使用中文Whispers算法进行人脸聚类

Question

我正在尝试使用中文耳语算法进行面部聚类.我已经使用DLIB和Python,用于提取每个面的特征和如在由Davisking描述映射到128 d矢量https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp.

然后我按照那里的指示构建了一个图表.我实现了中文耳语算法并应用于此图.谁能告诉我我做了什么错？任何人都可以使用中文耳语算法上传面部聚类的python代码吗？这是我的中国私语代码:

import networkx as nx
import random
from random import shuffle
import math
def chinese_whispers(nodes,edges,iterations):
    G = nx.Graph()
    G.add_nodes_from(nodes)
    #print(G.node)
    for n, v in enumerate(nodes):
        G.node[n]['class'] = v
        #print(n,v)
    G.add_edges_from(edges)
    #gn=G.nodes()
    #for node in gn:
    #print((node,G[node],G.node,G.node[node]))
    #(0, {16: {'weight': 0.49846761956907698}, 14: {'weight': 0.55778036559581601}, 7: {'weight': 0.43902511314524784}}, {'class': 0})
    for z in range(0, iterations):
        gn = G.nodes()
    # I randomize the nodes to give me an arbitrary start point
        shuffle(gn)
        for node in gn:
            neighs = G[node]
            classes = {}
       # do an inventory of the given nodes neighbours and edge weights
            for ne in neighs:
                if isinstance(ne, int):
                    key=G.node[ne]['class']
                    if key in classes:
                        classes[key] += G[node][ne]['weight']
                    else:
                        classes[key] = G[node][ne]['weight']
       # find the class with the highest edge weight sum

            max = 0
            maxclass = 0
            for c in classes:
                if classes[c] > max:
                    max = classes[c]
                    maxclass = c
       # set the class of target node to the winning local class
            G.node[node]['class'] = maxclass

    n_clusters = []
    for node in G.nodes():
        n_clusters.append(G.node[node]['class'])
    return(n_clusters)

这是128 D向量中每个面部的面部特征提取和编码的代码,以及用于应用中文低语的这些图形结构.

from sklearn import cluster
import cv2
import sys
import os
import dlib
import glob
from skimage import io
import numpy as np
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from matplotlib import pyplot as plt
import chinese
from chinese import chinese_whispers
predictor_path = "/home/deeplearning/Desktop/face_recognition 
examples/shape_predictor_68_face_landmarks.dat"
face_rec_model_path = "/home/deeplearning/Desktop/face_recognition 
examples/dlib_face_recognition_resnet_model_v1.dat"
faces_folder_path = "/home/deeplearning/Desktop/face_recognition 
examples/test11/"

# 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()
#print (detector)
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)

#win = dlib.image_window()

# Now process all the images
dict={}
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
    print("Processing file: {}".format(f))
    img = io.imread(f)
    dets = detector(img, 3)
    for k, d in enumerate(dets):
        shape = sp(img, d)
        face_descriptor = facerec.compute_face_descriptor(img, shape)
        a=np.array(face_descriptor)
        dict[(f,d)] = (a,f)



answ=np.array(list(dict.values()))
tmp=answ.shape[0]
ans=np.zeros((tmp,128))
for i in range(tmp):
    ans[i]=np.array(answ[i][0])
nodes=[]
for i in range(tmp):
    nodes.append(i)
edges=[]
for i in range(tmp):
    for j in range(i+1,tmp):
        dist=np.sqrt(np.sum((ans[i]-ans[j])**2))
        if dist < 0.6:
            edges.append((i,j,{'weight': dist}))


iterations=10
cluster=chinese_whispers(nodes,edges,iterations)

我不明白我在做什么错.谁能帮帮我呢？提前致谢.

Answer 1

我之前使用过Dlib进行人脸聚类。

很抱歉我没有正确理解你的问题。您是否收到错误或没有得到准确的结果？

假设您没有得到正确的结果，我建议使用shape_predictor_5_face_landmarks.dat而不是64 个面部标志，因为它在使用 Chinese Whisper 算法进行聚类时提供更好的结果。

你也可以尝试一下DLib自带的中文私语聚类功能，看看是否效果更好。

示例-face_clustering.py

#!/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-get 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))

# 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)

您还可以更改阈值和迭代次数，看看是否能获得更好的结果。

希望这可以帮助。