shape(idx*2+1));
- }
-
- // ------------------------------------------------------------------------------------
-
- inline unsigned long nearest_shape_point (
- const matrix<float,0,1>& shape,
- const dlib::vector<float,2>& pt
- )
- {
- // find the nearest part of the shape to this pixel
- float best_dist = std::numeric_limits<float>::infinity();
- const unsigned long num_shape_parts = shape.size()/2;
- unsigned long best_idx = 0;
- for (unsigned long j = 0; j < num_shape_parts; ++j)
- {
- const float dist = length_squared(location(shape,j)-pt);
- if (dist < best_dist)
- {
- best_dist = dist;
- best_idx = j;
- }
- }
- return best_idx;
- }
-
- // ------------------------------------------------------------------------------------
-
- inline void create_shape_relative_encoding (
- const matrix<float,0,1>& shape,
- const std::vector<dlib::vector<float,2> >& pixel_coordinates,
- std::vector<unsigned long>& anchor_idx,
- std::vector<dlib::vector<float,2> >& deltas
- )
- /*!
- requires
- - shape.size()%2 == 0
- - shape.size() > 0
- ensures
- - #anchor_idx.size() == pixel_coordinates.size()
- - #deltas.size() == pixel_coordinates.size()
- - for all valid i:
- - pixel_coordinates[i] == location(shape,#anchor_idx[i]) + #deltas[i]
- !*/
- {
- anchor_idx.resize(pixel_coordinates.size());
- deltas.resize(pixel_coordinates.size());
-
-
- for (unsigned long i = 0; i < pixel_coordinates.size(); ++i)
- {
- anchor_idx[i] = nearest_shape_point(shape, pixel_coordinates[i]);
- deltas[i] = pixel_coordinates[i] - location(shape,anchor_idx[i]);
- }
- }
-
- // ------------------------------------------------------------------------------------
-
- inline point_transform_affine find_tform_between_shapes (
- const matrix<float,0,1>& from_shape,
- const matrix<float,0,1>& to_shape
- )
- {
- DLIB_ASSERT(from_shape.size() == to_shape.size() && (from_shape.size()%2) == 0 && from_shape.size() > 0,"");
- std::vector<vector<float,2> > from_points, to_points;
- const unsigned long num = from_shape.size()/2;
- from_points.reserve(num);
- to_points.reserve(num);
- if (num == 1)
- {
- // Just use an identity transform if there is only one landmark.
- return point_transform_affine();
- }
-
- for (unsigned long i = 0; i < num; ++i)
- {
- from_points.push_back(location(from_shape,i));
- to_points.push_back(location(to_shape,i));
- }
- return find_similarity_transform(from_points, to_points);
- }
-
- // ------------------------------------------------------------------------------------
-
- inline point_transform_affine normalizing_tform (
- const rectangle& rect
- )
- /*!
- ensures
- - returns a transform that maps rect.tl_corner() to (0,0) and rect.br_corner()
- to (1,1).
- !*/
- {
- std::vector<vector<float,2> > from_points, to_points;
- from_points.push_back(rect.tl_corner()); to_points.push_back(point(0,0));
- from_points.push_back(rect.tr_corner()); to_points.push_back(point(1,0));
- from_points.push_back(rect.br_corner()); to_points.push_back(point(1,1));
- return find_affine_transform(from_points, to_points);
- }
-
- // ------------------------------------------------------------------------------------
-
- inline point_transform_affine unnormalizing_tform (
- const rectangle& rect
- )
- /*!
- ensures
- - returns a transform that maps (0,0) to rect.tl_corner() and (1,1) to
- rect.br_corner().
- !*/
- {
- std::vector<vector<float,2> > from_points, to_points;
- to_points.push_back(rect.tl_corner()); from_points.push_back(point(0,0));
- to_points.push_back(rect.tr_corner()); from_points.push_back(point(1,0));
- to_points.push_back(rect.br_corner()); from_points.push_back(point(1,1));
- return find_affine_transform(from_points, to_points);
- }
-
- // ------------------------------------------------------------------------------------
-
- template <typename image_type, typename feature_type>
- void extract_feature_pixel_values (
- const image_type& img_,
- const rectangle& rect,
- const matrix<float,0,1>& current_shape,
- const matrix<float,0,1>& reference_shape,
- const std::vector<unsigned long>& reference_pixel_anchor_idx,
- const std::vector<dlib::vector<float,2> >& reference_pixel_deltas,
- std::vector<feature_type>& feature_pixel_values
- )
- /*!
- requires
- - image_type == an image object that implements the interface defined in
- dlib/image_processing/generic_image.h
- - reference_pixel_anchor_idx.size() == reference_pixel_deltas.size()
- - current_shape.size() == reference_shape.size()
- - reference_shape.size()%2 == 0
- - max(mat(reference_pixel_anchor_idx)) < reference_shape.size()/2
- ensures
- - #feature_pixel_values.size() == reference_pixel_deltas.size()
- - for all valid i:
- - #feature_pixel_values[i] == the value of the pixel in img_ that
- corresponds to the pixel identified by reference_pixel_anchor_idx[i]
- and reference_pixel_deltas[i] when the pixel is located relative to
- current_shape rather than reference_shape.
- !*/
- {
- const matrix<float,2,2> tform = matrix_cast<float>(find_tform_between_shapes(reference_shape, current_shape).get_m());
- const point_transform_affine tform_to_img = unnormalizing_tform(rect);
-
- const rectangle area = get_rect(img_);
-
- const_image_view<image_type> img(img_);
- feature_pixel_values.resize(reference_pixel_deltas.size());
- for (unsigned long i = 0; i < feature_pixel_values.size(); ++i)
- {
- // Compute the point in the current shape corresponding to the i-th pixel and
- // then map it from the normalized shape space into pixel space.
- point p = tform_to_img(tform*reference_pixel_deltas[i] + location(current_shape, reference_pixel_anchor_idx[i]));
- if (area.contains(p))
- feature_pixel_values[i] = get_pixel_intensity(img[p.y()][p.x()]);
- else
- feature_pixel_values[i] = 0;
- }
- }
-
- } // end namespace impl
-
-// ----------------------------------------------------------------------------------------
-
- class shape_predictor
- {
- public:
-
-
- shape_predictor (
- )
- {}
-
- shape_predictor (
- const matrix<float,0,1>& initial_shape_,
- const std::vector<std::vector<impl::regression_tree> >& forests_,
- const std::vector<std::vector<dlib::vector<float,2> > >& pixel_coordinates
- ) : initial_shape(initial_shape_), forests(forests_)
- /*!
- requires
- - initial_shape.size()%2 == 0
- - forests.size() == pixel_coordinates.size() == the number of cascades
- - for all valid i:
- - all the index values in forests[i] are less than pixel_coordinates[i].size()
- - for all valid i and j:
- - forests[i][j].leaf_values.size() is a power of 2.
- (i.e. we require a tree with all the levels fully filled out.
- - forests[i][j].leaf_values.size() == forests[i][j].splits.size()+1
- (i.e. there need to be the right number of leaves given the number of splits in the tree)
- !*/
- {
- anchor_idx.resize(pixel_coordinates.size());
- deltas.resize(pixel_coordinates.size());
- // Each cascade uses a different set of pixels for its features. We compute
- // their representations relative to the initial shape now and save it.
- for (unsigned long i = 0; i < pixel_coordinates.size(); ++i)
- impl::create_shape_relative_encoding(initial_shape, pixel_coordinates[i], anchor_idx[i], deltas[i]);
- }
-
- unsigned long num_parts (
- ) const
- {
- return initial_shape.size()/2;
- }
-
- unsigned long num_features (
- ) const
- {
- unsigned long num = 0;
- for (unsigned long iter = 0; iter < forests.size(); ++iter)
- for (unsigned long i = 0; i < forests[iter].size(); ++i)
- num += forests[iter][i].num_leaves();
- return num;
- }
-
- template <typename image_type>
- full_object_detection operator()(
- const image_type& img,
- const rectangle& rect
- ) const
- {
- using namespace impl;
- matrix<float,0,1> current_shape = initial_shape;
- std::vector<float> feature_pixel_values;
- for (unsigned long iter = 0; iter < forests.size(); ++iter)
- {
- extract_feature_pixel_values(img, rect, current_shape, initial_shape,
- anchor_idx[iter], deltas[iter], feature_pixel_values);
- unsigned long leaf_idx;
- // evaluate all the trees at this level of the cascade.
- for (unsigned long i = 0; i < forests[iter].size(); ++i)
- current_shape += forests[iter][i](feature_pixel_values, leaf_idx);
- }
-
- // convert the current_shape into a full_object_detection
- const point_transform_affine tform_to_img = unnormalizing_tform(rect);
- std::vector<point> parts(current_shape.size()/2);
- for (unsigned long i = 0; i < parts.size(); ++i)
- parts[i] = tform_to_img(location(current_shape, i));
- return full_object_detection(rect, parts);
- }
-
- template <typename image_type, typename T, typename U>
- full_object_detection operator()(
- const image_type& img,
- const rectangle& rect,
- std::vector<std::pair<T,U> >& feats
- ) const
- {
- feats.clear();
- using namespace impl;
- matrix<float,0,1> current_shape = initial_shape;
- std::vector<float> feature_pixel_values;
- unsigned long feat_offset = 0;
- for (unsigned long iter = 0; iter < forests.size(); ++iter)
- {
- extract_feature_pixel_values(img, rect, current_shape, initial_shape,
- anchor_idx[iter], deltas[iter], feature_pixel_values);
- // evaluate all the trees at this level of the cascade.
- for (unsigned long i = 0; i < forests[iter].size(); ++i)
- {
- unsigned long leaf_idx;
- current_shape += forests[iter][i](feature_pixel_values, leaf_idx);
-
- feats.push_back(std::make_pair(feat_offset+leaf_idx, 1));
- feat_offset += forests[iter][i].num_leaves();
- }
- }
-
- // convert the current_shape into a full_object_detection
- const point_transform_affine tform_to_img = unnormalizing_tform(rect);
- std::vector<point> parts(current_shape.size()/2);
- for (unsigned long i = 0; i < parts.size(); ++i)
- parts[i] = tform_to_img(location(current_shape, i));
- return full_object_detection(rect, parts);
- }
-
- friend void serialize (const shape_predictor& item, std::ostream& out);
-
- friend void deserialize (shape_predictor& item, std::istream& in);
-
- private:
- matrix<float,0,1> initial_shape;
- std::vector<std::vector<impl::regression_tree> > forests;
- std::vector<std::vector<unsigned long> > anchor_idx;
- std::vector<std::vector<dlib::vector<float,2> > > deltas;
- };
-
- inline void serialize (const shape_predictor& item, std::ostream& out)
- {
- int version = 1;
- dlib::serialize(version, out);
- dlib::serialize(item.initial_shape, out);
- dlib::serialize(item.forests, out);
- dlib::serialize(item.anchor_idx, out);
- dlib::serialize(item.deltas, out);
- }
-
- inline void deserialize (shape_predictor& item, std::istream& in)
- {
- int version = 0;
- dlib::deserialize(version, in);
- if (version != 1)
- throw serialization_error("Unexpected version found while deserializing dlib::shape_predictor.");
- dlib::deserialize(item.initial_shape, in);
- dlib::deserialize(item.forests, in);
- dlib::deserialize(item.anchor_idx, in);
- dlib::deserialize(item.deltas, in);
- }
-
-// ----------------------------------------------------------------------------------------
-// ----------------------------------------------------------------------------------------
-// ----------------------------------------------------------------------------------------
-// ----------------------------------------------------------------------------------------
-
- template <
- typename image_array
- >
- double test_shape_predictor (
- const shape_predictor& sp,
- const image_array& images,
- const std::vector<std::vector<full_object_detection> >& objects,
- const std::vector<std::vector<double> >& scales
- )
- {
- // make sure requires clause is not broken
-#ifdef ENABLE_ASSERTS
- DLIB_CASSERT( images.size() == objects.size() ,
- "\t double test_shape_predictor()"
- << "\n\t Invalid inputs were given to this function. "
- << "\n\t images.size(): " << images.size()
- << "\n\t objects.size(): " << objects.size()
- );
- for (unsigned long i = 0; i < objects.size(); ++i)
- {
- for (unsigned long j = 0; j < objects[i].size(); ++j)
- {
- DLIB_CASSERT(objects[i][j].num_parts() == sp.num_parts(),
- "\t double test_shape_predictor()"
- << "\n\t Invalid inputs were given to this function. "
- << "\n\t objects["<<i<<"]["<<j<<"].num_parts(): " << objects[i][j].num_parts()
- << "\n\t sp.num_parts(): " << sp.num_parts()
- );
- }
- if (scales.size() != 0)
- {
- DLIB_CASSERT(objects[i].size() == scales[i].size(),
- "\t double test_shape_predictor()"
- << "\n\t Invalid inputs were given to this function. "
- << "\n\t objects["<<i<<"].size(): " << objects[i].size()
- << "\n\t scales["<<i<<"].size(): " << scales[i].size()
- );
-
- }
- }
-#endif
-
- running_stats<double> rs;
- for (unsigned long i = 0; i < objects.size(); ++i)
- {
- for (unsigned long j = 0; j < objects[i].size(); ++j)
- {
- // Just use a scale of 1 (i.e. no scale at all) if the caller didn't supply
- // any scales.
- const double scale = scales.size()==0 ? 1 : scales[i][j];
-
- full_object_detection det = sp(images[i], objects[i][j].get_rect());
-
- for (unsigned long k = 0; k < det.num_parts(); ++k)
- {
- if (objects[i][j].part(k) != OBJECT_PART_NOT_PRESENT)
- {
- double score = length(det.part(k) - objects[i][j].part(k))/scale;
- rs.add(score);
- }
- }
- }
- }
- return rs.mean();
- }
-
-// ----------------------------------------------------------------------------------------
-
- template <
- typename image_array
- >
- double test_shape_predictor (
- const shape_predictor& sp,
- const image_array& images,
- const std::vector<std::vector<full_object_detection> >& objects
- )
- {
- std::vector<std::vector<double> > no_scales;
- return test_shape_predictor(sp, images, objects, no_scales);
- }
-
-// ----------------------------------------------------------------------------------------
-
-}
-
-#endif // DLIB_SHAPE_PREDICToR_H_
-
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