def main(_):
tv_utils.set_gpus_to_use()
if FLAGS.input_image is None:
logging.error("No input_image was given.")
logging.info(
"Usage: python demo.py --input_image data/test.png "
"[--output_image output_image] [--logdir /path/to/weights] "
"[--gpus GPUs_to_use] ")
exit(1)
if FLAGS.logdir is None:
# Download and use weights from the MultiNet Paper
if 'TV_DIR_RUNS' in os.environ:
runs_dir = os.path.join(os.environ['TV_DIR_RUNS'], 'KittiSeg')
else:
runs_dir = 'RUNS'
maybe_download_and_extract(runs_dir)
logdir = os.path.join(runs_dir, default_run)
else:
logging.info("Using weights found in {}".format(FLAGS.logdir))
logdir = FLAGS.logdir
# Loading hyperparameters from logdir
hypes = tv_utils.load_hypes_from_logdir(logdir, base_path='hypes')
logging.info("Hypes loaded successfully.")
# Loading tv modules (encoder.py, decoder.py, eval.py) from logdir
modules = tv_utils.load_modules_from_logdir(logdir)
logging.info("Modules loaded successfully. Starting to build tf graph.")
# Create tf graph and build module.
with tf.Graph().as_default():
image_placehold = tf.placeholder(tf.float32, shape=[1, None, None, 3])
# build Tensorflow graph using the model from logdir
prediction = core.build_inference_graph(hypes,
modules,
image=image_placehold)
logging.info("Graph build successfully.")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver = tf.train.Saver()
# Load weights from logdir
core.load_weights(logdir, sess, saver)
# eval_dict,_=kitti_eval.evaluate_without_crf(hypes,sess,image_placehold,prediction)
eval_dict, _ = kitti_eval.evaluate_test(hypes, sess, image_placehold,
prediction)
for name, value in eval_dict:
logging.info(' %s %s : % 0.04f ' % (name, '(raw)', value))
def train_loop(myhypes=None):
utils.set_gpus_to_use()
try:
import tensorvision.train
import tensorflow_fcn.utils
except ImportError:
logging.error("Could not import the submodules.")
logging.error("Please execute:"
"'git submodule update --init --recursive'")
exit(1)
if tf.app.flags.FLAGS.hypes is None:
logging.error("No hype file is given.")
logging.info("Usage: python train.py --hypes hypes/KittiClass.json")
exit(1)
with open(myhypes, 'r') as f:
logging.info("f: %s", f)
hypes = commentjson.load(f)
utils.load_plugins()
if tf.app.flags.FLAGS.mod is not None:
import ast
mod_dict = ast.literal_eval(tf.app.flags.FLAGS.mod)
dict_merge(hypes, mod_dict)
if 'TV_DIR_RUNS' in os.environ:
os.environ['TV_DIR_RUNS'] = os.path.join(os.environ['TV_DIR_RUNS'],
'KittiSeg')
utils.set_dirs(hypes, tf.app.flags.FLAGS.hypes)
utils._add_paths_to_sys(hypes)
train.maybe_download_and_extract(hypes)
logging.info("Initialize training folder")
# TODO initialize the train folder and copy some arg files to it--------------------------------yu
train.initialize_training_folder(hypes)
logging.info("Start training")
train.do_training(hypes,
trainable_scopes=FLAGS.trainable_scopes,
exclude_scopes=FLAGS.checkpoint_exclude_scopes,
checkpoint_path=FLAGS.checkpoint_path)
def main(_):
"""Run main function."""
if FLAGS.hypes is None:
logging.error("No hypes are given.")
logging.error("Usage: tv-train --hypes hypes.json")
exit(1)
with open(tf.app.flags.FLAGS.hypes, 'r') as f:
logging.info("f: %s", f)
hypes = json.load(f)
utils.set_gpus_to_use()
utils.load_plugins()
utils.set_dirs(hypes, tf.app.flags.FLAGS.hypes)
logging.info("Initialize training folder")
initialize_training_folder(hypes)
maybe_download_and_extract(hypes)
logging.info("Start training")
do_training(hypes)
def main(_):
tv_utils.set_gpus_to_use()
if FLAGS.input_image is None:
logging.error("No input_image was given.")
logging.info(
"Usage: python demo.py --input_image data/test.png "
"[--output_image output_image] [--logdir /path/to/weights] "
"[--gpus GPUs_to_use] ")
exit(1)
if FLAGS.logdir is None:
# Download and use weights from the MultiNet Paper
if 'TV_DIR_RUNS' in os.environ:
runs_dir = os.path.join(os.environ['TV_DIR_RUNS'],
'KittiSeg')
else:
runs_dir = 'RUNS'
maybe_download_and_extract(runs_dir)
logdir = os.path.join(runs_dir, default_run)
else:
logging.info("Using weights found in {}".format(FLAGS.logdir))
logdir = FLAGS.logdir
# Loading hyperparameters from logdir
hypes = tv_utils.load_hypes_from_logdir(logdir, base_path='hypes')
logging.info("Hypes loaded successfully.")
# Loading tv modules (encoder.py, decoder.py, eval.py) from logdir
modules = tv_utils.load_modules_from_logdir(logdir)
logging.info("Modules loaded successfully. Starting to build tf graph.")
# Create tf graph and build module.
with tf.Graph().as_default():
# Create placeholder for input
# image_pl = tf.placeholder(tf.float32)
# image = tf.expand_dims(image_pl, 0)
image_placehold= tf.placeholder(tf.float32,shape=[1,None,None,3])
# build Tensorflow graph using the model from logdir
prediction = core.build_inference_graph(hypes, modules,
image=image_placehold)
logging.info("Graph build successfully.")
# Create a session for running Ops on the Graph.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver = tf.train.Saver()
# Load weights from logdir
core.load_weights(logdir, sess, saver)
logging.info("Weights loaded successfully.")
start_time = time.time()
logging.info('process start')
input_image = FLAGS.input_image
logging.info("Starting inference using {} as input".format(input_image))
# Load and resize input image
image = scp.misc.imread(input_image)
image= scp.misc.imresize(image,[150,150,3])
# image2 = scp.misc.imread('/home/yu/projects/KittiSeg/data/demo/demo2.png')
if hypes['jitter']['reseize_image']:
# Resize input only, if specified in hypes
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
image = scp.misc.imresize(image, size=(image_height, image_width),
interp='cubic')
# Run KittiSeg model on image
image_to_input=np.expand_dims(image,axis=0)
feed = {image_placehold: image_to_input}
softmax = prediction['softmax']
output = sess.run([softmax], feed_dict=feed)
logging.info('Finished in {}s\n'.format(
time.time() - start_time))
start_time = time.time()
print ('the output.shape =',output[0].shape)
# TODO test time -----------------------------------------------------------yu
for i in xrange(100):
output = sess.run([softmax],feed_dict=feed)
logging.info('Finished 100 time and average is in {}s\n'.format((
time.time() - start_time)/100.0))
# TODO test time ----------------------------------------------------------yu
# Reshape output from flat vector to 2D Image
shape = image.shape
print ('the image.shape =',shape)
#TODO if use CRF-----------------------------------------------------------------------------------yu
use_crf=False
street_prediction=np.empty([int(shape[0])*int(shape[1]),1])
if use_crf:
output_image = output[0][:, :].reshape(shape[0], shape[1], 2)
# output_image = post_crf.post_process_crf(image, output_image, 2)
else:
output_image = output[0][:, 1].reshape(shape[0], shape[1])
# output_image = output[0][:, 1].reshape(shape[0], shape[1])
print('the first output_image.shape =', output_image.shape)
# output_image=output[0][:,:].reshape(shape[0],shape[1],2)
print ('the second output_image.shape =',output_image.shape)
# output_image = my_array[:,:,:,1].reshape(shape[0],shape[1])
# print (output_image.shape)
# print (output_image.shape)
start_time=time.time()
# Plot confidences as red-blue overlay
# rb_image = seg.make_overlay(image, output_image)
# res=post_crf.post_process_crf(image,output_image,2)
output_image1=output[0][:,0].reshape(shape[0], shape[1])
street_prediction=output_image > output_image1
output_image2=output_image.reshape(shape[0],shape[1],-1)
output_image2=output_image2*255.0
# Accept all pixel with conf >= 0.5 as positive prediction
# This creates a `hard` prediction result for class street
threshold = 0.5
street_prediction = output_image > threshold
# street_prediction = res > threshold
# Plot the hard prediction as green overlay
green_image = tv_utils.fast_overlay(image, street_prediction)
# Save output images to disk.
if FLAGS.output_image is None:
output_base_name = input_image
else:
output_base_name = FLAGS.output_image
raw_image_name = output_base_name.split('.')[0] + '_raw.png'
rb_image_name = output_base_name.split('.')[0] + '_rb.png'
green_image_name = output_base_name.split('.')[0] + '_green.png'
# scp.misc.imshow(rb_image)
#TODO save the image--------------------------------------------------------------------------yu
save_image=False
if save_image:
# np.save(FLAGS.logdir+'182000' + '.png',output_image)
cv2.imwrite(FLAGS.logdir+'um_000031' + '.png', green_image)
# scp.misc.imsave(FLAGS.logdir+'182000' + '.png', output_image2)
scp.misc.imshow(green_image)
scp.misc.imsave('prediction_umm_000036.png',green_image)