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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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