def visualize(model, model_path, nr_visualize=100, output_dir='output'):
"""
Visualize some intermediate results (proposals, raw predictions) inside the pipeline.
"""
df = get_train_dataflow() # we don't visualize mask stuff
df.reset_state()
pred = OfflinePredictor(PredictConfig(
model=model,
session_init=get_model_loader(model_path),
input_names=['image', 'gt_boxes', 'gt_labels'],
output_names=[
'generate_{}_proposals/boxes'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals/scores'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_scores',
'output/boxes',
'output/scores',
'output/labels',
]))
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
utils.fs.mkdir_p(output_dir)
with tqdm.tqdm(total=nr_visualize) as pbar:
for idx, dp in itertools.islice(enumerate(df), nr_visualize):
img, gt_boxes, gt_labels = dp['image'], dp['gt_boxes'], dp['gt_labels']
rpn_boxes, rpn_scores, all_scores, \
final_boxes, final_scores, final_labels = pred(img, gt_boxes, gt_labels)
# draw groundtruth boxes
gt_viz = draw_annotation(img, gt_boxes, gt_labels)
# draw best proposals for each groundtruth, to show recall
proposal_viz, good_proposals_ind = draw_proposal_recall(img, rpn_boxes, rpn_scores, gt_boxes)
# draw the scores for the above proposals
score_viz = draw_predictions(img, rpn_boxes[good_proposals_ind], all_scores[good_proposals_ind])
results = [DetectionResult(*args) for args in
zip(final_boxes, final_scores, final_labels,
[None] * len(final_labels))]
final_viz = draw_final_outputs(img, results)
viz = tpviz.stack_patches([
gt_viz, proposal_viz,
score_viz, final_viz], 2, 2)
if os.environ.get('DISPLAY', None):
tpviz.interactive_imshow(viz)
cv2.imwrite("{}/{:03d}.png".format(output_dir, idx), viz)
pbar.update()
def draw_outputs(img, final_boxes, final_scores, final_labels, threshold=0.8):
"""
Args:
results: [DetectionResult]
"""
results = [DetectionResult(*args) for args in
zip(final_boxes, final_scores, final_labels,
[None] * len(final_labels)) if args[1]>threshold]
if len(results) == 0:
return img
tags = []
for r in results:
tags.append(
"{},{:.2f}".format(cfg.DATA.CLASS_NAMES[r.class_id], r.score))
boxes = np.asarray([r.box for r in results])
ret = viz.draw_boxes(img, boxes, tags)
for r in results:
if r.mask is not None:
ret = draw_mask(ret, r.mask)
return ret
def do_sanity_check(pred_func,
output_dir='/root/dentalpoc/logs/xxxxx',
font_rs=10,
thickness_rs=10):
# num_tower = max(cfg.TRAIN.NUM_GPUS, 1)
# graph_funcs = MultiTowerOfflinePredictor(
# pred_config, list(range(num_tower))).get_predictors()
os.makedirs(output_dir, exist_ok=True)
for dataset in cfg.DATA.VAL:
logger.info("sanity checking {} ...".format(dataset))
# dataflows = [
# get_eval_dataflow(dataset, shard=k, num_shards=num_tower, add_gt=True)
# for k in range(num_tower)]
# all_results = multithread_predict_dataflow(dataflows, graph_funcs)
coco_format_detection = DatasetRegistry.get(dataset)
coco_object = coco_format_detection.coco
for _im_id, _img_dic in list(coco_object.imgs.items())[1:]:
_img_path = _img_dic['path']
_img_seg_polygons = coco_object.imgToAnns[_im_id]
detection_ground_truths = list(
map(
lambda x: DetectionResult(
box=convert_box_mode_xywh_2_xyxy(x['bbox']),
score=1.0,
class_id=x['category_id'],
mask=coco_object.annToMask(x)), _img_seg_polygons))
print("S======check")
_predict_with_gt(pred_func=pred_func,
input_file=_img_path,
ground_truths=detection_ground_truths,
output_dir=output_dir,
font_rs=font_rs,
thickness_rs=thickness_rs)
xxx = 0
