顔のクラスタリング（Python, Dlib を使用）

ファイルは，cluster.py のようなファイル名で， C:\pytools\dlib\python_examples に保存すること．

次のソースコードは，Dlib に付属の python_examples\face_clustering.py （パブリックドメイン）を書き替えたもの．

太字が書き換え部分．顔画像のすべてを出力するように変更．

#!/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
#
#   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 -yV install cmake
#
#   Also note that this example requires Numpy which can be installed
#   via the command:
#       pip install numpy
import sys
import os
import dlib
import glob
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 = dlib.load_rgb_image(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))
if not os.path.isdir(output_folder_path):
    os.makedirs(output_folder_path)
# Find biggest class
biggest_class = None
biggest_class_length = 0
for i in range(0, num_classes):
    print(i)
    indices = []
    for k, label in enumerate(labels):
        if label == i:
            indices.append(k)
    for k, index in enumerate(indices):
        img, shape = images[index]
        file_path = os.path.join(output_folder_path, "face_" + str(i) + "_" + str(k))
        dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25)

顔のクラスタリング（Python, Dlib を使用）

前準備

Python, Dlib のインストール

学習済みデータの準備

顔のクラスタリング