1.找出最大聚类

2.将人脸存入最大聚类的文件夹



实现：

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_ ... et_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))

# 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)

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作者：_iorilan
来源：CSDN
原文：https://blog.csdn.net/lan_liang/article/details/84714569
版权声明：本文为博主原创文章，转载请附上博文链接！