def get_imdb(dataset, splitName):
imdbPaths = getImdbPaths(dataset, splitName)
imdb = coco_voc.coco_voc(dataset,
splitName,
image_path=imdbPaths['imageDir'],
captionJsonPath=imdbPaths['jsonPath'])
return imdb
import os
import sg_utils as utils
import coco_voc
import shutil
# Make directories
for i in xrange(60):
utils.mkdir_if_missing(os.path.join('..', 'data', 'images', '{:02d}'.format(i)))
# Copy files over
sets = ['train', 'val', 'test']
for set_ in sets:
imdb = coco_voc.coco_voc(set_)
for i in xrange(imdb.num_images):
in_file = os.path.join('../data', set_ + '2014', \
'COCO_{}2014_{:012d}.jpg'.format(set_, imdb.image_index[i]));
out_file = imdb.image_path_at(i)
# print in_file, out_file
shutil.copyfile(in_file, out_file)
utils.tic_toc_print(1, ' Copying images [{}]: {:06d} / {:06d}\n'.format(set_, i, imdb.num_images));
import os
import sg_utils as utils
import coco_voc
import shutil
# Make directories
for i in xrange(60):
utils.mkdir_if_missing(os.path.join('data', 'images', '{:02d}'.format(i)))
# Copy files over
sets = ['train', 'val', 'test']
for set_ in sets:
imdb = coco_voc.coco_voc(set_)
for i in xrange(imdb.num_images):
in_file = os.path.join(set_ + '2014', \
'COCO_{}2014_{:012d}.jpg'.format(set_, imdb.image_index[i]));
out_file = imdb.image_path_at(i)
# print in_file, out_file
shutil.copyfile(in_file, out_file)
utils.tic_toc_print(1, ' Copying images [{}]: {:06d} / {:06d}\n'.format(set_, i, imdb.num_images));
type=str)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
imdb = coco_voc.coco_voc('test')
vocab = utils.load_variables(args.vocab_file)
gt_label = preprocess.get_vocab_counts(
imdb.image_index,
imdb.coco_caption_data,
5,
vocab
)
det_file = args.det_file
det_dir = os.path.dirname(det_file) # get root dir of det_file
eval_file = os.path.join(det_dir, imdb.name + '_eval.pkl')
benchmark(imdb, vocab, gt_label, 5, det_file, eval_file=eval_file)
map_file = args.map_file
gt_label_det = preprocess.get_vocab_counts_det(
# # set up caffe
caffe.set_mode_gpu()
if args.gpu_id is not None:
caffe.set_device(args.gpu_id)
# Load the vocabulary
vocab = utils.load_variables(args.vocab_file)
if args.task == 'compute_targets':
imdb = []
output_dir = args.train_dir
sets = ['train', 'val']
for i, imset in enumerate([args.train_set, args.val_set]):
imdb.append(coco_voc.coco_voc(imset))
print 'Loaded dataset {:s}'.format(imdb[i].name)
# Compute targets for the file
counts = preprocess.get_vocab_counts(imdb[i].image_index, \
imdb[i].coco_caption_data, 5, vocab)
if args.write_labels:
label_file = os.path.join(output_dir, 'labels_' + sets[i] + '.h5')
print 'Writing labels to {}'.format(label_file)
with h5py.File(label_file, 'w') as f:
for j in xrange(imdb[i].num_images):
ind = imdb[i].image_index[j]
ind_str = '{:02d}/{:d}'.format(int(math.floor(ind)/1e4), ind)
l = f.create_dataset('/labels-{}'.format(ind_str), (1, 1, counts.shape[1], 1), dtype = 'f')
c = counts[j,:].copy(); c = c > 0; c = c.astype(np.float32); c = c.reshape((1, 1, c.size, 1))
def get_imdb(dataset, splitName): imdbPaths = getImdbPaths(dataset, splitName); imdb = coco_voc.coco_voc(dataset, splitName, image_path=imdbPaths['imageDir'], captionJsonPath = imdbPaths['jsonPath']) return imdb;