def object_from_point_cloud(path, number_of_points):
xyz_points, rgb_points = download_point_cloud.download_ply(path)
if number_of_points is not None and number_of_points > xyz_points.shape[
0]:
number_of_points = xyz_points.shape[0]
object_points = PointsObject()
object_points.set_points(xyz_points, rgb_points, number_of_points)
object_points.scale(0.5)
# visualization.visualize_object([object_points])
return object_points
def object_from_picture():
mask, depth, rgb = get_moving_mask()
object_mask = get_one_object_mask(mask / 255,
depth / 255,
depth_threshold=0.05,
number_of_object=1)
xyz_points, rgb_points = image_processing.calculate_point_cloud(
rgb / 255, depth * object_mask / 255)
pc = PointsObject()
pc.set_points(xyz_points, rgb_points)
visualization.visualize_object([pc])
def try_vrep_connection():
"""Function for checking if vrep_functions and PointsObject are working fine"""
client_id = vrep_functions.vrep_connection()
vrep_functions.vrep_start_sim(client_id)
kinect_rgb_id = vrep_functions.get_object_id(client_id, 'kinect_rgb')
kinect_depth_id = vrep_functions.get_object_id(client_id, 'kinect_depth')
depth_im, rgb_im = vrep_functions.vrep_get_kinect_images(
client_id, kinect_rgb_id, kinect_depth_id)
print(depth_im.shape, rgb_im.shape)
vrep_functions.vrep_stop_sim(client_id)
depth, rgb = vrep_functions.calculate_point_cloud(rgb_im, depth_im,
cam_angle,
near_clipping_plane,
far_clipping_plane)
background = PointsObject()
background.set_points(depth, rgb, number_of_active_points)
# visualization.visualize_object([background])
xyz = np.asarray(background.return_n_last_points(number_of_active_points))
print(xyz[0].shape)
def save_points_cloud():
points_cloud, points_color = create_points_cloud()
object = PointsObject()
object.set_points(points_cloud, points_color)
object.save_all_points("Test", "ball")
def objects_test_moving_figures_global():
classes = {}
rgb_im = image_processing.load_image("falling balls and cylinder",
"rgb_" + str(0) + ".png")
depth_im = image_processing.load_image("falling balls and cylinder",
"depth_" + str(0) + ".png",
"depth")
mog = RGBD_MoG(rgb_im, depth_im, number_of_gaussians=3)
for number_of_frame in range(1, 5):
color_mask = np.zeros([rgb_im.shape[0], rgb_im.shape[1], 3])
rgb_im = image_processing.load_image(
"falling balls and cylinder",
"rgb_" + str(number_of_frame) + ".png")
depth_im = image_processing.load_image(
"falling balls and cylinder",
"depth_" + str(number_of_frame) + ".png", "depth")
mask = mog.set_mask(rgb_im, depth_im)
depth_im = depth_im * (mask / 255).astype(int)
masks = region_growing(mask / 255,
depth_im / 255,
depth_threshold=0.01,
significant_number_of_points=10)
if len(masks) == 0:
print("No moving objects in the frame")
else:
for mask in masks:
xyz_points, rgb_points = image_processing.calculate_point_cloud(
rgb_im / 255, depth_im * mask / 255)
current_object = PointsObject()
current_object.set_points(xyz_points, rgb_points)
norms = current_object.get_normals()
compared_object_descriptor = GlobalCovarianceDescriptor(
xyz_points,
rgb_points,
norms,
depth_im,
rgb_im,
mask,
use_xyz=True,
use_rgb=True,
use_normals=True)
match_found = False
lengths = np.zeros([len(classes)])
if number_of_frame == 1:
match_found = False
else:
match_found = True
for object_number, object_class in enumerate(classes):
lengths[
object_number] = object_class.compare_descriptors(
compared_object_descriptor.
object_descriptor)
min_arg = np.argmin(lengths)
print(lengths)
for object_number, object_class in enumerate(classes):
if object_number == min_arg:
color_mask[:, :,
0] += mask * classes[object_class][0]
color_mask[:, :,
1] += mask * classes[object_class][1]
color_mask[:, :,
2] += mask * classes[object_class][2]
if not match_found:
classes[compared_object_descriptor] = np.random.rand(3)
color_mask[:, :, 0] += mask * classes[
compared_object_descriptor][0]
color_mask[:, :, 1] += mask * classes[
compared_object_descriptor][1]
color_mask[:, :, 2] += mask * classes[
compared_object_descriptor][2]
image_processing.save_image(color_mask,
"tracking_results",
frame_number=number_of_frame,
image_name="global_two_same")
def objects_test_moving_figures_local():
number_of_comparing_points = 100
classes = {}
rgb_im = image_processing.load_image("falling balls and cylinder",
"rgb_" + str(0) + ".png")
depth_im = image_processing.load_image("falling balls and cylinder",
"depth_" + str(0) + ".png",
"depth")
mog = RGBD_MoG(rgb_im, depth_im, number_of_gaussians=3)
for number_of_frame in range(1, 5):
color_mask = np.zeros([rgb_im.shape[0], rgb_im.shape[1], 3])
rgb_im = image_processing.load_image(
"falling balls and cylinder",
"rgb_" + str(number_of_frame) + ".png")
depth_im = image_processing.load_image(
"falling balls and cylinder",
"depth_" + str(number_of_frame) + ".png", "depth")
mask = mog.set_mask(rgb_im, depth_im)
depth_im = depth_im * (mask / 255).astype(int)
masks = region_growing(mask / 255,
depth_im / 255,
depth_threshold=0.01,
significant_number_of_points=10)
if len(masks) == 0:
print("No moving objects in the frame")
else:
for mask in masks:
xyz_points, rgb_points = image_processing.calculate_point_cloud(
rgb_im / 255, depth_im * mask / 255)
current_object = PointsObject()
current_object.set_points(xyz_points, rgb_points)
norms = current_object.get_normals()
compared_object_descriptor = CovarianceDescriptor(
xyz_points,
rgb_points,
norms,
k_nearest_neighbours=None,
relevant_distance=0.1,
use_alpha=True,
use_beta=True,
use_ro=True,
use_theta=True,
use_psi=True,
use_rgb=True)
match_found = False
lengths = np.zeros([
len(classes),
np.amin(
[number_of_comparing_points, xyz_points.shape[0]])
])
if number_of_frame == 1:
match_found = False
else:
match_found = True
for object_number, object_class in enumerate(classes):
lengths[
object_number] = object_class.compare_descriptors(
compared_object_descriptor.
object_descriptor,
number_of_comparing_points)
print(np.sum(mask))
min_args = np.argmin(
lengths, axis=0)[np.amin(lengths, axis=0) < 0.1]
unique, counts = np.unique(min_args,
return_counts=True)
best_match = unique[np.argmax(counts)]
for object_number, object_class in enumerate(classes):
if object_number == best_match:
color_mask[:, :,
0] += mask * classes[object_class][0]
color_mask[:, :,
1] += mask * classes[object_class][1]
color_mask[:, :,
2] += mask * classes[object_class][2]
if not match_found:
classes[compared_object_descriptor] = np.random.rand(3)
color_mask[:, :, 0] += mask * classes[
compared_object_descriptor][0]
color_mask[:, :, 1] += mask * classes[
compared_object_descriptor][1]
color_mask[:, :, 2] += mask * classes[
compared_object_descriptor][2]
image_processing.save_image(color_mask,
"tracking_results",
frame_number=number_of_frame,
image_name="local_two_same")