def network_entire(images):
'''
A tensorflow operation that extracts features for a batch of images.
Args:
images: Numpy array of shape (n, h, w, 3).
Returns:
embedding: Tensor of shape (n, 128).
'''
# Normalization.
images = images - tf.constant(_RGB_MEAN, dtype=tf.float32, shape=(1,1,1,3))
# Travel through the network and get the embedding.
with slim.arg_scope(resnet_arg_scope(batch_norm_decay=0.9, weight_decay=0.0)):
_, endpoints = resnet_v1_50(images, num_classes=None, is_training=False, global_pool=True)
endpoints['model_output'] = endpoints['global_pool'] = tf.reduce_mean(
endpoints['resnet_v1_50/block4'], [1, 2], name='pool5', keep_dims=False)
with tf.name_scope('head'):
endpoints = head(endpoints, embedding_dim, is_training=False)
embedding = endpoints['emb']
return embedding
def __init__(self, init_emb_model=True):
self.tf_sess = None
self.endpoints = None
self.tf_input_image = None
people_det_model_prototxt_path = "/home/asoriano/Escritorio/spaceai-evolution/Models/MobileNetSSD_caffe/deploy.prototxt"
people_det_model_caffemodel_path = "/home/asoriano/Escritorio/spaceai-evolution/Models/MobileNetSSD_caffe/MobileNetSSD_deploy.caffemodel"
people_embeddings_path = "/home/asoriano/Escritorio/spaceai-evolution/Models/person_embedding_tf/checkpoint-25000"
# - Reading people detection model
self.people_det_model = cv2.dnn.readNetFromCaffe(people_det_model_prototxt_path, people_det_model_caffemodel_path)
self.people_det_model_class_names = {0: 'background', 15: 'person'}
# - People embeddings model
if init_emb_model:
tf.Graph().as_default()
self.tf_sess = tf.Session()
self.tf_input_image = tf.zeros([1, 256, 128, 3], dtype=tf.float32)
endpoints, body_prefix = model.endpoints(self.tf_input_image, is_training=False)
with tf.name_scope('head'):
self.endpoints = head.head(endpoints, 128, is_training=False)
tf.train.Saver().restore(self.tf_sess, people_embeddings_path)
import glob
import h5py
import tensorflow as tf
import numpy as np
import nets.resnet_v1_50 as model
import heads.fc1024 as head
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
# Tensorflow descriptor model
tf.Graph().as_default()
sess = tf.Session()
images = tf.zeros([1, 256, 256, 3], dtype=tf.float32)
endpoints, body_prefix = model.endpoints(images, is_training=False)
with tf.name_scope('head'):
endpoints = head.head(endpoints, 128, is_training=False)
tf.train.Saver().restore(sess, 'exps/checkpoint-20000')
def sim_vector(img):
resize_img = cv2.resize(img, (256, 256))
resize_img = np.expand_dims(resize_img, axis=0)
emb = sess.run(endpoints['emb'], feed_dict={images: resize_img})
return emb
if __name__ == '__main__':
dir_images = './oxford/*.jpg'
imgs_path = glob.glob(dir_images)