dataset_dir = '/home/bradenhurl/GTAData/'
data_dir = dataset_dir + label_dir
startIdx = 0
endIdx = 50000
x = []
y = []
z = []
for idx in range(startIdx,endIdx):
sys.stdout.write("\rProcessing index {} / {}".format(
idx + 1 - startIdx, endIdx - startIdx))
filepath = data_dir + '/' + "{:06d}.txt".format(idx)
if os.stat(filepath).st_size != 0:
obj_list = obj_utils.read_labels(data_dir, idx)
for obj in obj_list:
if obj.type == 'Pedestrian':
x.append(obj.t[0])
y.append(obj.t[1])
z.append(obj.t[2])
#x/y for this visualization represent right/forward (x/z in kitti cam coords)
x = np.array(x)
y = np.array(z)
data = np.vstack([x,y]).T
# Create a figure with 6 plot areas
fig, axes = plt.subplots(ncols=6, nrows=1, figsize=(21, 5))
# Everything sarts with a Scatterplot
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.
"""
fig_size = (10, 6.1)
gt_classes = ['Car', 'Pedestrian', 'Cyclist']
# Output images directories
output_dir_base = 'images_2d'
data_dir = '../../DATA/Kitti/object/'
label_dir = data_dir + 'training/label_2'
image_dir = data_dir + 'training/image_2'
filepath = data_dir + 'val.txt'
calib_dir = data_dir + 'training/calib'
filenames = open(filepath, 'r').readlines()
filenames = [int(filename) for filename in filenames]
i = 0
i_max = len(filenames)
for filename in filenames:
##############################
# Ground Truth
##############################
# Get ground truth labels
gt_objects = obj_utils.read_labels(label_dir, filename)
boxes2d, _, _ = obj_utils.build_bbs_from_objects(
gt_objects, class_needed=gt_classes)
image_path = image_dir + "/%06d.png" % filename
image = Image.open(image_path)
image_size = image.size
prop_fig, prop_2d_axes, prop_3d_axes = \
vis_utils.visualization(image_dir,
filename,
display=False)
# Read the stereo calibration matrix for visualization
stereo_calib = calib_utils.read_calibration(calib_dir, filename)
calib_p2 = stereo_calib.p2
draw_gt(gt_objects, prop_2d_axes, prop_3d_axes, calib_p2)
out_name = output_dir_base + "/%06d.png" % filename
plt.savefig(out_name)
plt.close(prop_fig)
i += 1
print(str(i) + '/' + str(i_max))
print('\nDone')
def main():
"""
This demo shows example mini batch info for full MlodModel training.
This includes ground truth, ortho rotated ground truth,
negative proposal anchors, positive proposal anchors, and a sampled
mini batch.
The 2D iou can be modified to show the effect of changing the iou
threshold for mini batch sampling.
In order to let this demo run without training an RPN, the proposals
shown are being read from a text file.
Keys:
F1: Toggle ground truth
F2: Toggle ortho rotated ground truth
F3: Toggle negative proposal anchors
F4: Toggle positive proposal anchors
F5: Toggle mini batch anchors
"""
##############################
# Options
##############################
# Config file folder, default (<mlod_root>/data/outputs/<checkpoint_name>)
config_dir = None
# checkpoint_name = None
checkpoint_name = 'mlod_exp_example'
data_split = 'val_half'
# global_step = None
global_step = 100000
# # # Cars # # #
# sample_name = "000050"
sample_name = "000104"
# sample_name = "000764"
# # # People # # #
# 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
mini_batch_size = 512
neg_proposal_2d_iou_hi = 0.6
pos_proposal_2d_iou_lo = 0.65
bkg_proposals_line_width = 0.5
neg_proposals_line_width = 0.5
mid_proposals_line_width = 0.5
pos_proposals_line_width = 1.0
##############################
# End of Options
##############################
img_idx = int(sample_name)
print("Showing mini batch for sample {}".format(sample_name))
# Read proposals from file
if checkpoint_name is None:
# Use VAL Dataset
dataset = DatasetBuilder.build_kitti_dataset(DatasetBuilder.KITTI_VAL)
# Load demo proposals
proposals_and_scores_dir = mlod.top_dir() + \
'/demos/data/predictions/' + checkpoint_name + \
'/proposals_and_scores/' + dataset.data_split
else:
if config_dir is None:
config_dir = mlod.root_dir() + '/data/outputs/' + checkpoint_name
# Parse experiment config
pipeline_config_file = \
config_dir + '/' + checkpoint_name + '.config'
_, _, _, dataset_config = \
config_builder_util.get_configs_from_pipeline_file(
pipeline_config_file, is_training=False)
dataset_config.data_split = data_split
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Overwrite
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
mini_batch_utils.mlod_neg_iou_range[1] = neg_proposal_2d_iou_hi
mini_batch_utils.mlod_pos_iou_range[0] = pos_proposal_2d_iou_lo
# Load proposals from outputs folder
proposals_and_scores_dir = mlod.root_dir() + \
'/data/outputs/' + checkpoint_name + \
'/predictions/proposals_and_scores/' + dataset.data_split
# 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]
proposals_and_scores = np.loadtxt(
proposals_and_scores_dir +
"/{}/{}.txt".format(global_step, sample_name))
proposal_boxes_3d = proposals_and_scores[:, 0:7]
proposal_anchors = box_3d_encoder.box_3d_to_anchor(proposal_boxes_3d)
# Get filtered ground truth
obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)
filtered_objs = dataset.kitti_utils.filter_labels(obj_labels)
# Convert ground truth to anchors
gt_boxes_3d = np.asarray([
box_3d_encoder.object_label_to_box_3d(obj_label)
for obj_label in filtered_objs
])
gt_anchors = box_3d_encoder.box_3d_to_anchor(gt_boxes_3d,
ortho_rotate=True)
# Ortho rotate ground truth
gt_ortho_boxes_3d = box_3d_encoder.anchors_to_box_3d(gt_anchors)
gt_ortho_objs = [
box_3d_encoder.box_3d_to_object_label(box_3d, obj_type='OrthoGt')
for box_3d in gt_ortho_boxes_3d
]
# Project gt and anchors into BEV
gt_bev_anchors, _ = \
anchor_projector.project_to_bev(gt_anchors,
dataset.kitti_utils.bev_extents)
bev_anchors, _ = \
anchor_projector.project_to_bev(proposal_anchors,
dataset.kitti_utils.bev_extents)
# Reorder boxes into (y1, x1, y2, x2) order
gt_bev_anchors_tf_order = anchor_projector.reorder_projected_boxes(
gt_bev_anchors)
bev_anchors_tf_order = anchor_projector.reorder_projected_boxes(
bev_anchors)
# Convert to box_list format for iou calculation
gt_anchor_box_list = box_list.BoxList(
tf.cast(gt_bev_anchors_tf_order, tf.float32))
anchor_box_list = box_list.BoxList(
tf.cast(bev_anchors_tf_order, tf.float32))
# Get IoU for every anchor
tf_all_ious = box_list_ops.iou(gt_anchor_box_list, anchor_box_list)
valid_ious = True
# Make sure the calculated IoUs contain values. Since its a [N, M]
# tensor, if there are no gt's for instance, that entry will be zero.
if tf_all_ious.shape[0] == 0 or tf_all_ious.shape[1] == 0:
print('#################################################')
print('Warning: This sample does not contain valid IoUs')
print('#################################################')
valid_ious = False
if valid_ious:
tf_max_ious = tf.reduce_max(tf_all_ious, axis=0)
tf_max_iou_indices = tf.argmax(tf_all_ious, axis=0)
# Sample an RPN mini batch from the non empty anchors
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
# Overwrite mini batch size and sample a mini batch
mini_batch_utils.mlod_mini_batch_size = mini_batch_size
mb_mask_tf, _ = mini_batch_utils.sample_mlod_mini_batch(tf_max_ious)
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Run the graph to calculate ious for every proposal and
# to get the mini batch mask
all_ious, max_ious, max_iou_indices = sess.run(
[tf_all_ious, tf_max_ious, tf_max_iou_indices])
mb_mask = sess.run(mb_mask_tf)
mb_anchors = proposal_anchors[mb_mask]
mb_anchor_boxes_3d = box_3d_encoder.anchors_to_box_3d(mb_anchors)
mb_anchor_ious = max_ious[mb_mask]
else:
# We have no valid IoU's, so assume all IoUs are zeros
# and the mini-batch contains all the anchors since we cannot
# mask without IoUs.
max_ious = np.zeros(proposal_boxes_3d.shape[0])
mb_anchor_ious = max_ious
mb_anchors = proposal_anchors
mb_anchor_boxes_3d = box_3d_encoder.anchors_to_box_3d(mb_anchors)
# Create list of positive/negative proposals based on iou
pos_proposal_objs = []
mid_proposal_objs = []
neg_proposal_objs = []
bkg_proposal_objs = []
for i in range(len(proposal_boxes_3d)):
box_3d = proposal_boxes_3d[i]
if max_ious[i] == 0.0:
# Background proposals
bkg_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='BackgroundProposal'))
elif max_ious[i] < neg_proposal_2d_iou_hi:
# Negative proposals
neg_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='NegativeProposal'))
elif max_ious[i] < pos_proposal_2d_iou_lo:
# Middle proposals (in between negative and positive)
mid_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='MiddleProposal'))
elif max_ious[i] <= 1.0:
# Positive proposals
pos_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='PositiveProposal'))
else:
raise ValueError('Invalid IoU > 1.0')
print('{} bkg, {} neg, {} mid, {} pos proposals:'.format(
len(bkg_proposal_objs), len(neg_proposal_objs), len(mid_proposal_objs),
len(pos_proposal_objs)))
# Convert the mini_batch anchors to object list
mb_obj_list = []
for i in range(len(mb_anchor_ious)):
if valid_ious and (mb_anchor_ious[i] >
mini_batch_utils.mlod_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)
mb_obj_list.append(obj)
# Point cloud
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
points, point_colours = demo_utils.get_filtered_pc_and_colours(
dataset, image, img_idx)
# Visualize from here
vis_utils.visualization(dataset.rgb_image_dir, img_idx)
plt.show(block=False)
# VtkPointCloud
vtk_point_cloud = VtkPointCloud()
vtk_point_cloud.set_points(points, point_colours)
# VtkAxes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# VtkBoxes for ground truth
vtk_gt_boxes = VtkBoxes()
vtk_gt_boxes.set_objects(filtered_objs, COLOUR_SCHEME)
# VtkBoxes for ortho ground truth
vtk_gt_ortho_boxes = VtkBoxes()
vtk_gt_ortho_boxes.set_objects(gt_ortho_objs, COLOUR_SCHEME)
# VtkBoxes for background proposals
vtk_bkg_proposal_boxes = VtkBoxes()
vtk_bkg_proposal_boxes.set_objects(bkg_proposal_objs, COLOUR_SCHEME)
vtk_bkg_proposal_boxes.set_line_width(bkg_proposals_line_width)
# VtkBoxes for negative proposals
vtk_neg_proposal_boxes = VtkBoxes()
vtk_neg_proposal_boxes.set_objects(neg_proposal_objs, COLOUR_SCHEME)
vtk_neg_proposal_boxes.set_line_width(neg_proposals_line_width)
# VtkBoxes for middle proposals
vtk_mid_proposal_boxes = VtkBoxes()
vtk_mid_proposal_boxes.set_objects(mid_proposal_objs, COLOUR_SCHEME)
vtk_mid_proposal_boxes.set_line_width(mid_proposals_line_width)
# VtkBoxes for positive proposals
vtk_pos_proposal_boxes = VtkBoxes()
vtk_pos_proposal_boxes.set_objects(pos_proposal_objs, COLOUR_SCHEME)
vtk_pos_proposal_boxes.set_line_width(pos_proposals_line_width)
# Create VtkBoxes for mini batch anchors
vtk_mb_boxes = VtkBoxes()
vtk_mb_boxes.set_objects(mb_obj_list, COLOUR_SCHEME)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.SetBackground(0.2, 0.3, 0.4)
# Add actors
vtk_renderer.AddActor(axes)
vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_gt_ortho_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_bkg_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_neg_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_mid_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_pos_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_mb_boxes.vtk_actor)
# 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("MLOD 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_gt_boxes.vtk_actor,
vtk_gt_ortho_boxes.vtk_actor,
vtk_bkg_proposal_boxes.vtk_actor,
vtk_neg_proposal_boxes.vtk_actor,
vtk_mid_proposal_boxes.vtk_actor,
vtk_pos_proposal_boxes.vtk_actor,
vtk_mb_boxes.vtk_actor,
]))
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start()
def main():
"""
Visualization of anchor filtering using 3D integral images
"""
anchor_colour_scheme = {
"Car": (0, 255, 0), # Green
"Pedestrian": (255, 150, 50), # Orange
"Cyclist": (150, 50, 100), # Purple
"DontCare": (255, 0, 0), # Red
"Anchor": (0, 0, 255), # Blue
}
# Create Dataset
dataset = DatasetBuilder.build_kitti_dataset(DatasetBuilder.KITTI_TRAINVAL)
# Options
clusters, _ = dataset.get_cluster_info()
sample_name = "000000"
img_idx = int(sample_name)
anchor_stride = [0.5, 0.5]
ground_plane = obj_utils.get_road_plane(img_idx, dataset.planes_dir)
anchor_3d_generator = grid_anchor_3d_generator.GridAnchor3dGenerator(
anchor_3d_sizes=clusters, anchor_stride=anchor_stride)
area_extents = np.array([[-40, 40], [-5, 3], [0, 70]])
# Generate anchors in box_3d format
start_time = time.time()
anchor_boxes_3d = anchor_3d_generator.generate(area_3d=area_extents,
ground_plane=ground_plane)
end_time = time.time()
print("Anchors generated in {} s".format(end_time - start_time))
point_cloud = obj_utils.get_lidar_point_cloud(img_idx, dataset.calib_dir,
dataset.velo_dir)
offset_dist = 2.0
# Filter points within certain xyz range and offset from ground plane
offset_filter = obj_utils.get_point_filter(point_cloud, area_extents,
ground_plane, offset_dist)
# Filter points within 0.2m of the road plane
road_filter = obj_utils.get_point_filter(point_cloud, area_extents,
ground_plane, 0.1)
slice_filter = np.logical_xor(offset_filter, road_filter)
point_cloud = point_cloud.T[slice_filter]
# Generate Voxel Grid
vx_grid_3d = voxel_grid.VoxelGrid()
vx_grid_3d.voxelize(point_cloud, 0.1, area_extents)
# Anchors in anchor format
all_anchors = box_3d_encoder.box_3d_to_anchor(anchor_boxes_3d)
# Filter the boxes here!
start_time = time.time()
empty_filter = \
anchor_filter.get_empty_anchor_filter(anchors=all_anchors,
voxel_grid_3d=vx_grid_3d,
density_threshold=1)
anchor_boxes_3d = anchor_boxes_3d[empty_filter]
end_time = time.time()
print("Anchors filtered in {} s".format(end_time - start_time))
# Visualize GT boxes
# Grab ground truth
ground_truth_list = obj_utils.read_labels(dataset.label_dir, img_idx)
# ----------
# Test Sample extraction
# Visualize from here
vis_utils.visualization(dataset.rgb_image_dir, img_idx)
plt.show(block=False)
image_path = dataset.get_rgb_image_path(sample_name)
image_shape = np.array(Image.open(image_path)).shape
rgb_boxes, rgb_normalized_boxes = \
anchor_projector.project_to_image_space(all_anchors, dataset,
image_shape, img_idx)
# Overlay boxes on images
anchor_objects = []
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, 'Anchor')
# Append to a list for visualization in VTK later
anchor_objects.append(obj_label)
for idx in range(len(ground_truth_list)):
ground_truth_obj = ground_truth_list[idx]
# Append to a list for visualization in VTK later
anchor_objects.append(ground_truth_obj)
# Create VtkAxes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# Create VtkBoxes for boxes
vtk_boxes = VtkBoxes()
vtk_boxes.set_objects(anchor_objects, anchor_colour_scheme)
vtk_point_cloud = VtkPointCloud()
vtk_point_cloud.set_points(point_cloud)
vtk_voxel_grid = VtkVoxelGrid()
vtk_voxel_grid.set_voxels(vx_grid_3d)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.AddActor(vtk_boxes.vtk_actor)
# vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_voxel_grid.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(170.0)
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("Anchors")
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(
vtk.vtkInteractorStyleTrackballCamera())
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start() # Blocking
def main():
"""This demo runs through all samples in the trainval set, and checks
that the 3D box projection of all 'Car', 'Van', 'Pedestrian', and 'Cyclist'
objects are in the correct flipped 2D location after applying
modifications to the stereo p2 matrix.
"""
dataset = DatasetBuilder.build_kitti_dataset(DatasetBuilder.KITTI_TRAINVAL,
use_defaults=True)
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
all_samples = dataset.sample_names
all_pixel_errors = []
all_max_pixel_errors = []
total_flip_time = 0.0
for sample_idx in range(dataset.num_samples):
sys.stdout.write('\r{} / {}'.format(sample_idx,
dataset.num_samples - 1))
sample_name = all_samples[sample_idx]
img_idx = int(sample_name)
# Run the main loop to run throughout the images
frame_calibration_info = calib_utils.read_calibration(
dataset.calib_dir,
img_idx)
# Load labels
gt_labels = obj_utils.read_labels(dataset.label_dir, img_idx)
gt_labels = dataset.kitti_utils.filter_labels(
gt_labels, ['Car', 'Van', 'Pedestrian', 'Cyclist'])
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
image_size = [image.shape[1], image.shape[0]]
# Flip p2 matrix
calib_p2 = frame_calibration_info.p2
flipped_p2 = np.copy(calib_p2)
flipped_p2[0, 2] = image.shape[1] - flipped_p2[0, 2]
flipped_p2[0, 3] = -flipped_p2[0, 3]
for obj_idx in range(len(gt_labels)):
obj = gt_labels[obj_idx]
# Get original 2D bounding boxes
orig_box_3d = box_3d_encoder.object_label_to_box_3d(obj)
orig_bbox_2d = box_3d_projector.project_to_image_space(
orig_box_3d, calib_p2, truncate=True, image_size=image_size)
# Skip boxes outside image
if orig_bbox_2d is None:
continue
orig_bbox_2d_flipped = flip_box_2d(orig_bbox_2d, image_size)
# Do flipping
start_time = time.time()
flipped_obj = kitti_aug.flip_label_in_3d_only(obj)
flip_time = time.time() - start_time
total_flip_time += flip_time
box_3d_flipped = box_3d_encoder.object_label_to_box_3d(flipped_obj)
new_bbox_2d_flipped = box_3d_projector.project_to_image_space(
box_3d_flipped, flipped_p2, truncate=True,
image_size=image_size)
pixel_errors = new_bbox_2d_flipped - orig_bbox_2d_flipped
max_pixel_error = np.amax(np.abs(pixel_errors))
all_pixel_errors.append(pixel_errors)
all_max_pixel_errors.append(max_pixel_error)
if max_pixel_error > 5:
print(' Error > 5px', sample_idx, max_pixel_error)
print(np.round(orig_bbox_2d_flipped, 3),
np.round(new_bbox_2d_flipped, 3))
print('Avg flip time:', total_flip_time / dataset.num_samples)
# Convert to ndarrays
all_pixel_errors = np.asarray(all_pixel_errors)
all_max_pixel_errors = np.asarray(all_max_pixel_errors)
# Print max values
print(np.amax(all_max_pixel_errors))
# Plot pixel errors
fig, axes = plt.subplots(nrows=3, ncols=1)
ax0, ax1, ax2 = axes.flatten()
ax0.hist(all_pixel_errors[:, 0], 50, histtype='bar', facecolor='green')
ax1.hist(all_pixel_errors[:, 2], 50, histtype='bar', facecolor='green')
ax2.hist(all_max_pixel_errors, 50, histtype='bar', facecolor='green')
plt.show()
if __name__ == '__main__':
frame_id = sys.argv[1]
pointcloud_file_path = './kitti/velodyne/{0}.bin'.format(frame_id)
with open(pointcloud_file_path, 'rb') as fid:
data_array = np.fromfile(fid, np.single)
points = data_array.reshape(-1, 4)
calib_filename = os.path.join('./kitti/calib/', '{0}.txt'.format(frame_id))
calib = kitti_util.Calibration(calib_filename)
fig = draw_lidar(points)
# ground truth
obj_labels = obj_utils.read_labels('./kitti/label_2', int(frame_id))
gt_boxes = []
for obj in obj_labels:
if obj.type not in ['Car']:
continue
_, corners = kitti_util.compute_box_3d(obj, calib.P)
corners_velo = calib.project_rect_to_velo(corners)
gt_boxes.append(corners_velo)
fig = draw_gt_boxes3d(gt_boxes, fig, color=(1, 0, 0))
# proposals
proposal_objs = load_proposals(frame_id)
boxes = []
box_scores = []
for obj in proposal_objs:
_, corners = kitti_util.compute_box_3d(obj, calib.P)
def main():
# Setting Paths
cam = 2
# dataset_dir = '/media/bradenhurl/hd/gta/object/'
data_set = 'training'
dataset_dir = os.path.expanduser('~') + '/wavedata-dev/demos/gta'
#dataset_dir = os.path.expanduser('~') + '/Kitti/object/'
dataset_dir = os.path.expanduser(
'~') + '/GTAData/TruPercept/object_tru_percept8/'
#Set to true to see predictions (results) from all perspectives
use_results = True
altPerspective = False
perspID = 48133
perspStr = '%07d' % perspID
altPerspect_dir = os.path.join(dataset_dir, data_set + '/alt_perspective/')
if altPerspective:
data_set = data_set + '/alt_perspective/' + perspStr
fromWiseWindows = False
useEVE = False
if fromWiseWindows:
data_set = 'object'
if useEVE:
dataset_dir = '/media/bradenhurl/hd/data/eve/'
else:
dataset_dir = '/media/bradenhurl/hd/data/'
image_dir = os.path.join(dataset_dir, data_set) + '/image_2'
velo_dir = os.path.join(dataset_dir, data_set) + '/velodyne'
calib_dir = os.path.join(dataset_dir, data_set) + '/calib'
if use_results:
label_dir = os.path.join(dataset_dir, data_set) + '/predictions'
else:
label_dir = os.path.join(dataset_dir, data_set) + '/label_2'
base_dir = os.path.join(dataset_dir, data_set)
comparePCs = False
if comparePCs:
velo_dir2 = os.path.join(dataset_dir, data_set) + '/velodyne'
tracking = False
if tracking:
seq_idx = 1
data_set = '%04d' % seq_idx
dataset_dir = '/media/bradenhurl/hd/GTAData/August-01/tracking'
image_dir = os.path.join(dataset_dir, 'images', data_set)
label_dir = os.path.join(dataset_dir, 'labels', data_set)
velo_dir = os.path.join(dataset_dir, 'velodyne', data_set)
calib_dir = os.path.join(dataset_dir, 'training', 'calib', '0000')
#Used for visualizing inferences
#label_dir = '/media/bradenhurl/hd/avod/avod/data/outputs/pyramid_people_gta_40k'
#label_dir = label_dir + '/predictions/kitti_predictions_3d/test/0.02/154000/data/'
closeView = False
pitch = 170
pointSize = 3
zoom = 1
if closeView:
pitch = 180.5
pointSize = 3
zoom = 35
image_list = os.listdir(image_dir)
fulcrum_of_points = True
use_intensity = False
img_idx = 2
print('=== Loading image: {:06d}.png ==='.format(img_idx))
print(image_dir)
image = cv2.imread(image_dir + '/{:06d}.png'.format(img_idx))
image_shape = (image.shape[1], image.shape[0])
if use_intensity:
point_cloud, intensity = obj_utils.get_lidar_point_cloud(
img_idx, calib_dir, velo_dir, ret_i=use_intensity)
else:
point_cloud = obj_utils.get_lidar_point_cloud(img_idx,
calib_dir,
velo_dir,
im_size=image_shape)
if comparePCs:
point_cloud2 = obj_utils.get_lidar_point_cloud(img_idx,
calib_dir,
velo_dir2,
im_size=image_shape)
point_cloud = np.hstack((point_cloud, point_cloud2))
# Reshape points into N x [x, y, z]
all_points = np.array(point_cloud).transpose().reshape((-1, 3))
# Define Fixed Sizes for the voxel grid
x_min = -85
x_max = 85
y_min = -5
y_max = 5
z_min = 3
z_max = 85
x_min = min(point_cloud[0])
x_max = max(point_cloud[0])
y_min = min(point_cloud[1])
y_max = max(point_cloud[1])
#z_min = min(point_cloud[2])
z_max = max(point_cloud[2])
# Filter points within certain xyz range
area_filter = (point_cloud[0] > x_min) & (point_cloud[0] < x_max) & \
(point_cloud[1] > y_min) & (point_cloud[1] < y_max) & \
(point_cloud[2] > z_min) & (point_cloud[2] < z_max)
all_points = all_points[area_filter]
#point_colours = np.zeros(point_cloud.shape[1],0)
#print(point_colours.shape)
if fulcrum_of_points:
# Get point colours
point_colours = vis_utils.project_img_to_point_cloud(
all_points, image, calib_dir, img_idx)
print("Point colours shape: ", point_colours.shape)
print("Sample 0 of colour: ", point_colours[0])
elif use_intensity:
adjusted = intensity == 65535
intensity = intensity > 0
intensity = np.expand_dims(intensity, -1)
point_colours = np.hstack(
(intensity * 255, intensity * 255 - adjusted * 255,
intensity * 255 - adjusted * 255))
print("Intensity shape:", point_colours.shape)
print("Intensity sample: ", point_colours[0])
# Create Voxel Grid
voxel_grid = VoxelGrid()
voxel_grid_extents = [[x_min, x_max], [y_min, y_max], [z_min, z_max]]
print(voxel_grid_extents)
start_time = time.time()
voxel_grid.voxelize(all_points, 0.2, voxel_grid_extents)
end_time = time.time()
print("Voxelized in {} s".format(end_time - start_time))
# Get bounding boxes
gt_detections = obj_utils.read_labels(label_dir,
img_idx,
results=use_results)
if gt_detections is None:
gt_detections = []
#perspective_utils.to_world(gt_detections, base_dir, img_idx)
#perspective_utils.to_perspective(gt_detections, base_dir, img_idx)
for entity_str in os.listdir(altPerspect_dir):
if os.path.isdir(os.path.join(altPerspect_dir, entity_str)):
perspect_detections = perspective_utils.get_detections(
base_dir,
altPerspect_dir,
img_idx,
perspID,
entity_str,
results=use_results)
if perspect_detections != None:
if use_results:
stripped_detections = trust_utils.strip_objs(
perspect_detections)
gt_detections = gt_detections + stripped_detections
else:
gt_detections = gt_detections + perspect_detections
# Create VtkPointCloud for visualization
vtk_point_cloud = VtkPointCloud()
if fulcrum_of_points or use_intensity:
vtk_point_cloud.set_points(all_points, point_colours)
else:
vtk_point_cloud.set_points(all_points)
vtk_point_cloud.vtk_actor.GetProperty().SetPointSize(pointSize)
# Create VtkVoxelGrid for visualization
vtk_voxel_grid = VtkVoxelGrid()
vtk_voxel_grid.set_voxels(voxel_grid)
COLOUR_SCHEME_PAPER = {
"Car": (0, 0, 255), # Blue
"Pedestrian": (255, 0, 0), # Red
"Bus": (0, 0, 255), #Blue
"Cyclist": (150, 50, 100), # Purple
"Van": (255, 150, 150), # Peach
"Person_sitting": (150, 200, 255), # Sky Blue
"Truck": (0, 0, 255), # Light Grey
"Tram": (150, 150, 150), # Grey
"Misc": (100, 100, 100), # Dark Grey
"DontCare": (255, 255, 255), # White
}
# Create VtkBoxes for boxes
vtk_boxes = VtkBoxes()
vtk_boxes.set_objects(gt_detections,
COLOUR_SCHEME_PAPER) #vtk_boxes.COLOUR_SCHEME_KITTI)
# Create Axes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_voxel_grid.vtk_actor)
vtk_renderer.AddActor(vtk_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(pitch)
current_cam.Roll(180.0)
# Zooms out to fit all points on screen
vtk_renderer.ResetCamera()
# Zoom in slightly
current_cam.Zoom(zoom)
# Reset the clipping range to show all points
vtk_renderer.ResetCameraClippingRange()
# Setup Render Window
vtk_render_window = vtk.vtkRenderWindow()
vtk_render_window.SetWindowName(
"Point Cloud and Voxel Grid, Image {}".format(img_idx))
vtk_render_window.SetSize(1920, 1080)
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)
# Add custom interactor to toggle actor visibilities
vtk_render_window_interactor.SetInteractorStyle(
vis_utils.ToggleActorsInteractorStyle([
vtk_point_cloud.vtk_actor,
vtk_voxel_grid.vtk_actor,
vtk_boxes.vtk_actor,
]))
# Show image
image = cv2.imread(image_dir + "/%06d.png" % img_idx)
cv2.imshow("Press any key to continue", image)
cv2.waitKey()
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start() # Blocking
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()
def visualize_objects(objects,
img_idx,
show_results,
alt_persp,
perspID,
fulcrum_of_points=True,
use_intensity=False,
view_received_detections=True,
receive_from_perspective=-1,
only_receive_dets=False,
compare_pcs=False,
show_3d_point_count=False,
show_orientation=cfg.VISUALIZE_ORIENTATION,
final_results=False,
show_score=False,
compare_with_gt=True,
show_image=True,
vis_scores=False):
if cfg.VISUALIZE_AGG_EVALS:
show_image = False
# Setting Paths
cam = 2
dataset_dir = cfg.DATASET_DIR
print("dataset_dir: ", cfg.DATASET_DIR)
if img_idx == -1:
print(
"Please set the TEST_IDX in the config.py file to see a specific index."
)
img_idx = random.randint(0, 101)
print("Using random index: ", img_idx)
perspStr = '%07d' % perspID
altPerspect_dir = os.path.join(dataset_dir, 'alt_perspective')
if alt_persp:
dataset_dir = dataset_dir + '/alt_perspective/' + perspStr
else:
perspID = const.ego_id()
if show_results:
label_dir = os.path.join(dataset_dir, 'predictions')
else:
label_dir = os.path.join(dataset_dir, 'label_2')
COLOUR_SCHEME = {
"Car": (0, 0, 255), # Blue
"Pedestrian": (255, 0, 0), # Red
"Bus": (0, 0, 255), #Blue
"Cyclist": (150, 50, 100), # Purple
"Van": (255, 150, 150), # Peach
"Person_sitting": (150, 200, 255), # Sky Blue
"Truck": (0, 0, 255), # Light Grey
"Tram": (150, 150, 150), # Grey
"Misc": (100, 100, 100), # Dark Grey
"DontCare": (255, 255, 255), # White
"Received": (255, 150, 150), # Peach
"OwnObject": (51, 255, 255), # Cyan
"GroundTruth": (0, 255, 0), # Green
}
# Load points_in_3d_boxes for each object
if vis_scores:
text_positions = []
text_labels = []
else:
text_positions = None
text_labels = None
if objects is not None:
for obj in objects:
if vis_scores:
text_positions.append(obj.t)
txt = '{}'.format(obj.score)
text_labels.append(txt)
if compare_with_gt:
label_dir = os.path.join(dataset_dir, cfg.LABEL_DIR)
real_gt_data = obj_utils.read_labels(label_dir, img_idx, results=False)
if real_gt_data is not None:
for obj in real_gt_data:
obj.type = "GroundTruth"
objects = objects + real_gt_data
vis_utils.visualize_objects_in_pointcloud(
objects, COLOUR_SCHEME, dataset_dir, img_idx, fulcrum_of_points,
use_intensity, receive_from_perspective, compare_pcs,
show_3d_point_count, show_orientation, final_results, show_score,
compare_with_gt, show_image, text_positions, text_labels)
def load_samples(self, indices):
""" Loads input-output data for a set of samples. Should only be
called when a particular sample dict is required. Otherwise,
samples should be provided by the next_batch function
Args:
indices: A list of sample indices from the dataset.sample_list
to be loaded
Return:
samples: a list of data sample dicts
"""
sample_dicts = []
for sample_idx in indices:
sample = self.sample_list[sample_idx]
sample_name = sample.name
# Only read labels if they exist
if self.has_labels:
# Read mini batch first to see if it is empty
anchors_info = self.get_anchors_info(sample_name)
if (not anchors_info) and self.train_val_test == 'train' \
and (not self.train_on_all_samples):
empty_sample_dict = {
constants.KEY_SAMPLE_NAME: sample_name,
constants.KEY_ANCHORS_INFO: anchors_info
}
return [empty_sample_dict]
obj_labels = obj_utils.read_labels(self.label_dir,
int(sample_name))
# Only use objects that match dataset classes
obj_labels = self.kitti_utils.filter_labels(obj_labels)
else:
obj_labels = None
anchors_info = []
label_anchors = np.zeros((1, 6))
label_boxes_3d = np.zeros((1, 7))
label_classes = np.zeros(1)
img_idx = int(sample_name)
# Load image (BGR -> RGB)
cv_bgr_image = cv2.imread(self.get_rgb_image_path(sample_name))
rgb_image = cv_bgr_image[..., ::-1]
image_shape = rgb_image.shape[0:2]
image_input = rgb_image
# Get ground plane
ground_plane = obj_utils.get_road_plane(int(sample_name),
self.planes_dir)
# Get calibration
stereo_calib = calib_utils.read_calibration(
self.calib_dir, int(sample_name))
stereo_calib_p2 = stereo_calib.p2
point_cloud = self.kitti_utils.get_point_cloud(
self.bev_source, img_idx, image_shape)
# Augmentation (Flipping)
# WZN: the flipping augmentation flips both image(in camera frame), pointcloud (in Lidar frame), and calibration
#matrix(between cam and Lidar) so the correspondence is still true.
if kitti_aug.AUG_FLIPPING in sample.augs:
image_input = kitti_aug.flip_image(image_input)
point_cloud = kitti_aug.flip_point_cloud(point_cloud)
obj_labels = [
kitti_aug.flip_label_in_3d_only(obj) for obj in obj_labels
]
ground_plane = kitti_aug.flip_ground_plane(ground_plane)
stereo_calib_p2 = kitti_aug.flip_stereo_calib_p2(
stereo_calib_p2, image_shape)
# Augmentation (Image Jitter)
if kitti_aug.AUG_PCA_JITTER in sample.augs:
image_input[:, :,
0:3] = kitti_aug.apply_pca_jitter(image_input[:, :,
0:3])
if obj_labels is not None:
label_boxes_3d = np.asarray([
box_3d_encoder.object_label_to_box_3d(obj_label)
for obj_label in obj_labels
])
label_classes = [
self.kitti_utils.class_str_to_index(obj_label.type)
for obj_label in obj_labels
]
label_classes = np.asarray(label_classes, dtype=np.int32)
label_h2d = [
obj_label.y2 - obj_label.y1 for obj_label in obj_labels
]
# Return empty anchors_info if no ground truth after filtering
if len(label_boxes_3d) == 0:
anchors_info = []
if self.train_on_all_samples:
# If training without any positive labels, we cannot
# set these to zeros, because later on the offset calc
# uses log on these anchors. So setting any arbitrary
# number here that does not break the offset calculation
# should work, since the negative samples won't be
# regressed in any case.
dummy_anchors = [[-1000, -1000, -1000, 1, 1, 1]]
label_anchors = np.asarray(dummy_anchors)
dummy_boxes = [[-1000, -1000, -1000, 1, 1, 1, 0]]
label_boxes_3d = np.asarray(dummy_boxes)
else:
label_anchors = np.zeros((1, 6))
label_boxes_3d = np.zeros((1, 7))
label_classes = np.zeros(1)
label_h2d = np.zeros(1)
else:
label_anchors = box_3d_encoder.box_3d_to_anchor(
label_boxes_3d, ortho_rotate=True)
# Create BEV maps
bev_images = self.kitti_utils.create_bev_maps(
point_cloud, ground_plane, output_indices=self.output_indices)
#WZN produce input for sparse pooling
if self.output_indices:
voxel_indices = bev_images[1]
pts_in_voxel = bev_images[2]
bev_images = bev_images[0]
height_maps = bev_images.get('height_maps')
density_map = bev_images.get('density_map')
bev_input = np.dstack((*height_maps, density_map))
#shape: (H, W, C)
#import pdb
#pdb.set_trace()
#WZN produce input for sparse pooling
if self.output_indices:
feat_stride = 2**(int(
self.config.use_pyramid_level_at_SHPL[-1]))
spinput = gen_sparse_pooling_input_avod(pts_in_voxel, voxel_indices, stereo_calib, \
[image_shape[1], image_shape[0]], bev_input.shape[0:2])
spinput = produce_sparse_pooling_input(
spinput, stride=[feat_stride, feat_stride])
sparse_pooling_input = [spinput]
#sparse_pooling_input1 = produce_sparse_pooling_input(gen_sparse_pooling_input_avod(pts_in_voxel,voxel_indices,
# stereo_calib,[image_shape[1],image_shape[0]],bev_input.shape[0:2]),stride=[4,4]) #for retinaNet
#stereo_calib,[image_shape[1],image_shape[0]],bev_input.shape[0:2]),stride=[1,1])
#WZN: Note here avod padded the vgg input by 4, so add it
#bev_input_padded = np.copy(bev_input.shape[0:2])
#bev_input_padded[0] = bev_input_padded[0]+4
#sparse_pooling_input2 = produce_sparse_pooling_input(gen_sparse_pooling_input_avod(pts_in_voxel,voxel_indices,
# stereo_calib,[image_shape[1],image_shape[0]],bev_input_padded),stride=[8,8])
#sparse_pooling_input = [sparse_pooling_input1,sparse_pooling_input2]
else:
sparse_pooling_input = None
sample_dict = {
constants.KEY_LABEL_BOXES_3D: label_boxes_3d,
constants.KEY_LABEL_ANCHORS: label_anchors,
constants.KEY_LABEL_CLASSES: label_classes,
constants.KEY_LABEL_H2D: label_h2d,
constants.KEY_IMAGE_INPUT: image_input,
constants.KEY_BEV_INPUT: bev_input,
#WZN: for sparse pooling
constants.KEY_SPARSE_POOLING_INPUT: sparse_pooling_input,
constants.KEY_ANCHORS_INFO: anchors_info,
constants.KEY_POINT_CLOUD: point_cloud,
constants.KEY_GROUND_PLANE: ground_plane,
constants.KEY_STEREO_CALIB_P2: stereo_calib_p2,
constants.KEY_SAMPLE_NAME: sample_name,
constants.KEY_SAMPLE_AUGS: sample.augs
}
sample_dicts.append(sample_dict)
return sample_dicts
def visualize(img_idx, show_results, alt_persp, perspID, fulcrum_of_points,
use_intensity, view_received_detections,
receive_from_perspective, receive_det_id, only_receive_dets,
change_rec_colour, compare_pcs, alt_colour_peach=False,
show_3d_point_count=False, show_orientation=cfg.VISUALIZE_ORIENTATION,
final_results=False, show_score=False,
compare_with_gt=False, show_image=True,
filter_area=cfg.VISUALIZE_AREA_FILTER):
# Setting Paths
cam = 2
dataset_dir = cfg.DATASET_DIR
print("dataset_dir: ", cfg.DATASET_DIR)
if img_idx == -1:
print("Please set the TEST_IDX in the config.py file to see a specific index.")
img_idx = random.randint(0,101)
print("Using random index: ", img_idx)
global text_labels
global text_positions
global COLOUR_SCHEME
if show_3d_point_count or show_score:
text_labels = []
text_positions = []
perspStr = '%07d' % perspID
altPerspect_dir = os.path.join(dataset_dir,'alt_perspective')
if alt_persp:
dataset_dir = dataset_dir + '/alt_perspective/' + perspStr
else:
perspID = const.ego_id()
if show_results:
label_dir = os.path.join(dataset_dir, 'predictions')
else:
label_dir = os.path.join(dataset_dir, 'label_2')
# Load points_in_3d_boxes for each object
points_dict = points_in_3d_boxes.load_points_in_3d_boxes(img_idx, perspID)
gt_detections = []
# Get bounding boxes
if final_results:
if filter_area:
label_dir = os.path.join(dataset_dir, cfg.FINAL_DETS_SUBDIR_AF)
else:
label_dir = os.path.join(dataset_dir, cfg.FINAL_DETS_SUBDIR)
gt_detections = obj_utils.read_labels(label_dir, img_idx, results=show_results)
if compare_with_gt and not show_results:
for obj in gt_detections:
obj.type = "GroundTruth"
addScoreText(gt_detections, show_3d_point_count, show_score)
else:
if (not view_received_detections or receive_from_perspective != -1) and not only_receive_dets:
gt_detections = perspective_utils.get_detections(dataset_dir, dataset_dir, img_idx, perspID,
perspID, results=show_results, filter_area=filter_area)
setPointsText(gt_detections, points_dict, show_3d_point_count)
addScoreTextTrustObjs(gt_detections, show_3d_point_count, show_score)
gt_detections = trust_utils.strip_objs(gt_detections)
gt_detections[0].type = "OwnObject"
if view_received_detections:
stripped_detections = []
if receive_from_perspective == -1:
perspect_detections = perspective_utils.get_all_detections(img_idx, perspID, show_results, filter_area)
if change_rec_colour:
for obj_list in perspect_detections:
obj_list[0].obj.type = "OwnObject"
if obj_list[0].detector_id == perspID:
if compare_with_gt:
if obj_list is not None:
for obj in obj_list:
obj.obj.type = "GroundTruth"
continue
color_str = "Received{:07d}".format(obj_list[0].detector_id)
prime_val = obj_list[0].detector_id * 47
entity_colour = (prime_val + 13 % 255, (prime_val / 255) % 255, prime_val % 255)
COLOUR_SCHEME[color_str] = entity_colour
first_obj = True
for obj in obj_list:
if first_obj:
first_obj = False
continue
obj.obj.type = color_str
for obj_list in perspect_detections:
setPointsText(obj_list, points_dict, show_3d_point_count)
addScoreTextTrustObjs(obj_list, show_3d_point_count, show_score)
stripped_detections = trust_utils.strip_objs_lists(perspect_detections)
else:
receive_entity_str = '{:07d}'.format(receive_from_perspective)
receive_dir = os.path.join(altPerspect_dir, receive_entity_str)
if os.path.isdir(receive_dir):
print("Using detections from: ", receive_dir)
perspect_detections = perspective_utils.get_detections(dataset_dir, receive_dir, img_idx, receive_from_perspective,
receive_entity_str, results=show_results, filter_area=filter_area)
if perspect_detections is not None:
color_str = "Received{:07d}".format(receive_from_perspective)
prime_val = receive_from_perspective * 47
entity_colour = (prime_val + 13 % 255, (prime_val / 255) % 255, prime_val % 255)
COLOUR_SCHEME[color_str] = entity_colour
first_obj = True
for obj in perspect_detections:
if first_obj:
first_obj = False
continue
obj.obj.type = color_str
setPointsText(perspect_detections, points_dict, show_3d_point_count)
addScoreTextTrustObjs(perspect_detections, show_3d_point_count, show_score)
stripped_detections = trust_utils.strip_objs(perspect_detections)
else:
print("Could not find directory: ", receive_dir)
if receive_det_id != -1 and len(stripped_detections) > 0:
single_det = []
single_det.append(stripped_detections[receive_det_id])
stripped_detections = single_det
if change_rec_colour and alt_colour_peach:
for obj in stripped_detections:
obj.type = "Received"
if len(stripped_detections) > 0:
stripped_detections[0].type = "OwnObject"
if only_receive_dets:
gt_detections = stripped_detections
print("Not using main perspective detections")
else:
gt_detections = gt_detections + stripped_detections
if compare_with_gt and show_results:
label_dir = os.path.join(dataset_dir, cfg.LABEL_DIR)
real_gt_data = obj_utils.read_labels(label_dir, img_idx, results=False)
for obj in real_gt_data:
if obj.type != "DontCare":
obj.type = "GroundTruth"
gt_detections = gt_detections + real_gt_data
visualize_objects_in_pointcloud(gt_detections, COLOUR_SCHEME, dataset_dir,
img_idx, fulcrum_of_points, use_intensity,
receive_from_perspective, compare_pcs,
show_3d_point_count, show_orientation,
final_results, show_score,
compare_with_gt, show_image,
_text_positions=text_positions, _text_labels=text_labels)
