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hofee
2024-10-19 19:06:09 +08:00
parent 5dae3c53db
commit be7ec1a433
4 changed files with 71 additions and 42 deletions
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45
utils/vis.py
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@@ -40,7 +40,7 @@ class visualizeUtil:
all_combined_pts = []
for i in range(length):
path = DataLoadUtil.get_path(root, scene, i)
pts = DataLoadUtil.load_from_preprocessed_pts(path,"txt")
pts = DataLoadUtil.load_from_preprocessed_pts(path,"npy")
if pts.shape[0] == 0:
continue
all_combined_pts.append(pts)
@@ -73,41 +73,46 @@ class visualizeUtil:
mesh.export(model_path)
@staticmethod
def save_points_and_normals(root, scene, frame_idx, output_dir):
def save_points_and_normals(root, scene, frame_idx, output_dir, binocular=False):
target_mask_label = (0, 255, 0, 255)
path = DataLoadUtil.get_path(root, scene, frame_idx)
cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
depth_L,_ = DataLoadUtil.load_depth(
cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular, display_table_as_world_space_origin=False)
depth = DataLoadUtil.load_depth(
path, cam_info["near_plane"],
cam_info["far_plane"],
binocular=True,
binocular=binocular,
)
mask_L = DataLoadUtil.load_seg(path, binocular=True, left_only=True)
normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
if isinstance(depth, tuple):
depth = depth[0]
mask = DataLoadUtil.load_seg(path, binocular=binocular, left_only=True)
normal = DataLoadUtil.load_normal(path, binocular=binocular, left_only=True)
''' target points '''
target_mask_img_L = (mask_L == target_mask_label).all(axis=-1)
if mask is None:
target_mask_img = np.ones_like(depth, dtype=bool)
else:
target_mask_img = (mask == target_mask_label).all(axis=-1)
cam_intrinsic = cam_info["cam_intrinsic"]
z = depth_L[target_mask_img_L]
i, j = np.nonzero(target_mask_img_L)
z = depth[target_mask_img]
i, j = np.nonzero(target_mask_img)
x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
random_downsample_N = 1000
points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
normal_camera = normal_L[target_mask_img_L].reshape(-1, 3)
normal_camera = normal[target_mask_img].reshape(-1, 3)
sampled_target_points, idx = PtsUtil.random_downsample_point_cloud(
points_camera, random_downsample_N, require_idx=True
)
if len(sampled_target_points) == 0:
print("No target points")
offset = np.asarray([[1, 0, 0], [0, -1, 0], [0, 0, -1]])
sampled_normal_camera = normal_camera[idx]
sampled_normal_camera = np.dot(sampled_normal_camera, offset)
sampled_visualized_normal = []
sampled_normal_camera[:, 2] = -sampled_normal_camera[:, 2]
sampled_normal_camera[:, 1] = -sampled_normal_camera[:, 1]
num_samples = 10
for i in range(len(sampled_target_points)):
sampled_visualized_normal.append([sampled_target_points[i] + 0.02*t * sampled_normal_camera[i] for t in range(num_samples)])
@@ -123,10 +128,10 @@ class visualizeUtil:
if __name__ == "__main__":
root = r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\temp"
model_dir = r"H:\\AI\\Datasets\\scaled_object_box_meshes"
scene = "omniobject3d-box_030"
scene = "test_obj"
output_dir = r"C:\Document\Local Project\nbv_rec\nbv_reconstruction\test"
# visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir)
# visualizeUtil.save_all_combined_pts(root, scene, output_dir)
# visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene)
visualizeUtil.save_points_and_normals(root, scene, 0, output_dir)
visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir)
visualizeUtil.save_all_combined_pts(root, scene, output_dir)
visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene)
#visualizeUtil.save_points_and_normals(root, scene,"10", output_dir, binocular=True)