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Added face clustering example to Python API

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Visionworkz 2017-09-06 02:05:42 +08:00
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142
python_examples/face_clustering.py Executable file
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@ -0,0 +1,142 @@
#!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example shows how to use dlib's face recognition tool for clustering using chinese_whispers.
# This is useful when you have a collection of photographs which you know are linked to
# a particular person, but the person may be photographed with multiple other people.
# In this example, we assume the largest cluster will contain photos of the common person in the
# collection of photographs. Then, we save extracted images of the face in the largest cluster in
# a 150x150 px format which is suitable for jittering and loading to perform metric learning (as shown
# in the dnn_metric_learning_on_images_ex.cpp example.
# https://github.com/davisking/dlib/blob/master/examples/dnn_metric_learning_on_images_ex.cpp
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command:
# pip install dlib
#
# Alternatively, if you want to compile dlib yourself then go into the dlib
# root folder and run:
# python setup.py install
# or
# python setup.py install --yes USE_AVX_INSTRUCTIONS
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster. This code will also use CUDA if you have CUDA and cuDNN
# installed.
#
# Compiling dlib should work on any operating system so long as you have
# CMake and boost-python installed. On Ubuntu, this can be done easily by
# running the command:
# sudo apt-get install libboost-python-dev cmake
#
# Also note that this example requires scikit-image which can be installed
# via the command:
# pip install scikit-image
# Or downloaded from http://scikit-image.org/download.html.
import sys
import os
import dlib
import glob
from skimage import io
if len(sys.argv) != 5:
print(
"Call this program like this:\n"
" ./face_clustering.py shape_predictor_68_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces output_folder\n"
"You can download a trained facial shape predictor and recognition model from:\n"
" http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\n"
" http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
exit()
predictor_path = sys.argv[1]
face_rec_model_path = sys.argv[2]
faces_folder_path = sys.argv[3]
output_folder_path = sys.argv[4]
# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face, and finally the
# face recognition model.
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
descriptors = []
images = []
# Now process all the images
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
print("Processing file: {}".format(f))
img = io.imread(f)
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
# Now process each face we found.
for k, d in enumerate(dets):
# Get the landmarks/parts for the face in box d.
shape = sp(img, d)
# Draw the face landmarks on the screen so we can see what face is currently being processed.
# Compute the 128D vector that describes the face in img identified by
# shape. In general, if two face descriptor vectors have a Euclidean
# distance between them less than 0.6 then they are from the same
# person, otherwise they are from different people. Here we just print
# the vector to the screen.
face_descriptor = facerec.compute_face_descriptor(img, shape)
descriptors.append(face_descriptor)
images.append((img, shape))
# It should also be noted that you can also call this function like this:
# face_descriptor = facerec.compute_face_descriptor(img, shape, 100)
# The version of the call without the 100 gets 99.13% accuracy on LFW
# while the version with 100 gets 99.38%. However, the 100 makes the
# call 100x slower to execute, so choose whatever version you like. To
# explain a little, the 3rd argument tells the code how many times to
# jitter/resample the image. When you set it to 100 it executes the
# face descriptor extraction 100 times on slightly modified versions of
# the face and returns the average result. You could also pick a more
# middle value, such as 10, which is only 10x slower but still gets an
# LFW accuracy of 99.3%.
labels = facerec.cluster(descriptors)
label_classes = list(set(labels))
label_classes.sort()
num_classes = len(label_classes)
print("Number of clusters: {}".format(num_classes))
print("Labels classes: {}".format(str(label_classes)))
# Find biggest class
biggest_class = None
biggest_class_length = 0
for i in range(0, num_classes):
class_length = len([label for label in labels if label == i])
if class_length > biggest_class_length:
biggest_class_length = class_length
biggest_class = i
print("Biggest class: {}".format(biggest_class))
print("Biggest class length: {}".format(biggest_class_length))
# Find the indices for the biggest class
indices = []
for i, label in enumerate(labels):
if label == biggest_class:
indices.append(i)
print("Biggest class indices: {}".format(str(indices)))
# Ensure output directory exists
if not os.path.isdir(output_folder_path):
os.makedirs(output_folder_path)
# Save the extracted faces
for i, index in enumerate(indices):
img, shape = images[index]
file_path = os.path.join(output_folder_path, "face_" + str(i))
facerec.save_image_chip(img, shape, file_path)

84
tools/python/src/face_recognition.cpp
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@ -9,6 +9,8 @@
#include <dlib/dnn.h>
#include <dlib/image_transforms.h>
#include "indexing.h"
#include <dlib/image_io.h>
#include <dlib/clustering.h>
using namespace dlib;
@ -37,6 +39,79 @@ public:
cropper->set_max_rotation_degrees(3);
}
boost::python::list cluster(boost::python::list descriptors)
{
boost::python::list clusters;
size_t num_descriptors = len(descriptors);
// In particular, one simple thing we can do is face clustering. This next bit of code
// creates a graph of connected faces and then uses the Chinese whispers graph clustering
// algorithm to identify how many people there are and which faces belong to whom.
std::vector<sample_pair> edges;
std::vector<unsigned long> labels;
for (size_t i = 0; i < num_descriptors; ++i)
{
for (size_t j = i+1; j < num_descriptors; ++j)
{
// Faces are connected in the graph if they are close enough. Here we check if
// the distance between two face descriptors is less than 0.6, which is the
// decision threshold the network was trained to use. Although you can
// certainly use any other threshold you find useful.
matrix<double,0,1> first_descriptor = boost::python::extract<matrix<double,0,1>>(descriptors[i]);
matrix<double,0,1> second_descriptor = boost::python::extract<matrix<double,0,1>>(descriptors[j]);
if (length(first_descriptor-second_descriptor) < 0.6)
edges.push_back(sample_pair(i,j));
}
}
const auto num_clusters = chinese_whispers(edges, labels);
for (size_t i = 0; i < labels.size(); ++i)
{
clusters.append(labels[i]);
}
return clusters;
}
void save_image_chip (
object img,
const full_object_detection& face,
const std::string& chip_filename
)
{
std::vector<full_object_detection> faces(1, face);
save_image_chips(img, faces, chip_filename);
return;
}
void save_image_chips (
object img,
const std::vector<full_object_detection>& faces,
const std::string& chip_filename
)
{
int num_faces = faces.size();
std::vector<chip_details> dets;
for (auto& f : faces)
dets.push_back(get_face_chip_details(f, 150, 0.25));
dlib::array<matrix<rgb_pixel>> face_chips;
extract_image_chips(numpy_rgb_image(img), dets, face_chips);
int i=0;
for (auto& chip : face_chips) {
i++;
if(num_faces > 1)
{
const std::string& file_name = chip_filename + "_" + std::to_string(i) + ".jpg";
save_jpeg(chip, file_name);
}
else
{
const std::string& file_name = chip_filename + ".jpg";
save_jpeg(chip, file_name);
}
}
}
matrix<double,0,1> compute_face_descriptor (
object img,
const full_object_detection& face,
@ -155,6 +230,15 @@ void bind_face_recognition()
.def("compute_face_descriptor", &face_recognition_model_v1::compute_face_descriptors, (arg("img"),arg("faces"),arg("num_jitters")=0),
"Takes an image and an array of full_object_detections that reference faces in that image and converts them into 128D face descriptors. "
"If num_jitters>1 then each face will be randomly jittered slightly num_jitters times, each run through the 128D projection, and the average used as the face descriptor."
)
.def("save_image_chip", &face_recognition_model_v1::save_image_chip, (arg("img"),arg("face"),arg("chip_filename")),
"Takes an image and a full_object_detection that references a face in that image and saves the face with the specified file name prefix"
)
.def("save_image_chips", &face_recognition_model_v1::save_image_chips, (arg("img"),arg("faces"),arg("chip_filename")),
"Takes an image and a full_object_detections object that reference faces in that image and saves the faces with the specified file name prefix"
)
.def("cluster", &face_recognition_model_v1::cluster, (arg("descriptors")),
"Takes a list of descriptors and returns a list that contains a label for each descriptor. Clustering is done using chinese_whispers."
);
}