本帖使用TensorFlow做一個根據臉部推斷照片人物年齡和性別的練習，網上有很多類似app。

訓練資料 – Adience資料集

Adience資料集來源為Flickr相簿，由使用者使用iPhone或者其它智慧手機裝置拍攝，該資料集主要用於進行年齡和性別的未經過濾的面孔估計。同時，裡面還進行了相應的landmark的標註，其中包含2284個類別和26580張圖片。

由於資料來源ftp站點被牆，我只能使用梯子，下載過程非常漫長和痛苦。為了讓你免受折磨，我傳了一份到網盤。

程式碼

import os
import glob
import tensorflow as tf # 0.12
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
import numpy as np
from random import shuffle
 
age_table=['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']
sex_table=['f','m']  # f:女; m:男
 
# AGE==True 訓練年齡模型，False,訓練性別模型
AGE = False
 
if AGE == True:
	lables_size = len(age_table) # 年齡
else:
	lables_size = len(sex_table) # 性別
 
face_set_fold = 'AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification'
 
fold_0_data = os.path.join(face_set_fold, 'fold_0_data.txt')
fold_1_data = os.path.join(face_set_fold, 'fold_1_data.txt')
fold_2_data = os.path.join(face_set_fold, 'fold_2_data.txt')
fold_3_data = os.path.join(face_set_fold, 'fold_3_data.txt')
fold_4_data = os.path.join(face_set_fold, 'fold_4_data.txt')
 
face_image_set = os.path.join(face_set_fold, 'aligned')
 
def parse_data(fold_x_data):
	data_set = []
 
	with open(fold_x_data, 'r') as f:
		line_one = True
		for line in f:
			tmp = []
			if line_one == True:
				line_one = False
				continue
 
			tmp.append(line.split('\t')[0])
			tmp.append(line.split('\t')[1])
			tmp.append(line.split('\t')[3])
			tmp.append(line.split('\t')[4])
 
			file_path = os.path.join(face_image_set, tmp[0])
			if os.path.exists(file_path):
				filenames = glob.glob(file_path + "/*.jpg")
				for filename in filenames:
					if tmp[1] in filename:
						break
				if AGE == True:
					if tmp[2] in age_table:
						data_set.append([filename, age_table.index(tmp[2])])
				else:
					if tmp[3] in sex_table:
						data_set.append([filename, sex_table.index(tmp[3])])
 
	return data_set
 
data_set_0 = parse_data(fold_0_data)
data_set_1 = parse_data(fold_1_data)
data_set_2 = parse_data(fold_2_data)
data_set_3 = parse_data(fold_3_data)
data_set_4 = parse_data(fold_4_data)
 
data_set = data_set_0 + data_set_1 + data_set_2 + data_set_3 + data_set_4
shuffle(data_set)
 
# 縮放影象的大小
IMAGE_HEIGHT = 227
IMAGE_WIDTH = 227
# 讀取縮放影象
jpg_data = tf.placeholder(dtype=tf.string)
decode_jpg = tf.image.decode_jpeg(jpg_data, channels=3)
resize = tf.image.resize_images(decode_jpg, [IMAGE_HEIGHT, IMAGE_WIDTH])
resize = tf.cast(resize, tf.uint8) / 255
def resize_image(file_name):
	with tf.gfile.FastGFile(file_name, 'r') as f:
		image_data = f.read()
	with tf.Session() as sess:
		image = sess.run(resize, feed_dict={jpg_data: image_data})
	return image
 
pointer = 0
# 有點慢(先睡了)，應該先處理好圖片或使用string_input_producer
def get_next_batch(data_set, batch_size=128):
	global pointer
	batch_x = []
	batch_y = []
	for i in range(batch_size):
		batch_x.append(resize_image(data_set[pointer][0]))
		batch_y.append(data_set[pointer][1])
		pointer += 1
	return batch_x, batch_y
 
batch_size = 128
num_batch = len(data_set) // batch_size
 
X = tf.placeholder(dtype=tf.float32, shape=[batch_size, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
Y = tf.placeholder(dtype=tf.int32, shape=[batch_size])
 
def conv_net(nlabels, images, pkeep=1.0):
	weights_regularizer = tf.contrib.layers.l2_regularizer(0.0005)
	with tf.variable_scope("conv_net", "conv_net", [images]) as scope:
		with tf.contrib.slim.arg_scope([convolution2d, fully_connected], weights_regularizer=weights_regularizer, biases_initializer=tf.constant_initializer(1.), weights_initializer=tf.random_normal_initializer(stddev=0.005), trainable=True):
			with tf.contrib.slim.arg_scope([convolution2d], weights_initializer=tf.random_normal_initializer(stddev=0.01)):
				conv1 = convolution2d(images, 96, [7,7], [4, 4], padding='VALID', biases_initializer=tf.constant_initializer(0.), scope='conv1')
				pool1 = max_pool2d(conv1, 3, 2, padding='VALID', scope='pool1')
				norm1 = tf.nn.local_response_normalization(pool1, 5, alpha=0.0001, beta=0.75, name='norm1')
				conv2 = convolution2d(norm1, 256, [5, 5], [1, 1], padding='SAME', scope='conv2') 
				pool2 = max_pool2d(conv2, 3, 2, padding='VALID', scope='pool2')
				norm2 = tf.nn.local_response_normalization(pool2, 5, alpha=0.0001, beta=0.75, name='norm2')
				conv3 = convolution2d(norm2, 384, [3, 3], [1, 1], biases_initializer=tf.constant_initializer(0.), padding='SAME', scope='conv3')
				pool3 = max_pool2d(conv3, 3, 2, padding='VALID', scope='pool3')
				flat = tf.reshape(pool3, [-1, 384*6*6], name='reshape')
				full1 = fully_connected(flat, 512, scope='full1')
				drop1 = tf.nn.dropout(full1, pkeep, name='drop1')
				full2 = fully_connected(drop1, 512, scope='full2')
				drop2 = tf.nn.dropout(full2, pkeep, name='drop2')
	with tf.variable_scope('output') as scope:
		weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0, stddev=0.01), name='weights')
		biases = tf.Variable(tf.constant(0.0, shape=[nlabels], dtype=tf.float32), name='biases')
		output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
	return output
 
def training():
	logits = conv_net(lables_size, X)
 
	def optimizer(eta, loss_fn):
		global_step = tf.Variable(0, trainable=False)
		optz = lambda lr: tf.train.MomentumOptimizer(lr, 0.9)
		lr_decay_fn = lambda lr,global_step : tf.train.exponential_decay(lr, global_step, 100, 0.97, staircase=True)
		return tf.contrib.layers.optimize_loss(loss_fn, global_step, eta, optz, clip_gradients=4., learning_rate_decay_fn=lr_decay_fn)
 
	def loss(logits, labels):
		cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)
		cross_entropy_mean = tf.reduce_mean(cross_entropy)
		regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
		total_loss = cross_entropy_mean + 0.01 * sum(regularization_losses)
		loss_averages = tf.train.ExponentialMovingAverage(0.9)
		loss_averages_op = loss_averages.apply([cross_entropy_mean] + [total_loss])
		with tf.control_dependencies([loss_averages_op]):
		    total_loss = tf.identity(total_loss)
		return total_loss
	# loss
	total_loss = loss(logits, Y)
	# optimizer
	train_op = optimizer(0.001, total_loss)
 
	saver = tf.train.Saver(tf.global_variables())
	with tf.Session() as sess:
		sess.run(tf.global_variables_initializer())
 
		global pointer
		epoch = 0
		while True:
			pointer = 0
			for batch in range(num_batch):
				batch_x, batch_y = get_next_batch(data_set, batch_size)
				_, loss_value = sess.run([train_op, total_loss], feed_dict={X:batch_x, Y:batch_y})
				print(epoch, batch, loss_value)
			saver.save(sess, 'age.module' if AGE == True else 'sex.module')
			epoch += 1
 
training()
 
"""
# 檢測性別和年齡
# 把batch_size改為1
def detect_age_or_sex(image_path):
	logits = conv_net(lables_size, X)
	saver = tf.train.Saver()
 
	with tf.Session() as sess:
		saver.restore(sess, './age.module' if AGE == True else './sex.module')
		
		softmax_output = tf.nn.softmax(logits)
		res = sess.run(softmax_output, feed_dict={X:[resize_image(image_path)]})
		res = np.argmax(res)
 
		if AGE == True:
			return age_table[res]
		else:
			return sex_table[res]
"""
 

後續：使用Opencv檢測提取人臉，然後使用訓練好的模型判斷性別和年齡。

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