def main():
"""
Visualization of the mini batch anchors for RpnModel training.
Keys:
F1: Toggle mini batch anchors
F2: Toggle positive/negative proposal anchors
F3: Toggle easy ground truth objects (Green)
F4: Toggle medium ground truth objects (Orange)
F5: Toggle hard ground truth objects (Red)
F6: Toggle all ground truth objects (default off)
F7: Toggle ground-plane
"""
anchor_colour_scheme = {
"Car": (255, 0, 0), # Red
"Pedestrian": (255, 150, 50), # Orange
"Cyclist": (150, 50, 100), # Purple
"DontCare": (255, 255, 255), # White
"Anchor": (150, 150, 150), # Gray
"Positive": (0, 255, 255), # Teal
"Negative": (255, 0, 255) # Bright Purple
}
##############################
# Options
##############################
show_orientations = True
# Classes name
config_name = 'car'
# config_name = 'ped'
# config_name = 'cyc'
# config_name = 'ppl'
# # # Random sample # # #
sample_name = None
# Small cars
# sample_name = '000008'
# sample_name = '000639'
# # # Cars # # #
# sample_name = "000001"
# sample_name = "000050"
# sample_name = "000112"
# sample_name = "000169"
# sample_name = "000191"
# # # People # # #
# sample_name = '000000'
# val_half
# sample_name = '000001' # Hard, 1 far cyc
# sample_name = '000005' # Easy, 1 ped
# sample_name = '000122' # Easy, 1 cyc
# sample_name = '000134' # Hard, lots of people
# sample_name = '000167' # Medium, 1 ped, 2 cycs
# sample_name = '000187' # Medium, 1 ped on left
# sample_name = '000381' # Easy, 1 ped
# sample_name = '000398' # Easy, 1 ped
# sample_name = '000401' # Hard, obscured peds
# sample_name = '000407' # Easy, 1 ped
sample_name = '000448' # Hard, several far people
# sample_name = '000486' # Hard 2 obscured peds
# sample_name = '000509' # Easy, 1 ped
# sample_name = '000718' # Hard, lots of people
# sample_name = '002216' # Easy, 1 cyc
# sample_name = "000000"
# sample_name = "000011"
# sample_name = "000015"
# sample_name = "000028"
# sample_name = "000035"
# sample_name = "000134"
# sample_name = "000167"
# sample_name = '000379'
# sample_name = '000381'
# sample_name = '000397'
# sample_name = '000398'
# sample_name = '000401'
# sample_name = '000407'
# sample_name = '000486'
# sample_name = '000509'
# # Cyclists # # #
# sample_name = '000122'
# sample_name = '000448'
# # # Multiple classes # # #
# sample_name = "000764"
##############################
# End of Options
##############################
# Dataset config
dataset_config_path = mlod.top_dir() + \
'/demos/configs/mb_rpn_{}.config'.format(config_name)
# Create Dataset
dataset = DatasetBuilder.load_dataset_from_config(
dataset_config_path)
# Random sample
if sample_name is None:
sample_idx = np.random.randint(0, dataset.num_samples)
sample_name = dataset.sample_list[sample_idx].name
anchor_strides = dataset.kitti_utils.anchor_strides
img_idx = int(sample_name)
print("Showing mini batch for sample {}".format(sample_name))
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
image_shape = [image.shape[1], image.shape[0]]
# KittiUtils class
dataset_utils = dataset.kitti_utils
ground_plane = obj_utils.get_road_plane(img_idx, dataset.planes_dir)
point_cloud = obj_utils.get_depth_map_point_cloud(img_idx,
dataset.calib_dir,
dataset.depth_dir,
image_shape)
points = point_cloud.T
point_colours = vis_utils.project_img_to_point_cloud(points, image,
dataset.calib_dir,
img_idx)
clusters, _ = dataset.get_cluster_info()
anchor_generator = grid_anchor_3d_generator.GridAnchor3dGenerator()
# Read mini batch info
anchors_info = dataset_utils.get_anchors_info(
dataset.classes_name, anchor_strides, sample_name)
if not anchors_info:
# Exit early if anchors_info is empty
print("Anchors info is empty, please try a different sample")
return
# Generate anchors for all classes
all_anchor_boxes_3d = []
for class_idx in range(len(dataset.classes)):
anchor_boxes_3d = anchor_generator.generate(
area_3d=dataset.kitti_utils.area_extents,
anchor_3d_sizes=clusters[class_idx],
anchor_stride=anchor_strides[class_idx],
ground_plane=ground_plane)
all_anchor_boxes_3d.extend(anchor_boxes_3d)
all_anchor_boxes_3d = np.asarray(all_anchor_boxes_3d)
# Use anchors info
indices, ious, offsets, classes = anchors_info
# Get non empty anchors from the indices
anchor_boxes_3d = all_anchor_boxes_3d[indices]
# Sample an RPN mini batch from the non empty anchors
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
mb_mask_tf, _ = mini_batch_utils.sample_rpn_mini_batch(ious)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
mb_mask = sess.run(mb_mask_tf)
mb_anchor_boxes_3d = anchor_boxes_3d[mb_mask]
mb_anchor_ious = ious[mb_mask]
# ObjectLabel list that hold all boxes to visualize
obj_list = []
num_positives = 0
# Convert the mini_batch anchors to object list
mini_batch_size = mini_batch_utils.rpn_mini_batch_size
for i in range(mini_batch_size):
if mb_anchor_ious[i] > mini_batch_utils.rpn_pos_iou_range[0]:
obj_type = "Positive"
num_positives += 1
else:
obj_type = "Negative"
obj = box_3d_encoder.box_3d_to_object_label(mb_anchor_boxes_3d[i],
obj_type)
obj_list.append(obj)
print('Num positives', num_positives)
# Convert all non-empty anchors to object list
non_empty_anchor_objs = \
[box_3d_encoder.box_3d_to_object_label(
anchor_box_3d, obj_type='Anchor')
for anchor_box_3d in anchor_boxes_3d]
##############################
# Ground Truth
##############################
if dataset.has_labels:
easy_gt_objs, medium_gt_objs, \
hard_gt_objs, all_gt_objs = demo_utils.get_gts_based_on_difficulty(
dataset, img_idx)
else:
easy_gt_objs = medium_gt_objs = hard_gt_objs = all_gt_objs = []
# Visualize 2D image
vis_utils.visualization(dataset.rgb_image_dir, img_idx)
plt.show(block=False)
# Create VtkAxes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# Create VtkBoxes for mini batch anchors
vtk_pos_anchor_boxes = VtkBoxes()
vtk_pos_anchor_boxes.set_objects(obj_list, anchor_colour_scheme)
# VtkBoxes for non empty anchors
vtk_non_empty_anchors = VtkBoxes()
vtk_non_empty_anchors.set_objects(non_empty_anchor_objs,
anchor_colour_scheme)
vtk_non_empty_anchors.set_line_width(0.1)
# Create VtkBoxes for ground truth
vtk_easy_gt_boxes, vtk_medium_gt_boxes, \
vtk_hard_gt_boxes, vtk_all_gt_boxes = \
demo_utils.create_gt_vtk_boxes(easy_gt_objs,
medium_gt_objs,
hard_gt_objs,
all_gt_objs,
show_orientations)
vtk_point_cloud = VtkPointCloud()
vtk_point_cloud.set_points(points, point_colours)
vtk_point_cloud.vtk_actor.GetProperty().SetPointSize(2)
vtk_ground_plane = VtkGroundPlane()
vtk_ground_plane.set_plane(ground_plane, dataset.kitti_utils.bev_extents)
# vtk_voxel_grid = VtkVoxelGrid()
# vtk_voxel_grid.set_voxels(vx_grid)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_ground_plane.vtk_actor)
vtk_renderer.AddActor(vtk_hard_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_medium_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_easy_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_all_gt_boxes.vtk_actor)
# vtk_renderer.AddActor(vtk_voxel_grid.vtk_actor)
vtk_renderer.AddActor(vtk_non_empty_anchors.vtk_actor)
vtk_renderer.AddActor(vtk_pos_anchor_boxes.vtk_actor)
vtk_renderer.AddActor(axes)
vtk_renderer.SetBackground(0.2, 0.3, 0.4)
# Setup Camera
current_cam = vtk_renderer.GetActiveCamera()
current_cam.Pitch(160.0)
current_cam.Roll(180.0)
# Zooms out to fit all points on screen
vtk_renderer.ResetCamera()
# Zoom in slightly
current_cam.Zoom(2.5)
# Reset the clipping range to show all points
vtk_renderer.ResetCameraClippingRange()
# Setup Render Window
vtk_render_window = vtk.vtkRenderWindow()
mb_iou_thresholds = np.round(
[mini_batch_utils.rpn_neg_iou_range[1],
mini_batch_utils.rpn_pos_iou_range[0]], 3)
vtk_render_window.SetWindowName(
'Sample {} RPN Mini Batch {}/{}, '
'Num Positives {}'.format(
sample_name,
mb_iou_thresholds[0],
mb_iou_thresholds[1],
num_positives))
vtk_render_window.SetSize(900, 500)
vtk_render_window.AddRenderer(vtk_renderer)
# Setup custom interactor style, which handles mouse and key events
vtk_render_window_interactor = vtk.vtkRenderWindowInteractor()
vtk_render_window_interactor.SetRenderWindow(vtk_render_window)
vtk_render_window_interactor.SetInteractorStyle(
vis_utils.ToggleActorsInteractorStyle([
vtk_non_empty_anchors.vtk_actor,
vtk_pos_anchor_boxes.vtk_actor,
vtk_easy_gt_boxes.vtk_actor,
vtk_medium_gt_boxes.vtk_actor,
vtk_hard_gt_boxes.vtk_actor,
vtk_all_gt_boxes.vtk_actor,
vtk_ground_plane.vtk_actor
]))
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start()
def main(dataset=None):
"""Generates anchors info which is used for mini batch sampling.
Processing on 'Cars' can be split into multiple processes, see the Options
section for configuration.
Args:
dataset: KittiDataset (optional)
If dataset is provided, only generate info for that dataset.
If no dataset provided, generates info for all 3 classes.
"""
if dataset is not None:
do_preprocessing(dataset, None)
return
car_dataset_config_path = mlod.root_dir() + \
'/configs/mb_preprocessing/rpn_cars.config'
ped_dataset_config_path = mlod.root_dir() + \
'/configs/mb_preprocessing/rpn_pedestrians.config'
cyc_dataset_config_path = mlod.root_dir() + \
'/configs/mb_preprocessing/rpn_cyclists.config'
ppl_dataset_config_path = mlod.root_dir() + \
'/configs/mb_preprocessing/rpn_people.config'
##############################
# Options
##############################
# Serial vs parallel processing
in_parallel = True
process_car = True # Cars
process_ped = False # Pedestrians
process_cyc = False # Cyclists
process_ppl = True # People (Pedestrians + Cyclists)
# Number of child processes to fork, samples will
# be divided evenly amongst the processes (in_parallel must be True)
num_car_children = 8
num_ped_children = 8
num_cyc_children = 8
num_ppl_children = 8
##############################
# Dataset setup
##############################
if process_car:
car_dataset = DatasetBuilder.load_dataset_from_config(
car_dataset_config_path)
if process_ped:
ped_dataset = DatasetBuilder.load_dataset_from_config(
ped_dataset_config_path)
if process_cyc:
cyc_dataset = DatasetBuilder.load_dataset_from_config(
cyc_dataset_config_path)
if process_ppl:
ppl_dataset = DatasetBuilder.load_dataset_from_config(
ppl_dataset_config_path)
##############################
# Serial Processing
##############################
if not in_parallel:
if process_car:
do_preprocessing(car_dataset, None)
if process_ped:
do_preprocessing(ped_dataset, None)
if process_cyc:
do_preprocessing(cyc_dataset, None)
if process_ppl:
do_preprocessing(ppl_dataset, None)
print('All Done (Serial)')
##############################
# Parallel Processing
##############################
else:
# List of all child pids to wait on
all_child_pids = []
# Cars
if process_car:
car_indices_split = split_indices(car_dataset, num_car_children)
split_work(all_child_pids, car_dataset, car_indices_split,
num_car_children)
# Pedestrians
if process_ped:
ped_indices_split = split_indices(ped_dataset, num_ped_children)
split_work(all_child_pids, ped_dataset, ped_indices_split,
num_ped_children)
# Cyclists
if process_cyc:
cyc_indices_split = split_indices(cyc_dataset, num_cyc_children)
split_work(all_child_pids, cyc_dataset, cyc_indices_split,
num_cyc_children)
# People (Pedestrians + Cyclists)
if process_ppl:
ppl_indices_split = split_indices(ppl_dataset, num_ppl_children)
split_work(all_child_pids, ppl_dataset, ppl_indices_split,
num_ppl_children)
# Wait to child processes to finish
print('num children:', len(all_child_pids))
for i, child_pid in enumerate(all_child_pids):
os.waitpid(child_pid, 0)
print('All Done (Parallel)')
def main():
# Create Dataset
dataset_config_path = mlod.root_dir() + \
'/configs/mb_preprocessing/rpn_cars.config'
dataset = DatasetBuilder.load_dataset_from_config(dataset_config_path)
# Random sample
sample_name = '000169'
anchor_strides = dataset.kitti_utils.anchor_strides
img_idx = int(sample_name)
print("Showing mini batch for sample {}".format(sample_name))
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
image_shape = [image.shape[1], image.shape[0]]
# KittiUtils class
dataset_utils = dataset.kitti_utils
ground_plane = obj_utils.get_road_plane(img_idx, dataset.planes_dir)
point_cloud = obj_utils.get_depth_map_point_cloud(img_idx,
dataset.calib_dir,
dataset.depth_dir,
image_shape)
# Grab ground truth
ground_truth_list = obj_utils.read_labels(dataset.label_dir, img_idx)
ground_truth_list = dataset_utils.filter_labels(ground_truth_list)
stereo_calib_p2 = calib_utils.read_calibration(dataset.calib_dir,
img_idx).p2
##############################
# Flip sample info
##############################
start_time = time.time()
flipped_image = kitti_aug.flip_image(image)
flipped_point_cloud = kitti_aug.flip_point_cloud(point_cloud)
flipped_gt_list = [kitti_aug.flip_label_in_3d_only(obj)
for obj in ground_truth_list]
flipped_ground_plane = kitti_aug.flip_ground_plane(ground_plane)
flipped_calib_p2 = kitti_aug.flip_stereo_calib_p2(
stereo_calib_p2, image_shape)
flipped_points = flipped_point_cloud.T
print('flip sample', time.time() - start_time)
##############################
# Generate anchors
##############################
clusters, _ = dataset.get_cluster_info()
anchor_generator = grid_anchor_3d_generator.GridAnchor3dGenerator()
# Read mini batch info
anchors_info = dataset_utils.get_anchors_info(
dataset.classes_name,
anchor_strides,
sample_name)
all_anchor_boxes_3d = []
all_ious = []
for class_idx in range(len(dataset.classes)):
anchor_boxes_3d = anchor_generator.generate(
area_3d=dataset.kitti_utils.area_extents,
anchor_3d_sizes=clusters[class_idx],
anchor_stride=anchor_strides[class_idx],
ground_plane=ground_plane)
if anchors_info:
indices, ious, offsets, classes = anchors_info
# Get non empty anchors from the indices
non_empty_anchor_boxes_3d = anchor_boxes_3d[indices]
all_anchor_boxes_3d.extend(non_empty_anchor_boxes_3d)
all_ious.extend(ious)
if not len(all_anchor_boxes_3d) > 0:
# Exit early if anchors_info is empty
print("No anchors, Please try a different sample")
return
# Convert to ndarrays
all_anchor_boxes_3d = np.asarray(all_anchor_boxes_3d)
all_ious = np.asarray(all_ious)
##############################
# Flip anchors
##############################
start_time = time.time()
flipped_anchor_boxes_3d = kitti_aug.flip_boxes_3d(all_anchor_boxes_3d,
flip_ry=False)
print('flip anchors', time.time() - start_time)
# Overwrite with flipped things
all_anchor_boxes_3d = flipped_anchor_boxes_3d
points = flipped_points
ground_truth_list = flipped_gt_list
ground_plane = flipped_ground_plane
def main():
"""Flip RPN Mini Batch
Visualization of the mini batch anchors for RpnModel training.
Keys:
F1: Toggle mini batch anchors
F2: Flipped
"""
anchor_colour_scheme = {
"Car": (255, 0, 0), # Red
"Pedestrian": (255, 150, 50), # Orange
"Cyclist": (150, 50, 100), # Purple
"DontCare": (255, 255, 255), # White
"Anchor": (150, 150, 150), # Gray
"Regressed Anchor": (255, 255, 0), # Yellow
"Positive": (0, 255, 255), # Teal
"Negative": (255, 0, 255) # Purple
}
dataset_config_path = mlod.root_dir() + \
'/configs/mb_rpn_demo_cars.config'
# dataset_config_path = mlod.root_dir() + \
# '/configs/mb_rpn_demo_people.config'
##############################
# Options
##############################
# # # Random sample # # #
sample_name = None
# # # Cars # # #
# sample_name = "000001"
# sample_name = "000050"
# sample_name = "000104"
# sample_name = "000112"
# sample_name = "000169"
# sample_name = "000191"
sample_name = "003801"
# # # Pedestrians # # #
# sample_name = "000000"
# sample_name = "000011"
# sample_name = "000015"
# sample_name = "000028"
# sample_name = "000035"
# sample_name = "000134"
# sample_name = "000167"
# sample_name = '000379'
# sample_name = '000381'
# sample_name = '000397'
# sample_name = '000398'
# sample_name = '000401'
# sample_name = '000407'
# sample_name = '000486'
# sample_name = '000509'
# # Cyclists # # #
# sample_name = '000122'
# sample_name = '000448'
# # # Multiple classes # # #
# sample_name = "000764"
##############################
# End of Options
##############################
# Create Dataset
dataset = DatasetBuilder.load_dataset_from_config(dataset_config_path)
# Random sample
if sample_name is None:
sample_idx = np.random.randint(0, dataset.num_samples)
sample_name = dataset.sample_list[sample_idx]
anchor_strides = dataset.kitti_utils.anchor_strides
img_idx = int(sample_name)
print("Showing mini batch for sample {}".format(sample_name))
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
image_shape = [image.shape[1], image.shape[0]]
# KittiUtils class
dataset_utils = dataset.kitti_utils
ground_plane = obj_utils.get_road_plane(img_idx, dataset.planes_dir)
point_cloud = obj_utils.get_depth_map_point_cloud(img_idx,
dataset.calib_dir,
dataset.depth_dir,
image_shape)
points = point_cloud.T
# Grab ground truth
ground_truth_list = obj_utils.read_labels(dataset.label_dir, img_idx)
ground_truth_list = dataset_utils.filter_labels(ground_truth_list)
stereo_calib_p2 = calib_utils.read_calibration(dataset.calib_dir,
img_idx).p2
##############################
# Flip sample info
##############################
start_time = time.time()
flipped_image = kitti_aug.flip_image(image)
flipped_point_cloud = kitti_aug.flip_point_cloud(point_cloud)
flipped_gt_list = [
kitti_aug.flip_label_in_3d_only(obj) for obj in ground_truth_list
]
flipped_ground_plane = kitti_aug.flip_ground_plane(ground_plane)
flipped_calib_p2 = kitti_aug.flip_stereo_calib_p2(stereo_calib_p2,
image_shape)
print('flip sample', time.time() - start_time)
flipped_points = flipped_point_cloud.T
point_colours = vis_utils.project_img_to_point_cloud(
points, image, dataset.calib_dir, img_idx)
##############################
# Generate anchors
##############################
clusters, _ = dataset.get_cluster_info()
anchor_generator = grid_anchor_3d_generator.GridAnchor3dGenerator()
# Read mini batch info
anchors_info = dataset_utils.get_anchors_info(sample_name)
all_anchor_boxes_3d = []
all_ious = []
all_offsets = []
for class_idx in range(len(dataset.classes)):
anchor_boxes_3d = anchor_generator.generate(
area_3d=dataset.kitti_utils.area_extents,
anchor_3d_sizes=clusters[class_idx],
anchor_stride=anchor_strides[class_idx],
ground_plane=ground_plane)
if len(anchors_info[class_idx]) > 0:
indices, ious, offsets, classes = anchors_info[class_idx]
# Get non empty anchors from the indices
non_empty_anchor_boxes_3d = anchor_boxes_3d[indices]
all_anchor_boxes_3d.extend(non_empty_anchor_boxes_3d)
all_ious.extend(ious)
all_offsets.extend(offsets)
if not len(all_anchor_boxes_3d) > 0:
# Exit early if anchors_info is empty
print("No anchors, Please try a different sample")
return
# Convert to ndarrays
all_anchor_boxes_3d = np.asarray(all_anchor_boxes_3d)
all_ious = np.asarray(all_ious)
all_offsets = np.asarray(all_offsets)
##############################
# Flip anchors
##############################
start_time = time.time()
# Flip anchors and offsets
flipped_anchor_boxes_3d = kitti_aug.flip_boxes_3d(all_anchor_boxes_3d,
flip_ry=False)
all_offsets[:, 0] = -all_offsets[:, 0]
print('flip anchors and offsets', time.time() - start_time)
# Overwrite with flipped things
all_anchor_boxes_3d = flipped_anchor_boxes_3d
points = flipped_points
ground_truth_list = flipped_gt_list
ground_plane = flipped_ground_plane
##############################
# Mini batch sampling
##############################
# Sample an RPN mini batch from the non empty anchors
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
mb_mask_tf, _ = mini_batch_utils.sample_rpn_mini_batch(all_ious)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
mb_mask = sess.run(mb_mask_tf)
mb_anchor_boxes_3d = all_anchor_boxes_3d[mb_mask]
mb_anchor_ious = all_ious[mb_mask]
mb_anchor_offsets = all_offsets[mb_mask]
# ObjectLabel list that hold all boxes to visualize
obj_list = []
# Convert the mini_batch anchors to object list
for i in range(len(mb_anchor_boxes_3d)):
if mb_anchor_ious[i] > mini_batch_utils.rpn_pos_iou_range[0]:
obj_type = "Positive"
else:
obj_type = "Negative"
obj = box_3d_encoder.box_3d_to_object_label(mb_anchor_boxes_3d[i],
obj_type)
obj_list.append(obj)
# Convert all non-empty anchors to object list
non_empty_anchor_objs = \
[box_3d_encoder.box_3d_to_object_label(
anchor_box_3d, obj_type='Anchor')
for anchor_box_3d in all_anchor_boxes_3d]
##############################
# Regress Positive Anchors
##############################
# Convert anchor_boxes_3d to anchors and apply offsets
mb_pos_mask = mb_anchor_ious > mini_batch_utils.rpn_pos_iou_range[0]
mb_pos_anchor_boxes_3d = mb_anchor_boxes_3d[mb_pos_mask]
mb_pos_anchor_offsets = mb_anchor_offsets[mb_pos_mask]
mb_pos_anchors = box_3d_encoder.box_3d_to_anchor(mb_pos_anchor_boxes_3d)
regressed_pos_anchors = anchor_encoder.offset_to_anchor(
mb_pos_anchors, mb_pos_anchor_offsets)
# Convert regressed anchors to ObjectLabels for visualization
regressed_anchor_boxes_3d = box_3d_encoder.anchors_to_box_3d(
regressed_pos_anchors, fix_lw=True)
regressed_anchor_objs = \
[box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='Regressed Anchor')
for box_3d in regressed_anchor_boxes_3d]
##############################
# Visualization
##############################
cv2.imshow('{} flipped'.format(sample_name), flipped_image)
cv2.waitKey()
# Create VtkAxes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# Create VtkBoxes for mini batch anchors
vtk_pos_anchor_boxes = VtkBoxes()
vtk_pos_anchor_boxes.set_objects(obj_list, anchor_colour_scheme)
# VtkBoxes for non empty anchors
vtk_non_empty_anchors = VtkBoxes()
vtk_non_empty_anchors.set_objects(non_empty_anchor_objs,
anchor_colour_scheme)
vtk_non_empty_anchors.set_line_width(0.1)
# VtkBoxes for regressed anchors
vtk_regressed_anchors = VtkBoxes()
vtk_regressed_anchors.set_objects(regressed_anchor_objs,
anchor_colour_scheme)
vtk_regressed_anchors.set_line_width(5.0)
# Create VtkBoxes for ground truth
vtk_gt_boxes = VtkBoxes()
vtk_gt_boxes.set_objects(ground_truth_list,
anchor_colour_scheme,
show_orientations=True)
vtk_point_cloud = VtkPointCloud()
vtk_point_cloud.set_points(points, point_colours)
vtk_ground_plane = VtkGroundPlane()
vtk_ground_plane.set_plane(ground_plane, dataset.kitti_utils.bev_extents)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_non_empty_anchors.vtk_actor)
vtk_renderer.AddActor(vtk_pos_anchor_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_regressed_anchors.vtk_actor)
vtk_renderer.AddActor(vtk_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_ground_plane.vtk_actor)
vtk_renderer.AddActor(axes)
vtk_renderer.SetBackground(0.2, 0.3, 0.4)
# Setup Camera
current_cam = vtk_renderer.GetActiveCamera()
current_cam.Pitch(160.0)
current_cam.Roll(180.0)
# Zooms out to fit all points on screen
vtk_renderer.ResetCamera()
# Zoom in slightly
current_cam.Zoom(2.5)
# Reset the clipping range to show all points
vtk_renderer.ResetCameraClippingRange()
# Setup Render Window
vtk_render_window = vtk.vtkRenderWindow()
vtk_render_window.SetWindowName("RPN Mini Batch")
vtk_render_window.SetSize(900, 500)
vtk_render_window.AddRenderer(vtk_renderer)
# Setup custom interactor style, which handles mouse and key events
vtk_render_window_interactor = vtk.vtkRenderWindowInteractor()
vtk_render_window_interactor.SetRenderWindow(vtk_render_window)
vtk_render_window_interactor.SetInteractorStyle(
vis_utils.ToggleActorsInteractorStyle([
vtk_non_empty_anchors.vtk_actor,
vtk_pos_anchor_boxes.vtk_actor,
vtk_regressed_anchors.vtk_actor,
vtk_ground_plane.vtk_actor,
]))
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start()
