def process_sequence(seq,
detections_path,
add_masks,
tracker_options,
optical_flow_path,
temp_out,
n_timesteps=None,
start_time_at_1=False):
assert n_timesteps is not None
#n_timesteps = TIMESTEPS_PER_SEQ[seq]
det_boxes, det_scores, reid_features, det_classes, det_masks = \
import_detections_for_sequence(seq, n_timesteps, detections_path, "", 0, add_masks)
if tracker_options["mask_iou_weight_car"] > 0.0 or \
tracker_options["mask_iou_weight_pedestrian"] > 0.0 or \
tracker_options["bbox_iou_weight_car"] > 0.0 or \
tracker_options["bbox_iou_weight_pedestrian"] > 0.0:
optical_flow = load_optical_flow(seq, optical_flow_path)
else:
optical_flow = None
hyp_tracks = track_single_sequence(tracker_options,
det_boxes,
det_scores,
reid_features,
det_classes,
det_masks,
optical_flow=optical_flow)
hyp_tracks = make_disjoint(hyp_tracks, "score")
export_tracking_result_in_kitti_format(seq,
hyp_tracks,
add_masks,
"",
temp_out,
start_time_at_1=start_time_at_1)
def forward(self):
out_folder = "forwarded/" + self.model_name + "/tracking_data_bbox_refined/"
tf.gfile.MakeDirs(out_folder)
for n in range(21):
open(out_folder + "%04d" % n + ".txt", "w")
data = self.val_data
n_examples_per_epoch = data.n_examples_per_epoch()
extraction_keys = [Extractions.DET_MASKS, DataKeys.IMAGE_FILENAMES, DataKeys.IDS]
tracks = {} # tag -> list of lists of trackelems
for n in range(n_examples_per_epoch):
res = self.trainer.validation_step(extraction_keys=extraction_keys)
masks = res[Extractions.EXTRACTIONS][Extractions.DET_MASKS][0][0]
#if len(masks) > 0:
# import matplotlib.pyplot as plt
# for mask in masks:
# plt.imshow(mask)
# plt.show()
filename = res[Extractions.EXTRACTIONS][DataKeys.IMAGE_FILENAMES][0][0].decode("utf-8")
sp = filename.split("/")
seq = sp[-2]
t = int(sp[-1].replace(".png", "").replace(".jpg", ""))
ids = res[Extractions.EXTRACTIONS][DataKeys.IDS][0][0]
masks_encoded = [cocomask.encode(np.asfortranarray(mask)) for mask in masks]
if seq not in tracks:
tracks[seq] = []
while len(tracks[seq]) < t + 1:
tracks[seq].append([])
assert len(masks_encoded) == len(ids)
for id_, mask_ in zip(ids, masks_encoded):
x0, y0, w, h = cocomask.toBbox(mask_)
box = [x0, y0, x0 + w, y0 + h]
obj = data.tracking_result[seq][id_][t]
class_str = obj.class_
if class_str == "Car":
class_id = 1
elif class_str == "Pedestrian":
class_id = 2
else:
assert False, ("unknown class str", class_str)
score = obj.score
tracks[seq][t].append(TrackElement(box=box, track_id=id_, class_=class_id, score=score, mask=mask_))
print(n, "/", n_examples_per_epoch, masks.shape, filename, ids)
for seq in tracks.keys():
tracks[seq] = make_disjoint(tracks[seq], self.mask_disjoint_strategy)
# write out data
export_tracking_result_in_kitti_format(seq, tracks[seq], True, self.config.string("model"), out_folder)
def make_disjoint_helper(self, tracks):
return make_disjoint(tracks, self.mask_disjoint_strategy)