def load_data_dir(argv):
gt_dir, pred_dir, mode = process_arguments(argv)
if not os.path.exists(gt_dir):
help('Given GT directory ' + gt_dir + ' does not exist!\n')
if not os.path.exists(pred_dir):
help('Given prediction directory ' + pred_dir + ' does not exist!\n')
filenames = glob.glob(gt_dir + "/*.json")
gtFramesAll = []
prFramesAll = []
for i in range(len(filenames)):
# load each annotation json file
with open(filenames[i]) as data_file:
data = json.load(data_file)
if (not "annolist" in data):
data = convert_videos(data)[0]
gt = data["annolist"]
for imgidx in range(len(gt)):
gt[imgidx]["seq_id"] = i
gt[imgidx]["seq_name"] = os.path.basename(filenames[i]).split('.')[0]
for ridxGT in range(len(gt[imgidx]["annorect"])):
if ("track_id" in gt[imgidx]["annorect"][ridxGT].keys()):
# adjust track_ids to make them unique across all sequences
assert(gt[imgidx]["annorect"][ridxGT]["track_id"][0] < MAX_TRACK_ID)
gt[imgidx]["annorect"][ridxGT]["track_id"][0] += i*MAX_TRACK_ID
gtFramesAll += gt
gtBasename = os.path.basename(filenames[i])
predFilename = pred_dir + gtBasename
if (not os.path.exists(predFilename)):
raise IOError('Prediction file ' + predFilename + ' does not exist')
# load predictions
with open(predFilename) as data_file:
data = json.load(data_file)
if (not "annolist" in data):
data = convert_videos(data)[0]
pr = data["annolist"]
if (len(pr) <> len(gt)):
raise Exception('# prediction frames %d <> # GT frames %d for %s' % (len(pr),len(gt),predFilename))
for imgidx in range(len(pr)):
track_id_frame = []
for ridxPr in range(len(pr[imgidx]["annorect"])):
if ("track_id" in pr[imgidx]["annorect"][ridxPr].keys()):
track_id = pr[imgidx]["annorect"][ridxPr]["track_id"][0]
track_id_frame += [track_id]
# adjust track_ids to make them unique across all sequences
assert(track_id < MAX_TRACK_ID)
pr[imgidx]["annorect"][ridxPr]["track_id"][0] += i*MAX_TRACK_ID
track_id_frame_unique = np.unique(np.array(track_id_frame)).tolist()
if (len(track_id_frame) <> len(track_id_frame_unique)):
raise Exception('Non-unique tracklet IDs found in frame %s of prediction %s' % (pr[imgidx]["image"][0]["name"],predFilename))
prFramesAll += pr
gtFramesAll,prFramesAll = cleanupData(gtFramesAll,prFramesAll)
gtFramesAll,prFramesAll = removeIgnoredPoints(gtFramesAll,prFramesAll)
return gtFramesAll, prFramesAll
def load_data_dir(argv):
gt_dir, pred_dir, mode = process_arguments(argv)
if not os.path.exists(gt_dir):
help('Given GT directory ' + gt_dir + ' does not exist!\n')
if not os.path.exists(pred_dir):
help('Given prediction directory ' + pred_dir + ' does not exist!\n')
filenames = glob.glob(gt_dir + "/*.json")
gtFramesAll = []
prFramesAll = []
for i in range(len(filenames)):
# load each annotation json file
#print('GT:')
with open(filenames[i]) as data_file:
data = json.load(data_file)
if (not "annolist" in data):
data = convert_videos(data)[0]
gt = data["annolist"]
for imgidx in range(len(gt)):
gt[imgidx]["seq_id"] = i
gt[imgidx]["seq_name"] = os.path.basename(
filenames[i]).split('.')[0]
for ridxGT in range(len(gt[imgidx]["annorect"])):
if ("track_id" in gt[imgidx]["annorect"][ridxGT].keys()):
# adjust track_ids to make them unique across all sequences
# print(os.path.basename(filenames[i]).split('.')[0])
# print(gt[imgidx]["annorect"][ridxGT]["track_id"][0])
assert (gt[imgidx]["annorect"][ridxGT]["track_id"][0] <
MAX_TRACK_ID)
gt[imgidx]["annorect"][ridxGT]["track_id"][
0] += i * MAX_TRACK_ID
gtFramesAll += gt
gtBasename = os.path.basename(filenames[i])
predFilename = pred_dir + gtBasename
#print(xy)
if (not os.path.exists(predFilename)):
raise IOError('Prediction file ' + predFilename +
' does not exist')
# load predictions
# print('PRED:')
with open(predFilename) as data_file:
data = json.load(data_file)
if (not "annolist" in data):
data = convert_videos(data)[0]
pr = data["annolist"]
if (len(pr) != len(gt)):
raise Exception('# prediction frames %d != # GT frames %d for %s' %
(len(pr), len(gt), predFilename))
for imgidx in range(len(pr)):
for ridxPr in range(len(pr[imgidx]["annorect"])):
if ("track_id" in pr[imgidx]["annorect"][ridxPr].keys()):
# adjust track_ids to make them unique across all sequences
# print(os.path.basename(filenames[i]).split('.')[0])
# print(pr[imgidx]["annorect"][ridxPr]["track_id"][0])
assert (pr[imgidx]["annorect"][ridxPr]["track_id"][0] <
MAX_TRACK_ID)
pr[imgidx]["annorect"][ridxPr]["track_id"][
0] += i * MAX_TRACK_ID
prFramesAll += pr
# print(xy)
gtFramesAll, prFramesAll = cleanupData(gtFramesAll, prFramesAll)
gtFramesAll, prFramesAll = removeIgnoredPoints(gtFramesAll, prFramesAll)
return gtFramesAll, prFramesAll