def predict_task():
num_files = 1
flag1=True
# print 'hhhhhh'
seq_len = 32
batch_size = 1
num_classes = 19
model_prefix = './'
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
sess = tf.InteractiveSession()
feature, networks = net.c3d_biclstm(x, num_classes, False, False)
feature = feature.outputs
sess.run(tf.global_variables_initializer())
load_params = tl.files.load_npz(name='%s/beihang_dataset_birnn_model_epoch_10.npz' % (model_prefix))
tl.files.assign_params(sess, load_params, networks)
print("restore done")
while True:
if not data_queue.empty():
command = data_queue.get()
# print 'The output queue size: {}'.format(data_queue.qsize())
if command == "quit":
break
else:
file_dir = '/home/e829/Documents/lxj/1128/data/' + 'train_' + str(num_files) + '/'
if not os.path.isdir(file_dir):
os.mkdir(file_dir)
save_file = os.path.join(os.getcwd(), 'data/train_' + str(num_files) + '/')
url_path = 'http://192.168.1.104/train_' + str(num_files) + '/'
nums = string.atoi(command[8:])
for i in range(nums):
url = url_path + str(i + 1) + '.png'
path_name = save_file + str(i + 1) + '.png'
# 下载
download(url, path_name)
# Data preprocessing
current_path = os.getcwd()
nni_path = os.path.join(current_path, 'data/')
os.chdir(nni_path)
list_nni = os.listdir(nni_path)
list_nni.sort(key=lambda x: int(x[6:]))
file_nni = list_nni[len(list_nni) - 2]
img_path = os.path.join(nni_path, file_nni)
starttime3 = time.clock()
NNI(img_path)
rename_lxj(img_path)
os.chdir(current_path)
with open("./train1.txt", "w") as f:
f.write(img_path+'/'+' '+str(32)+ ' '+str(0)+'\n')
time_preprocess_done = time.clock()
print ("the NNI time is %f" % (time_preprocess_done - starttime3))
#lxj
if flag1:
# print("please perform a new gesture！")
print("This may be a gesture,do you want to register a new class?")
print ("please input:[y/n]")
starttime4 = time.clock()
data_show_queue.put((-1, img_path,True))
last_label = -1
while True:
# print "while"
if not data_rec.empty():
input_data = data_rec.get()
if input_data == 'y':
new_class_feature = data_input('./train1.txt', networks, feature, x, y, sess)
# print("Register completed")
flag1 = False
print "Congratulations!Register a new class successfully!"
break
elif input_data == 'n':
# new_class_feature = new_class_feature
print "Please continue!"
break
else:
print "Invalid operation!"
break
endtime4=time.clock()
# print "time"
if data_rec.empty() and (endtime4-starttime4)>10.0:
print "Time is up! Please continue!"
break
# break
# else:
# print "ok"
# new_class_feature=data_input('./train1.txt',networks,feature,x,y,sess)
# print("Register completed")
# flag1 = False
else:
# print new_class_feature
test_features=data_input('./train1.txt',networks,feature,x,y,sess)
feature_extraction_done = time.clock()
print ("the preprocess and feature extraction time is %f" % (feature_extraction_done - time_preprocess_done))
distances = []
for i in range(len(new_class_feature)):
distances.append(distance_encludian(new_class_feature[i], test_features))
if min(distances) >= 0.423:
print("The test gesture does not belong to any class,do you want to register a new class?")
print ("please input:[y/n]")
# cv.destroyAllWindows()
starttime5 = time.clock()
# data_show_queue.put((-1, img_path))
if not last_label == -1:
data_show_queue.put((-1, img_path,True))
last_label = -1
else:
data_show_queue.put((-1, img_path,False))
last_label = -1
while True:
if not data_rec.empty():
input_data = data_rec.get()
if input_data == 'y':
tmp = np.array(test_features)
new_class_feature.append(tmp)
print "Congratulations!Register a new class successfully!"
break
elif input_data == 'n':
new_class_feature = new_class_feature
print "Please continue your test!"
break
else:
print "Invalid operation!"
break
endtime5 = time.clock()
if data_rec.empty() and (endtime5 - starttime5) > 10.0:
print "Time is up! Please continue!"
break
else:
judge_distance_done = time.clock()
print ("the judge distance time is %f" % (judge_distance_done - feature_extraction_done))
pre_label = distances.index(min(distances))
print("The test gesture belongs to class %d" % (pre_label+1))
if last_label == -1:
data_show_queue.put((pre_label, img_path, True))
else:
data_show_queue.put((pre_label, img_path, False))
last_label = pre_label
endtime3 = time.clock()
print ("the predict time is %f" % (endtime3 - judge_distance_done))
print "total predict classes time: ", (endtime3 - starttime3)
print "-----------------------------------------------------------"
num_files += 1
d = curtime.split(' ')[0]
t = curtime.split(' ')[1]
strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2],
t.split(':')[0],t.split(':')[1],t.split(':')[2])
saved_stdout = sys.stdout
mem_log = cStringIO.StringIO()
sys.stdout = mem_log
logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
log = open(logfile, 'w')
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
_,networks = net.c3d_biclstm(x, num_classes, False, True)
networks_y = networks.outputs
networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
networks_cost = tl.cost.cross_entropy(networks_y, y)
correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
_,predictions = net.c3d_biclstm(x, num_classes, True, False)
prediction_y_op = tf.argmax(tf.nn.softmax(predictions.outputs),1)
prediction_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(prediction_y_op, tf.int32), y), tf.float32))
l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[14]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[20]) + \
model_prefix = './'
curtime = '%s' % datetime.now()
d = curtime.split(' ')[0]
t = curtime.split(' ')[1]
strtime = '%s%s%s-%s%s%s' % (d.split('-')[0], d.split('-')[1], d.split('-')[2],
t.split(':')[0], t.split(':')[1], t.split(':')[2])
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
y = tf.placeholder(tf.int32, shape=[
batch_size,
], name='y')
sess = tf.InteractiveSession()
_, networks = net.c3d_biclstm(x, num_classes, False, False)
network_pred = tf.nn.softmax(networks.outputs)
network_y_op = tf.argmax(tf.nn.softmax(networks.outputs), 1)
network_accu = tf.reduce_mean(
tf.cast(tf.equal(tf.cast(network_y_op, tf.int32), y), tf.float32))
networks_cost = tl.cost.cross_entropy(networks_y, y)
sess.run(tf.initialize_all_variables())
# RGB
testing_datalist = './dataset_splits/valid_rgb_list.txt'
X_test, y_test = data.load_video_list(testing_datalist)
X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
rgb_prediction = np.zeros((len(y_test), num_classes), dtype=np.float32)
load_params = tl.files.load_npz(name='%s/isogr_rgb_birnn_model_epoch_10.npz' %
t = curtime.split(' ')[1]
strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2],
t.split(':')[0],t.split(':')[1],t.split(':')[2])
saved_stdout = sys.stdout
mem_log = cStringIO.StringIO()
sys.stdout = mem_log
logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
log = open(logfile, 'w')
"""size of pictures"""
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
#training
_,networks = net.c3d_biclstm(x, num_classes, False, True)
networks_y = networks.outputs
networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
networks_cost = tl.cost.cross_entropy(networks_y, y,name='cost')
correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('tr_accuracy', networks_accu)
#test
_,predictions = net.c3d_biclstm(x, num_classes, True, False)
prediction_y_op = tf.argmax(tf.nn.softmax(predictions.outputs),1)
prediction_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(prediction_y_op, tf.int32), y), tf.float32))
tf.summary.scalar('te_accuracy', prediction_accu)
l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
def predict_task():
num_files = 1
flag1 = True
# print 'hhhhhh'
seq_len = 32
batch_size = 1
num_classes = 19
model_prefix = './'
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3],
name='x')
y = tf.placeholder(tf.int32, shape=[
batch_size,
], name='y')
sess = tf.InteractiveSession()
feature, networks = net.c3d_biclstm(x, num_classes, False, False)
feature = feature.outputs
sess.run(tf.global_variables_initializer())
load_params = tl.files.load_npz(
name='%s/beihang_dataset_birnn_model_epoch_10.npz' % (model_prefix))
tl.files.assign_params(sess, load_params, networks)
print("restore done")
while True:
if not data_queue.empty():
command = data_queue.get()
# print 'The output queue size: {}'.format(data_queue.qsize())
if command == "quit":
break
else:
file_dir = '/home/e829/Documents/lxj/1128/data/' + 'train_' + str(
num_files) + '/'
if not os.path.isdir(file_dir):
os.mkdir(file_dir)
save_file = os.path.join(os.getcwd(),
'data/train_' + str(num_files) + '/')
url_path = 'http://192.168.1.104/train_' + str(num_files) + '/'
nums = string.atoi(command[8:])
for i in range(nums):
url = url_path + str(i + 1) + '.png'
path_name = save_file + str(i + 1) + '.png'
# 下载
download(url, path_name)
# Data preprocessing
current_path = os.getcwd()
nni_path = os.path.join(current_path, 'data/')
os.chdir(nni_path)
list_nni = os.listdir(nni_path)
list_nni.sort(key=lambda x: int(x[6:]))
file_nni = list_nni[len(list_nni) - 2]
img_path = os.path.join(nni_path, file_nni)
starttime3 = time.clock()
NNI(img_path)
rename_lxj(img_path)
os.chdir(current_path)
with open("./train1.txt", "w") as f:
f.write(img_path + '/' + ' ' + str(32) + ' ' + str(0) +
'\n')
time_preprocess_done = time.clock()
print("the NNI time is %f" %
(time_preprocess_done - starttime3))
#lxj
if flag1:
# print("please perform a new gesture！")
print(
"This may be a gesture,do you want to register a new class?"
)
print("please input:[y/n]")
starttime4 = time.clock()
data_show_queue.put((-1, img_path, True))
last_label = -1
while True:
# print "while"
if not data_rec.empty():
input_data = data_rec.get()
if input_data == 'y':
new_class_feature = data_input(
'./train1.txt', networks, feature, x, y,
sess)
# print("Register completed")
flag1 = False
print "Congratulations!Register a new class successfully!"
break
elif input_data == 'n':
# new_class_feature = new_class_feature
print "Please continue!"
break
else:
print "Invalid operation!"
break
endtime4 = time.clock()
# print "time"
if data_rec.empty() and (endtime4 - starttime4) > 10.0:
print "Time is up! Please continue!"
break
# break
# else:
# print "ok"
# new_class_feature=data_input('./train1.txt',networks,feature,x,y,sess)
# print("Register completed")
# flag1 = False
else:
# print new_class_feature
test_features = data_input('./train1.txt', networks,
feature, x, y, sess)
feature_extraction_done = time.clock()
print("the preprocess and feature extraction time is %f" %
(feature_extraction_done - time_preprocess_done))
distances = []
for i in range(len(new_class_feature)):
distances.append(
distance_encludian(new_class_feature[i],
test_features))
if min(distances) >= 0.423:
print(
"The test gesture does not belong to any class,do you want to register a new class?"
)
print("please input:[y/n]")
# cv.destroyAllWindows()
starttime5 = time.clock()
# data_show_queue.put((-1, img_path))
if not last_label == -1:
data_show_queue.put((-1, img_path, True))
last_label = -1
else:
data_show_queue.put((-1, img_path, False))
last_label = -1
while True:
if not data_rec.empty():
input_data = data_rec.get()
if input_data == 'y':
tmp = np.array(test_features)
new_class_feature.append(tmp)
print "Congratulations!Register a new class successfully!"
break
elif input_data == 'n':
new_class_feature = new_class_feature
print "Please continue your test!"
break
else:
print "Invalid operation!"
break
endtime5 = time.clock()
if data_rec.empty() and (endtime5 -
starttime5) > 10.0:
print "Time is up! Please continue!"
break
else:
judge_distance_done = time.clock()
print("the judge distance time is %f" %
(judge_distance_done - feature_extraction_done))
pre_label = distances.index(min(distances))
print("The test gesture belongs to class %d" %
(pre_label + 1))
if last_label == -1:
data_show_queue.put((pre_label, img_path, True))
else:
data_show_queue.put((pre_label, img_path, False))
last_label = pre_label
endtime3 = time.clock()
print("the predict time is %f" %
(endtime3 - judge_distance_done))
print "total predict classes time: ", (endtime3 -
starttime3)
print "-----------------------------------------------------------"
num_files += 1
num_classes = 19
dataset_name = 'beihang_dataset'
model_prefix = './'
curtime = '%s' % datetime.now()
d = curtime.split(' ')[0]
t = curtime.split(' ')[1]
strtime = '%s%s%s-%s%s%s' % (d.split('-')[0], d.split('-')[1], d.split('-')[2],
t.split(':')[0], t.split(':')[1], t.split(':')[2])
x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
sess = tf.InteractiveSession()
feature, networks = net.c3d_biclstm(x, num_classes, False, False)
feature=feature.outputs
network_pred = tf.nn.softmax(networks.outputs)
network_y_op = tf.argmax(tf.nn.softmax(networks.outputs), 1)
pre_label=tf.cast(network_y_op,tf.int32)
network_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(network_y_op, tf.int32), y), tf.float32))
sess.run(tf.global_variables_initializer())
# Depth
testing_datalist = './dataset_splits/test_samples.txt'
features=[]
labels=[]
true_labels=[]
X_test, y_test = data.load_video_list(testing_datalist)
X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
