def test_data_splits(self):
bad_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_UNITTEST)
# Test invalid splits
bad_config.data_split = "bad"
self.assertRaises(ValueError, KittiDataset, bad_config)
# Should be "train"
bad_config.data_split = "training"
self.assertRaises(ValueError, KittiDataset, bad_config)
# Should be "val"
bad_config.data_split = "validation"
self.assertRaises(ValueError, KittiDataset, bad_config)
# Should be "test"
bad_config.data_split = "testing"
self.assertRaises(ValueError, KittiDataset, bad_config)
# Train split
train_dataset = self.get_fake_dataset('train', self.fake_kitti_dir)
self.assertEqual(train_dataset.num_samples, 7)
# Validation split
validation_dataset = self.get_fake_dataset('val', self.fake_kitti_dir)
self.assertEqual(validation_dataset.num_samples, 6)
# Train + validation split
trainval_dataset = self.get_fake_dataset('trainval',
self.fake_kitti_dir)
self.assertEqual(trainval_dataset.num_samples, 13)
# Test split
test_dataset = self.get_fake_dataset('test', self.fake_kitti_dir)
self.assertEqual(test_dataset.num_samples, 10)
def main():
dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_UNITTEST)
dataset_config.data_split = "trainval"
unittest_dataset = DatasetBuilder.build_kitti_dataset(dataset_config)
gen_label_clusters.main(unittest_dataset)
gen_mini_batches.main(unittest_dataset)
def get_fake_dataset(self, data_split, directory):
dataset_config = DatasetBuilder.copy_config(
DatasetBuilder.PANOPTIC_UNITTEST)
# Overwrite config values
dataset_config.data_split = data_split
dataset_config.dataset_dir = directory
dataset = DatasetBuilder.build_panoptic_dataset(dataset_config)
return dataset
def test_project_to_image_space_tensors(self):
anchors = np.asarray([[0, 0, 3, 2, 0, 6], [3, 0, 3, 2, 0, 2]],
dtype=np.float64)
img_idx = int('000217')
img_shape = [375, 1242]
dataset_config = DatasetBuilder.copy_config(
DatasetBuilder.KITTI_UNITTEST)
dataset_config.data_split = 'train'
dataset_config.dataset_dir = tests.test_path() + \
"/datasets/Kitti/object"
dataset = DatasetBuilder().build_kitti_dataset(dataset_config)
stereo_calib_p2 = calib_utils.read_calibration(dataset.calib_dir,
img_idx).p2
# Project the 3D points in numpy space
img_corners, img_corners_norm = anchor_projector.project_to_image_space(
anchors, stereo_calib_p2, img_shape)
# convert the required params to tensors
tf_stereo_calib_p2 = tf.convert_to_tensor(stereo_calib_p2,
dtype=tf.float32)
tf_anchors = tf.convert_to_tensor(anchors, dtype=tf.float32)
tf_img_shape = tf.convert_to_tensor(img_shape, dtype=tf.float32)
# Project the 3D points in tensor space
img_corners_tensor, img_corners_norm_tensor = \
anchor_projector.tf_project_to_image_space(tf_anchors,
tf_stereo_calib_p2,
tf_img_shape)
sess = tf.Session()
with sess.as_default():
img_corners_out = img_corners_tensor.eval()
img_corners_norm_out = img_corners_norm_tensor.eval()
np.testing.assert_allclose(img_corners,
img_corners_out,
atol=1e-04,
err_msg='Incorrect corner projection')
np.testing.assert_allclose(
img_corners_norm,
img_corners_norm_out,
atol=1e-04,
err_msg='Incorrect normalized corner projection')
def setUpClass(cls):
dataset_config = DatasetBuilder.copy_config(
DatasetBuilder.KITTI_UNITTEST)
cls.dataset = DatasetBuilder.build_kitti_dataset(dataset_config)
cls.label_dir = cls.dataset.label_dir
def main():
"""This demo shows RPN proposals and AVOD predictions in 3D
and 2D in image space. Given certain thresholds for proposals
and predictions, it selects and draws the bounding boxes on
the image sample. It goes through the entire proposal and
prediction samples for the given dataset split.
The proposals, overlaid, and prediction images can be toggled on or off
separately in the options section.
The prediction score and IoU with ground truth can be toggled on or off
as well, shown as (score, IoU) above the detection.
"""
dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)
##############################
# Options
##############################
dataset_config = DatasetBuilder.merge_defaults(dataset_config)
dataset_config.data_split = 'val'
fig_size = (10, 6.1)
rpn_score_threshold = 0.1
avod_score_threshold = 0.1
# gt_classes = ['Car']
gt_classes = ['Pedestrian']
# gt_classes = ['Pedestrian', 'Cyclist']
# Overwrite this to select a specific checkpoint
global_step = 115000 # None
checkpoint_name = 'pplp_pedestrian_kitti'
# Drawing Toggles
draw_proposals_separate = False
draw_overlaid = True # To draw both proposal and predcition bounding boxes
draw_predictions_separate = False
# Show orientation for both GT and proposals/predictions
draw_orientations_on_prop = True
draw_orientations_on_pred = True
# Draw 2D bounding boxes
draw_projected_2d_boxes = True
# Draw pointclouds
draw_point_cloud = True
point_cloud_source = 'lidar'
slices_config = \
"""
slices {
height_lo: -1.0 # -0.2
height_hi: 2 # 2.3
num_slices: 1 # 5
}
"""
print('slices_config = ', slices_config)
text_format.Merge(slices_config,
dataset_config.kitti_utils_config.bev_generator)
# Save images for samples with no detections
save_empty_images = False
draw_score = True
draw_iou = True
##############################
# End of Options
##############################
# Get the dataset
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Setup Paths
predictions_dir = pplp.root_dir() + \
'/data/outputs/' + checkpoint_name + '/predictions'
proposals_and_scores_dir = predictions_dir + \
'/proposals_and_scores/' + dataset.data_split
predictions_and_scores_dir = predictions_dir + \
'/final_predictions_and_scores/' + dataset.data_split
# Output images directories
output_dir_base = predictions_dir + '/images_2d'
# Get checkpoint step
steps = os.listdir(proposals_and_scores_dir)
steps.sort(key=int)
print('Available steps: {}'.format(steps))
# Use latest checkpoint if no index provided
if global_step is None:
global_step = steps[-1]
if draw_proposals_separate:
prop_out_dir = output_dir_base + '/proposals/{}/{}/{}'.format(
dataset.data_split, global_step, rpn_score_threshold)
if not os.path.exists(prop_out_dir):
os.makedirs(prop_out_dir)
print('Proposal images saved to:', prop_out_dir)
if draw_overlaid:
overlaid_out_dir = output_dir_base + '/overlaid/{}/{}/{}'.format(
dataset.data_split, global_step, avod_score_threshold)
if not os.path.exists(overlaid_out_dir):
os.makedirs(overlaid_out_dir)
print('Overlaid images saved to:', overlaid_out_dir)
if draw_predictions_separate:
pred_out_dir = output_dir_base + '/predictions/{}/{}/{}'.format(
dataset.data_split, global_step, avod_score_threshold)
if not os.path.exists(pred_out_dir):
os.makedirs(pred_out_dir)
print('Prediction images saved to:', pred_out_dir)
# Rolling average array of times for time estimation
avg_time_arr_length = 10
last_times = np.repeat(time.time(), avg_time_arr_length) + \
np.arange(avg_time_arr_length)
# for sample_idx in range(dataset.num_samples):
for sample_idx in [6]:
# Estimate time remaining with 5 slowest times
start_time = time.time()
last_times = np.roll(last_times, -1)
last_times[-1] = start_time
avg_time = np.mean(np.sort(np.diff(last_times))[-5:])
samples_remaining = dataset.num_samples - sample_idx
est_time_left = avg_time * samples_remaining
# Print progress and time remaining estimate
sys.stdout.write('\rSaving {} / {}, Avg Time: {:.3f}s, '
'Time Remaining: {:.2f}s'.format(
sample_idx + 1, dataset.num_samples, avg_time,
est_time_left))
sys.stdout.flush()
sample_name = dataset.sample_names[sample_idx]
img_idx = int(sample_name)
##############################
# Proposals
##############################
if draw_proposals_separate or draw_overlaid:
# Load proposals from files
proposals_file_path = proposals_and_scores_dir + \
"/{}/{}.txt".format(global_step, sample_name)
if not os.path.exists(proposals_file_path):
print('Sample {}: No proposals, skipping'.format(sample_name))
continue
print('Sample {}: Drawing proposals'.format(sample_name))
proposals_and_scores = np.loadtxt(proposals_file_path)
proposal_boxes_3d = proposals_and_scores[:, 0:7]
proposal_scores = proposals_and_scores[:, 7]
# Apply score mask to proposals
score_mask = proposal_scores > rpn_score_threshold
proposal_boxes_3d = proposal_boxes_3d[score_mask]
proposal_scores = proposal_scores[score_mask]
proposal_objs = \
[box_3d_encoder.box_3d_to_object_label(proposal,
obj_type='Proposal')
for proposal in proposal_boxes_3d]
##############################
# Predictions
##############################
if draw_predictions_separate or draw_overlaid:
predictions_file_path = predictions_and_scores_dir + \
"/{}/{}.txt".format(global_step,
sample_name)
if not os.path.exists(predictions_file_path):
continue
# Load predictions from files
predictions_and_scores = np.loadtxt(
predictions_and_scores_dir +
"/{}/{}.txt".format(global_step, sample_name))
prediction_boxes_3d = predictions_and_scores[:, 0:7]
prediction_scores = predictions_and_scores[:, 7]
prediction_class_indices = predictions_and_scores[:, 8]
# process predictions only if we have any predictions left after
# masking
if len(prediction_boxes_3d) > 0:
# Apply score mask
avod_score_mask = prediction_scores >= avod_score_threshold
prediction_boxes_3d = prediction_boxes_3d[avod_score_mask]
prediction_scores = prediction_scores[avod_score_mask]
prediction_class_indices = \
prediction_class_indices[avod_score_mask]
# # Swap l, w for predictions where w > l
# swapped_indices = \
# prediction_boxes_3d[:, 4] > prediction_boxes_3d[:, 3]
# prediction_boxes_3d = np.copy(prediction_boxes_3d)
# prediction_boxes_3d[swapped_indices, 3] = \
# prediction_boxes_3d[swapped_indices, 4]
# prediction_boxes_3d[swapped_indices, 4] = \
# prediction_boxes_3d[swapped_indices, 3]
##############################
# Ground Truth
##############################
# Get ground truth labels
if dataset.has_labels:
gt_objects = obj_utils.read_labels(dataset.label_dir, img_idx)
else:
gt_objects = []
# Filter objects to desired difficulty
filtered_gt_objs = dataset.kitti_utils.filter_labels(
gt_objects, classes=gt_classes)
boxes2d, _, _ = obj_utils.build_bbs_from_objects(
filtered_gt_objs, class_needed=gt_classes)
image_path = dataset.get_rgb_image_path(sample_name)
image = Image.open(image_path)
image_size = image.size
# Read the stereo calibration matrix for visualization
stereo_calib = calib_utils.read_calibration(dataset.calib_dir, img_idx)
calib_p2 = stereo_calib.p2
##############################
# Reformat and prepare to draw
##############################
if draw_proposals_separate or draw_overlaid:
proposals_as_anchors = box_3d_encoder.box_3d_to_anchor(
proposal_boxes_3d)
proposal_boxes, _ = anchor_projector.project_to_image_space(
proposals_as_anchors, calib_p2, image_size)
num_of_proposals = proposal_boxes_3d.shape[0]
prop_fig, prop_2d_axes, prop_3d_axes = \
vis_utils.visualization(dataset.rgb_image_dir,
img_idx,
display=False)
draw_proposals(filtered_gt_objs, calib_p2, num_of_proposals,
proposal_objs, proposal_boxes, prop_2d_axes,
prop_3d_axes, draw_orientations_on_prop)
if draw_point_cloud:
# Filter the useful pointclouds from all points
kitti_utils = dataset.kitti_utils
x, y, z, i = calib_utils.read_lidar(dataset.velo_dir, img_idx)
point_cloud = np.vstack((x, y, z))
# pts = calib_utils.lidar_to_cam_frame(pts, frame_calib)
# point_cloud = kitti_utils.get_point_cloud(
# point_cloud_source, img_idx, image_size)
point_cloud = kitti_utils.get_point_cloud(
point_cloud_source, img_idx, image_size)
print('point_cloud = ', point_cloud)
ground_plane = kitti_utils.get_ground_plane(sample_name)
all_points = np.transpose(point_cloud)
# print('dataset_config.kitti_utils_config.bev_generator.\
# slices.height_lo = ', dataset_config.kitti_utils_config.bev_generator.slices.height_lo)
height_lo = dataset_config.kitti_utils_config.bev_generator.slices.height_lo
height_hi = dataset_config.kitti_utils_config.bev_generator.slices.height_hi
# config = dataset.config.kitti_utils_config
# print('config = ', config)
area_extents = [[0., 70.], [-40, 40], [-1.1, -1]]
# ?,x,?
area_extents = np.reshape(area_extents, (3, 2))
print('area_extents = ', area_extents)
print('ground_plane = ', ground_plane)
slice_filter = get_point_filter(point_cloud,
area_extents,
ground_plane=None,
offset_dist=2.0)
# slice_filter = kitti_utils.create_slice_filter(
# point_cloud,
# area_extents,
# ground_plane,
# height_lo,
# height_hi)
# Apply slice filter
slice_points = all_points[slice_filter]
print('slice_points = ', slice_points)
# print('slice_points =', slice_points)
# Project the useful pointclouds on 2D image
point_2d = obj_utils.get_lidar_points_on_2D_image(
img_idx, dataset.calib_dir, dataset.velo_dir, image_size,
True, slice_points)
draw_points(prop_2d_axes, point_2d, 'red')
if draw_proposals_separate:
# Save just the proposals
filename = prop_out_dir + '/' + sample_name + '.png'
plt.savefig(filename)
if not draw_overlaid:
plt.close(prop_fig)
if draw_overlaid or draw_predictions_separate:
if len(prediction_boxes_3d) > 0:
# Project the 3D box predictions to image space
image_filter = []
final_boxes_2d = []
for i in range(len(prediction_boxes_3d)):
box_3d = prediction_boxes_3d[i, 0:7]
img_box = box_3d_projector.project_to_image_space(
box_3d,
calib_p2,
truncate=True,
image_size=image_size,
discard_before_truncation=False)
if img_box is not None:
image_filter.append(True)
final_boxes_2d.append(img_box)
else:
image_filter.append(False)
final_boxes_2d = np.asarray(final_boxes_2d)
final_prediction_boxes_3d = prediction_boxes_3d[image_filter]
final_scores = prediction_scores[image_filter]
final_class_indices = prediction_class_indices[image_filter]
num_of_predictions = final_boxes_2d.shape[0]
# Convert to objs
final_prediction_objs = \
[box_3d_encoder.box_3d_to_object_label(
prediction, obj_type='Prediction')
for prediction in final_prediction_boxes_3d]
for (obj, score) in zip(final_prediction_objs, final_scores):
obj.score = score
else:
if save_empty_images:
pred_fig, pred_2d_axes, pred_3d_axes = \
vis_utils.visualization(dataset.rgb_image_dir,
img_idx,
display=False,
fig_size=fig_size)
filename = pred_out_dir + '/' + sample_name + '.png'
plt.savefig(filename)
plt.close(pred_fig)
continue
if draw_overlaid:
# Overlay prediction boxes on image
draw_predictions(filtered_gt_objs, calib_p2,
num_of_predictions, final_prediction_objs,
final_class_indices, final_boxes_2d,
prop_2d_axes, prop_3d_axes, draw_score,
draw_iou, gt_classes,
draw_orientations_on_pred)
filename = overlaid_out_dir + '/' + sample_name + '.png'
plt.savefig(filename)
plt.close(prop_fig)
if draw_predictions_separate:
# Now only draw prediction boxes on images
# on a new figure handler
if draw_projected_2d_boxes:
pred_fig, pred_2d_axes, pred_3d_axes = \
vis_utils.visualization(dataset.rgb_image_dir,
img_idx,
display=False,
fig_size=fig_size)
draw_predictions(filtered_gt_objs, calib_p2,
num_of_predictions, final_prediction_objs,
final_class_indices, final_boxes_2d,
pred_2d_axes, pred_3d_axes, draw_score,
draw_iou, gt_classes,
draw_orientations_on_pred)
else:
pred_fig, pred_3d_axes = \
vis_utils.visualize_single_plot(
dataset.rgb_image_dir, img_idx, display=False)
draw_3d_predictions(filtered_gt_objs, calib_p2,
num_of_predictions,
final_prediction_objs,
final_class_indices, final_boxes_2d,
pred_3d_axes, draw_score, draw_iou,
gt_classes, draw_orientations_on_pred)
filename = pred_out_dir + '/' + sample_name + '.png'
plt.savefig(filename)
plt.close(pred_fig)
print('\nDone')
def main():
"""
Displays the bird's eye view maps for a KITTI sample.
"""
##############################
# Options
##############################
bev_generator = 'slices'
slices_config = \
"""
slices {
height_lo: 0.9 # -0.2
height_hi: 1.0 # 2.3
num_slices: 1 # 5
}
"""
# Use None for a random image
# img_idx = None
img_idx = 571
# img_idx = 191
show_ground_truth = True # Whether to overlay ground_truth boxes
point_cloud_source = 'lidar'
##############################
# End of Options
##############################
dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)
dataset_config = DatasetBuilder.merge_defaults(dataset_config)
# Overwrite bev_generator
if bev_generator == 'slices':
text_format.Merge(slices_config,
dataset_config.kitti_utils_config.bev_generator)
else:
raise ValueError('Invalid bev_generator')
print('dataset_config = ', dataset_config)
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
if img_idx is None:
img_idx = int(random.random() * dataset.num_samples)
sample_name = "{:06}".format(img_idx)
print('=== Showing BEV maps for image: {}.png ==='.format(sample_name))
# Load image
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
image_shape = image.shape[0:2]
kitti_utils = dataset.kitti_utils
point_cloud = kitti_utils.get_point_cloud(point_cloud_source, img_idx,
image_shape)
ground_plane = kitti_utils.get_ground_plane(sample_name)
bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)
height_maps = np.array(bev_images.get("height_maps"))
density_map = np.array(bev_images.get("density_map"))
box_points, box_points_norm = [None, None]
if show_ground_truth:
# Get projected boxes
obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)
filtered_objs = dataset.kitti_utils.filter_labels(
obj_labels, ['Car', 'Pedestrian'])
# filtered_objs = dataset.kitti_utils.filter_labels(
# obj_labels, ['Car', 'Van', 'Pedestrian', 'Cyclist'])
print('filtered_objs = ', filtered_objs)
label_boxes = []
for label in filtered_objs:
box = box_3d_encoder.object_label_to_box_3d(label)
label_boxes.append(box)
label_boxes = np.array(label_boxes)
box_points, box_points_norm = box_3d_projector.project_to_bev(
label_boxes, [[-40, 40], [0, 70]])
rgb_img_size = (np.array((1242, 375)) * 0.75).astype(np.int16)
img_x_start = 60
img_y_start = 330
img_x = img_x_start
img_y = img_y_start
img_w = 400
img_h = 350
img_titlebar_h = 20
# Show images
vis_utils.cv2_show_image("Image",
image,
size_wh=rgb_img_size,
location_xy=(img_x, 0))
# Height maps
for map_idx in range(len(height_maps)):
height_map = height_maps[map_idx]
height_map = draw_boxes(height_map, box_points_norm)
vis_utils.cv2_show_image("Height Map {}".format(map_idx),
height_map,
size_wh=(img_w, img_h),
location_xy=(img_x, img_y))
img_x += img_w
# Wrap around
if (img_x + img_w) > 1920:
img_x = img_x_start
img_y += img_h + img_titlebar_h
# Density map
density_map = draw_boxes(density_map, box_points_norm)
vis_utils.cv2_show_image("Density Map",
density_map,
size_wh=(img_w, img_h),
location_xy=(img_x, img_y))
cv2.waitKey()
def setUpClass(cls):
dataset_config = DatasetBuilder.copy_config(
DatasetBuilder.PANOPTIC_UNITTEST)
cls.dataset = DatasetBuilder.build_panoptic_dataset(dataset_config)
cls.label_dir = cls.dataset.label_dir
def main():
"""
Visualization of 3D grid anchor generation, showing 2D projections
in BEV and image space, and a 3D display of the anchors
"""
dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_TRAIN)
dataset_config.num_clusters[0] = 1
dataset = DatasetBuilder.build_kitti_dataset(dataset_config)
label_cluster_utils = LabelClusterUtils(dataset)
clusters, _ = label_cluster_utils.get_clusters()
# Options
img_idx = 1
# fake_clusters = np.array([[5, 4, 3], [6, 5, 4]])
# fake_clusters = np.array([[3, 3, 3], [4, 4, 4]])
fake_clusters = np.array([[4, 2, 3]])
fake_anchor_stride = [5.0, 5.0]
ground_plane = [0, -1, 0, 1.72]
anchor_3d_generator = grid_anchor_3d_generator.GridAnchor3dGenerator()
area_extents = np.array([[-40, 40], [-5, 5], [0, 70]])
# Generate anchors for cars only
start_time = time.time()
anchor_boxes_3d = anchor_3d_generator.generate(
area_3d=dataset.kitti_utils.area_extents,
anchor_3d_sizes=fake_clusters,
anchor_stride=fake_anchor_stride,
ground_plane=ground_plane)
all_anchors = box_3d_encoder.box_3d_to_anchor(anchor_boxes_3d)
end_time = time.time()
print("Anchors generated in {} s".format(end_time - start_time))
# Project into bev
bev_boxes, bev_normalized_boxes = \
anchor_projector.project_to_bev(all_anchors, area_extents[[0, 2]])
bev_fig, (bev_axes, bev_normalized_axes) = \
plt.subplots(1, 2, figsize=(16, 7))
bev_axes.set_xlim(0, 80)
bev_axes.set_ylim(70, 0)
bev_normalized_axes.set_xlim(0, 1.0)
bev_normalized_axes.set_ylim(1, 0.0)
plt.show(block=False)
for box in bev_boxes:
box_w = box[2] - box[0]
box_h = box[3] - box[1]
rect = patches.Rectangle((box[0], box[1]),
box_w,
box_h,
linewidth=2,
edgecolor='b',
facecolor='none')
bev_axes.add_patch(rect)
for normalized_box in bev_normalized_boxes:
box_w = normalized_box[2] - normalized_box[0]
box_h = normalized_box[3] - normalized_box[1]
rect = patches.Rectangle((normalized_box[0], normalized_box[1]),
box_w,
box_h,
linewidth=2,
edgecolor='b',
facecolor='none')
bev_normalized_axes.add_patch(rect)
rgb_fig, rgb_2d_axes, rgb_3d_axes = \
vis_utils.visualization(dataset.rgb_image_dir, img_idx)
plt.show(block=False)
image_path = dataset.get_rgb_image_path(dataset.sample_names[img_idx])
image_shape = np.array(Image.open(image_path)).shape
stereo_calib_p2 = calib_utils.read_calibration(dataset.calib_dir,
img_idx).p2
start_time = time.time()
rgb_boxes, rgb_normalized_boxes = \
anchor_projector.project_to_image_space(all_anchors,
stereo_calib_p2,
image_shape)
end_time = time.time()
print("Anchors projected in {} s".format(end_time - start_time))
# Read the stereo calibration matrix for visualization
stereo_calib = calib_utils.read_calibration(dataset.calib_dir, 0)
p = stereo_calib.p2
# Overlay boxes on images
for anchor_idx in range(len(anchor_boxes_3d)):
anchor_box_3d = anchor_boxes_3d[anchor_idx]
obj_label = box_3d_encoder.box_3d_to_object_label(anchor_box_3d)
# Draw 3D boxes
vis_utils.draw_box_3d(rgb_3d_axes, obj_label, p)
# Draw 2D boxes
rgb_box_2d = rgb_boxes[anchor_idx]
box_x1 = rgb_box_2d[0]
box_y1 = rgb_box_2d[1]
box_w = rgb_box_2d[2] - box_x1
box_h = rgb_box_2d[3] - box_y1
rect = patches.Rectangle((box_x1, box_y1),
box_w,
box_h,
linewidth=2,
edgecolor='b',
facecolor='none')
rgb_2d_axes.add_patch(rect)
if anchor_idx % 32 == 0:
rgb_fig.canvas.draw()
plt.show(block=True)