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update strategy and overlap rate compute method

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hofee
2024-08-30 16:49:21 +08:00
parent a14bdc2c55
commit 71676e2f4e
9 changed files with 123 additions and 149 deletions
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56
utils/data_load.py
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@@ -17,11 +17,50 @@ class DataLoadUtil:
return path
@staticmethod
def load_model_points(root, scene_name):
model_path = os.path.join(root, scene_name, "sampled_model_points.txt")
def get_sampled_model_points_path(root, scene_name):
path = os.path.join(root,scene_name, f"sampled_model_points.txt")
return path
@staticmethod
def get_scene_seq_length(root, scene_name):
camera_params_path = os.path.join(root, scene_name, "camera_params")
return len(os.listdir(camera_params_path))
@staticmethod
def load_downsampled_world_model_points(root, scene_name):
model_path = DataLoadUtil.get_sampled_model_points_path(root, scene_name)
model_points = np.loadtxt(model_path)
return model_points
@staticmethod
def save_downsampled_world_model_points(root, scene_name, model_points):
model_path = DataLoadUtil.get_sampled_model_points_path(root, scene_name)
np.savetxt(model_path, model_points)
@staticmethod
def load_original_model_points(model_dir, object_name):
model_path = os.path.join(model_dir, object_name, "mesh.obj")
mesh = trimesh.load(model_path)
return mesh.vertices
@staticmethod
def load_scene_info(root, scene_name):
scene_info_path = os.path.join(root, scene_name, "scene_info.json")
with open(scene_info_path, "r") as f:
scene_info = json.load(f)
return scene_info
@staticmethod
def load_target_object_pose(root, scene_name):
scene_info = DataLoadUtil.load_scene_info(root, scene_name)
target_name = scene_info["target_name"]
transformation = scene_info[target_name]
location = transformation["location"]
rotation_euler = transformation["rotation_euler"]
pose_mat = trimesh.transformations.euler_matrix(*rotation_euler)
pose_mat[:3, 3] = location
return pose_mat
@staticmethod
def load_depth(path):
depth_path = os.path.join(os.path.dirname(path), "depth", os.path.basename(path) + ".png")
@@ -83,8 +122,9 @@ class DataLoadUtil:
y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
mask = mask.reshape(-1, 3)
target_mask = np.all(mask == target_mask_label)
mask = mask.reshape(-1)
target_mask = mask == target_mask_label
target_points_camera = points_camera[target_mask]
target_points_camera_aug = np.concatenate([target_points_camera, np.ones((target_points_camera.shape[0], 1))], axis=-1)
@@ -104,10 +144,10 @@ class DataLoadUtil:
return point_cloud['points_world']
@staticmethod
def get_point_cloud_list_from_seq(root, seq_idx, num_frames):
def get_point_cloud_list_from_seq(root, scene_name, num_frames):
point_cloud_list = []
for idx in range(num_frames):
path = DataLoadUtil.get_path(root, seq_idx, idx)
for frame_idx in range(num_frames):
path = DataLoadUtil.get_path(root, scene_name, frame_idx)
point_cloud = DataLoadUtil.get_point_cloud_world_from_path(path)
point_cloud_list.append(point_cloud)
return point_cloud_list

17
utils/pts.py Normal file
View File

@@ -0,0 +1,17 @@
import numpy as np
import open3d as o3d
class PtsUtil:
@staticmethod
def voxel_downsample_point_cloud(point_cloud, voxel_size=0.005):
o3d_pc = o3d.geometry.PointCloud()
o3d_pc.points = o3d.utility.Vector3dVector(point_cloud)
downsampled_pc = o3d_pc.voxel_down_sample(voxel_size)
return np.asarray(downsampled_pc.points)
@staticmethod
def transform_point_cloud(points, pose_mat):
points_h = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1)
points_h = np.dot(pose_mat, points_h.T).T
return points_h[:, :3]

74
utils/reconstruction.py
View File

@@ -1,6 +1,6 @@
import numpy as np
import open3d as o3d
from scipy.spatial import cKDTree
from utils.pts import PtsUtil
class ReconstructionUtil:
@@ -13,18 +13,12 @@ class ReconstructionUtil:
return coverage_rate
@staticmethod
def compute_overlap_rate(point_cloud1, point_cloud2, threshold=0.01):
kdtree1 = cKDTree(point_cloud1)
kdtree2 = cKDTree(point_cloud2)
distances1, _ = kdtree2.query(point_cloud1)
distances2, _ = kdtree1.query(point_cloud2)
overlapping_points1 = np.sum(distances1 < threshold)
overlapping_points2 = np.sum(distances2 < threshold)
overlap_rate1 = overlapping_points1 / point_cloud1.shape[0]
overlap_rate2 = overlapping_points2 / point_cloud2.shape[0]
return (overlap_rate1 + overlap_rate2) / 2
def compute_overlap_rate(new_point_cloud, combined_point_cloud, threshold=0.01):
kdtree = cKDTree(combined_point_cloud)
distances, _ = kdtree.query(new_point_cloud)
overlapping_points = np.sum(distances < threshold)
overlap_rate = overlapping_points / new_point_cloud.shape[0]
return overlap_rate
@staticmethod
def combine_point_with_view_sequence(point_list, view_sequence):
@@ -41,46 +35,14 @@ class ReconstructionUtil:
for view_index, view in enumerate(views):
candidate_views = combined_point_cloud + [view]
down_sampled_combined_point_cloud = ReconstructionUtil.downsample_point_cloud(candidate_views, threshold)
down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(candidate_views, threshold)
new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
coverage_increase = new_coverage - current_coverage
if coverage_increase > best_coverage_increase:
best_coverage_increase = coverage_increase
best_view = view_index
return best_view, best_coverage_increase
@staticmethod
def compute_next_best_view_sequence(target_point_cloud, point_cloud_list, threshold=0.01):
selected_views = []
current_coverage = 0.0
remaining_views = list(range(len(point_cloud_list)))
view_sequence = []
target_point_cloud = ReconstructionUtil.downsample_point_cloud(target_point_cloud, threshold)
while remaining_views:
best_view = None
best_coverage_increase = -1
for view_index in remaining_views:
candidate_views = selected_views + [point_cloud_list[view_index]]
combined_point_cloud = np.vstack(candidate_views)
down_sampled_combined_point_cloud = ReconstructionUtil.downsample_point_cloud(combined_point_cloud,threshold)
new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
coverage_increase = new_coverage - current_coverage
if coverage_increase > best_coverage_increase:
best_coverage_increase = coverage_increase
best_view = view_index
if best_view is not None:
if best_coverage_increase <=1e-3:
break
selected_views.append(point_cloud_list[best_view])
current_coverage += best_coverage_increase
view_sequence.append((best_view, current_coverage))
remaining_views.remove(best_view)
return view_sequence, remaining_views
@staticmethod
def compute_next_best_view_sequence_with_overlap(target_point_cloud, point_cloud_list, threshold=0.01, overlap_threshold=0.3):
@@ -88,7 +50,6 @@ class ReconstructionUtil:
current_coverage = 0.0
remaining_views = list(range(len(point_cloud_list)))
view_sequence = []
target_point_cloud = ReconstructionUtil.downsample_point_cloud(target_point_cloud, threshold)
while remaining_views:
best_view = None
@@ -98,15 +59,15 @@ class ReconstructionUtil:
if selected_views:
combined_old_point_cloud = np.vstack(selected_views)
down_sampled_old_point_cloud = ReconstructionUtil.downsample_point_cloud(combined_old_point_cloud,threshold)
down_sampled_new_view_point_cloud = ReconstructionUtil.downsample_point_cloud(point_cloud_list[view_index],threshold)
overlap_rate = ReconstructionUtil.compute_overlap_rate(down_sampled_old_point_cloud,down_sampled_new_view_point_cloud , threshold)
down_sampled_old_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_old_point_cloud,threshold)
down_sampled_new_view_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud_list[view_index],threshold)
overlap_rate = ReconstructionUtil.compute_overlap_rate(down_sampled_new_view_point_cloud,down_sampled_old_point_cloud, threshold)
if overlap_rate < overlap_threshold:
continue
candidate_views = selected_views + [point_cloud_list[view_index]]
combined_point_cloud = np.vstack(candidate_views)
down_sampled_combined_point_cloud = ReconstructionUtil.downsample_point_cloud(combined_point_cloud,threshold)
down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
new_coverage = ReconstructionUtil.compute_coverage_rate(target_point_cloud, down_sampled_combined_point_cloud, threshold)
coverage_increase = new_coverage - current_coverage
#print(f"view_index: {view_index}, coverage_increase: {coverage_increase}")
@@ -128,12 +89,5 @@ class ReconstructionUtil:
break
return view_sequence, remaining_views
def downsample_point_cloud(point_cloud, voxel_size=0.005):
o3d_pc = o3d.geometry.PointCloud()
o3d_pc.points = o3d.utility.Vector3dVector(point_cloud)
downsampled_pc = o3d_pc.voxel_down_sample(voxel_size)
return np.asarray(downsampled_pc.points)