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Diffstat (limited to 'ml/dlib/tools/python/src/cnn_face_detector.cpp')
| -rw-r--r-- | ml/dlib/tools/python/src/cnn_face_detector.cpp | 183 |
1 files changed, 0 insertions, 183 deletions
diff --git a/ml/dlib/tools/python/src/cnn_face_detector.cpp b/ml/dlib/tools/python/src/cnn_face_detector.cpp deleted file mode 100644 index f18d99d95..000000000 --- a/ml/dlib/tools/python/src/cnn_face_detector.cpp +++ /dev/null @@ -1,183 +0,0 @@ -// Copyright (C) 2017 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. - -#include "opaque_types.h" -#include <dlib/python.h> -#include <dlib/matrix.h> -#include <dlib/dnn.h> -#include <dlib/image_transforms.h> -#include "indexing.h" -#include <pybind11/stl_bind.h> - -using namespace dlib; -using namespace std; - -namespace py = pybind11; - - -class cnn_face_detection_model_v1 -{ - -public: - - cnn_face_detection_model_v1(const std::string& model_filename) - { - deserialize(model_filename) >> net; - } - - std::vector<mmod_rect> detect ( - py::object pyimage, - const int upsample_num_times - ) - { - pyramid_down<2> pyr; - std::vector<mmod_rect> rects; - - // Copy the data into dlib based objects - matrix<rgb_pixel> image; - if (is_gray_python_image(pyimage)) - assign_image(image, numpy_gray_image(pyimage)); - else if (is_rgb_python_image(pyimage)) - assign_image(image, numpy_rgb_image(pyimage)); - else - throw dlib::error("Unsupported image type, must be 8bit gray or RGB image."); - - // Upsampling the image will allow us to detect smaller faces but will cause the - // program to use more RAM and run longer. - unsigned int levels = upsample_num_times; - while (levels > 0) - { - levels--; - pyramid_up(image, pyr); - } - - auto dets = net(image); - - // Scale the detection locations back to the original image size - // if the image was upscaled. - for (auto&& d : dets) { - d.rect = pyr.rect_down(d.rect, upsample_num_times); - rects.push_back(d); - } - - return rects; - } - - std::vector<std::vector<mmod_rect> > detect_mult ( - py::list imgs, - const int upsample_num_times, - const int batch_size = 128 - ) - { - pyramid_down<2> pyr; - std::vector<matrix<rgb_pixel> > dimgs; - dimgs.reserve(len(imgs)); - - for(int i = 0; i < len(imgs); i++) - { - // Copy the data into dlib based objects - matrix<rgb_pixel> image; - py::object tmp = imgs[i].cast<py::object>(); - if (is_gray_python_image(tmp)) - assign_image(image, numpy_gray_image(tmp)); - else if (is_rgb_python_image(tmp)) - assign_image(image, numpy_rgb_image(tmp)); - else - throw dlib::error("Unsupported image type, must be 8bit gray or RGB image."); - - for(int i = 0; i < upsample_num_times; i++) - { - pyramid_up(image); - } - dimgs.push_back(image); - } - - for(int i = 1; i < dimgs.size(); i++) - { - if - ( - dimgs[i - 1].nc() != dimgs[i].nc() || - dimgs[i - 1].nr() != dimgs[i].nr() - ) - throw dlib::error("Images in list must all have the same dimensions."); - - } - - auto dets = net(dimgs, batch_size); - std::vector<std::vector<mmod_rect> > all_rects; - - for(auto&& im_dets : dets) - { - std::vector<mmod_rect> rects; - rects.reserve(im_dets.size()); - for (auto&& d : im_dets) { - d.rect = pyr.rect_down(d.rect, upsample_num_times); - rects.push_back(d); - } - all_rects.push_back(rects); - } - - return all_rects; - } - -private: - - template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>; - template <long num_filters, typename SUBNET> using con5 = con<num_filters,5,5,1,1,SUBNET>; - - template <typename SUBNET> using downsampler = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>; - template <typename SUBNET> using rcon5 = relu<affine<con5<45,SUBNET>>>; - - using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>; - - net_type net; -}; - -// ---------------------------------------------------------------------------------------- - -void bind_cnn_face_detection(py::module& m) -{ - { - py::class_<cnn_face_detection_model_v1>(m, "cnn_face_detection_model_v1", "This object detects human faces in an image. The constructor loads the face detection model from a file. You can download a pre-trained model from http://dlib.net/files/mmod_human_face_detector.dat.bz2.") - .def(py::init<std::string>()) - .def( - "__call__", - &cnn_face_detection_model_v1::detect_mult, - py::arg("imgs"), py::arg("upsample_num_times")=0, py::arg("batch_size")=128, - "takes a list of images as input returning a 2d list of mmod rectangles" - ) - .def( - "__call__", - &cnn_face_detection_model_v1::detect, - py::arg("img"), py::arg("upsample_num_times")=0, - "Find faces in an image using a deep learning model.\n\ - - Upsamples the image upsample_num_times before running the face \n\ - detector." - ); - } - - m.def("set_dnn_prefer_smallest_algorithms", &set_dnn_prefer_smallest_algorithms, "Tells cuDNN to use slower algorithms that use less RAM."); - - auto cuda = m.def_submodule("cuda", "Routines for setting CUDA specific properties."); - cuda.def("set_device", &dlib::cuda::set_device, py::arg("device_id"), - "Set the active CUDA device. It is required that 0 <= device_id < get_num_devices()."); - cuda.def("get_device", &dlib::cuda::get_device, "Get the active CUDA device."); - cuda.def("get_num_devices", &dlib::cuda::get_num_devices, "Find out how many CUDA devices are available."); - - { - typedef mmod_rect type; - py::class_<type>(m, "mmod_rectangle", "Wrapper around a rectangle object and a detection confidence score.") - .def_readwrite("rect", &type::rect) - .def_readwrite("confidence", &type::detection_confidence); - } - { - typedef std::vector<mmod_rect> type; - py::bind_vector<type>(m, "mmod_rectangles", "An array of mmod rectangle objects.") - .def("extend", extend_vector_with_python_list<mmod_rect>); - } - { - typedef std::vector<std::vector<mmod_rect> > type; - py::bind_vector<type>(m, "mmod_rectangless", "A 2D array of mmod rectangle objects.") - .def("extend", extend_vector_with_python_list<std::vector<mmod_rect>>); - } -} |