mirror of https://github.com/davisking/dlib.git
128 lines
4.9 KiB
Python
Executable File
128 lines
4.9 KiB
Python
Executable File
#!/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 installed. On Ubuntu, this can be done easily by running the
|
|
# command:
|
|
# sudo apt-get install 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_5_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_5_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 find all the faces and compute 128D face descriptors for each face.
|
|
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)
|
|
|
|
# Compute the 128D vector that describes the face in img identified by
|
|
# shape.
|
|
face_descriptor = facerec.compute_face_descriptor(img, shape)
|
|
descriptors.append(face_descriptor)
|
|
images.append((img, shape))
|
|
|
|
# Now let's cluster the faces.
|
|
labels = dlib.chinese_whispers_clustering(descriptors, 0.5)
|
|
num_classes = len(set(labels))
|
|
print("Number of clusters: {}".format(num_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 cluster id number: {}".format(biggest_class))
|
|
print("Number of faces in biggest cluster: {}".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("Indices of images in the biggest cluster: {}".format(str(indices)))
|
|
|
|
# Ensure output directory exists
|
|
if not os.path.isdir(output_folder_path):
|
|
os.makedirs(output_folder_path)
|
|
|
|
# Save the extracted faces
|
|
print("Saving faces in largest cluster to output folder...")
|
|
for i, index in enumerate(indices):
|
|
img, shape = images[index]
|
|
file_path = os.path.join(output_folder_path, "face_" + str(i))
|
|
# The size and padding arguments are optional with default size=150x150 and padding=0.25
|
|
dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25)
|
|
|
|
|
|
|
|
|