基于dlib实现人脸聚类

转载自：https://www.pythonheidong.com/blog/article/182166/

Dlib 是一个现代化的 C ++ 工具包，包含用于创建复杂软件的机器学习算法和工具 ” 。它使您能够直接在 Python 中运行许多任务，其中一个例子就是人脸检测。

一、先安装

以Ubuntu为例子

step 1：

# for Ubuntu

sudo apt-get install build-essential cmake

sudo apt-get install libgtk-3-dev

sudo apt-get install libboost-all-dev

step 2：

pip install dlib

 

二、实现过程

1、官方提供的模型文件 http://dlib.net/files/

http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2

http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2

dlib相关文档  http://accu.cc/content/daze/dlib/install/

下载下面的model

 

 

2、程序目录如下

 

 

faces 存放要聚类的人脸图片，model  存放下载下来的model，out是输出目录

 

3、face_clustering.py如下

# coding: utf-8 """ @author: xhb """ import sys import os import dlib import glob import cv2 # 指定路径 current_path = os.getcwd() model_path = current_path + '/model/' shape_predictor_model = model_path + '/shape_predictor_5_face_landmarks.dat' face_rec_model = model_path + '/dlib_face_recognition_resnet_model_v1.dat' face_folder = current_path + '/faces/' output_folder = current_path + '/output/' # 导入模型 detector = dlib.get_frontal_face_detector() shape_detector = dlib.shape_predictor(shape_predictor_model) face_recognizer = dlib.face_recognition_model_v1(face_rec_model) # 为后面操作方便，建了几个列表 descriptors = [] images = [] # 遍历faces文件夹中所有的图片 for f in glob.glob(os.path.join(face_folder, "*.jpg")): print('Processing file：{}'.format(f)) # 读取图片 img = cv2.imread(f) # 转换到rgb颜色空间 img2 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 检测人脸 dets = detector(img2, 1) print("Number of faces detected: {}".format(len(dets))) # 遍历所有的人脸 for index, face in enumerate(dets): # 检测人脸特征点 shape = shape_detector(img2, face) # 投影到128D face_descriptor = face_recognizer.compute_face_descriptor(img2, shape) # 保存相关信息 descriptors.append(face_descriptor) images.append((img2, shape)) # 聚类 labels = dlib.chinese_whispers_clustering(descriptors, 0.5) print("labels: {}".format(labels)) num_classes = len(set(labels)) print("Number of clusters: {}".format(num_classes)) # 为了方便操作，用字典类型保存 face_dict = {} for i in range(num_classes): face_dict[i] = [] # print face_dict for i in range(len(labels)): face_dict[labels[i]].append(images[i]) # print face_dict.keys() # 遍历字典，保存结果 for key in face_dict.keys(): file_dir = os.path.join(output_folder, str(key)) if not os.path.isdir(file_dir): os.makedirs(file_dir) for index, (image, shape) in enumerate(face_dict[key]): file_path = os.path.join(file_dir, 'face_' + str(index)) dlib.save_face_chip(image, shape, file_path, size=150, padding=0.25)

4、运行  python3 face_clustering.py