mirror of https://github.com/davisking/dlib.git
Add a python wrapper for using the mmod face detector (#753)
Adam Geitgey
7 years ago
committed by
Davis E. King
parent
af88b0d56f
commit
b6d2329c5e
@ -0,0 +1,72 @@
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#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This example shows how to run a CNN based face detector using dlib. The
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# example loads a pretrained model and uses it to find faces in images. The
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# CNN model is much more accurate than the HOG based model shown in the
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# face_detector.py example, but takes much more computational power to
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# run, and is meant to be executed on a GPU to attain reasonable speed.
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#
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# You can download the pre-trained model from:
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# http://dlib.net/files/mmod_human_face_detector.dat.bz2
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#
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# The examples/faces folder contains some jpg images of people. You can run
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# this program on them and see the detections by executing the
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# following command:
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# ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg
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#
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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# or
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# python setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA
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# if you have a CPU that supports AVX instructions, you have an Nvidia GPU
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# and you have CUDA installed since this makes things run *much* faster.
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake and boost-python installed. On Ubuntu, this can be done easily by
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# running the command:
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# sudo apt-get install libboost-python-dev cmake
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#
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# Also note that this example requires scikit-image which can be installed
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# via the command:
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# pip install scikit-image
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# Or downloaded from http://scikit-image.org/download.html.
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import sys
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import dlib
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from skimage import io
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if len(sys.argv) < 3:
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print(
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"Call this program like this:\n"
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" ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg\n"
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"You can get the mmod_human_face_detector.dat file from:\n"
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" http://dlib.net/files/mmod_human_face_detector.dat.bz2")
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exit()
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cnn_face_detection_model = dlib.cnn_face_detection_model_v1(sys.argv[1])
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win = dlib.image_window()
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for f in sys.argv[2:]:
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print("Processing file: {}".format(f))
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img = io.imread(f)
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# The 1 in the second argument indicates that we should upsample the image
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# 1 time. This will make everything bigger and allow us to detect more
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# faces.
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dets = cnn_face_detection_model.cnn_face_detector(img, 1)
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print("Number of faces detected: {}".format(len(dets)))
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for i, d in enumerate(dets):
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print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
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i, d.left(), d.top(), d.right(), d.bottom()))
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win.clear_overlay()
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win.set_image(img)
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win.add_overlay(dets)
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dlib.hit_enter_to_continue()
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@ -0,0 +1,95 @@
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// Copyright (C) 2017 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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#include <dlib/python.h>
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#include <boost/shared_ptr.hpp>
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#include <dlib/matrix.h>
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#include <boost/python/slice.hpp>
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#include <dlib/geometry/vector.h>
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#include <dlib/dnn.h>
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#include <dlib/image_transforms.h>
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#include "indexing.h"
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using namespace dlib;
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using namespace std;
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using namespace boost::python;
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typedef matrix<double,0,1> cv;
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class cnn_face_detection_model_v1
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{
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public:
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cnn_face_detection_model_v1(const std::string& model_filename)
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{
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deserialize(model_filename) >> net;
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}
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std::vector<rectangle> cnn_face_detector (
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object pyimage,
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const int upsample_num_times
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)
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{
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pyramid_down<2> pyr;
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std::vector<rectangle> rects;
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// Copy the data into dlib based objects
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matrix<rgb_pixel> image;
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if (is_gray_python_image(pyimage))
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assign_image(image, numpy_gray_image(pyimage));
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else if (is_rgb_python_image(pyimage))
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assign_image(image, numpy_rgb_image(pyimage));
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else
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throw dlib::error("Unsupported image type, must be 8bit gray or RGB image.");
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// Upsampling the image will allow us to detect smaller faces but will cause the
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// program to use more RAM and run longer.
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unsigned int levels = upsample_num_times;
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while (levels > 0)
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{
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levels--;
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pyramid_up(image, pyr);
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}
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auto dets = net(image);
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// Scale the detection locations back to the original image size
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// if the image was upscaled.
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for (auto&& d : dets) {
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d.rect = pyr.rect_down(d.rect, upsample_num_times);
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rects.push_back(d.rect);
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}
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return rects;
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}
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private:
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template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
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template <long num_filters, typename SUBNET> using con5 = con<num_filters,5,5,1,1,SUBNET>;
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template <typename SUBNET> using downsampler = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
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template <typename SUBNET> using rcon5 = relu<affine<con5<45,SUBNET>>>;
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using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
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net_type net;
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};
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// ----------------------------------------------------------------------------------------
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void bind_cnn_face_detection()
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{
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using boost::python::arg;
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{
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class_<cnn_face_detection_model_v1>("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.", init<std::string>())
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.def("cnn_face_detector", &cnn_face_detection_model_v1::cnn_face_detector, (arg("img"), arg("upsample_num_times")=0),
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"Find faces in an image using a deep learning model.\n\
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- Upsamples the image upsample_num_times before running the face \n\
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detector."
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);
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}
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}
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