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/probs'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_probs',
'final_boxes',
'final_probs',
'final_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.get_data()),
nr_visualize):
img = dp[0]
if cfg.MODE_MASK:
gt_boxes, gt_labels, gt_masks = dp[-3:]
else:
gt_boxes, gt_labels = dp[-2:]
rpn_boxes, rpn_scores, all_probs, \
final_boxes, final_probs, 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_probs[good_proposals_ind])
results = [
DetectionResult(*args)
for args in zip(final_boxes, final_probs, 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 visualize(model_path, nr_visualize=50, output_dir='output'):
df = get_train_dataflow_coco() # 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_rpn_proposals/boxes',
'generate_rpn_proposals/probs',
'fastrcnn_all_probs',
'final_boxes',
'final_probs',
'final_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.get_data()),
nr_visualize):
img, _, _, gt_boxes, gt_labels = dp
rpn_boxes, rpn_scores, all_probs, \
final_boxes, final_probs, 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_probs[good_proposals_ind])
if config.USE_SECOND_HEAD:
results = [
SecondDetectionResult(*args)
for args in zip(final_boxes, final_probs, final_labels,
[None] * len(final_labels))
]
else:
results = [
DetectionResult(*args)
for args in zip(final_boxes, final_probs, 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 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 = dp[0]
if cfg.MODE_MASK:
gt_boxes, gt_labels, gt_masks = dp[-3:]
else:
gt_boxes, gt_labels = dp[-2:]
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 visualize(model_path, nr_visualize=50, output_dir='output'):
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(model_path),
input_names=['image', 'gt_boxes', 'gt_labels'],
output_names=[
'generate_rpn_proposals/boxes',
'generate_rpn_proposals/probs',
'fastrcnn_all_probs',
'fastrcnn_fg_probs',
'fastrcnn_fg_boxes',
]))
df = get_train_dataflow()
df.reset_state()
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.get_data()),
nr_visualize):
img, _, _, gt_boxes, gt_labels = dp
rpn_boxes, rpn_scores, all_probs, fg_probs, fg_boxes = pred(
img, gt_boxes, gt_labels)
gt_viz = draw_annotation(img, gt_boxes, gt_labels)
proposal_viz, good_proposals_ind = draw_proposal_recall(
img, rpn_boxes, rpn_scores, gt_boxes)
score_viz = draw_predictions(img, rpn_boxes[good_proposals_ind],
all_probs[good_proposals_ind])
fg_boxes = clip_boxes(fg_boxes, img.shape[:2])
fg_viz = draw_predictions(img, fg_boxes, fg_probs)
results = nms_fastrcnn_results(fg_boxes, fg_probs)
final_viz = draw_final_outputs(img, results)
viz = tpviz.stack_patches(
[gt_viz, proposal_viz, score_viz, fg_viz, final_viz], 2, 3)
if os.environ.get('DISPLAY', None):
tpviz.interactive_imshow(viz)
cv2.imwrite("{}/{:03d}.png".format(output_dir, idx), viz)
pbar.update()
def do_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=['images', 'orig_image_dims', 'gt_boxes', 'gt_labels'],
output_names=[
'generate_{}_proposals_topk_per_image/boxes'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals_topk_per_image/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['images'], dp['gt_boxes'], dp[
'gt_labels']
orig_shape = img.shape[:2]
rpn_boxes, rpn_scores, all_scores, \
final_boxes, final_scores, final_labels = pred(np.expand_dims(img, axis=0),
np.expand_dims(np.array(img.shape), axis=0),
np.expand_dims(gt_boxes, axis=0),
np.expand_dims(gt_labels, axis=0))
# draw groundtruth boxes
gt_viz = draw_annotation(img, gt_boxes, gt_labels)
# draw best proposals for each groundtruth, to show recall
# custom op creates different shape for boxes, convert back to original
rpn_boxes = np.array([i[1:] for i in rpn_boxes])
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()
