def main():
model_file = 'model/ssd300_vgg16_short.pb'
graph = load_graph(model_file)
with tf.Session(graph=graph) as sess:
image_input = sess.graph.get_tensor_by_name(
'import/define_input/image_input:0')
result = sess.graph.get_tensor_by_name("import/result/result:0")
image_path = 'demo/test.jpg'
img = cv2.imread(image_path)
img = np.float32(img)
img = cv2.resize(img, (300, 300))
img = np.expand_dims(img, axis=0)
print('image_input', image_input)
print('img', type(img), img.shape, img[0][1][1])
enc_boxes = sess.run(result, feed_dict={image_input: img})
print('enc_boxes', type(enc_boxes), len(enc_boxes), type(enc_boxes[0]),
enc_boxes[0].shape)
print('detect_result_[0][0]', enc_boxes[0][0])
lid2name = {
0: 'Aeroplane',
1: 'Bicycle',
2: 'Bird',
3: 'Boat',
4: 'Bottle',
5: 'Bus',
6: 'Car',
7: 'Cat',
8: 'Chair',
9: 'Cow',
10: 'Diningtable',
11: 'Dog',
12: 'Horse',
13: 'Motorbike',
14: 'Person',
15: 'Pottedplant',
16: 'Sheep',
17: 'Sofa',
18: 'Train',
19: 'Tvmonitor'
}
preset = get_preset_by_name('vgg300')
anchors = get_anchors_for_preset(preset)
print('anchors', type(anchors))
boxes = decode_boxes(enc_boxes[0], anchors, 0.5, lid2name, None)
boxes = suppress_overlaps(boxes)[:200]
print('boxes', boxes)
img = cv2.imread(image_path)
for box in boxes:
color = (31, 119, 180)
draw_box(img, box[1], color)
box_data = '{} {} {} {} {} {}\n'.format(
box[1].label, box[1].labelid, box[1].center.x, box[1].center.y,
box[1].size.w, box[1].size.h)
print('box_data', box_data)
cv2.imwrite(image_path + '_out.jpg', img)
def main():
#---------------------------------------------------------------------------
# Parse the commandline
#---------------------------------------------------------------------------
parser = argparse.ArgumentParser(description='Process a dataset for SSD')
parser.add_argument('--data-source', default='brats18', help='data source')
parser.add_argument('--data-dir',
default='/data/tng016/brats18',
help='data directory')
parser.add_argument('--validation-fraction',
type=float,
default=0.025,
help='fraction of the data to be used for validation')
parser.add_argument('--expand-probability',
type=float,
default=0.5,
help='probability of running sample expander')
parser.add_argument('--sampler-trials',
type=int,
default=50,
help='number of time a sampler tries to find a sample')
parser.add_argument('--annotate',
type=str2bool,
default='False',
help="Annotate the data samples")
parser.add_argument('--compute-td',
type=str2bool,
default='True',
help="Compute training data")
parser.add_argument('--preset',
default='vgg300',
choices=['vgg300', 'vgg512'],
help="The neural network preset")
parser.add_argument('--process-test',
type=str2bool,
default='False',
help="process the test dataset")
args = parser.parse_args()
print('[i] Data source: ', args.data_source)
print('[i] Data directory: ', args.data_dir)
print('[i] Validation fraction: ', args.validation_fraction)
print('[i] Expand probability: ', args.expand_probability)
print('[i] Sampler trials: ', args.sampler_trials)
print('[i] Annotate: ', args.annotate)
print('[i] Compute training data:', args.compute_td)
print('[i] Preset: ', args.preset)
print('[i] Process test dataset: ', args.process_test)
#---------------------------------------------------------------------------
# Load the data source
#---------------------------------------------------------------------------
print('[i] Configuring the data source...')
#try:
source = load_data_source(args.data_source)
source.load_trainval_data(args.data_dir, args.validation_fraction)
if args.process_test:
source.load_test_data(args.data_dir)
print('[i] # training samples: ', source.num_train)
print('[i] # validation samples: ', source.num_valid)
print('[i] # testing samples: ', source.num_test)
print('[i] # classes: ', source.num_classes)
#except (ImportError, AttributeError, RuntimeError) as e:
# print('[!] Unable to load data source:', str(e))
# return 1
#---------------------------------------------------------------------------
# Annotate samples
#---------------------------------------------------------------------------
if args.annotate:
print('[i] Annotating samples...')
annotate(args.data_dir, source.train_samples, source.colors, 'train')
annotate(args.data_dir, source.valid_samples, source.colors, 'valid')
if args.process_test:
annotate(args.data_dir, source.test_samples, source.colors,
'test ')
#---------------------------------------------------------------------------
# Compute the training data
#---------------------------------------------------------------------------
if args.compute_td:
preset = get_preset_by_name(args.preset)
with open(args.data_dir + '/train-samples.pkl', 'wb') as f:
pickle.dump(source.train_samples, f)
with open(args.data_dir + '/valid-samples.pkl', 'wb') as f:
pickle.dump(source.valid_samples, f)
with open(args.data_dir + '/training-data.pkl', 'wb') as f:
data = {
'preset':
preset,
'num-classes':
source.num_classes,
'colors':
source.colors,
'lid2name':
source.lid2name,
'lname2id':
source.lname2id,
'train-transforms':
build_train_transforms(preset, source.num_classes,
args.sampler_trials,
args.expand_probability),
'valid-transforms':
build_valid_transforms(preset, source.num_classes)
}
pickle.dump(data, f)
return 0
def main():
checkpoint_file = 'model/e25.ckpt'
metagraph_file = checkpoint_file + '.meta'
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
preset = get_preset_by_name('vgg300')
anchors = get_anchors_for_preset(preset)
net = SSDVGG(sess, preset)
net.build_from_metagraph(metagraph_file, checkpoint_file)
#for tensor in tf.get_default_graph().as_graph_def().node: print(tensor.name)
image_path = 'demo/test.jpg'
img = cv2.imread(image_path)
img = np.float32(img)
img = cv2.resize(img, (300, 300))
img = np.expand_dims(img, axis=0)
print('image_input', net.image_input)
print('img', type(img), img.shape, img[0][1][1])
#exit()
enc_boxes = sess.run(net.result, feed_dict={net.image_input: img})
print('enc_boxes', type(enc_boxes), len(enc_boxes), type(enc_boxes[0]),
enc_boxes[0].shape)
lid2name = {
0: 'Aeroplane',
1: 'Bicycle',
2: 'Bird',
3: 'Boat',
4: 'Bottle',
5: 'Bus',
6: 'Car',
7: 'Cat',
8: 'Chair',
9: 'Cow',
10: 'Diningtable',
11: 'Dog',
12: 'Horse',
13: 'Motorbike',
14: 'Person',
15: 'Pottedplant',
16: 'Sheep',
17: 'Sofa',
18: 'Train',
19: 'Tvmonitor'
}
print('anchors', type(anchors))
boxes = decode_boxes(enc_boxes[0], anchors, 0.5, lid2name, None)
boxes = suppress_overlaps(boxes)[:200]
img = cv2.imread(image_path)
for box in boxes:
color = (31, 119, 180)
draw_box(img, box[1], color)
box_data = '{} {} {} {} {} {}\n'.format(
box[1].label, box[1].labelid, box[1].center.x, box[1].center.y,
box[1].size.w, box[1].size.h)
print('box_data', box_data)
cv2.imwrite(image_path + '_out.jpg', img)
def main():
# Parse the commandline
parser = argparse.ArgumentParser(description='Process a dataset for SSD')
parser.add_argument('--data-source',
default='pascal_voc',
help='data source')
parser.add_argument('--data-dir',
default='pascal-voc',
help='data directory')
parser.add_argument('--expand-probability',
type=float,
default=0.5,
help='probability of running sample expander')
parser.add_argument('--sampler-trials',
type=int,
default=50,
help='number of time a sampler tries to find a sample')
parser.add_argument('--preset',
default='vgg300',
choices=['vgg300', 'vgg512'])
args = parser.parse_args()
print('[i] Data source: ', args.data_source)
print('[i] Data directory: ', args.data_dir)
print('[i] Expand probability: ', args.expand_probability)
print('[i] Sampler trials: ', args.sampler_trials)
print('[i] Preset: ', args.preset)
# Load the data source
print('[i] Configuring the data source...')
try:
source = load_data_source(args.data_source)
source.load_trainval_data(args.data_dir)
print('[i] # training samples: ', source.num_train)
print('[i] # validation samples: ', source.num_valid)
print('[i] # classes: ', source.num_classes)
except (ImportError, AttributeError, RuntimeError) as e:
print('[!] Unable to load data source:', str(e))
return 1
# Compute the training data
preset = get_preset_by_name(args.preset)
with open(args.data_dir + '/train-samples.pkl', 'wb') as f:
pickle.dump(source.train_samples, f)
with open(args.data_dir + '/valid-samples.pkl', 'wb') as f:
pickle.dump(source.valid_samples, f)
with open(args.data_dir + '/training-data.pkl', 'wb') as f:
data = {
'preset':
preset,
'num-classes':
source.num_classes,
'colors':
source.colors,
'lid2name':
source.lid2name,
'lname2id':
source.lname2id,
'train-transforms':
build_train_transforms(preset, source.num_classes,
args.sampler_trials,
args.expand_probability),
'valid-transforms':
build_valid_transforms(preset, source.num_classes)
}
pickle.dump(data, f)
return 0
def main():
parser = argparse.ArgumentParser(description='Data prepropcess for SSD')
parser.add_argument('--data_dir',
default='/home/gs/data/VOCdevkit.../VOC2007'
) # KITTI PATH: '/home/gs/data/KITTI'
parser.add_argument('--train_save_dir', default=os.path.dirname(__file__))
parser.add_argument('--preset',
default='vgg300',
choices=['vgg300', 'vgg512'])
parser.add_argument('--data_set', default='VOC', choices=['VOC', 'KITTI'])
parser.add_argument('--sampler_trials', type=int, default=50)
parser.add_argument('--expand_probability', type=float, default=0.5)
args = parser.parse_args()
source = DATASETSource(args.data_set)
source.load_trainval_data(args.data_dir, args.data_set)
preset = get_preset_by_name(args.preset)
with open(os.path.join(args.train_save_dir, 'train-samples.pkl'),
'wb') as fp:
pickle.dump(source.train_samples, fp)
# pickle 모듈을 이용하면 원하는 데이터를 자료형의 변경없이 파일로 저장하여 그대로 로드할 수 있다.
with open(
os.path.join(args.train_save_dir, 'train-details.pkl'), 'wb'
) as fp: #pickle로 데이터를 저장하거나 불러올때는 파일을 바이트형식으로 읽거나 써야한다. (wb, rb)
data = {
'preset':
preset,
'num_classes':
source.num_classes,
'idx2label':
source.idx_to_label,
'label2idx':
source.label_to_idx,
'train_transforms':
build_train_transform(preset, source.num_classes,
args.sampler_trials, args.expand_probability,
source.label_to_idx, args.data_set)
}
pickle.dump(data, fp)
source = DATASETSource(args.data_set)
source.load_validval_data(args.data_dir, args.data_set)
with open(os.path.join(args.train_save_dir, 'valid-samples.pkl'),
'wb') as fp:
pickle.dump(source.valid_samples, fp)
# pickle 모듈을 이용하면 원하는 데이터를 자료형의 변경없이 파일로 저장하여 그대로 로드할 수 있다.
with open(
os.path.join(args.train_save_dir, 'valid-details.pkl'), 'wb'
) as fp: #pickle로 데이터를 저장하거나 불러올때는 파일을 바이트형식으로 읽거나 써야한다. (wb, rb)
data = {
'preset':
preset,
'num_classes':
source.num_classes,
'idx2label':
source.idx_to_label,
'label2idx':
source.label_to_idx,
'valid_transforms':
build_valid_transforms(preset, source.num_classes,
source.label_to_idx, args.data_set)
}
pickle.dump(data, fp)
return 0