def identify(image):
with tf.Graph().as_default():
pic = pad_image(image)
data = cut_image(pic)
image = tf.convert_to_tensor(data)
image = tf.expand_dims(image, -1)
image = tf.cast(image, dtype=tf.float32)
Finger_model = alexnet.alexNet(image, 0.5, 2, False)
logits = Finger_model.fc_2
moving_average_op = tf.train.ExponentialMovingAverage(
decay=alexnet.moving_average_decay)
variables_to_restore = moving_average_op.variables_to_restore()
saver = tf.train.Saver(var_list=variables_to_restore)
with tf.Session() as sess:
checkpoint_proto = tf.train.get_checkpoint_state(
checkpoint_dir=checkpoint_path)
if checkpoint_proto and checkpoint_proto.model_checkpoint_path:
saver.restore(sess, checkpoint_proto.model_checkpoint_path)
else:
print('checkpoint file not found!')
return
predict = sess.run(logits)
predict = np.array(predict)
"""
Vote Mechanism
"""
Num_0 = sum(predict == 0)
Num_1 = sum(predict == 1)
if Num_0[0] <= Num_1[0]:
return "Fake"
else:
return "Alive"
def evaluate():
with tf.Graph().as_default() as g:
img_batch, label_batch = preprocess.read_and_decode('test.tfrecords',
batch_size,
flag=True) #读入测试集
Finger_model = alexnet.alexNet(img_batch, 0.5, 2, False)
logits = Finger_model.fc_2
# 判断targets是否在前k个predictions里面，当k=1时等价于常规的计算正确率的方法，sess.run(predict_true_or_false)会执行符号计
predict_true_or_false = tf.nn.in_top_k(predictions=logits,
targets=label_batch,
k=1)
# 恢复moving average操作后的模型参数
moving_average_op = tf.train.ExponentialMovingAverage(
decay=alexnet.moving_average_decay)
# 返回要恢复的names到Variables的映射，也即一个map映射。如果一个变量有moving average,就使用moving average变量名作为the restore
# name, 否则就使用变量名
variables_to_restore = moving_average_op.variables_to_restore()
saver = tf.train.Saver(var_list=variables_to_restore)
summary_op = tf.summary.merge_all() # 创建序列化后的summary对象
# 创建一个event file,用于之后写summary对象到logdir目录下的文件中
summary_writer = tf.summary.FileWriter(logdir='./event-log-test',
graph=g)
eval_once(summary_op, summary_writer, saver, predict_true_or_false)
def train():
with tf.Graph().as_default(): # 指定当前图为默认graph
global_step = tf.Variable(initial_value=0, trainable=False)
img_batch, label_batch = preprocess.read_and_decode(tfrecords,
batch_size,
flag=True)
Finger_model = alexnet.alexNet(img_batch, 0.5, 2, True)
logits = Finger_model.fc_2
total_loss = alexnet.loss(logits, label_batch) # 计算损失
one_step_gradient_update = alexnet.one_step_train(
total_loss, global_step) # 返回一步梯度更新操作
# 创建一个saver对象，用于保存参数到文件中
saver = tf.train.Saver(var_list=tf.all_variables(
)) # tf.all_variables return a list of `Variable` objects
all_summary_obj = tf.summary.merge_all(
) # 返回所有summary对象先merge再serialize后的的字符串类型tensor
initiate_variables = tf.initialize_all_variables()
with tf.Session(config=tf.ConfigProto(
log_device_placement=False)) as sess:
sess.run(initiate_variables) # 变量初始化
tf.train.start_queue_runners(sess=sess) # 启动所有的queuerunners
if not os.path.exists(event_log_path):
os.makedirs(event_log_path)
Event_writer = tf.summary.FileWriter(logdir=event_log_path,
graph=sess.graph)
for step in range(max_iter_num):
_, loss_value = sess.run(
fetches=[one_step_gradient_update, total_loss])
assert not np.isnan(loss_value) # 用于验证当前迭代计算出的loss_value是否合理
if step % 100 == 0:
# 添加`Summary`协议缓存到事件文件中，故不能写total_loss变量到事件文件中，因为这里的total_loss为普通的tensor类型
print('step %d, the loss_value is %.2f' %
(step, loss_value))
all_summaries = sess.run(all_summary_obj)
Event_writer.add_summary(summary=all_summaries,
global_step=step)
if step % 1000 == 0 or (step + 1) == max_iter_num:
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
variables_save_path = os.path.join(
checkpoint_path,
'model-parameters.bin') # 路径合并，返回合并后的字符串
saver.save(
sess, variables_save_path, global_step=step
) # 把所有变量（包括moving average前后的模型参数）保存在variables_save_path路径下
evaluate.evaluate()
def load_model(self):
dropoutPro = 1
classNum = 1000
skip = []
self.imgMean = np.array([104, 117, 124], np.float)
self.x = tf.placeholder("float", [1, 227, 227, 3])
model = alexnet.alexNet(self.x, dropoutPro, classNum, skip)
score = model.fc3
self.softmax = tf.nn.softmax(score)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
config = tf.ConfigProto(gpu_options=gpu_options)
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
model.loadModel(self.sess)
def main():
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
imagePath = "./testImages"
imageName = ["000001.jpg"]
withPath = lambda imgName: '{}/{}'.format(imagePath, imgName)
# testImg = dict((imgName,cv2.imread(withPath(imgName))) for imgName in imageName)
testImg = dict(
(imgName, Image.open(withPath(imgName))) for imgName in imageName)
# noinspection PyUnboundLocalVariable
if testImg.values():
#some params
dropoutPro = 1
classNum = 1000
skip = []
imgMean = np.array([104, 117, 124], np.float)
x = tf.placeholder("float", [1, 227, 227, 3])
model = alexnet.alexNet(x, dropoutPro, classNum, skip)
score = model.fc3
softmax = tf.nn.softmax(score)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
model.loadModel(sess)
for key, img in testImg.items():
#img preprocess
# resized = cv2.resize(img.astype(np.float), (227, 227)) - imgMean
resized = np.array(img.resize((227, 227))) - imgMean
maxx = np.argmax(
sess.run(softmax,
feed_dict={x: resized.reshape((1, 227, 227, 3))}))
res = caffe_classes.class_names[maxx]
print("{}: {}\n----".format(key, res))
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
return image
testImg = {args.path: url2img(args.path)}
# noinspection PyUnboundLocalVariable
if testImg.values():
# some params
dropoutPro = 1
classNum = 1000
skip = []
imgMean = np.array([104, 117, 124], np.float)
x = tf.placeholder("float", [1, 227, 227, 3])
model = alexnet.alexNet(x, dropoutPro, classNum, skip)
score = model.fc3
softmax = tf.nn.softmax(score)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
model.loadModel(sess)
for key, img in testImg.items():
# img preprocess
resized = cv2.resize(img.astype(np.float), (227, 227)) - imgMean
maxx = np.argmax(
sess.run(softmax,
feed_dict={x: resized.reshape((1, 227, 227, 3))}))
res = caffe_classes.class_names[maxx]
BATCH_SIZE = 108
CAPACITY = 2000
dropout = 1.0
train_txt = 'train.txt'
val_txt = 'val.txt'
model_dir = './model_sample/'
if not os.path.exists('./features'):
os.mkdir('./features')
with tf.Graph().as_default():
batch, label_batch, n = make_data.get_batch(val_txt, IMG_W, IMG_H,
BATCH_SIZE, CAPACITY)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])
train_model = alexnet.alexNet(x, dropout, N_CLASSES)
feature_fc = train_model.fc2
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
IMG_W = 100
IMG_H = 100
train_filename = '/home/wu/TF_Project/action/sample_TFrecord/train1.tfrecords'
#dir = '/home/wu/TF_Project/action/feature_tensorboard/'
dir = '/home/wu/TF_Project/action/features/'
train_img, train_label = tfrecord.read_and_decode(train_filename)
train_batch, train_label_batch = tf.train.shuffle_batch(
[train_img, train_label],
batch_size=100,
num_threads=64,
capacity=2000,
min_after_dequeue=1000)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])
train_model = alexnet.alexNet(x)
conv1_feature = train_model.conv1
"""
state = tf.Variable(0, name='counter')
one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)
"""
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
train_model.load_initial_weights(sess)
#summary_op = tf.summary.merge_all()
return image
testImg = {args.path: url2img(args.path)}
# noinspection PyUnboundLocalVariable
if testImg.values():
# some params
dropoutPro = 1
classNum = 1000
skip = []
imgMean = np.array([104, 117, 124], np.float)
x = tf.placeholder("float", [1, 227, 227, 3])
model = alexnet.alexNet(x, dropoutPro, classNum, skip)
score = model.fc3
softmax = tf.nn.softmax(score)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
model.loadModel(sess)
for key, img in testImg.items():
# img preprocess
resized = cv2.resize(img.astype(np.float), (227, 227)) - imgMean
maxx = np.argmax(sess.run(softmax, feed_dict={x: resized.reshape((1, 227, 227, 3))}))
res = caffe_classes.class_names[maxx]
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, res, (int(img.shape[0] / 3), int(img.shape[1] / 3)), font, 1, (0, 255, 0), 2)
