def process_input(self):
"""
Function to extract data from the input array.
Input array is the output of the optimization step
which holds the generated sparse model of the object
and the relative scene transformations.
"""
#get the relative scene transforamtions from input array
self.scene_tfs = np.empty((0, 4, 4))
for input_arr in self.input_arrays:
num_scenes = input_arr['ref'].shape[0]
out_ts = input_arr['scenes'][:(num_scenes) * 3].reshape(
(num_scenes, 3))
out_qs = input_arr['scenes'][(num_scenes) * 3:(num_scenes) *
7].reshape((num_scenes, 4))
out_tfs = np.asarray([
tfa.compose(t, tfq.quat2mat(q), np.ones(3))
for t, q in zip(out_ts, out_qs)
])
self.scene_tfs = np.vstack((self.scene_tfs, out_tfs))
self.object_model = []
for obj in self.model_paths:
#read sparse model from input array
sparse_model = SparseModel().reader(obj)
#read dense model from .PLY file
dense_model = o3d.io.read_point_cloud(
os.path.join(os.path.dirname(obj), "dense.ply"))
self.object_model.append(
[sparse_model, np.asarray(dense_model.points)])
return
def process_input(self):
"""
Function to extract data from the input array.
Input array is the output of the optimization step
which holds the generated sparse model of the object
and the relative scene transformations.
"""
#get the relative scene transforamtions from input array
out_ts = self.input_array['scenes'][ :(self.num_scenes)*3].reshape((self.num_scenes, 3))
out_qs = self.input_array['scenes'][(self.num_scenes)*3 : (self.num_scenes)*7].reshape((self.num_scenes, 4))
out_tfs = np.asarray([tfa.compose(t, tfq.quat2mat(q), np.ones(3)) for t,q in zip(out_ts, out_qs)])
self.scene_tfs = out_tfs
#get object model from input_array
self.object_model = SparseModel().reader(self.model_path)
return
def closest_points(self, transformation):
pcd_tree = o3d.geometry.KDTreeFlann(self.ply_model)
# Get sparse model points in scene frame
sparse_points = np.asarray(self.sparse_model.points)
sparse_points = np.vstack(
[sparse_points.transpose(),
np.ones(sparse_points.shape[0])])
sparse_points = np.dot(transformation, sparse_points).transpose()
# Find the closest point in PLY model to each sparse point
closest_points = []
for point in sparse_points:
[_, nn_idx, _] = pcd_tree.search_knn_vector_3d(point[:3], 1)
nn_point = np.asarray(self.ply_model.points)[nn_idx][0]
closest_points.append(nn_point)
# Create PointCloud of nearest neightbors
nn_points = np.asarray(closest_points)
nn_points = np.vstack(
[nn_points.transpose(),
np.ones(nn_points.shape[0])])
nn_points = np.dot(np.linalg.inv(transformation),
nn_points).transpose()
nn_pcd = o3d.geometry.PointCloud()
nn_pcd.points = o3d.utility.Vector3dVector(nn_points[:, :3])
write_path = os.path.join(os.path.dirname(self.path_to_sparse_model),
"gt_sparse.txt")
SparseModel().writer(np.asarray(nn_pcd.points), write_path, True)
write_path = os.path.join(os.path.dirname(self.path_to_sparse_model),
"gt_cad.ply")
o3d.io.write_point_cloud(
write_path,
self.ply_model.transform(np.linalg.inv(transformation)))
return
def __init__(self, dataset_dir_path, sparse_model_path, dense_model_path, scene_meta_path, visualize=False):
"""
Constructor for Annotations class.
Input arguments:
dataset_dir_path - path to root dataset directory
sparse_model_path - path to sparse model (*.txt)
dense_model_path - path to dense model (*.ply)
scene_meta_path - path to scenes' meta info (*.npz)
visualize - set 'True' to visualize
"""
self.dataset_path = dataset_dir_path
self.input_array = np.load(scene_meta_path)
self.visualize = visualize
#read sparse model from input array
sparse_model = SparseModel().reader(sparse_model_path)
#read dense model from .PLY file
dense_model = o3d.io.read_point_cloud(dense_model_path)
dense_model = dense_model.voxel_down_sample(voxel_size=0.005)
#read camera intrinsics matrix from camera.txt in root directory
self.cam_mat = np.eye(3)
with open(os.path.join(self.dataset_path, 'camera.txt'), 'r') as file:
camera_intrinsics = file.readlines()[0].split()
camera_intrinsics = list(map(float, camera_intrinsics))
self.cam_mat[0,0] = camera_intrinsics[0]
self.cam_mat[1,1] = camera_intrinsics[1]
self.cam_mat[0,2] = camera_intrinsics[2]
self.cam_mat[1,2] = camera_intrinsics[3]
#get number of scenes and number of keypoints
self.num_scenes = int(self.input_array['scenes'].shape[0]/7)
self.num_keypts = sparse_model.shape[0]
#paths to each of the scene dirs inside root dir
self.list_of_scene_dirs = [d for d in os.listdir(self.dataset_path)
if os.path.isdir(os.path.join(self.dataset_path, d))]
self.list_of_scene_dirs.sort()
self.list_of_scene_dirs = self.list_of_scene_dirs[:self.num_scenes]
print("List of scenes: ", self.list_of_scene_dirs)
print("Number of scenes: ", self.num_scenes)
print("Number of keypoints: ", self.num_keypts)
#excect images to be 640x480
self.width = 640
self.height = 480
#bounding-box needs to scaled up to avoid excessive cropping
self.bbox_scale = 1.5
#define a ratio of labeled samples to produce
self.ratio = 10
#this is the object model
self.object_model = [sparse_model, np.asarray(dense_model.points)]
#these are the relative scene transformations
self.scene_tfs = []
def define_grasp_point(self, ply_path):
"""
Function to define grasp pose.
"""
# create output dir if not exists
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
if (self.scene_kpts.shape) != (1, 3, 2):
raise Exception("2 3D points must exist to define a grasp pose.")
if not os.path.exists(ply_path):
raise Exception("%s path does not exist" % ply_path)
#get 3 user-defined 3D points
point_1 = self.scene_kpts.transpose(0, 2, 1)[0, 0]
point_2 = self.scene_kpts.transpose(0, 2, 1)[0, 1]
#read point cloud and get normals
scene_cloud = o3d.io.read_point_cloud(ply_path)
scene_cloud.estimate_normals()
scene_cloud.normalize_normals()
scene_points = scene_cloud.points
scene_normals = scene_cloud.normals
scene_tree = o3d.geometry.KDTreeFlann(scene_cloud)
#get neightbors and find normal direction
[_, idx, _] = scene_tree.search_knn_vector_3d(point_1, 200)
normals = np.asarray(scene_normals)[list(idx)]
normal_dir = normals.mean(0)
normal_dir = normal_dir / np.linalg.norm(normal_dir)
#get intersection of point and plane
# d = (a.x_0 + b.y_0 + c.z_0)/(a.x_u + b.y_u + c.z_u)
lamda = np.dot(normal_dir, point_1) / np.dot(normal_dir, point_2)
point_i = lamda * point_2
vector_x = (point_i - point_1)
vector_y = np.cross(normal_dir, vector_x)
#normalize
vector_x = vector_x / np.linalg.norm(vector_x)
vector_y = vector_y / np.linalg.norm(vector_y)
#create rotation matrix
rot_mat = np.array([vector_x, vector_y, normal_dir])
tf_mat = tfa.compose(point_1, rot_mat, np.ones(3))
#save the grasp point
SparseModel().grasp_writer(
tf_mat, os.path.join(self.output_dir, "sparse_model.txt"))
#visualize in open3d
vis_mesh_list = []
vis_mesh_list.append(scene_cloud)
coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
0.2)
coordinate_frame.transform(tf_mat)
vis_mesh_list.append(coordinate_frame)
o3d.visualization.draw_geometries(vis_mesh_list)
return True, tf_mat
def get_true_poses(self):
"""
Function to find 3D positions of each defined keypoints in the frame of
every scene origin. Uses the .npz zipped archive to get relative scene
transformations, the selection matrix etc.
Returns a list of (Nx3) 2D numpy arrays where each i-th array in the list
holds the 3D keypoint pose configuration of the object in the i-th scene.
"""
list_of_poses = []
# get selected points from input npz array
ref_keypts = self.input_array['ref']
# get selection matrix frrom input npz array
select_mat = self.input_array['sm']
# convert selection matrix to a binary matrix
col_splits = np.hsplit(select_mat, select_mat.shape[1] // 3)
row_splits = [np.vsplit(col, col.shape[0] // 3) for col in col_splits]
vis_list = [
sum(map(lambda x: (x == np.eye(3)).all(), mat))
for mat in row_splits
]
# binary matrix of shape (num of scenes x num of keypoints)
vis_mat = np.reshape(vis_list, [self.num_scenes, self.num_keypts])
for sce_id, visibility in zip(range(len(self.list_of_scene_dirs)),
vis_mat):
# read true model from .pp file
tru_model = SparseModel().reader(self.picked_pts, 1 / 1000)
# partial user-annotated 3D model
obj_manual = ref_keypts[sce_id].transpose()[np.nonzero(visibility)
[0]]
# select corresponding points in true model
tru_model_part = tru_model[np.nonzero(visibility)[0]]
# use procrustes analysis to align true model to annotated points
_, _, tform = procrustes(tru_model_part, obj_manual, False)
T = tfa.compose(tform['translation'],
np.linalg.inv(tform['rotation']), np.ones(3))
T = np.linalg.inv(T)
true_object = np.asarray([(T[:3, :3].dot(pt) + T[:3, 3])
for pt in tru_model])
list_of_poses.append(true_object)
return list_of_poses
def __init__(self, sparse_model_path, ply_model_path, pp_file_path,
visualize):
# paths to model files
self.path_to_sparse_model = sparse_model_path
self.path_to_ply_model = ply_model_path
self.path_to_picked_points = pp_file_path
# visualization flag
self.visualize = visualize
# icp max-correspondence-distance
self.threshold = 0.01 # 1cm distance threshold
# read sparse model
sparse_model_array = SparseModel().reader(self.path_to_sparse_model)
self.sparse_model = o3d.geometry.PointCloud()
self.sparse_model.points = o3d.utility.Vector3dVector(
sparse_model_array)
# read ply model
self.ply_model = o3d.io.read_point_cloud(self.path_to_ply_model)
self.ply_model.scale(PLY_SCALE, np.zeros(3))
def process(self):
# read picked points
pp_array = SparseModel().reader(self.path_to_picked_points, PP_SCALE)
pp_model = o3d.geometry.PointCloud()
pp_model.points = o3d.utility.Vector3dVector(pp_array)
corr = np.zeros((len(pp_array), 2))
corr[:, 0] = list(range(len(pp_array)))
corr[:, 1] = list(range(len(pp_array)))
# estimate rough transformation using correspondences
p2p = o3d.registration.TransformationEstimationPointToPoint()
trans_init = p2p.compute_transformation(
self.sparse_model, pp_model, o3d.utility.Vector2iVector(corr))
# point-to-point ICP for refinement
reg_p2p = o3d.registration.registration_icp(self.sparse_model,
self.ply_model,
self.threshold, trans_init)
# visualize if required
if self.visualize:
draw_registration_result(self.sparse_model, self.ply_model,
reg_p2p.transformation)
return reg_p2p.transformation
def compute(self, sparse_model_flag=False):
"""
Function to compute the sparse model and the relative scene transformations
through optimization. Output directory will be created if not exists.
Returns a success boolean.
"""
# create output dir if not exists
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
#populate selection matrix from select_vec
total_kpt_count = len(self.select_vec)
found_kpt_count = len(np.nonzero(self.select_vec)[0])
selection_matrix = np.zeros((found_kpt_count * 3, total_kpt_count * 3))
for idx, nz_idx in enumerate(np.nonzero(self.select_vec)[0]):
selection_matrix[(idx * 3):(idx * 3) + 3,
(nz_idx * 3):(nz_idx * 3) + 3] = np.eye(3)
computed_vector = []
success_flag = False
if sparse_model_flag:
object_model = SparseModel().reader(self.sparse_model_file)
success_flag, res = app.geo_constrain.predict(
object_model, self.scene_kpts.transpose(0, 2, 1),
self.select_vec)
scene_t = np.asarray([np.array(i[:3, 3]) for i in res])
scene_q = np.asarray(
[tfq.mat2quat(np.array(i[:3, :3])) for i in res])
computed_vector = np.concatenate(
(scene_t.flatten(), scene_q.flatten()))
else:
#initialize quaternions and translations for each scene
scene_t_ini = np.array([[0, 0,
0]]).repeat(self.scene_kpts.shape[0],
axis=0)
scene_q_ini = np.array([[1, 0, 0,
0]]).repeat(self.scene_kpts.shape[0],
axis=0)
scene_P_ini = np.array([[[0, 0,
0]]]).repeat(self.scene_kpts.shape[2],
axis=0)
#initialize with known keypoints
scene_P_ini = self.scene_kpts[0].transpose()[:, np.newaxis, :]
#main optimization step
res = app.optimize.predict(self.scene_kpts, scene_t_ini,
scene_q_ini, scene_P_ini,
selection_matrix)
#extract generated sparse object model optimization output
len_ts = scene_t_ini[1:].size
len_qs = scene_q_ini[1:].size
object_model = res.x[len_ts + len_qs:].reshape(scene_P_ini.shape)
object_model = object_model.squeeze()
#save the generated sparse object model
SparseModel().writer(
object_model, os.path.join(self.output_dir,
"sparse_model.txt"), True)
computed_vector = np.concatenate([
np.zeros(3), res.x[:len_ts],
tfq.qeye(), res.x[len_ts:len_ts + len_qs]
])
success_flag = res.success
#save the input and the output from optimization step
out_fn = os.path.join(self.output_dir, 'saved_meta_data')
np.savez(out_fn,
model=object_model,
scenes=computed_vector,
ref=self.scene_kpts,
sm=selection_matrix)
if success_flag:
print("--------\n--------\n--------")
print("Computed results saved at {}".format(out_fn))
print("--------\n--------\n--------")
return success_flag, object_model
class PartialModel:
def __init__(self, dataset_path, input_arr_path, input_model_path):
"""
Constructor for PartialModel class.
Input arguments:
dataset_path - path to root dataset directory
input_arr_path - path to input npz zipped archive
input_model_path - path to sparse model file
"""
self.dataset_path = dataset_path
self.input_array = np.load(input_arr_path)
self.model_path = input_model_path
#get number of scenes and number of keypoints
self.num_scenes = self.input_array['ref'].shape[0]
self.num_keypts = self.input_array['ref'].shape[2]
#paths to each of the scene dirs inside root dir
self.list_of_scene_dirs = [d for d in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, d))]
self.list_of_scene_dirs.sort()
self.list_of_scene_dirs = self.list_of_scene_dirs[:self.num_scenes]
print("List of scenes: ", self.list_of_scene_dirs)
print("Number of scenes: ", self.num_scenes)
print("Number of keypoints: ", self.num_keypts)
#this is the object model
self.object_model = []
#these are the relative scene transformations
self.scene_tfs = []
def process_input(self):
"""
Function to extract data from the input array.
Input array is the output of the optimization step
which holds the generated sparse model of the object
and the relative scene transformations.
"""
#get the relative scene transforamtions from input array
out_ts = self.input_array['scenes'][ :(self.num_scenes)*3].reshape((self.num_scenes, 3))
out_qs = self.input_array['scenes'][(self.num_scenes)*3 : (self.num_scenes)*7].reshape((self.num_scenes, 4))
out_tfs = np.asarray([tfa.compose(t, tfq.quat2mat(q), np.ones(3)) for t,q in zip(out_ts, out_qs)])
self.scene_tfs = out_tfs
#get object model from input_array
self.object_model = SparseModel().reader(self.model_path)
return
def get_fragments_by_radius(self, radius=0.05):
"""
Function to extract point cloud clusters using a radius vector
around each keypoint in sparse model, in each scene. Returns the
list of partial models transformed into world frame.
"""
point_cloud_list = []
#iterate through a zip of list of scene dirs and the relative scene tfs
for data_dir_idx, (cur_scene_dir, sce_t) in enumerate(zip(self.list_of_scene_dirs, self.scene_tfs)):
#load scene point cloud from .PLY file
cur_ply_path = os.path.join(self.dataset_path, cur_scene_dir, cur_scene_dir + '.ply')
pcd = o3d.io.read_point_cloud(cur_ply_path)
#build KDTree
pcd_tree = o3d.geometry.KDTreeFlann(pcd)
#get object keypoints in scene frame
model_points = np.vstack([self.object_model.transpose(),
np.ones(self.object_model.shape[0])])
model_points = np.dot(sce_t, model_points).transpose()
fragment = o3d.geometry.PointCloud()
for keypt in model_points:
[_, idx, _] = pcd_tree.search_radius_vector_3d(keypt[:3], radius)
fragment.points.extend(o3d.utility.Vector3dVector(np.asarray(pcd.points)[idx]))
fragment.transform(np.linalg.inv(sce_t))
point_cloud_list.append(fragment)
return point_cloud_list
def get_regions(self):
"""
Function to extract point cloud clusters using region-growing clustering
with keypoints as initial seed points. Returns the
list of partial models transformed into world frame.
"""
point_cloud_list = []
#initialize region growing class
reg = RegionGrowing((5/180)*3.14, 1.0)
#iterate through a zip of list of scene dirs and the relative scene tfs
for data_dir_idx, (cur_scene_dir, sce_t) in enumerate(zip(self.list_of_scene_dirs, self.scene_tfs)):
#get object keypoints in scene frame
model_points = np.vstack([self.object_model.transpose(),
np.ones(self.object_model.shape[0])])
model_points = np.dot(sce_t, model_points).transpose()
#load scene point cloud from .PLY file
cur_ply_path = os.path.join(self.dataset_path, cur_scene_dir, cur_scene_dir + '.ply')
pcd = o3d.io.read_point_cloud(cur_ply_path)
#extract regions using model points as initial seeds
reg.set_input_cloud(pcd)
reg.box_crop(model_points[:,:3].mean(0), 0.003, 0.2)
reg.set_seeds(model_points[:,:3])
regions = reg.extract()
#transform each region to scene tf and add to list
point_cloud_list.extend([region.transform(np.linalg.inv(sce_t)) for region in regions])
return point_cloud_list
def process(input_array, num_out_scenes, output_dir):
#process input
scene_kpts = input_array['ref']
selection_matrix = input_array['sm']
#get number of scene and keypoints in original experiment
num_scenes = scene_kpts.shape[0]
num_keypts = scene_kpts.shape[2]
# convert selection matrix to a binary matrix
col_splits = np.hsplit(selection_matrix, selection_matrix.shape[1] // 3)
row_splits = [np.vsplit(col, col.shape[0] // 3) for col in col_splits]
vis_list = [
sum(map(lambda x: (x == np.eye(3)).all(), mat)) for mat in row_splits
]
# binary matrix of shape (num of scenes x num of keypoints)
vis_mat = np.reshape(vis_list, [num_scenes, num_keypts])
#slice binary matrix
vis_mat = vis_mat[:num_out_scenes]
# convert binary matrix back to selection matrix
select_vec = vis_mat.flatten()
total_kpt_count = len(select_vec)
found_kpt_count = len(np.nonzero(select_vec)[0])
selection_matrix = np.zeros((found_kpt_count * 3, total_kpt_count * 3))
for idx, nz_idx in enumerate(np.nonzero(select_vec)[0]):
selection_matrix[(idx * 3):(idx * 3) + 3,
(nz_idx * 3):(nz_idx * 3) + 3] = np.eye(3)
#slice scene keypoints
scene_kpts = scene_kpts[:num_out_scenes]
#initialize quaternions and translations for each scene
scene_t_ini = np.array([[0, 0, 0]]).repeat(scene_kpts.shape[0], axis=0)
scene_q_ini = np.array([[1, 0, 0, 0]]).repeat(scene_kpts.shape[0], axis=0)
scene_P_ini = np.array([[[0, 0, 0]]]).repeat(scene_kpts.shape[2], axis=0)
#initialize with known keypoints
scene_P_ini = scene_kpts[0].transpose()[:, np.newaxis, :]
#main optimization step
res = app.optimize.predict(scene_kpts, scene_t_ini, scene_q_ini,
scene_P_ini, selection_matrix)
#extract generated sparse object model optimization output
len_ts = scene_t_ini[1:].size
len_qs = scene_q_ini[1:].size
object_model = res.x[len_ts + len_qs:].reshape(scene_P_ini.shape)
object_model = object_model.squeeze()
#save the generated sparse object model
SparseModel().writer(object_model,
os.path.join(output_dir, "sparse_model.txt"))
computed_vector = res.x[:(len_ts + len_qs)]
success_flag = res.success
#save the input and the output from optimization step
out_fn = os.path.join(output_dir, 'saved_meta_data')
np.savez(out_fn,
model=object_model,
scenes=computed_vector,
ref=scene_kpts,
sm=selection_matrix)
if success_flag:
print("--------\n--------\n--------")
print("Computed results saved at {}".format(out_fn))
print("--------\n--------\n--------")
return object_model
#set scale factors
PLY_SCALE = 1
PP_SCALE = 1
path_to_dataset_dir = opt.dataset
path_to_experiment_dir = opt.experiment
# Set up paths (hard-coded for now)
path_to_sparse_model = os.path.join(opt.experiment, "sparse_model.txt")
path_to_pp_model = os.path.join(opt.dataset, os.path.basename(opt.dataset) + "_picked_points.pp")
path_to_input_tfs = os.path.join(opt.experiment, "saved_meta_data.npz")
path_to_output_tfs = os.path.join(opt.experiment, "gt_meta_data.npz")
# Read sparse model
sparse_model_array = SparseModel().reader(path_to_sparse_model)
sparse_model = o3d.geometry.PointCloud()
sparse_model.points = o3d.utility.Vector3dVector(sparse_model_array)
# Read PickedPoints model
pp_array = SparseModel().reader(path_to_pp_model, PP_SCALE)
pp_model = o3d.geometry.PointCloud()
pp_model.points = o3d.utility.Vector3dVector(pp_array)
# Paths to each of the scene dirs inside root dir
list_of_scene_dirs = [d for d in os.listdir(path_to_dataset_dir)
if os.path.isdir(os.path.join(path_to_dataset_dir, d))]
list_of_scene_dirs.sort()
num_scenes = len(list_of_scene_dirs)
num_keypts = len(pp_model.points)
print("List of scenes: ", list_of_scene_dirs)
