08-人脸识别-FaceNet-classify.py代码阅读（说明见注释）

"""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
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# in the Software without restriction, including without limitation the rights
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# copies of the Software, and to permit persons to whom the Software is
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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:]))