coco
140 lines
5.6 KiB
C++
140 lines
5.6 KiB
C++
// Copyright (C) 2014 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_LBP_ABSTRACT_Hh_
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#ifdef DLIB_LBP_ABSTRACT_Hh_
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#include "../image_processing/generic_image.h"
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#include "../pixel.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename image_type,
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typename image_type2
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>
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void make_uniform_lbp_image (
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const image_type& img,
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image_type2& lbp
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);
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/*!
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requires
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- image_type == an image object that implements the interface defined in
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dlib/image_processing/generic_image.h
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- image_type2 == an image object that implements the interface defined in
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dlib/image_processing/generic_image.h
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- image_type2 should contain a grayscale pixel type such as unsigned char.
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ensures
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- #lbp.nr() == img.nr()
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- #lbp.nc() == img.nc()
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- This function extracts the uniform local-binary-pattern feature at every pixel
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and stores it into #lbp. In particular, we have the following for all valid
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r and c:
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- #lbp[r][c] == the uniform LBP for the 3x3 pixel window centered on img[r][c].
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In particular, this is a value in the range 0 to 58 inclusive.
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- We use the idea of uniform LBPs from the paper:
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Face Description with Local Binary Patterns: Application to Face Recognition
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by Ahonen, Hadid, and Pietikainen.
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename image_type,
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typename T
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>
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void extract_histogram_descriptors (
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const image_type& img,
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const point& loc,
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std::vector<T>& histograms,
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const unsigned int cell_size = 10,
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const unsigned int block_size = 4,
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const unsigned int max_val = 58
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);
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/*!
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requires
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- image_type == an image object that implements the interface defined in
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dlib/image_processing/generic_image.h
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- image_type contains unsigned char valued pixels.
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- T is some scalar type like int or double
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- All pixel values in img are <= max_val
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- cell_size >= 1
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- block_size >= 1
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- max_val < 256
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ensures
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- This function extracts histograms of pixel values from block_size*block_size
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windows in the area in img immediately around img[loc.y()][loc.x()]. The
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histograms are appended onto the end of #histograms. Each window is
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cell_size pixels wide and tall. Moreover, the windows do not overlap.
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- #histograms.size() == histograms.size() + block_size*block_size*(max_val+1)
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename image_type,
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typename T
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>
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void extract_uniform_lbp_descriptors (
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const image_type& img,
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std::vector<T>& feats,
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const unsigned int cell_size = 10
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);
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/*!
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requires
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- cell_size >= 1
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- T is some scalar type like int or double
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ensures
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- Extracts histograms of uniform local-binary-patterns from img. The
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histograms are from densely tiled windows that are cell_size pixels wide and
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tall. The windows do not overlap and cover all of img.
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- #feats.size() == 59*(number of windows that fit into img)
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(i.e. #feats contains the LBP histograms)
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- We will have taken the square root of all the histogram elements. That is,
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#feats[i] is the square root of the number of LBPs that appeared in its
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corresponding window.
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename image_type,
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typename T
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>
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void extract_highdim_face_lbp_descriptors (
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const image_type& img,
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const full_object_detection& det,
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std::vector<T>& feats
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);
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/*!
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requires
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- T is some scalar type like int or double
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- det.num_parts() == 68
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ensures
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- This function extracts the high-dimensional LBP feature described in the
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paper:
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Blessing of Dimensionality: High-dimensional Feature and Its Efficient
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Compression for Face Verification by Dong Chen, Xudong Cao, Fang Wen, and
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Jian Sun
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- #feats == the high-dimensional LBP descriptor. It is the concatenation of
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many LBP histograms, each extracted from different scales and from different
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windows around different face landmarks. We also take the square root of
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each histogram element before storing it into #feats.
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- #feats.size() == 99120
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- This function assumes img has already been aligned and normalized to a
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standard size.
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- This function assumes det contains a human face detection with face parts
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annotated using the annotation scheme from the iBUG 300-W face landmark
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dataset. This means that det.part(i) gives the locations of different face
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landmarks according to the iBUG 300-W annotation scheme.
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_LBP_ABSTRACT_Hh_
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