def approximate_environment(environment_points,
step=0.1,
min_number_of_approximated_points=30):
temp = PointsObject()
temp.add_points(environment_points)
normals = temp.get_normals()
found_shapes = shape_recognition.RANSAC(
environment_points,
normals,
number_of_points_threshold=environment_points.shape[0] * 0.1,
number_of_iterations=10,
min_pc_number=environment_points.shape[0] * 0.3,
number_of_subsets=10,
use_planes=True,
use_box=False,
use_sphere=False,
use_cylinder=True,
use_cone=False)
points = np.empty((0, 3))
normals = np.empty((0, 3))
for s in found_shapes:
s = np.round(s / step) * step
temp = PointsObject()
temp.add_points(s)
points = np.append(points, s, axis=0)
normals = np.append(normals, temp.get_normals(), axis=0)
return points, normals
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")