coco
928 lines
34 KiB
C++
928 lines
34 KiB
C++
// Copyright (C) 2015 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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#ifndef DLIB_DNn_INPUT_H_
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#define DLIB_DNn_INPUT_H_
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#include "input_abstract.h"
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#include "../matrix.h"
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#include "../array2d.h"
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#include "../pixel.h"
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#include "../image_processing.h"
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#include <sstream>
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#include <array>
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#include "../cuda/tensor_tools.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <typename T>
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class input
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{
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const static bool always_false = sizeof(T)!=sizeof(T);
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static_assert(always_false, "Unsupported type given to input<>. input<> only supports "
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"dlib::matrix and dlib::array2d objects.");
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};
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// ----------------------------------------------------------------------------------------
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template <size_t NR, size_t NC=NR>
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class input_rgb_image_sized;
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class input_rgb_image
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{
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public:
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typedef matrix<rgb_pixel> input_type;
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input_rgb_image (
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) :
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avg_red(122.782),
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avg_green(117.001),
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avg_blue(104.298)
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{
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}
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input_rgb_image (
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float avg_red_,
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float avg_green_,
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float avg_blue_
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) : avg_red(avg_red_), avg_green(avg_green_), avg_blue(avg_blue_)
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{}
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template <size_t NR, size_t NC>
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inline input_rgb_image (
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const input_rgb_image_sized<NR,NC>& item
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);
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float get_avg_red() const { return avg_red; }
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float get_avg_green() const { return avg_green; }
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float get_avg_blue() const { return avg_blue; }
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bool image_contained_point ( const tensor& data, const point& p) const { return get_rect(data).contains(p); }
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drectangle tensor_space_to_image_space ( const tensor& /*data*/, drectangle r) const { return r; }
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drectangle image_space_to_tensor_space ( const tensor& /*data*/, double /*scale*/, drectangle r ) const { return r; }
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template <typename forward_iterator>
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void to_tensor (
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forward_iterator ibegin,
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forward_iterator iend,
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resizable_tensor& data
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) const
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{
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DLIB_CASSERT(std::distance(ibegin,iend) > 0);
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const auto nr = ibegin->nr();
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const auto nc = ibegin->nc();
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// make sure all the input matrices have the same dimensions
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for (auto i = ibegin; i != iend; ++i)
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{
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DLIB_CASSERT(i->nr()==nr && i->nc()==nc,
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"\t input_rgb_image::to_tensor()"
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<< "\n\t All matrices given to to_tensor() must have the same dimensions."
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<< "\n\t nr: " << nr
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<< "\n\t nc: " << nc
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<< "\n\t i->nr(): " << i->nr()
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<< "\n\t i->nc(): " << i->nc()
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);
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}
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// initialize data to the right size to contain the stuff in the iterator range.
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data.set_size(std::distance(ibegin,iend), 3, nr, nc);
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const size_t offset = nr*nc;
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auto ptr = data.host();
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for (auto i = ibegin; i != iend; ++i)
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{
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for (long r = 0; r < nr; ++r)
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{
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for (long c = 0; c < nc; ++c)
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{
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rgb_pixel temp = (*i)(r,c);
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auto p = ptr++;
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*p = (temp.red-avg_red)/256.0;
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p += offset;
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*p = (temp.green-avg_green)/256.0;
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p += offset;
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*p = (temp.blue-avg_blue)/256.0;
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p += offset;
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}
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}
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ptr += offset*(data.k()-1);
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}
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}
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friend void serialize(const input_rgb_image& item, std::ostream& out)
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{
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serialize("input_rgb_image", out);
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serialize(item.avg_red, out);
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serialize(item.avg_green, out);
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serialize(item.avg_blue, out);
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}
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friend void deserialize(input_rgb_image& item, std::istream& in)
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{
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std::string version;
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deserialize(version, in);
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if (version != "input_rgb_image" && version != "input_rgb_image_sized")
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throw serialization_error("Unexpected version found while deserializing dlib::input_rgb_image.");
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deserialize(item.avg_red, in);
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deserialize(item.avg_green, in);
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deserialize(item.avg_blue, in);
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// read and discard the sizes if this was really a sized input layer.
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if (version == "input_rgb_image_sized")
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{
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size_t nr, nc;
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deserialize(nr, in);
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deserialize(nc, in);
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}
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}
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friend std::ostream& operator<<(std::ostream& out, const input_rgb_image& item)
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{
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out << "input_rgb_image("<<item.avg_red<<","<<item.avg_green<<","<<item.avg_blue<<")";
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return out;
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}
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friend void to_xml(const input_rgb_image& item, std::ostream& out)
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{
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out << "<input_rgb_image r='"<<item.avg_red<<"' g='"<<item.avg_green<<"' b='"<<item.avg_blue<<"'/>";
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}
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private:
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float avg_red;
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float avg_green;
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float avg_blue;
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};
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// ----------------------------------------------------------------------------------------
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template <size_t NR, size_t NC>
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class input_rgb_image_sized
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{
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public:
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static_assert(NR != 0 && NC != 0, "The input image can't be empty.");
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typedef matrix<rgb_pixel> input_type;
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input_rgb_image_sized (
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) :
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avg_red(122.782),
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avg_green(117.001),
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avg_blue(104.298)
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{
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}
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input_rgb_image_sized (
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const input_rgb_image& item
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) : avg_red(item.get_avg_red()),
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avg_green(item.get_avg_green()),
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avg_blue(item.get_avg_blue())
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{}
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input_rgb_image_sized (
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float avg_red_,
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float avg_green_,
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float avg_blue_
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) : avg_red(avg_red_), avg_green(avg_green_), avg_blue(avg_blue_)
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{}
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float get_avg_red() const { return avg_red; }
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float get_avg_green() const { return avg_green; }
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float get_avg_blue() const { return avg_blue; }
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bool image_contained_point ( const tensor& data, const point& p) const { return get_rect(data).contains(p); }
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drectangle tensor_space_to_image_space ( const tensor& /*data*/, drectangle r) const { return r; }
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drectangle image_space_to_tensor_space ( const tensor& /*data*/, double /*scale*/, drectangle r ) const { return r; }
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template <typename forward_iterator>
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void to_tensor (
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forward_iterator ibegin,
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forward_iterator iend,
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resizable_tensor& data
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) const
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{
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DLIB_CASSERT(std::distance(ibegin,iend) > 0);
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// make sure all input images have the correct size
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for (auto i = ibegin; i != iend; ++i)
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{
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DLIB_CASSERT(i->nr()==NR && i->nc()==NC,
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"\t input_rgb_image_sized::to_tensor()"
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<< "\n\t All input images must have "<<NR<<" rows and "<<NC<< " columns, but we got one with "<<i->nr()<<" rows and "<<i->nc()<<" columns."
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);
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}
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// initialize data to the right size to contain the stuff in the iterator range.
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data.set_size(std::distance(ibegin,iend), 3, NR, NC);
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const size_t offset = NR*NC;
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auto ptr = data.host();
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for (auto i = ibegin; i != iend; ++i)
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{
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for (size_t r = 0; r < NR; ++r)
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{
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for (size_t c = 0; c < NC; ++c)
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{
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rgb_pixel temp = (*i)(r,c);
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auto p = ptr++;
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*p = (temp.red-avg_red)/256.0;
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p += offset;
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*p = (temp.green-avg_green)/256.0;
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p += offset;
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*p = (temp.blue-avg_blue)/256.0;
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p += offset;
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}
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}
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ptr += offset*(data.k()-1);
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}
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}
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friend void serialize(const input_rgb_image_sized& item, std::ostream& out)
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{
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serialize("input_rgb_image_sized", out);
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serialize(item.avg_red, out);
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serialize(item.avg_green, out);
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serialize(item.avg_blue, out);
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serialize(NR, out);
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serialize(NC, out);
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}
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friend void deserialize(input_rgb_image_sized& item, std::istream& in)
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{
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std::string version;
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deserialize(version, in);
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if (version != "input_rgb_image_sized")
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throw serialization_error("Unexpected version found while deserializing dlib::input_rgb_image_sized.");
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deserialize(item.avg_red, in);
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deserialize(item.avg_green, in);
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deserialize(item.avg_blue, in);
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size_t nr, nc;
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deserialize(nr, in);
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deserialize(nc, in);
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if (nr != NR || nc != NC)
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{
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std::ostringstream sout;
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sout << "Wrong image dimensions found while deserializing dlib::input_rgb_image_sized.\n";
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sout << "Expected "<<NR<<" rows and "<<NC<< " columns, but found "<<nr<<" rows and "<<nc<<" columns.";
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throw serialization_error(sout.str());
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}
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}
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friend std::ostream& operator<<(std::ostream& out, const input_rgb_image_sized& item)
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{
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out << "input_rgb_image_sized("<<item.avg_red<<","<<item.avg_green<<","<<item.avg_blue<<") nr="<<NR<<" nc="<<NC;
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return out;
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}
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friend void to_xml(const input_rgb_image_sized& item, std::ostream& out)
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{
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out << "<input_rgb_image_sized r='"<<item.avg_red<<"' g='"<<item.avg_green<<"' b='"<<item.avg_blue<<"' nr='"<<NR<<"' nc='"<<NC<<"'/>";
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}
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private:
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float avg_red;
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float avg_green;
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float avg_blue;
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};
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// ----------------------------------------------------------------------------------------
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template <size_t NR, size_t NC>
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input_rgb_image::
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input_rgb_image (
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const input_rgb_image_sized<NR,NC>& item
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) : avg_red(item.get_avg_red()),
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avg_green(item.get_avg_green()),
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avg_blue(item.get_avg_blue())
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{}
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// ----------------------------------------------------------------------------------------
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template <typename T, long NR, long NC, typename MM, typename L>
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class input<matrix<T,NR,NC,MM,L>>
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{
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public:
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typedef matrix<T,NR,NC,MM,L> input_type;
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input() {}
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input(const input&) {}
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template <typename mm>
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input(const input<array2d<T,mm>>&) {}
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bool image_contained_point ( const tensor& data, const point& p) const { return get_rect(data).contains(p); }
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drectangle tensor_space_to_image_space ( const tensor& /*data*/, drectangle r) const { return r; }
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drectangle image_space_to_tensor_space ( const tensor& /*data*/, double /*scale*/, drectangle r ) const { return r; }
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template <typename forward_iterator>
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void to_tensor (
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forward_iterator ibegin,
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forward_iterator iend,
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resizable_tensor& data
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) const
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{
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DLIB_CASSERT(std::distance(ibegin,iend) > 0);
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const auto nr = ibegin->nr();
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const auto nc = ibegin->nc();
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// make sure all the input matrices have the same dimensions
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for (auto i = ibegin; i != iend; ++i)
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{
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DLIB_CASSERT(i->nr()==nr && i->nc()==nc,
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"\t input::to_tensor()"
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<< "\n\t All matrices given to to_tensor() must have the same dimensions."
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<< "\n\t nr: " << nr
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<< "\n\t nc: " << nc
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<< "\n\t i->nr(): " << i->nr()
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<< "\n\t i->nc(): " << i->nc()
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);
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}
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// initialize data to the right size to contain the stuff in the iterator range.
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data.set_size(std::distance(ibegin,iend), pixel_traits<T>::num, nr, nc);
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typedef typename pixel_traits<T>::basic_pixel_type bptype;
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const size_t offset = nr*nc;
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auto ptr = data.host();
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for (auto i = ibegin; i != iend; ++i)
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{
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for (long r = 0; r < nr; ++r)
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{
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for (long c = 0; c < nc; ++c)
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{
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auto temp = pixel_to_vector<float>((*i)(r,c));
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auto p = ptr++;
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for (long j = 0; j < temp.size(); ++j)
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{
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if (is_same_type<bptype,unsigned char>::value)
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*p = temp(j)/256.0;
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else
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*p = temp(j);
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p += offset;
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}
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}
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}
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ptr += offset*(data.k()-1);
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}
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}
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friend void serialize(const input& /*item*/, std::ostream& out)
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{
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serialize("input<matrix>", out);
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}
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friend void deserialize(input& /*item*/, std::istream& in)
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{
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std::string version;
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deserialize(version, in);
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if (version != "input<matrix>")
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throw serialization_error("Unexpected version found while deserializing dlib::input.");
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}
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friend std::ostream& operator<<(std::ostream& out, const input& /*item*/)
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{
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out << "input<matrix>";
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return out;
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}
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friend void to_xml(const input& /*item*/, std::ostream& out)
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{
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out << "<input/>";
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}
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};
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// ----------------------------------------------------------------------------------------
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template <typename T, long NR, long NC, typename MM, typename L, size_t K>
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class input<std::array<matrix<T,NR,NC,MM,L>,K>>
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{
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public:
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typedef std::array<matrix<T,NR,NC,MM,L>,K> input_type;
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input() {}
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input(const input&) {}
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bool image_contained_point ( const tensor& data, const point& p) const { return get_rect(data).contains(p); }
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drectangle tensor_space_to_image_space ( const tensor& /*data*/, drectangle r) const { return r; }
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drectangle image_space_to_tensor_space ( const tensor& /*data*/, double /*scale*/, drectangle r ) const { return r; }
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template <typename forward_iterator>
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void to_tensor (
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forward_iterator ibegin,
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forward_iterator iend,
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resizable_tensor& data
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) const
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{
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DLIB_CASSERT(std::distance(ibegin,iend) > 0);
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DLIB_CASSERT(ibegin->size() != 0, "When using std::array<matrix> inputs you can't give 0 sized arrays.");
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const auto nr = (*ibegin)[0].nr();
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const auto nc = (*ibegin)[0].nc();
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// make sure all the input matrices have the same dimensions
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for (auto i = ibegin; i != iend; ++i)
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{
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for (size_t k = 0; k < K; ++k)
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{
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const auto& arr = *i;
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DLIB_CASSERT(arr[k].nr()==nr && arr[k].nc()==nc,
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"\t input::to_tensor()"
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<< "\n\t When using std::array<matrix> as input, all matrices in a batch must have the same dimensions."
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<< "\n\t nr: " << nr
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<< "\n\t nc: " << nc
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<< "\n\t k: " << k
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<< "\n\t arr[k].nr(): " << arr[k].nr()
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<< "\n\t arr[k].nc(): " << arr[k].nc()
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);
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}
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}
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// initialize data to the right size to contain the stuff in the iterator range.
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data.set_size(std::distance(ibegin,iend), K, nr, nc);
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auto ptr = data.host();
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for (auto i = ibegin; i != iend; ++i)
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{
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for (size_t k = 0; k < K; ++k)
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{
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for (long r = 0; r < nr; ++r)
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{
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for (long c = 0; c < nc; ++c)
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{
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if (is_same_type<T,unsigned char>::value)
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*ptr++ = (*i)[k](r,c)/256.0;
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else
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*ptr++ = (*i)[k](r,c);
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}
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}
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}
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}
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}
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friend void serialize(const input& /*item*/, std::ostream& out)
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{
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serialize("input<array<matrix>>", out);
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}
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friend void deserialize(input& /*item*/, std::istream& in)
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{
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std::string version;
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deserialize(version, in);
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if (version != "input<array<matrix>>")
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throw serialization_error("Unexpected version found while deserializing dlib::input<array<matrix>>.");
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}
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friend std::ostream& operator<<(std::ostream& out, const input& /*item*/)
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{
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out << "input<array<matrix>>";
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return out;
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}
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friend void to_xml(const input& /*item*/, std::ostream& out)
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{
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out << "<input/>";
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}
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};
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// ----------------------------------------------------------------------------------------
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template <typename T, typename MM>
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class input<array2d<T,MM>>
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{
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public:
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typedef array2d<T,MM> input_type;
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input() {}
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input(const input&) {}
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template <long NR, long NC, typename mm, typename L>
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input(const input<matrix<T,NR,NC,mm,L>>&) {}
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bool image_contained_point ( const tensor& data, const point& p) const { return get_rect(data).contains(p); }
|
|
drectangle tensor_space_to_image_space ( const tensor& /*data*/, drectangle r) const { return r; }
|
|
drectangle image_space_to_tensor_space ( const tensor& /*data*/, double /*scale*/, drectangle r ) const { return r; }
|
|
|
|
template <typename forward_iterator>
|
|
void to_tensor (
|
|
forward_iterator ibegin,
|
|
forward_iterator iend,
|
|
resizable_tensor& data
|
|
) const
|
|
{
|
|
DLIB_CASSERT(std::distance(ibegin,iend) > 0);
|
|
const auto nr = ibegin->nr();
|
|
const auto nc = ibegin->nc();
|
|
// make sure all the input matrices have the same dimensions
|
|
for (auto i = ibegin; i != iend; ++i)
|
|
{
|
|
DLIB_CASSERT(i->nr()==nr && i->nc()==nc,
|
|
"\t input::to_tensor()"
|
|
<< "\n\t All array2d objects given to to_tensor() must have the same dimensions."
|
|
<< "\n\t nr: " << nr
|
|
<< "\n\t nc: " << nc
|
|
<< "\n\t i->nr(): " << i->nr()
|
|
<< "\n\t i->nc(): " << i->nc()
|
|
);
|
|
}
|
|
|
|
|
|
// initialize data to the right size to contain the stuff in the iterator range.
|
|
data.set_size(std::distance(ibegin,iend), pixel_traits<T>::num, nr, nc);
|
|
typedef typename pixel_traits<T>::basic_pixel_type bptype;
|
|
|
|
const size_t offset = nr*nc;
|
|
auto ptr = data.host();
|
|
for (auto i = ibegin; i != iend; ++i)
|
|
{
|
|
for (long r = 0; r < nr; ++r)
|
|
{
|
|
for (long c = 0; c < nc; ++c)
|
|
{
|
|
auto temp = pixel_to_vector<float>((*i)[r][c]);
|
|
auto p = ptr++;
|
|
for (long j = 0; j < temp.size(); ++j)
|
|
{
|
|
if (is_same_type<bptype,unsigned char>::value)
|
|
*p = temp(j)/256.0;
|
|
else
|
|
*p = temp(j);
|
|
p += offset;
|
|
}
|
|
}
|
|
}
|
|
ptr += offset*(data.k()-1);
|
|
}
|
|
|
|
}
|
|
|
|
friend void serialize(const input& item, std::ostream& out)
|
|
{
|
|
serialize("input<array2d>", out);
|
|
}
|
|
|
|
friend void deserialize(input& item, std::istream& in)
|
|
{
|
|
std::string version;
|
|
deserialize(version, in);
|
|
if (version != "input<array2d>")
|
|
throw serialization_error("Unexpected version found while deserializing dlib::input.");
|
|
}
|
|
friend std::ostream& operator<<(std::ostream& out, const input& item)
|
|
{
|
|
out << "input<array2d>";
|
|
return out;
|
|
}
|
|
|
|
friend void to_xml(const input& item, std::ostream& out)
|
|
{
|
|
out << "<input/>";
|
|
}
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
namespace detail {
|
|
template <typename PYRAMID_TYPE>
|
|
class input_image_pyramid
|
|
{
|
|
public:
|
|
|
|
virtual ~input_image_pyramid() = 0;
|
|
|
|
typedef PYRAMID_TYPE pyramid_type;
|
|
|
|
unsigned long get_pyramid_padding() const { return pyramid_padding; }
|
|
void set_pyramid_padding(unsigned long value) { pyramid_padding = value; }
|
|
|
|
unsigned long get_pyramid_outer_padding() const { return pyramid_outer_padding; }
|
|
void set_pyramid_outer_padding(unsigned long value) { pyramid_outer_padding = value; }
|
|
|
|
bool image_contained_point(
|
|
const tensor& data,
|
|
const point& p
|
|
) const
|
|
{
|
|
auto&& rects = any_cast<std::vector<rectangle>>(data.annotation());
|
|
DLIB_CASSERT(rects.size() > 0);
|
|
return rects[0].contains(p + rects[0].tl_corner());
|
|
}
|
|
|
|
drectangle tensor_space_to_image_space(
|
|
const tensor& data,
|
|
drectangle r
|
|
) const
|
|
{
|
|
auto&& rects = any_cast<std::vector<rectangle>>(data.annotation());
|
|
return tiled_pyramid_to_image<pyramid_type>(rects, r);
|
|
}
|
|
|
|
drectangle image_space_to_tensor_space (
|
|
const tensor& data,
|
|
double scale,
|
|
drectangle r
|
|
) const
|
|
{
|
|
DLIB_CASSERT(0 < scale && scale <= 1, "scale: " << scale);
|
|
auto&& rects = any_cast<std::vector<rectangle>>(data.annotation());
|
|
return image_to_tiled_pyramid<pyramid_type>(rects, scale, r);
|
|
}
|
|
|
|
protected:
|
|
|
|
template <typename forward_iterator>
|
|
void to_tensor_init (
|
|
forward_iterator ibegin,
|
|
forward_iterator iend,
|
|
resizable_tensor &data,
|
|
unsigned int k
|
|
) const
|
|
{
|
|
|
|
DLIB_CASSERT(std::distance(ibegin, iend) > 0);
|
|
auto nr = ibegin->nr();
|
|
auto nc = ibegin->nc();
|
|
// make sure all the input matrices have the same dimensions
|
|
for (auto i = ibegin; i != iend; ++i)
|
|
{
|
|
DLIB_CASSERT(i->nr() == nr && i->nc() == nc,
|
|
"\t input_grayscale_image_pyramid::to_tensor()"
|
|
<< "\n\t All matrices given to to_tensor() must have the same dimensions."
|
|
<< "\n\t nr: " << nr
|
|
<< "\n\t nc: " << nc
|
|
<< "\n\t i->nr(): " << i->nr()
|
|
<< "\n\t i->nc(): " << i->nc()
|
|
);
|
|
}
|
|
|
|
long NR, NC;
|
|
pyramid_type pyr;
|
|
auto& rects = data.annotation().get<std::vector<rectangle>>();
|
|
impl::compute_tiled_image_pyramid_details(pyr, nr, nc, pyramid_padding, pyramid_outer_padding, rects,
|
|
NR, NC);
|
|
|
|
// initialize data to the right size to contain the stuff in the iterator range.
|
|
data.set_size(std::distance(ibegin, iend), k, NR, NC);
|
|
|
|
// We need to zero the image before doing the pyramid, since the pyramid
|
|
// creation code doesn't write to all parts of the image. We also take
|
|
// care to avoid triggering any device to hosts copies.
|
|
auto ptr = data.host_write_only();
|
|
for (size_t i = 0; i < data.size(); ++i)
|
|
ptr[i] = 0;
|
|
|
|
}
|
|
|
|
// now build the image pyramid into data. This does the same thing as
|
|
// standard create_tiled_pyramid(), except we use the GPU if one is available.
|
|
void create_tiled_pyramid (
|
|
const std::vector<rectangle>& rects,
|
|
resizable_tensor& data
|
|
) const
|
|
{
|
|
for (size_t i = 1; i < rects.size(); ++i) {
|
|
alias_tensor src(data.num_samples(), data.k(), rects[i - 1].height(), rects[i - 1].width());
|
|
alias_tensor dest(data.num_samples(), data.k(), rects[i].height(), rects[i].width());
|
|
|
|
auto asrc = src(data, data.nc() * rects[i - 1].top() + rects[i - 1].left());
|
|
auto adest = dest(data, data.nc() * rects[i].top() + rects[i].left());
|
|
|
|
tt::resize_bilinear(adest, data.nc(), data.nr() * data.nc(),
|
|
asrc, data.nc(), data.nr() * data.nc());
|
|
}
|
|
}
|
|
|
|
unsigned long pyramid_padding = 10;
|
|
unsigned long pyramid_outer_padding = 11;
|
|
};
|
|
|
|
template <typename PYRAMID_TYPE>
|
|
input_image_pyramid<PYRAMID_TYPE>::~input_image_pyramid() {}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename PYRAMID_TYPE>
|
|
class input_grayscale_image_pyramid : public detail::input_image_pyramid<PYRAMID_TYPE>
|
|
{
|
|
public:
|
|
typedef matrix<unsigned char> input_type;
|
|
typedef PYRAMID_TYPE pyramid_type;
|
|
|
|
template <typename forward_iterator>
|
|
void to_tensor (
|
|
forward_iterator ibegin,
|
|
forward_iterator iend,
|
|
resizable_tensor& data
|
|
) const
|
|
{
|
|
this->to_tensor_init(ibegin, iend, data, 1);
|
|
|
|
const auto rects = data.annotation().get<std::vector<rectangle>>();
|
|
if (rects.size() == 0)
|
|
return;
|
|
|
|
// copy the first raw image into the top part of the tiled pyramid. We need to
|
|
// do this for each of the input images/samples in the tensor.
|
|
auto ptr = data.host_write_only();
|
|
for (auto i = ibegin; i != iend; ++i)
|
|
{
|
|
auto& img = *i;
|
|
ptr += rects[0].top()*data.nc();
|
|
for (long r = 0; r < img.nr(); ++r)
|
|
{
|
|
auto p = ptr+rects[0].left();
|
|
for (long c = 0; c < img.nc(); ++c)
|
|
p[c] = (img(r,c))/256.0;
|
|
ptr += data.nc();
|
|
}
|
|
ptr += data.nc()*(data.nr()-rects[0].bottom()-1);
|
|
}
|
|
|
|
this->create_tiled_pyramid(rects, data);
|
|
}
|
|
|
|
friend void serialize(const input_grayscale_image_pyramid& item, std::ostream& out)
|
|
{
|
|
serialize("input_grayscale_image_pyramid", out);
|
|
serialize(item.pyramid_padding, out);
|
|
serialize(item.pyramid_outer_padding, out);
|
|
}
|
|
|
|
friend void deserialize(input_grayscale_image_pyramid& item, std::istream& in)
|
|
{
|
|
std::string version;
|
|
deserialize(version, in);
|
|
if (version != "input_grayscale_image_pyramid")
|
|
throw serialization_error("Unexpected version found while deserializing dlib::input_grayscale_image_pyramid.");
|
|
deserialize(item.pyramid_padding, in);
|
|
deserialize(item.pyramid_outer_padding, in);
|
|
}
|
|
|
|
friend std::ostream& operator<<(std::ostream& out, const input_grayscale_image_pyramid& item)
|
|
{
|
|
out << "input_grayscale_image_pyramid()";
|
|
out << " pyramid_padding="<<item.pyramid_padding;
|
|
out << " pyramid_outer_padding="<<item.pyramid_outer_padding;
|
|
return out;
|
|
}
|
|
|
|
friend void to_xml(const input_grayscale_image_pyramid& item, std::ostream& out)
|
|
{
|
|
out << "<input_grayscale_image_pyramid"
|
|
<<"' pyramid_padding='"<<item.pyramid_padding
|
|
<<"' pyramid_outer_padding='"<<item.pyramid_outer_padding
|
|
<<"'/>";
|
|
}
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
template <typename PYRAMID_TYPE>
|
|
class input_rgb_image_pyramid : public detail::input_image_pyramid<PYRAMID_TYPE>
|
|
{
|
|
public:
|
|
typedef matrix<rgb_pixel> input_type;
|
|
typedef PYRAMID_TYPE pyramid_type;
|
|
|
|
input_rgb_image_pyramid (
|
|
) :
|
|
avg_red(122.782),
|
|
avg_green(117.001),
|
|
avg_blue(104.298)
|
|
{
|
|
}
|
|
|
|
input_rgb_image_pyramid (
|
|
float avg_red_,
|
|
float avg_green_,
|
|
float avg_blue_
|
|
) : avg_red(avg_red_), avg_green(avg_green_), avg_blue(avg_blue_)
|
|
{}
|
|
|
|
float get_avg_red() const { return avg_red; }
|
|
float get_avg_green() const { return avg_green; }
|
|
float get_avg_blue() const { return avg_blue; }
|
|
|
|
template <typename forward_iterator>
|
|
void to_tensor (
|
|
forward_iterator ibegin,
|
|
forward_iterator iend,
|
|
resizable_tensor& data
|
|
) const
|
|
{
|
|
this->to_tensor_init(ibegin, iend, data, 3);
|
|
|
|
const auto rects = data.annotation().get<std::vector<rectangle>>();
|
|
if (rects.size() == 0)
|
|
return;
|
|
|
|
// copy the first raw image into the top part of the tiled pyramid. We need to
|
|
// do this for each of the input images/samples in the tensor.
|
|
auto ptr = data.host_write_only();
|
|
for (auto i = ibegin; i != iend; ++i)
|
|
{
|
|
auto& img = *i;
|
|
ptr += rects[0].top()*data.nc();
|
|
for (long r = 0; r < img.nr(); ++r)
|
|
{
|
|
auto p = ptr+rects[0].left();
|
|
for (long c = 0; c < img.nc(); ++c)
|
|
p[c] = (img(r,c).red-avg_red)/256.0;
|
|
ptr += data.nc();
|
|
}
|
|
ptr += data.nc()*(data.nr()-rects[0].bottom()-1);
|
|
|
|
ptr += rects[0].top()*data.nc();
|
|
for (long r = 0; r < img.nr(); ++r)
|
|
{
|
|
auto p = ptr+rects[0].left();
|
|
for (long c = 0; c < img.nc(); ++c)
|
|
p[c] = (img(r,c).green-avg_green)/256.0;
|
|
ptr += data.nc();
|
|
}
|
|
ptr += data.nc()*(data.nr()-rects[0].bottom()-1);
|
|
|
|
ptr += rects[0].top()*data.nc();
|
|
for (long r = 0; r < img.nr(); ++r)
|
|
{
|
|
auto p = ptr+rects[0].left();
|
|
for (long c = 0; c < img.nc(); ++c)
|
|
p[c] = (img(r,c).blue-avg_blue)/256.0;
|
|
ptr += data.nc();
|
|
}
|
|
ptr += data.nc()*(data.nr()-rects[0].bottom()-1);
|
|
}
|
|
|
|
this->create_tiled_pyramid(rects, data);
|
|
}
|
|
|
|
friend void serialize(const input_rgb_image_pyramid& item, std::ostream& out)
|
|
{
|
|
serialize("input_rgb_image_pyramid2", out);
|
|
serialize(item.avg_red, out);
|
|
serialize(item.avg_green, out);
|
|
serialize(item.avg_blue, out);
|
|
serialize(item.pyramid_padding, out);
|
|
serialize(item.pyramid_outer_padding, out);
|
|
}
|
|
|
|
friend void deserialize(input_rgb_image_pyramid& item, std::istream& in)
|
|
{
|
|
std::string version;
|
|
deserialize(version, in);
|
|
if (version != "input_rgb_image_pyramid" && version != "input_rgb_image_pyramid2")
|
|
throw serialization_error("Unexpected version found while deserializing dlib::input_rgb_image_pyramid.");
|
|
deserialize(item.avg_red, in);
|
|
deserialize(item.avg_green, in);
|
|
deserialize(item.avg_blue, in);
|
|
if (version == "input_rgb_image_pyramid2")
|
|
{
|
|
deserialize(item.pyramid_padding, in);
|
|
deserialize(item.pyramid_outer_padding, in);
|
|
}
|
|
else
|
|
{
|
|
item.pyramid_padding = 10;
|
|
item.pyramid_outer_padding = 11;
|
|
}
|
|
}
|
|
|
|
friend std::ostream& operator<<(std::ostream& out, const input_rgb_image_pyramid& item)
|
|
{
|
|
out << "input_rgb_image_pyramid("<<item.avg_red<<","<<item.avg_green<<","<<item.avg_blue<<")";
|
|
out << " pyramid_padding="<<item.pyramid_padding;
|
|
out << " pyramid_outer_padding="<<item.pyramid_outer_padding;
|
|
return out;
|
|
}
|
|
|
|
friend void to_xml(const input_rgb_image_pyramid& item, std::ostream& out)
|
|
{
|
|
out << "<input_rgb_image_pyramid r='"<<item.avg_red<<"' g='"<<item.avg_green
|
|
<<"' b='"<<item.avg_blue
|
|
<<"' pyramid_padding='"<<item.pyramid_padding
|
|
<<"' pyramid_outer_padding='"<<item.pyramid_outer_padding
|
|
<<"'/>";
|
|
}
|
|
|
|
private:
|
|
float avg_red;
|
|
float avg_green;
|
|
float avg_blue;
|
|
};
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
}
|
|
|
|
#endif // DLIB_DNn_INPUT_H_
|
|
|