def gen_frozen_graph():
tf.reset_default_graph()
image_batch = tf.placeholder(dtype=tf.float32, shape=placeholder_shape)
net_out = nets.vgg16NetvladPca(image_batch)
print('-------------------', net_out.name)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, nets.defaultCheckpoint())
batch = np.ones(input_shape, dtype=np.float32)
result = sess.run(net_out, feed_dict={image_batch: batch})
with open("tf_python_checkpoint_output.txt", "w") as f:
for r in result.flatten():
f.write("%.8f\n" % r)
frozen_graph = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_node_names=output_node_name)
frozen_graph = tf.compat.v1.graph_util.remove_training_nodes(
frozen_graph)
graph_io.write_graph(frozen_graph,
'',
output_graph_name,
as_text=False)
print('gen_frozen_graph done.')
def test():
pub = rospy.Publisher('chatter', String, queue_size=10)
rospy.init_node('talker', anonymous=True)
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
tf.reset_default_graph()
image_batch = tf.placeholder(dtype=tf.float32,
shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
# images = [cv2.imread(file) for file in glob.glob("/Users/benjaminramtoula/Documents/Cours/POLYMTL/MISTLAB/SLAM/datasets/kitti/00_color/image_2/00000*.png")]
# images = [cv2.cvtColor(image, cv2.COLOR_BGR2RGB) for image in images]
# print(images)
inim = cv2.imread(nfm.exampleImgPath())
batch = np.expand_dims(inim, axis=0)
# inim = cv2.imread(nfm.exampleImgPath())
# inim = cv2.cvtColor(inim, cv2.COLOR_BGR2RGB)
# batch = [np.expand_dims(inim, axis=0) for inim in images]
result = sess.run(net_out, feed_dict={image_batch: batch})
hello_str = "hello world %s" % rospy.get_time()
rospy.loginfo(result)
pub.publish(result)
rate.sleep()
def compute_map_features(ref_map_images):
ref_desc=[]
tf.reset_default_graph()
image_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
for img in ref_map_images:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (640,480), interpolation=cv2.INTER_CUBIC)
batch = np.expand_dims(img, axis=0)
t1=time.time()
desc = sess.run(net_out, feed_dict={image_batch: batch})#[0][0:1024]
print('Encode Time: ', time.time()-t1)
ref_desc.append(desc)
print(desc.shape)
return ref_desc
def __init__(self):
self.dim = rospy.get_param('~dim', 4096)
self.scale = rospy.get_param('~scale', 1.0)
rospy.loginfo("Parameter dim=%d", self.dim)
rospy.loginfo("Parameter scale=%d", self.scale)
tf.reset_default_graph()
self.image_batch = tf.placeholder(dtype=tf.float32,
shape=[None, None, None, 3])
self.net_out = nets.vgg16NetvladPca(self.image_batch)
self.saver = tf.train.Saver()
self.sess = tf.Session()
self.saver.restore(self.sess, nets.defaultCheckpoint())
self.pub = rospy.Publisher('netvlad_descriptor',
GlobalDescriptor,
queue_size=1)
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("image",
Image,
self.callback,
queue_size=1)
def __init__(self, is_grayscale=False):
self.is_grayscale = is_grayscale
if is_grayscale:
self.tf_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 1])
else:
self.tf_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
self.net_out = nets.vgg16NetvladPca(self.tf_batch)
saver = tf.train.Saver()
self.sess = tf.Session()
saver.restore(self.sess, nets.defaultCheckpoint())
def __init__(self, is_grayscale=False):
self.is_grayscale = is_grayscale
if is_grayscale:
self.tf_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 1])
else:
self.tf_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
self.net_out = nets.vgg16NetvladPca(self.tf_batch)
saver = tf.train.Saver()
self.sess = tf.Session()
saver.restore(self.sess, nets.defaultCheckpoint())
def getSessVars():
tf.reset_default_graph()
image_batch = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
return sess, net_out, image_batch
def testVgg16NetvladPca():
tf.reset_default_graph()
image_batch = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
saver.restore(sess, nets.defaultCheckpoint())
print('Initialization Finished!')
overwrite = False
data_name = "corridor"
dataset_id = 2
data_path = f"/home/frog/Desktop/lifelongSLAM/Examples/RGB-D/data/{data_name}-1-package/{data_name}-1-{dataset_id}"
out_put_path = data_path + "/vlad"
if not os.path.exists(out_put_path):
os.mkdir(out_put_path)
data_names = open(os.path.join(data_path, "color.txt"))
for i_data_name in data_names:
i_time = i_data_name.strip('\n').split(' ')[0]
i_name = i_data_name.strip('\n').split(' ')[-1]
im2dl = os.path.join(data_path, i_name)
if (os.path.isfile(im2dl) and np.all(cv2.imread(im2dl) != None)):
start = time.time()
dl2vlad = os.path.join(out_put_path, f"{i_time}.txt")
if os.path.exists(dl2vlad) and not overwrite:
continue
img = cv2.imread(im2dl)
if isinstance(img, type(np.nan)):
continue
#if img == None:
# continue
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(img, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: batch})
result = sess.run(net_out, feed_dict={image_batch: batch})
end = time.time()
print('Took %f seconds' % (end - start))
#dl2vlad = "/home/wangrong/netvlad_tf_open-master/result.txt"
#4096
np.savetxt(dl2vlad, result, fmt='%f')
def compute_query_desc(image_query):
image_query=cv2.resize(image_query, (640,480), interpolation=cv2.INTER_CUBIC)
tf.reset_default_graph()
image_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
batch = np.expand_dims(image_query, axis=0)
query_desc = sess.run(net_out, feed_dict={image_batch: batch})#[0][0:1024]
print(query_desc.shape)
return query_desc
def testVgg16NetvladPca(self):
''' Need example_stats.mat in matlab folder, which can be generated
with get_example_stats.m. Also need translated checkpoint, can be
generated with mat_to_checkpoint.py. '''
tf.reset_default_graph()
image_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
inim = cv2.imread(nfm.exampleImgPath())
#print(nfm.exampleImgPath()) #/home/wangrong/netvlad_tf_open-master/example.jpg
inim = cv2.cvtColor(inim, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(inim, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: batch})
t = time.time()
result = sess.run(net_out, feed_dict={image_batch: batch})
#print(result.size) #4096
#print(result) #[[-0.00681984 -0.00276458 -0.00730957 ... 0.01022749 -0.00939475
#-0.0027039 ]]
print('Took %f seconds' % (time.time() - t))
#%% Load Matlab results
mat = scio.loadmat(nfm.exampleStatPath(),
struct_as_record=False, squeeze_me=True)
#print(nfm.exampleStatPath()) #/home/wangrong/netvlad_tf_open-master/matlab/example_stats.mat
mat_outs = mat['outs']
#%% Compare final output
out_diff = np.abs(mat_outs[-1] - result)
self.assertLess(np.linalg.norm(out_diff), 0.0053)
print('Error of final vector is %f' % np.linalg.norm(out_diff))
def testVgg16NetvladPca(self):
tf.reset_default_graph()
image_batch = tf.placeholder(dtype=tf.float32,
shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
rimg = cv2.imread('data/images/office_1.png')
rimg = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
rbatch = np.expand_dims(rimg, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: rbatch})
rt = time.time()
rresult = sess.run(net_out, feed_dict={image_batch: rbatch})
print('Took %f seconds' % (time.time() - rt))
qimg = cv2.imread('data/images/office_3.png')
qimg = cv2.cvtColor(qimg, cv2.COLOR_BGR2RGB)
qbatch = np.expand_dims(qimg, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: qbatch})
qt = time.time()
qresult = sess.run(net_out, feed_dict={image_batch: qbatch})
print('Took %f seconds' % (time.time() - qt))
#%% Compare final output
out_diff = np.abs(qresult - rresult)
print('Image presentation distance: %f', np.linalg.norm(out_diff))
def testVgg16NetvladPca(self):
''' Need example_stats.mat in matlab folder, which can be generated
with get_example_stats.m. Also need translated checkpoint, can be
generated with mat_to_checkpoint.py. '''
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
image_batch = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.compat.v1.train.Saver()
sess = tf.compat.v1.Session()
saver.restore(sess, nets.defaultCheckpoint())
inim = cv2.imread(nfm.exampleImgPath())
inim = cv2.cvtColor(inim, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(inim, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: batch})
t = time.time()
result = sess.run(net_out, feed_dict={image_batch: batch})
print('Took %f seconds' % (time.time() - t))
#%% Load Matlab results
mat = scio.loadmat(nfm.exampleStatPath(),
struct_as_record=False,
squeeze_me=True)
mat_outs = mat['outs']
#%% Compare final output
out_diff = np.abs(mat_outs[-1] - result)
self.assertLess(np.linalg.norm(out_diff), 0.0053)
print('Error of final vector is %f' % np.linalg.norm(out_diff))
def testVgg16NetvladPca(self):
''' Need example_stats.mat in matlab folder, which can be generated
with get_example_stats.m. Also need translated checkpoint, can be
generated with mat_to_checkpoint.py. '''
tf.reset_default_graph()
image_batch = tf.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
inim = cv2.imread(nfm.exampleImgPath())
inim = cv2.cvtColor(inim, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(inim, axis=0)
#%% Generate TF results
for _ in range(2):
sess.run(net_out, feed_dict={image_batch: batch})
t = time.time()
result = sess.run(net_out, feed_dict={image_batch: batch})
print('Took %f seconds' % (time.time() - t))
#%% Load Matlab results
mat = scio.loadmat(nfm.exampleStatPath(),
struct_as_record=False, squeeze_me=True)
mat_outs = mat['outs']
#%% Compare final output
out_diff = np.abs(mat_outs[-1] - result)
self.assertLess(np.linalg.norm(out_diff), 0.0053)
print('Error of final vector is %f' % np.linalg.norm(out_diff))
import cv2
import numpy as np
import tensorflow as tf
import netvlad_tf.net_from_mat as nfm
import netvlad_tf.nets as nets
tf.compat.v1.reset_default_graph
image_batch = tf.compat.v1.placeholder(
dtype=tf.float32, shape=[None, None, None, 3])
net_out = nets.vgg16NetvladPca(image_batch)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, nets.defaultCheckpoint())
inim = cv2.imread(nfm.exampleImgPath())
inim = cv2.cvtColor(inim, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(inim, axis=0)
result = sess.run(net_out, feed_dict={image_batch: batch})
def __init__(self):
self.images_l_queue = collections.deque()
self.images_r_queue = collections.deque()
self.images_rgb_queue = collections.deque()
self.geometric_feats = collections.deque()
self.images_rgb_kf = collections.deque()
self.timestamps_kf = collections.deque()
self.local_descriptors = []
self.nb_descriptors_already_sent = 0
self.received_descriptors = []
self.separators_found = collections.deque()
self.local_kf_already_used = collections.deque()
self.other_kf_already_used = collections.deque()
self.frames_kept_pairs_ignored = collections.deque()
self.nb_kf_skipped = 0
self.original_ids_of_kf = collections.deque()
self.orig_id_last_img_in_q = 0
self.nb_kf_odom = 0
self.kf_ids_of_frames_kept = collections.deque()
tf.reset_default_graph()
self.image_batch = tf.placeholder(dtype=tf.float32,
shape=[None, None, None, 3])
self.net_out = nets.vgg16NetvladPca(self.image_batch)
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
saver.restore(self.sess, nets.defaultCheckpoint())
self.bridge = CvBridge()
self.local_robot_id = rospy.get_param("local_robot_id")
self.other_robot_id = rospy.get_param("other_robot_id")
self.log_gps = rospy.get_param("log_gps")
if self.log_gps:
from dji_sdk.msg import GlobalPosition
rospy.Subscriber("gps_topic", GlobalPosition, self.save_gps_queue)
self.gps_data_queue = collections.deque()
self.s_add_seps_pose_graph = rospy.ServiceProxy(
'add_separators_pose_graph', ReceiveSeparators)
self.s_get_feats = rospy.ServiceProxy('get_features_and_descriptor',
GetFeatsAndDesc)
self.logs_location = rospy.get_param("logs_location")
self.send_estimates_of_poses = rospy.get_param(
"use_estimates_of_poses")
if self.send_estimates_of_poses:
self.s_get_pose_estimates = rospy.ServiceProxy(
'get_pose_estimates', PoseEstimates)
# Read params
self.netvlad_distance = rospy.get_param("netvlad_distance")
self.netvlad_dimensions = rospy.get_param("netvlad_dimensions")
self.netvlad_batch_size = rospy.get_param("netvlad_batch_size")
self.netvlad_max_matches_nb = rospy.get_param("netvlad_max_matches_nb")
self.number_of_kf_skipped = rospy.get_param("number_of_kf_skipped")
# Log params to file
with open(
self.logs_location + 'params_' + str(self.local_robot_id) +
'.txt', 'a') as file:
file.write('netvlad_distance: ' + str(self.netvlad_distance) +
'\nnetvlad_dimensions: ' +
str(self.netvlad_dimensions) +
'\nnetvlad_batch_size: ' +
str(self.netvlad_batch_size) +
'\nnetvlad_max_matches_nb: ' +
str(self.netvlad_max_matches_nb) +
'\nnumber_of_kf_skipped: ' +
str(self.number_of_kf_skipped) +
'\nseparators_min_inliers: ' +
str(rospy.get_param("separators_min_inliers")) + '\n')
import tensorflow as tf from netvlad_tf.net_from_mat import netFromMat from netvlad_tf.nets import defaultCheckpoint tf.reset_default_graph() layers = netFromMat() saver = tf.train.Saver() sess = tf.Session() tf.global_variables_initializer().run(session=sess) saver.save(sess, defaultCheckpoint())
import tensorflow as tf from netvlad_tf.net_from_mat import netFromMat from netvlad_tf.nets import defaultCheckpoint tf.reset_default_graph() layers = netFromMat() saver = tf.train.Saver() sess = tf.Session() tf.global_variables_initializer().run(session=sess) saver.save(sess, defaultCheckpoint())
import cv2
import numpy as np
import tensorflow as tf
import netvlad_tf.net_from_mat as nfm
import netvlad_tf.nets as nets
from keras.models import Sequential
import netvlad_keras
import os
# This script loads the tensorflow model and transforms it into a Keras model
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
checkpoint = nets.defaultCheckpoint()
# start tensorflow session
with tf.Session() as sess:
# import graph
saver = tf.train.import_meta_graph(checkpoint + ".meta")
# load weights for graph
saver.restore(sess, checkpoint)
# get all global variables (including model variables)
vars_global = tf.global_variables()
# get their name and value and put them into dictionary
sess.as_default()
model_vars = {}
