def example_load_letter():
"""
"""
logg = logging.getLogger(f"c.{__name__}.example_load_letter")
logg.debug(f"Starting example_load_letter")
letter_dir = Path(__file__).resolve().parent / "letters"
logg.debug(f"letter_dir: {letter_dir}")
letter_file_path = letter_dir / "l_lower.tsv"
scale = 10
spline_sequence = utils.load_points(letter_file_path, scale)
print(spline_sequence)
xlim = 0 * scale, 124 * scale
ylim = -250 * scale, 0 * scale
# draw_long_spline(spline_sequence, xlim, ylim, plot_vectors=True)
draw_long_spline(spline_sequence, xlim, ylim, plot_vectors=False)
def generate_figure(idx):
scan, origin, spacing = load_itk("./training/dataset0%d/image0%d.mhd" %
(idx, idx))
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
for image_id in range(4):
path = "./training/dataset0%d/vessel%s/reference.txt" % (idx,
str(image_id))
print(path)
points = load_points(path)
X, Y, Z = points[:, 0], points[:, 1], points[:, 2]
X, Y, Z = X / spacing[1], Y / spacing[1], Z / spacing[0]
ax.scatter(X, Y, Z, s=0.3, c=np.arange(len(points)))
plt.axis("square")
plt.savefig("./visualizations/" + str(idx) + ".png")
def get_batch(dataset_idx, vessel_idx, batch_size=32):
"""
:param dataset_idx: dataset to use
:param vessel_idx: vessel to use
:param batch_size: size of the batch gee
:return: a batch of data from the given dataset and vessel
"""
print("-", end="")
reference_points = load_reference_points("./preprocessing/reference_directions.txt")
probs, radii, directions, input_data = [], [], [], []
points_path = join(training_dir, "dataset0%d/vessel%s/reference.txt" % (dataset_idx, str(vessel_idx)))
points = load_points(points_path)
image, _, _ = load_itk(join(training_dir, "dataset0%d/image0%d.mhd" % (dataset_idx, dataset_idx)))
idxs = np.random.randint(300, len(points) - 300, batch_size)
for idx in idxs:
radius, direction = create_sample(idx, points, reference_points)
point = world_to_voxel(points[idx, :3])
patch = segment_image(image, point).copy()
if patch.shape == (19, 19, 19):
input_data.append(patch)
probs.append(1.)
radii.append(radius)
directions.append(direction)
input_data = np.asarray(input_data).reshape(-1, 19, 19, 19, 1)
radii = np.asarray(radii).reshape(-1, 1)
directions = np.asarray(directions).reshape(-1, 500)
probs = np.asarray(probs).reshape(-1, 1)
# print(input_data.shape, radii.shape, directions.shape, probs.shape)
return input_data, [probs, radii, directions]
# save this intermediate step
penrose_segments = penrose.segments
LOGN("\tsegments", len(penrose_segments))
with open("d%i_penrose.segments" % depth, "w") as fd:
utils.write_segments(penrose_segments, fd)
########################################################################
# TSP
########################################################################
trajs = []
if ask_for.tour != [None]:
for tour in ask_for.tour:
with open(tour) as fd:
trajs.append(utils.load_points(fd))
if ask_for.notsp:
if ask_for.tour == [None] or not ask_for.pheromones:
LOGN(
"If you do not want to solve the TSP, you must provide a solution tour (--tour) and a pheromones matrix (--pheromones)"
)
sys.exit(error_codes["NO-TSP"])
if ask_for.pheromones:
with open(ask_for.pheromones) as fd:
phero = utils.load_matrix(fd)
else:
LOGN("Solve the TSP with an Ant Colony Algorithm")
from preprocessing import *
from utils import load_points
if __name__ == "__main__":
# change_voxel_spacing()
dataset_idx, image_idx = 0, 0
path = "./training/dataset0%d/vessel%s/reference.txt" % (dataset_idx,
str(image_idx))
points = load_points(path)
reference_points = load_reference_points(
"./preprocessing/reference_directions.txt")
print(points.shape, reference_points.shape)
print(points[0, :3], points[20, :3])
cat = find_direction_cat(points[0, :3], points[20, :3], reference_points)
print(reference_points[cat])
# X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
# X = np.random.rand(20,3)
# nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(X)
# distances, indices = nbrs.kneighbors(X)
# Load points
if not exists(args.pcl_path):
exit(args.pcl_path + " could not be found")
ext = splitext(args.pcl_path)[-1].lower()
if ext is None or ext not in [".pcd", ".txt", ".xyz"]:
exit("Point-cloud file has wrong extension")
# http://www.open3d.org/docs/release/tutorial/Basic/file_io.html
pcd = load_points(args.pcl_path, "pcd" if ext[1:] == "pcd" else "xyz")
# o3d.visualization.draw_geometries([pcd])
print(len(pcd.points), "points found")
# pcd_array = np.asarray(pcd.points)
# search_knn_vector_3d - returns a list of indices of the k nearest neighbors of the anchor point
# [_, idx, _] = pcd_tree.search_knn_vector_3d(pcd.points[0], args.nn)
# print(np.asarray(pcd.points)[idx[1:], :])
# search_radius_vector_3d - all points with distances to the anchor point less than a given radius
# [k, idx, _] = pcd_tree.search_radius_vector_3d(pcd.points[0], args.r)
# print(np.asarray(pcd.points)[idx[1:], :])
# search_hybrid_vector_3d - at most k nearest neighbors that have distances to the anchor point less than a given radius
# Combines criteria of KNN search and RNN search
# [_, idx, _] = pcd_tree.search_hybrid_vector_3d(pcd.points[0], args.r, args.nn)
LOGN( "\tsegments",len(penrose_segments) )
with open("d%i_penrose.segments" % depth, "w") as fd:
utils.write_segments( penrose_segments, fd )
########################################################################
# TSP
########################################################################
trajs = []
if ask_for.tour != [None]:
for tour in ask_for.tour:
with open(tour) as fd:
trajs.append( utils.load_points(fd) )
if ask_for.notsp:
if ask_for.tour == [None] or not ask_for.pheromones:
LOGN( "If you do not want to solve the TSP, you must provide a solution tour (--tour) and a pheromones matrix (--pheromones)" )
sys.exit(error_codes["NO-TSP"])
if ask_for.pheromones:
with open(ask_for.pheromones) as fd:
phero = utils.load_matrix(fd)
else:
LOGN( "Solve the TSP with an Ant Colony Algorithm" )
LOGN( "\tConvert the segment list into an adjacency list graph" )
G = graph.graph_of( penrose_segments )