"""An example of how to use your own dataset to train a classifier that recognizes people.
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# The above copyright notice and this permission notice shall be included in all
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# @ 调用格式：
# @
# @ 训练模型记住人脸（不是训练网络，网络在这之前已经先训练好了）。
# @ ../lfw/ 是lfw数据集经过 mtcnn 截取以后的结果。否则会影响效果（去除数据集中的人脸外部干扰）
# @ python classifier.py TRAIN ../lfw/ 20170511-185253/ train_20180419_2048.pkl
# @
# @ 测试模型记住人脸的结果。（../data 是测试用的图的路径。）
# @ python classifier.py CLASSIFY ../data/ 20170511-185253/ train_20180419_2048.pkl
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import argparse
import facenet
import os
import sys
import math
import pickle
from sklearn.svm import SVC
# @ args内中参数见函数 parse_arguments
def main(args):
# @ 声明一个计算图，都这么写，没有就是默认一个。
with tf.Graph().as_default():
# @ 声明一个 Session
with tf.Session() as sess:
# @ Part I
# @ 这部分是计算人脸的 embedding 特征。费时。
# @
# @ 加随机数seed，调用np.random.random()的结果都会相同。
np.random.seed(seed=args.seed)
if args.use_split_dataset:
dataset_tmp = facenet.get_dataset(args.data_dir)
train_set, test_set = split_dataset(dataset_tmp, args.min_nrof_images_per_class, args.nrof_train_images_per_class)
if (args.mode=='TRAIN'):
dataset = train_set
elif (args.mode=='CLASSIFY'):
dataset = test_set
else:
dataset = facenet.get_dataset(args.data_dir)
# Check that there are at least one training image per class
# @ cls.image_paths 是每张图的路径，包含文件名。
for cls in dataset:
assert(len(cls.image_paths)>0, 'There must be at least one image for each class in the dataset')
# @ 分离出图片路径名paths，和类型labels（人脸所属人名）
paths, labels = facenet.get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Load the model
# @ 这里加的 model 使用于生成人脸的 embedding 特征的网络。
# @ 这个网络是事先已经生成好的。
# @ 网络可以根据运行的平台，设计成不同大小。比如基于GoogleNet/AlexNet等
print('Loading feature extraction model')
facenet.load_model(args.model)
# Get input and output tensors
# @ TensorFlow的参数准备。embeddings 是网络的输出，是后续分类的输入。
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
nrof_images = len(paths) # @ 图片总数
nrof_batches_per_epoch = int(math.ceil(1.0*nrof_images / args.batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
start_index = i*args.batch_size
end_index = min((i+1)*args.batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, args.image_size)
feed_dict = { images_placeholder:images, phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
# @ emb_array 是 embedding 结果。一个 embedding 有 18 维。
# @ 接下来就是用机器学习的方法分类。
classifier_filename_exp = os.path.expanduser(args.classifier_filename)
# @ Part II 也较费时。
# @ 这部分是训练分类人脸的机器学习模型，这里使用的SVC，是SVM的一种。
# @ 若是 CLASSIFY ，则是加载训练结果，建立 SVC 分类器。
if (args.mode=='TRAIN'):
# Train classifier
# @ SVC是SVM的一种Type，是用来的做分类的；同样还有SVR，是SVM的另一种Type，是用来的做回归的。
print('Training classifier')
model = SVC(kernel='linear', probability=True)
model.fit(emb_array, labels) # @ 训练过程
# @ 训练结束，保存数据
# Create a list of class names
class_names = [ cls.name.replace('_', ' ') for cls in dataset]
# Saving classifier model
with open(classifier_filename_exp, 'wb') as outfile:
pickle.dump((model, class_names), outfile)
print('Saved classifier model to file "%s"' % classifier_filename_exp)
elif (args.mode=='CLASSIFY'):
# Classify images
print('Testing classifier')
# @ 加载数据，建立分类器
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('Loaded classifier model from file "%s"' % classifier_filename_exp)
# @ 预测，标签结果应该是 one_hot 的。
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1) # @ 输出每列最大的序号。
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
for i in range(len(best_class_indices)):
print('%4d %s: %.3f' % (i, class_names[best_class_indices[i]], best_class_probabilities[i]))
# @ 评估结果。labels 是测试集的实际结果，best_class_indices是预测结果。
accuracy = np.mean(np.equal(best_class_indices, labels))
print('Accuracy: %.3f' % accuracy)
# @ 将数据集分成训练集和测试集
def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class):
train_set = []
test_set = []
for cls in dataset:
paths = cls.image_paths
# Remove classes with less than min_nrof_images_per_class
if len(paths)>=min_nrof_images_per_class:
np.random.shuffle(paths)
train_set.append(facenet.ImageClass(cls.name, paths[:nrof_train_images_per_class]))
test_set.append(facenet.ImageClass(cls.name, paths[nrof_train_images_per_class:]))
return train_set, test_set
# @ 命令行参数，使用的系统库 argparse
# @ ** 写法值得记住 **
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'],
help='Indicates if a new classifier should be trained or a classification ' +
'model should be used for classification', default='CLASSIFY')
parser.add_argument('data_dir', type=str,
help='Path to the data directory containing aligned LFW face patches.')
parser.add_argument('model', type=str,
help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('classifier_filename',
help='Classifier model file name as a pickle (.pkl) file. ' +
'For training this is the output and for classification this is an input.')
parser.add_argument('--use_split_dataset',
help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +
'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true')
parser.add_argument('--test_data_dir', type=str,
help='Path to the test data directory containing aligned images used for testing.')
parser.add_argument('--batch_size', type=int,
help='Number of images to process in a batch.', default=90)
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--seed', type=int,
help='Random seed.', default=666)
parser.add_argument('--min_nrof_images_per_class', type=int,
help='Only include classes with at least this number of images in the dataset', default=20)
parser.add_argument('--nrof_train_images_per_class', type=int,
help='Use this number of images from each class for training and the rest for testing', default=10)
return parser.parse_args(argv)
# @ 主函数
# @ sys.argv[1:] 就是命令行输入的 classify.py 后面的所有字符串，以空格分隔。
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))