人脸聚类

人脸聚类属于无监督学习. 当你有许多未标记的待分类的照片时, 使用人脸聚类是非常有用的. 其 python 脚本位于 /python_examples/face_clustering.py.

待分类目录下有两张神仙的肖像和两张观海的肖像, 使用如下代码可以将神仙和观海分开.

import os

import sys

import dlib

import skimage.draw

import skimage.io

predictor_path = 'shape_predictor_68_face_landmarks.dat'

face_rec_model_path = 'dlib_face_recognition_resnet_model_v1.dat'

faces_folder_path = sys.argv[1]

detector = dlib.get_frontal_face_detector()

sp = dlib.shape_predictor(predictor_path)

facerec = dlib.face_recognition_model_v1(face_rec_model_path)

face_results = []

for entry in os.scandir(faces_folder_path):

print(f'Processing file: {entry.path}')

img = skimage.io.imread(entry.path)

dets = detector(img, 1)

print("Number of faces detected: {}".format(len(dets)))

for k, d in enumerate(dets):

shape = sp(img, d)

face_descriptor = facerec.compute_face_descriptor(img, shape)

face_results.append({

'path': entry.path,

'det': d,

'shape': shape,

'descriptor': face_descriptor

})

labels = dlib.chinese_whispers_clustering([e['descriptor'] for e in face_results], 0.5)

num_classes = len(set(labels))

print("Number of clusters: {}".format(num_classes))

for i, r in enumerate(face_results):

r['label'] = labels[i]

for e in face_results:

path = e['path']

d = e['det']

det = [d.top(), d.left(), d.bottom(), d.right()]

label = e['label']

print(label, path, det)

执行结果

$ python3 run.py face_tests/

Processing file: face_tests/crystal_01.jpg

Number of faces detected: 1

Processing file: face_tests/crystal_02.jpg

Number of faces detected: 1

Processing file: face_tests/obama_01.jpg

Number of faces detected: 1

Processing file: face_tests/obama_02.jpg

Number of faces detected: 1

Number of clusters: 2

0 face_tests/crystal_01.jpg [116, 201, 270, 356]

0 face_tests/crystal_02.jpg [64, 236, 219, 390]

1 face_tests/obama_01.jpg [38, 146, 113, 220]

1 face_tests/obama_02.jpg [63, 71, 138, 146]