def __init__(self, track_groundtruth_file):
frameids, infos = util.read_imagelist(track_groundtruth_file)
frameids = map(int, frameids)
self.objectset = {}
self.id_objects_map = {}
for i in xrange(len(frameids)):
groundbox = GroundBox(frameids[i], infos[i])
if frameids[i] not in self.objectset:
self.objectset[frameids[i]] = []
self.objectset[frameids[i]].append(groundbox)
if groundbox.trackid not in self.id_objects_map:
self.id_objects_map[groundbox.trackid] = []
self.id_objects_map[groundbox.trackid].append(groundbox)
def process(args):
images, labels = util.read_imagelist(args.image_list)
net = caffeutil.get_net_test(args.prototxt, args.model, gpu=args.gpu)
scores = caffeutil.batch_process_images(net, images, output_layers=args.result_layer, resize=args.size,
batch_size=args.batch_size, mean=args.mean, scale=args.scale,
return_format='origin')
# Write the result in bag.
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
print 'result:',args.output_dir
scoreoutfile = open(args.scorefile + '.npz', 'wb')
pickle.dump(images, scoreoutfile, True)
pickle.dump(scores, scoreoutfile, True)
pickle.dump(labels, scoreoutfile, True)
scoreoutfile.close()
def read_label_name(filename):
labelfile = filename
labels = []
sm_num = []
if filename is None:
for i in range(200):
labels.append('label' + str(i))
sm_num.append(2)
else:
labels, o_sm_num = util.read_imagelist(filename)
o_sm_num = util.format_labels_todigits(o_sm_num)
for sn in o_sm_num:
if len(sn) == 0:
sm_num.append(2)
else:
sm_num.append(sn[0])
return labels, sm_num
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('dataset_id')
parser.add_argument('--origin', action='store_true')
args = parser.parse_args()
dataset_num = args.dataset_id
print 'dataset_num: ', dataset_num
if args.origin:
track_file = 'origin_track_n' + dataset_num + '.log'
else:
track_file = 'track_n' + dataset_num + '.txt'
tracksystem = GroundBoxSystem(track_file)
ground_file = '12groundtruth/n' + dataset_num + '.txt'
groundsystem = GroundBoxSystem(ground_file)
pictures, _ = util.read_imagelist('images_all/n' + dataset_num + '.list')
trackframes = tracksystem.objectset.keys()
trackframes = sorted(trackframes)
trackids = tracksystem.id_objects_map.keys()
# In[38]:
mcount, scount = 0, 0
keepd_ground_box = set()
location_count = 0
location_set = []
track_data = []
track_data_index = AutoListMap()
track_data_location_index = {}