def visualize_detections_for_sequence(tag, det_boxes, det_scores, det_classes, det_masks, imgs,
add_masks, interactive_visualization, model_str):
if len(imgs) > len(det_boxes):
print("warning, len of imgs and det_boxes does not match", len(imgs), len(det_boxes), file=log.v1)
imgs = imgs[:len(det_boxes)]
assert len(det_boxes) == len(imgs)
for t, (boxes, scores, classes, masks, img) in enumerate(zip(det_boxes, det_scores, det_classes, det_masks, imgs)):
if add_masks:
masks_decoded = [cocomask.decode(m) for m in masks]
else:
masks_decoded = [None for _ in boxes]
if interactive_visualization:
out_filename = None
else:
out_folder = "forwarded/" + model_str + "/vis/detections/" + tag
os.makedirs(out_folder, exist_ok=True)
out_filename = out_folder + "/%06d.jpg" % t
visualize_detections(boxes, classes, masks_decoded, scores, img, None, out_filename)
def visualize_tracks(tag, tracks, imgs, add_masks, interactive_visualization, model_str, box_is_xywh=False):
if len(imgs) > len(tracks):
print("warning, len of imgs and tracks does not match", len(imgs), len(tracks), file=log.v1)
imgs = imgs[:len(tracks)]
assert len(tracks) == len(imgs), (len(tracks), len(imgs))
for t, (track, img) in enumerate(zip(tracks, imgs)):
boxes = [te.box for te in track]
classes = [te.class_ for te in track]
if add_masks:
masks = [cocomask.decode(te.mask) for te in track]
else:
masks = [None for _ in track]
scores = [1.0 for _ in track]
ids = [te.track_id for te in track]
if interactive_visualization:
out_filename = None
else:
out_folder = "forwarded/" + model_str + "/vis/tracks/" + tag
os.makedirs(out_folder, exist_ok=True)
out_filename = out_folder + "/%06d.jpg" % t
visualize_detections(boxes, classes, masks, scores, img, ids, out_filename, box_is_xywh=box_is_xywh)