def predictor2(image_tru, num_face):
# image = align_face(image_tru)
image = image_tru
######**********************************************************#####
# Encode
data_file = 'tmp_aug.tfrecords'
writer = tf.python_io.TFRecordWriter(data_file)
# image = load_image('./')
gender = np.array([0])
race = np.array([0])
age = np.array([0])
addrs = ['0']
feature = {
'val/gender': _int64_feature(gender.astype(np.int8)),
'val/race': _int64_feature(race.astype(np.int8)),
'val/age': _int64_feature(age.astype(np.int8)),
'val/image': _bytes_feature(tf.compat.as_bytes(image.tostring())),
'val/address': _bytes_feature(os.path.basename(addrs[0].encode()))
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
sys.stdout.flush()
# decode
tf.reset_default_graph()
filename_queue = tf.train.string_input_producer([data_file])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'val/gender':
tf.FixedLenFeature([], tf.int64),
'val/race':
tf.FixedLenFeature([], tf.int64),
'val/age':
tf.FixedLenFeature([], tf.int64),
'val/image':
tf.FixedLenFeature([], tf.string),
'val/address':
tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['val/image'], tf.float32)
image = tf.cast(image, tf.uint8)
image.set_shape([200 * 200 * 3])
image = tf.reshape(image, [200, 200, 3])
# image = tf.reverse_v2(image, [-1])
image = tf.image.per_image_standardization(image)
images = tf.train.shuffle_batch([image],
batch_size=1,
capacity=256,
num_threads=2,
min_after_dequeue=32)
train_mode = tf.placeholder(tf.bool)
logits_gender, logits_race, logits_age, end_points, _ = build_model(
images, train_mode)
end_points['Predictions/gender'] = tf.nn.softmax(logits_gender,
name='Predictions/gender')
end_points['Predictions/race'] = tf.nn.softmax(logits_race,
name='Predictions/race')
end_points['Predictions/age'] = tf.nn.softmax(logits_age,
name='Predictions/age')
predictions1 = tf.argmax(end_points['Predictions/gender'], -1)
predictions2 = tf.argmax(end_points['Predictions/race'], -1)
predictions3 = tf.argmax(end_points['Predictions/age'], -1)
pr1 = tf.to_float(tf.to_int32(predictions1))
pr2 = tf.to_float(tf.to_int32(predictions2))
pr3 = tf.to_float(tf.to_int32(predictions3))
with tf.Session() as sess:
#
# init_op = tf.group(tf.local_variables_initializer())
# sess.run(init_op)
saver = tf.train.Saver(max_to_keep=100)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("restore and start evaluation!")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# writer = tf.summary.FileWriter(log_dir, graph=tf.get_default_graph())
pred1, pred2 = [], []
# for step in range(num_steps_per_epoch):
acc1, acc2, acc3 = sess.run([pr1, pr2, pr3], {train_mode: False})
print(acc1, acc2, acc3)
# Log some information
# logging.info('Step %s: gender Accuracy: %.4f race Accuracy: %.4f loss: %.4f (%.2f sec/step)'step, acc1, acc2, l, time_elapsed)
# writer.add_summary(curr_summary, step)
# pred1.append(acc1)
# pred2.append(acc2)
# coord.request_stop()
# coord.join(threads)
# saver.save(sess, final_checkpoint_file)
sess.close()
# average_acc1 = np.mean(accuracies1)
# average_acc2 = np.mean(accuracies2)
# average_loss = np.mean(loss_list)
gender = int(acc1)
race = int(acc2)
age = int(acc3)
global Result
Result = [gender, race, age, num_face]
def predictor(image_tru):
if len(sys.argv) != 1:
print('python predictor.py file_path_to_image')
else:
#image_path = sys.argv[1]
#image_path = '/home/pagand/PycharmProjects/RaceRec/crop_part1/19_1_4_20170103233712235.jpg.chip.jpg'
# image_path = project_dir + '23_1_0_20170103180703224.jpg'
# image_path = project_dir + '111.jpg'
# image_path = project_dir + '03.jpg'
#image_path = project_dir + 'pedram.jpg'
# image = img
#image = load_image(image_path)
# image = align_face(image_tru)
image = image_tru
######**********************************************************#####
#Encode
data_file = 'tmp_aug.tfrecords'
writer = tf.python_io.TFRecordWriter(data_file)
# image = load_image('./')
gender = np.array([0])
race = np.array([0])
addrs = ['0']
feature = {
'val/gender': _int64_feature(gender.astype(np.int8)),
'val/race': _int64_feature(race.astype(np.int8)),
'val/image': _bytes_feature(tf.compat.as_bytes(image.tostring())),
'val/address': _bytes_feature(os.path.basename(addrs[0].encode()))
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
sys.stdout.flush()
# decode
tf.reset_default_graph()
filename_queue = tf.train.string_input_producer([data_file])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'val/gender':
tf.FixedLenFeature([],
tf.int64),
'val/race':
tf.FixedLenFeature([],
tf.int64),
'val/image':
tf.FixedLenFeature([],
tf.string),
'val/address':
tf.FixedLenFeature([],
tf.string)
})
# features = tf.parse_single_example(
# example,
# features={'val/gender': tf.FixedLenFeature([], tf.int64),
# 'val/race': tf.FixedLenFeature([], tf.int64),
# 'val/image': tf.FixedLenFeature([], tf.string),
# 'val/address': tf.FixedLenFeature([], tf.string)})
image = tf.decode_raw(features['val/image'], tf.float32)
image = tf.cast(image, tf.uint8)
image.set_shape([200 * 200 * 3])
image = tf.reshape(image, [200, 200, 3])
# image = tf.reverse_v2(image, [-1])
image = tf.image.per_image_standardization(image)
images = tf.train.shuffle_batch([image],
batch_size=1,
capacity=256,
num_threads=2,
min_after_dequeue=32)
train_mode = tf.placeholder(tf.bool)
logits_gender, logits_race, end_points, _ = build_model(
images, train_mode)
end_points['Predictions/gender'] = tf.nn.softmax(
logits_gender, name='Predictions/gender')
end_points['Predictions/race'] = tf.nn.softmax(logits_race,
name='Predictions/race')
predictions1 = tf.argmax(end_points['Predictions/gender'], -1)
predictions2 = tf.argmax(end_points['Predictions/race'], -1)
pr1 = tf.to_float(tf.to_int32(predictions1))
pr2 = tf.to_float(tf.to_int32(predictions2))
with tf.Session() as sess:
#
# init_op = tf.group(tf.local_variables_initializer())
# sess.run(init_op)
saver = tf.train.Saver(max_to_keep=100)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("restore and start evaluation!")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#writer = tf.summary.FileWriter(log_dir, graph=tf.get_default_graph())
pred1, pred2 = [], []
#for step in range(num_steps_per_epoch):
acc1, acc2 = sess.run([pr1, pr2], {train_mode: False})
# Log some information
#logging.info('Step %s: gender Accuracy: %.4f race Accuracy: %.4f loss: %.4f (%.2f sec/step)'step, acc1, acc2, l, time_elapsed)
#writer.add_summary(curr_summary, step)
# pred1.append(acc1)
# pred2.append(acc2)
# coord.request_stop()
# coord.join(threads)
# saver.save(sess, final_checkpoint_file)
sess.close()
#average_acc1 = np.mean(accuracies1)
#average_acc2 = np.mean(accuracies2)
#average_loss = np.mean(loss_list)
# logging.info('Average gender Accuracy: %s', average_acc1)
# print('Average gender accuracy: ', int(np.mean(acc1)))
# print('Average gender accuracy: ', int(np.mean(acc2)))
gender = int(np.mean(acc1))
race = int(np.mean(acc1))
# logging.info('Average race Accuracy: %s', average_acc2)
#logging.info('Average loss: %s', average_loss)
#######***************************************************8######
# pred_gender, pred_race = run(image)
# pred_gender, pred_race = run(image)
#print(pred_gender, pred_race)
# print('You are a ', PGender[pred_gender[0]], ' and your race is ', PRase[pred_race[0]])
# draw_label(image, )
return gender, race