def input_fn():
with tf.name_scope('post_forward'):
out_shape = [FLAGS.train_image_size] * 2
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ),
(0.725, ), (0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., .5),
(1., 2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] *
all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=FLAGS.match_threshold,
ignore_threshold=FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
# global global_anchor_info
# global_anchor_info = {'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors(pred, num_anchors_per_layer),
# 'num_anchors_per_layer': num_anchors_per_layer,
# 'all_num_anchors_depth': all_num_anchors_depth,
# 'encode_fn': lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(glabels_, gbboxes_, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)}
image_preprocessing_fn = lambda image_, labels_, bboxes_: ssd_preprocessing.preprocess_image(
image_,
labels_,
bboxes_,
out_shape,
is_training=is_training,
data_format=FLAGS.data_format,
output_rgb=False)
# anchor_encoder_fn = lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(glabels_, gbboxes_, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
filenames = tf.placeholder(tf.string, shape=[None])
arga = tf.constant(True)
dataset = tf.data.TFRecordDataset(training_files)
dataset = dataset.map(lambda x: data_mapping_fn(
x, is_training, image_preprocessing_fn))
dataset = dataset.repeat() # repeat the input infinitely
dataset = dataset.batch(batch_size) # set the batch size
iterator = dataset.make_initializable_iterator()
# return image, {'shape': shape, 'loc_targets': loc_targets, 'cls_targets': cls_targets, 'match_scores': match_scores}
return dataset
def input_fn():
out_shape = [FLAGS.train_image_size] * 2
ssd300_anchor_params = {'layers_shapes': [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)], 'anchor_scales': [(0.1,), (0.2,), (0.375,), (0.55,), (0.725,), (0.9,)],
'extra_anchor_scales': [(0.1414,), (0.2739,), (0.4541,), (0.6315,), (0.8078,), (0.9836,)],
'anchor_ratios': [(1., 2., .5), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., .5), (1., 2., .5)],
'layer_steps': [8, 16, 32, 64, 100, 300]}
ssd512_anchor_params = {'layers_shapes': [(64, 64), (32, 32), (16, 16), (8, 8), (4, 4), (2, 2), (1, 1)],
'anchor_scales': [(0.07,), (0.15,), (0.3,), (0.45,), (0.6,), (0.75,), (0.9,)],
'extra_anchor_scales': [(0.1025,), (0.2121,), (0.3674,), (0.5196,), (0.6708,), (0.8216,), (0.9721,)],
'anchor_ratios': [(1., 2., .5), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., .5), (1., 2., .5)],
'layer_steps': [8, 16, 32, 64, 128, 256, 512]}
if FLAGS.train_image_size == 512:
net_params = ssd512_anchor_params
print('using ssd512 model')
else:
net_params = ssd300_anchor_params
print('using ssd300 model')
anchor_creator = anchor_manipulator.AnchorCreator(out_shape, **net_params)
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors()
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] * all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(allowed_borders = [1.0] * len(net_params['layer_steps']),
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
image_preprocessing_fn = lambda image_, labels_, bboxes_ : ssd_preprocessing.preprocess_image(image_, labels_, bboxes_, out_shape, is_training=is_training, data_format=FLAGS.data_format, output_rgb=False)
anchor_encoder_fn = lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(glabels_, gbboxes_, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
image, filename, shape, loc_targets, cls_targets, match_scores = dataset_common.slim_get_batch(FLAGS.num_classes,
batch_size,
('train' if is_training else 'val'),
os.path.join(FLAGS.data_dir, dataset_pattern),
FLAGS.num_readers,
FLAGS.num_preprocessing_threads,
image_preprocessing_fn,
anchor_encoder_fn,
num_epochs=FLAGS.train_epochs,
is_training=is_training)
global global_anchor_info
global_anchor_info = {'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors(pred, num_anchors_per_layer),
'num_anchors_per_layer': num_anchors_per_layer,
'all_num_anchors_depth': all_num_anchors_depth }
return {'image': image, 'filename': filename, 'shape': shape, 'loc_targets': loc_targets, 'cls_targets': cls_targets, 'match_scores': match_scores}, None
def input_fn():
out_shape = [FLAGS.train_image_size] * 2
anchor_creator = anchor_manipulator.AnchorCreator(out_shape,
layers_shapes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)],
anchor_scales = [(0.1,), (0.2,), (0.375,), (0.55,), (0.725,), (0.9,)],
extra_anchor_scales = [(0.1414,), (0.2739,), (0.4541,), (0.6315,), (0.8078,), (0.9836,)],
anchor_ratios = [(1., 2., .5), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333), (1., 2., .5), (1., 2., .5)],
layer_steps = [8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors()
# all_anchors: [[(38x38x1),(38x38x1),(4x1),(4x1)],[(19x19x1),(19x19x1),(4x1),(4x1)]... ] -> recording all the anchors information
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] * all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(allowed_borders = [1.0] * 6,
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
image_preprocessing_fn = lambda image_, labels_, bboxes_ : ssd_preprocessing.preprocess_image(image_, labels_, bboxes_, out_shape, is_training=is_training, data_format=FLAGS.data_format, output_rgb=False)
anchor_encoder_fn = lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(glabels_, gbboxes_, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
anchor_decoder_fn = lambda pred : anchor_encoder_decoder.decode_all_anchors(pred, num_anchors_per_layer)
image, _, shape, loc_targets, cls_targets, match_scores = dataset_common.slim_get_batch(FLAGS.num_classes,
batch_size,
('train' if is_training else 'val'),
os.path.join(FLAGS.data_dir, dataset_pattern),
FLAGS.num_readers,
FLAGS.num_preprocessing_threads,
image_preprocessing_fn,
anchor_encoder_fn,
num_epochs=FLAGS.train_epochs,
is_training=is_training)
global global_anchor_info
global_anchor_info = {'decode_fn': anchor_decoder_fn,
'num_anchors_per_layer': num_anchors_per_layer,
'all_num_anchors_depth': all_num_anchors_depth }
return image, {'shape': shape, 'loc_targets': loc_targets, 'cls_targets': cls_targets, 'match_scores': match_scores}
def main(_):
with tf.Graph().as_default():
out_shape = [FLAGS.train_image_size] * 2
image_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
shape_input = tf.placeholder(tf.int32, shape=(2, ))
features = ssd_preprocessing.preprocess_for_eval(
image_input,
out_shape,
data_format=FLAGS.data_format,
output_rgb=False)
features = tf.expand_dims(features, axis=0)
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(2., .5),
(2., 3., .5, 0.3333), (2., 3., .5, 0.3333),
(2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
decode_fn = lambda pred: anchor_encoder_decoder.ext_decode_all_anchors(
pred, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
with tf.variable_scope(FLAGS.model_scope,
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
backbone = ssd_net.VGG16Backbone(FLAGS.data_format)
feature_layers = backbone.forward(features, training=False)
location_pred, cls_pred = ssd_net.multibox_head(
feature_layers,
FLAGS.num_classes,
all_num_anchors_depth,
data_format=FLAGS.data_format)
if FLAGS.data_format == 'channels_first':
cls_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in cls_pred
]
location_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in location_pred
]
cls_pred = [
tf.reshape(pred, [-1, FLAGS.num_classes]) for pred in cls_pred
]
location_pred = [
tf.reshape(pred, [-1, 4]) for pred in location_pred
]
cls_pred = tf.concat(cls_pred, axis=0)
location_pred = tf.concat(location_pred, axis=0)
with tf.device('/cpu:0'):
bboxes_pred = decode_fn(location_pred)
bboxes_pred = tf.concat(bboxes_pred, axis=0)
selected_bboxes, selected_scores = parse_by_class(
cls_pred, bboxes_pred, FLAGS.num_classes,
FLAGS.select_threshold, FLAGS.min_size, FLAGS.keep_topk,
FLAGS.nms_topk, FLAGS.nms_threshold)
labels_list = []
scores_list = []
bboxes_list = []
for k, v in selected_scores.items():
labels_list.append(tf.ones_like(v, tf.int32) * k)
scores_list.append(v)
bboxes_list.append(selected_bboxes[k])
all_labels = tf.concat(labels_list, axis=0)
all_scores = tf.concat(scores_list, axis=0)
all_bboxes = tf.concat(bboxes_list, axis=0)
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, get_checkpoint())
np_image = imread('./demo/test.jpg')
labels_, scores_, bboxes_ = sess.run(
[all_labels, all_scores, all_bboxes],
feed_dict={
image_input: np_image,
shape_input: np_image.shape[:-1]
})
img_to_draw = draw_toolbox.bboxes_draw_on_img(np_image,
labels_,
scores_,
bboxes_,
thickness=2)
imsave('./demo/test_out.jpg', img_to_draw)
def main(_):
with tf.Graph().as_default():
out_shape = [FLAGS.train_image_size] * 2
with tf.name_scope('define_input'):
image_input = tf.placeholder(tf.uint8,
shape=(None, None, 3),
name='image_input')
features = ssd_preprocessing.preprocess_for_eval(
image_input,
out_shape,
data_format=FLAGS.data_format,
output_rgb=False)
features = tf.expand_dims(features, axis=0)
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333),
#(2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
def decode_fn(pred):
return anchor_encoder_decoder.ext_decode_all_anchors(
pred, all_anchors, all_num_anchors_depth,
all_num_anchors_spatial)
with tf.variable_scope(FLAGS.model_scope,
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
backbone = ssd_net.VGG16Backbone(FLAGS.data_format)
feature_layers = backbone.forward(features, training=False)
location_pred, cls_pred = ssd_net.multibox_head(
feature_layers,
FLAGS.num_classes,
all_num_anchors_depth,
data_format=FLAGS.data_format)
if FLAGS.data_format == 'channels_first':
cls_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in cls_pred
]
location_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in location_pred
]
cls_pred = [
tf.reshape(pred, [-1, FLAGS.num_classes]) for pred in cls_pred
]
location_pred = [
tf.reshape(pred, [-1, 4]) for pred in location_pred
]
with tf.variable_scope('cls_pred'):
cls_pred = tf.concat(cls_pred, axis=0)
with tf.variable_scope('location_pred'):
location_pred = tf.concat(location_pred, axis=0)
with tf.device('/cpu:0'):
bboxes_pred = decode_fn(location_pred)
bboxes_pred = tf.concat(bboxes_pred, axis=0)
selected_bboxes, selected_scores = parse_by_class(
cls_pred, bboxes_pred, FLAGS.num_classes,
FLAGS.select_threshold, FLAGS.min_size, FLAGS.keep_topk,
FLAGS.nms_topk, FLAGS.nms_threshold)
labels_list = []
scores_list = []
bboxes_list = []
for k, v in selected_scores.items():
labels_list.append(tf.ones_like(v, tf.int32) * k)
scores_list.append(v)
bboxes_list.append(selected_bboxes[k])
all_labels = tf.concat(labels_list, axis=0)
all_scores = tf.concat(scores_list, axis=0)
all_bboxes = tf.concat(bboxes_list, axis=0)
saver = tf.train.Saver()
'''
config = tf.ConfigProto(allow_soft_placement=True, inter_op_parallelism_threads=1, intra_op_parallelism_threads=1)
config.mlu_options.data_parallelism = 1
config.mlu_options.model_parallelism = 1
config.mlu_options.core_num = 1
config.mlu_options.core_version = 'MLU270'
config.mlu_options.precision = 'float'
with tf.Session(config = config) as sess:
'''
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, get_checkpoint())
np_image = imread('demo/test.jpg')
labels_, scores_, bboxes_ = sess.run(
[all_labels, all_scores, all_bboxes],
feed_dict={image_input: np_image})
#print('labels_', labels_, type(labels_), labels_.shape)
#print('scores_', scores_, type(scores_), scores_.shape)
#print('bboxes_', bboxes_, type(bboxes_), bboxes_.shape, bboxes_.shape[0])
img_to_draw = draw_toolbox.bboxes_draw_on_img(np_image,
labels_,
scores_,
bboxes_,
thickness=2)
imsave('demo/test_out.jpg', img_to_draw)
saver.save(sess, 'model/ssd300_vgg16/ssd300_vgg16', global_step=0)
def slim_get_split(file_pattern='{}_????'):
# Features in Pascal VOC TFRecords.
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
}
items_to_handlers = {
'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'shape': slim.tfexample_decoder.Tensor('image/shape'),
'object/bbox': slim.tfexample_decoder.BoundingBox(
['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)
dataset = slim.dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=100,
items_to_descriptions=None,
num_classes=21,
labels_to_names=None)
with tf.name_scope('dataset_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=2,
common_queue_capacity=32,
common_queue_min=8,
shuffle=True,
num_epochs=1)
[org_image, shape, glabels_raw, gbboxes_raw, isdifficult] = provider.get(['image', 'shape',
'object/label',
'object/bbox',
'object/difficult'])
image, glabels, gbboxes = ssd_preprocessing.preprocess_image(org_image, glabels_raw, gbboxes_raw, [300, 300],
is_training=True, data_format='channels_last',
output_rgb=True)
anchor_creator = anchor_manipulator.AnchorCreator([300] * 2,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1,), (0.2,), (0.375,), (0.55,), (0.725,),
(0.9,)],
extra_anchor_scales=[(0.1414,), (0.2739,), (0.4541,), (0.6315,),
(0.8078,), (0.9836,)],
anchor_ratios=[(2., .5), (2., 3., .5, 0.3333),
(2., 3., .5, 0.3333), (2., 3., .5, 0.3333),
(2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors()
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] * all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(allowed_borders=[1.0] * 6,
positive_threshold=0.5,
ignore_threshold=0.5,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
gt_targets, gt_labels, gt_scores = anchor_encoder_decoder.encode_all_anchors(glabels, gbboxes, all_anchors,
all_num_anchors_depth,
all_num_anchors_spatial, True)
anchors = anchor_encoder_decoder._all_anchors
# split by layers
gt_targets, gt_labels, gt_scores, anchors = tf.split(gt_targets, num_anchors_per_layer, axis=0), \
tf.split(gt_labels, num_anchors_per_layer, axis=0), \
tf.split(gt_scores, num_anchors_per_layer, axis=0), \
[tf.split(anchor, num_anchors_per_layer, axis=0) for anchor in anchors]
save_image_op = tf.py_func(save_image_with_bbox,
[ssd_preprocessing.unwhiten_image(image),
tf.clip_by_value(tf.concat(gt_labels, axis=0), 0, tf.int64.max),
tf.concat(gt_scores, axis=0),
tf.concat(gt_targets, axis=0)],
tf.int64, stateful=True)
return save_image_op
def data_mapping_fn(example_proto, is_training, image_preprocessing_fn):
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string,
default_value='jpeg'),
'image/filename': tf.FixedLenFeature((), tf.string, default_value=''),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
}
items_to_handlers = {
'image':
slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'filename':
slim.tfexample_decoder.Tensor('image/filename'),
'shape':
slim.tfexample_decoder.Tensor('image/shape'),
'object/bbox':
slim.tfexample_decoder.BoundingBox(['ymin', 'xmin', 'ymax', 'xmax'],
'image/object/bbox/'),
'object/label':
slim.tfexample_decoder.Tensor('image/object/bbox/label'),
'object/difficult':
slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
'object/truncated':
slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
}
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
[org_image, filename, shape, glabels_raw, gbboxes_raw,
isdifficult] = decoder.decode(example_proto, [
'image', 'filename', 'shape', 'object/label', 'object/bbox',
'object/difficult'
])
if is_training:
# if all is difficult, then keep the first one
isdifficult_mask = tf.cond(
tf.count_nonzero(isdifficult,
dtype=tf.int32) < tf.shape(isdifficult)[0],
lambda: isdifficult < tf.ones_like(isdifficult),
lambda: tf.one_hot(0,
tf.shape(isdifficult)[0],
on_value=True,
off_value=False,
dtype=tf.bool))
glabels_raw = tf.boolean_mask(glabels_raw, isdifficult_mask)
gbboxes_raw = tf.boolean_mask(gbboxes_raw, isdifficult_mask)
# Pre-processing image, labels and bboxes.
out_shape = [FLAGS.train_image_size] * 2
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ), (0.6315, ),
(0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] *
all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=FLAGS.match_threshold,
ignore_threshold=FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
global global_anchor_info
global_anchor_info = {
'decode_fn':
lambda pred: anchor_encoder_decoder.decode_all_anchors(
pred, num_anchors_per_layer),
'num_anchors_per_layer':
num_anchors_per_layer,
'all_num_anchors_depth':
all_num_anchors_depth,
'encode_fn':
lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(
glabels_, gbboxes_, all_anchors, all_num_anchors_depth,
all_num_anchors_spatial)
}
# global global_anchor_info
# global_anchor_info = {'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors(pred, num_anchors_per_layer)}
if is_training:
image, glabels, gbboxes = image_preprocessing_fn(
org_image, glabels_raw, gbboxes_raw)
else:
image = image_preprocessing_fn(org_image, glabels_raw, gbboxes_raw)
glabels, gbboxes = glabels_raw, gbboxes_raw
gt_targets, gt_labels, gt_scores = global_anchor_info['encode_fn'](glabels,
gbboxes)
# return [image, filename, shape, gt_targets, gt_labels, gt_scores]
return image, {
'shape': shape,
'loc_targets': gt_targets,
'cls_targets': gt_labels,
'match_scores': gt_scores
}
def main(_):
with tf.Graph().as_default():
out_shape = [FLAGS.train_image_size] * 2
image_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
shape_input = tf.placeholder(tf.int32, shape=(2, ))
features = ssd_preprocessing.preprocess_for_eval(
image_input,
out_shape,
data_format=FLAGS.data_format,
output_rgb=False)
features = tf.expand_dims(features, axis=0) #(N, W, H, C)
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
decode_fn = lambda pred: anchor_encoder_decoder.ext_decode_all_anchors(
pred, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
with tf.variable_scope(FLAGS.model_scope,
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
#backbone = ssd_net.VGG16Backbone(FLAGS.data_format)
backbone = ssd_net.MobileNetV2Backbone(FLAGS.data_format)
feature_layers = backbone.forward(features, training=False)
location_pred, cls_pred = ssd_net.multibox_head(
feature_layers,
FLAGS.num_classes,
all_num_anchors_depth,
data_format=FLAGS.data_format)
if FLAGS.data_format == 'channels_first':
cls_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in cls_pred
]
location_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in location_pred
]
cls_pred = [
tf.reshape(pred, [-1, FLAGS.num_classes]) for pred in cls_pred
]
location_pred = [
tf.reshape(pred, [-1, 4]) for pred in location_pred
]
cls_pred = tf.concat(cls_pred, axis=0)
location_pred = tf.concat(location_pred, axis=0)
with tf.device('/cpu:0'):
bboxes_pred = decode_fn(location_pred)
bboxes_pred = tf.concat(bboxes_pred, axis=0) #
selected_bboxes, selected_scores = parse_by_class(
cls_pred, bboxes_pred, FLAGS.num_classes,
FLAGS.select_threshold, FLAGS.min_size, FLAGS.keep_topk,
FLAGS.nms_topk, FLAGS.nms_threshold)
labels_list = []
scores_list = []
bboxes_list = []
for k, v in selected_scores.items():
labels_list.append(tf.ones_like(v, tf.int32) * k)
scores_list.append(v)
bboxes_list.append(selected_bboxes[k])
all_labels = tf.concat(labels_list, axis=0)
all_scores = tf.concat(scores_list, axis=0)
all_bboxes = tf.concat(bboxes_list, axis=0)
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, get_checkpoint())
while (video.isOpened()):
ret, frame = video.read()
if ret == False:
break
else:
timer2 = cv2.getTickCount()
if undistort == 'y':
######################################## Undistortion Parts ########################################
dim2 = None
dim3 = None
timer = cv2.getTickCount()
dim1 = frame.shape[:
2][::
-1] # dim1 is the dimension of input image to un-distort
assert dim1[0] / dim1[1] == DIM[0] / DIM[
1], "Image to undistort needs to have same aspect ratio as the ones used in calibration"
if not dim2:
dim2 = dim1
if not dim3:
dim3 = dim1
scaled_K = K * dim1[0] / DIM[
0] # The values of K is to scale with image dimension.
scaled_K[2][
2] = 1.0 # Except that K[2][2] is always 1.0
# This is how scaled_K, dim2 and balance are used to determine the final K used to un-distort image. OpenCV document failed to make this clear!
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(
scaled_K, D, dim2, np.eye(3), balance=0)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(
scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)
frame_r = cv2.remap(frame,
map1,
map2,
interpolation=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT)
t = (cv2.getTickCount() -
timer) / cv2.getTickFrequency()
# frame_r = cv2.resize(dst, (640, 360))
# frame_r = cv2.putText(frame_r, "Undistortion processing time: %.3f sec" % t, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 255), 2) #(1.0 / (end - start))
# ###############################################################################################
#np_image = imread('./demo/test.jpg')
labels_, scores_, bboxes_ = sess.run(
[all_labels, all_scores, all_bboxes],
feed_dict={
image_input: frame,
shape_input: frame.shape[:-1]
})
img_to_draw = draw_toolbox.bboxes_draw_on_img(frame,
labels_,
scores_,
bboxes_,
thickness=2)
fps = cv2.getTickFrequency() / (cv2.getTickCount() -
timer2)
img_to_draw = cv2.putText(img_to_draw, "FPS : %.1f" % fps,
(10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 255), 2) # dst_r
cv2.imshow('Object detector', img_to_draw) # dst_r
if cv2.waitKey(1) == ord('q'):
break
def main(_):
with tf.Graph().as_default():
out_shape = [FLAGS.train_image_size] * 2
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333),
#(2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
def decode_fn(pred):
return anchor_encoder_decoder.ext_decode_all_anchors(
pred, all_anchors, all_num_anchors_depth,
all_num_anchors_spatial)
with tf.name_scope('define_input'):
image_input = tf.placeholder(tf.float32,
shape=(1, 300, 300, 3),
name='image_input')
print('image_input', image_input)
with tf.variable_scope(FLAGS.model_scope,
default_name=None,
values=[image_input],
reuse=tf.AUTO_REUSE):
backbone = ssd_net.VGG16Backbone(FLAGS.data_format)
feature_layers = backbone.forward(image_input, training=False)
location_pred, cls_pred = ssd_net.multibox_head(
feature_layers,
FLAGS.num_classes,
all_num_anchors_depth,
data_format=FLAGS.data_format)
if FLAGS.data_format == 'channels_first':
cls_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in cls_pred
]
location_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in location_pred
]
cls_pred = [
tf.reshape(pred, [-1, FLAGS.num_classes]) for pred in cls_pred
]
location_pred = [
tf.reshape(pred, [-1, 4]) for pred in location_pred
]
with tf.variable_scope('cls_pred'):
cls_pred = tf.concat(cls_pred, axis=0)
with tf.variable_scope('location_pred'):
location_pred = tf.concat(location_pred, axis=0)
with tf.device('/cpu:0'):
bboxes_pred = decode_fn(location_pred)
bboxes_pred = tf.concat(bboxes_pred, axis=0)
selected_bboxes, selected_scores = parse_by_class(
cls_pred, bboxes_pred, FLAGS.num_classes,
FLAGS.select_threshold, FLAGS.min_size, FLAGS.keep_topk,
FLAGS.nms_topk, FLAGS.nms_threshold)
labels_list = []
scores_list = []
bboxes_list = []
for k, v in selected_scores.items():
labels_list.append(tf.ones_like(v, tf.int32) * k)
scores_list.append(v)
bboxes_list.append(selected_bboxes[k])
all_labels = tf.concat(labels_list, axis=0)
all_scores = tf.concat(scores_list, axis=0)
all_bboxes = tf.concat(bboxes_list, axis=0)
saver = tf.train.Saver()
'''
config = tf.ConfigProto(allow_soft_placement=True, inter_op_parallelism_threads=1, intra_op_parallelism_threads=1)
config.mlu_options.data_parallelism = 1
config.mlu_options.model_parallelism = 1
config.mlu_options.core_num = 1
config.mlu_options.core_version = 'MLU270'
config.mlu_options.precision = 'float'
with tf.Session(config = config) as sess:
'''
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, get_checkpoint())
_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94
means = [
_B_MEAN,
_G_MEAN,
_R_MEAN,
]
np_image = cv2.imread('demo/test.jpg')
image = cv2.resize(
np_image, (FLAGS.train_image_size, FLAGS.train_image_size))
image = (image - means) # / 255.0
image = np.expand_dims(image, axis=0)
print('image', type(image), image.shape)
'''
image = tf.to_float(np_image)
image = tf.image.resize_images(image, out_shape,
method=tf.image.ResizeMethod.BILINEAR, align_corners=False)
image.set_shape(out_shape + [3])
num_channels = image.get_shape().as_list()[-1]
channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
for i in range(num_channels):
channels[i] -= means[i]
image = tf.concat(axis=2, values=channels)
image_channels = tf.unstack(image, axis=-1, name='split_rgb')
image = tf.stack([image_channels[2], image_channels[1], image_channels[0]], axis=-1, name='merge_bgr')
'''
labels_, scores_, bboxes_ = sess.run(
[all_labels, all_scores, all_bboxes],
feed_dict={image_input: image})
#print('labels_', labels_, type(labels_), labels_.shape)
#print('scores_', scores_, type(scores_), scores_.shape)
#print('bboxes_', bboxes_, type(bboxes_), bboxes_.shape, bboxes_.shape[0])
img_to_draw = draw_toolbox.bboxes_draw_on_img(np_image,
labels_,
scores_,
bboxes_,
thickness=2)
cv2.imwrite('demo/test_out.jpg', img_to_draw)
saver.save(sess,
'model/ssd300_vgg16/ssd300_vgg16_short',
global_step=0)
class_ind] = nms_bboxes(
selected_scores[class_ind],
selected_bboxes[class_ind], nms_topk,
nms_threshold, 'nms_bboxes_{}'.format(class_ind))
return selected_bboxes, selected_scores
out_shape = [300] * 2
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333),
#(2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
def input_fn():
out_shape = [300, 510] #[FLAGS.train_image_size] * 2
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 64), (19, 32), (10, 16), (5, 8), (3, 6),
(1, 4)],
anchor_scales=[(0.05, ), (0.1, ), (0.2, ), (0.3, ), (0.4, ),
(0.5, )],
extra_anchor_scales=[(0.07, ), (0.1414, ), (0.245, ), (0.346, ),
(0.447, ), (0.547, )],
anchor_ratios=[(1., ), (1., ), (1., ), (1., ), (1., ), (1., )],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
num_anchors_per_layer = []
for ind in range(len(all_anchors)):
num_anchors_per_layer.append(all_num_anchors_depth[ind] *
all_num_anchors_spatial[ind])
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=FLAGS.match_threshold,
ignore_threshold=FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
image_preprocessing_fn = lambda image_, labels_, bboxes_: ssd_preprocessing.preprocess_image(
image_,
labels_,
bboxes_,
out_shape,
is_training=is_training,
data_format=FLAGS.data_format,
output_rgb=False)
anchor_encoder_fn = lambda glabels_, gbboxes_: anchor_encoder_decoder.encode_all_anchors(
glabels_, gbboxes_, all_anchors, all_num_anchors_depth,
all_num_anchors_spatial)
image, filename, shape, loc_targets, cls_targets, match_scores = dataset_common.slim_get_batch(
FLAGS.num_classes,
batch_size, ('train' if is_training else 'val'),
os.path.join(FLAGS.data_dir, dataset_pattern),
FLAGS.num_readers,
FLAGS.num_preprocessing_threads,
image_preprocessing_fn,
anchor_encoder_fn,
num_epochs=FLAGS.train_epochs,
is_training=is_training)
global global_anchor_info
global_anchor_info = {
'decode_fn':
lambda pred: anchor_encoder_decoder.decode_all_anchors(
pred, num_anchors_per_layer),
'num_anchors_per_layer':
num_anchors_per_layer,
'all_num_anchors_depth':
all_num_anchors_depth
}
return {
'image': image,
'filename': filename,
'shape': shape,
'loc_targets': loc_targets,
'cls_targets': cls_targets,
'match_scores': match_scores
}, None
def main(_):
with tf.Graph().as_default():
out_shape = [FLAGS.train_image_size] * 2
image_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
shape_input = tf.placeholder(tf.int32, shape=(2, ))
features = ssd_preprocessing.preprocess_for_eval(
image_input,
out_shape,
data_format=FLAGS.data_format,
output_rgb=False)
features = tf.expand_dims(features, axis=0)
anchor_creator = anchor_manipulator.AnchorCreator(
out_shape,
layers_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3),
(1, 1)],
anchor_scales=[(0.1, ), (0.2, ), (0.375, ), (0.55, ), (0.725, ),
(0.9, )],
extra_anchor_scales=[(0.1414, ), (0.2739, ), (0.4541, ),
(0.6315, ), (0.8078, ), (0.9836, )],
anchor_ratios=[(1., 2., .5), (1., 2., 3., .5, 0.3333),
(1., 2., 3., .5, 0.3333), (1., 2., 3., .5, 0.3333),
(1., 2., .5), (1., 2., .5)],
#anchor_ratios = [(2., .5), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., 3., .5, 0.3333), (2., .5), (2., .5)],
layer_steps=[8, 16, 32, 64, 100, 300])
all_anchors, all_num_anchors_depth, all_num_anchors_spatial = anchor_creator.get_all_anchors(
)
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(
allowed_borders=[1.0] * 6,
positive_threshold=None,
ignore_threshold=None,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
decode_fn = lambda pred: anchor_encoder_decoder.ext_decode_all_anchors(
pred, all_anchors, all_num_anchors_depth, all_num_anchors_spatial)
with tf.variable_scope(FLAGS.model_scope,
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
backbone = ssd_net.VGG16Backbone(FLAGS.data_format)
feature_layers = backbone.forward(features, training=False)
location_pred, cls_pred = ssd_net.multibox_head(
feature_layers,
FLAGS.num_classes,
all_num_anchors_depth,
data_format=FLAGS.data_format)
if FLAGS.data_format == 'channels_first':
cls_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in cls_pred
]
location_pred = [
tf.transpose(pred, [0, 2, 3, 1]) for pred in location_pred
]
cls_pred = [
tf.reshape(pred, [-1, FLAGS.num_classes]) for pred in cls_pred
]
location_pred = [
tf.reshape(pred, [-1, 4]) for pred in location_pred
]
cls_pred = tf.concat(cls_pred, axis=0)
location_pred = tf.concat(location_pred, axis=0)
with tf.device('/cpu:0'):
bboxes_pred = decode_fn(location_pred)
bboxes_pred = tf.concat(bboxes_pred, axis=0)
selected_bboxes, selected_scores = parse_by_class(
cls_pred, bboxes_pred, FLAGS.num_classes,
FLAGS.select_threshold, FLAGS.min_size, FLAGS.keep_topk,
FLAGS.nms_topk, FLAGS.nms_threshold)
labels_list = []
scores_list = []
bboxes_list = []
for k, v in selected_scores.items():
labels_list.append(tf.ones_like(v, tf.int32) * k)
scores_list.append(v)
bboxes_list.append(selected_bboxes[k])
all_labels = tf.concat(labels_list, axis=0)
all_scores = tf.concat(scores_list, axis=0)
all_bboxes = tf.concat(bboxes_list, axis=0)
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, get_checkpoint())
for i in range(video_frame_cnt):
ret, img_ori = vid.read()
# height_ori, width_ori = img_ori.shape[:2]
# img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB)
np_image = np.asarray(img, np.float32)
start_time = time.time()
labels_, scores_, bboxes_ = sess.run(
[all_labels, all_scores, all_bboxes],
feed_dict={
image_input: np_image,
shape_input: np_image.shape[:-1]
})
end_time = time.time()
img_to_draw = draw_toolbox.bboxes_draw_on_img(np_image,
labels_,
scores_,
bboxes_,
thickness=2)
cv2.putText(img_to_draw,
'{:.2f}ms'.format((end_time - start_time) * 1000),
(40, 40),
0,
fontScale=1,
color=(0, 255, 0),
thickness=2)
imsave('./test_out.jpg', img_to_draw)
new_img = cv2.imread('./test_out.jpg')
cv2.imshow('image', new_img)
videoWriter.write(new_img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
vid.release()
videoWriter.release()
