update preprocessor
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@ -7,9 +7,9 @@ runner:
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name: debug
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root_dir: experiments
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generate:
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port: 5002
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from: 2200
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to: 2300 # -1 means all
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port: 5005
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from: 2300
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to: 2800 # -1 means all
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object_dir: /media/hofee/data/data/scaled_object_meshes
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table_model_path: /media/hofee/data/data/others/table.obj
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output_dir: /media/hofee/repository/new_data_with_normal
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@ -49,4 +49,4 @@ runner:
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Light:
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location: [0,0,3.5]
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orientation: [0,0,0]
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power: 150
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power: 150
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@ -30,13 +30,15 @@ def save_target_points(root, scene, frame_idx, target_points: np.ndarray, file_t
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os.makedirs(os.path.join(root,scene, "target_pts"))
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save_np_pts(pts_path, target_points, file_type)
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def save_mask_idx(root, scene, frame_idx, mask_idx: np.ndarray,filtered_idx, file_type="txt"):
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indices_path = os.path.join(root,scene, "mask_idx", f"{frame_idx}.{file_type}")
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def save_mask_idx(root, scene, frame_idx, mask_train_input: np.ndarray, mask_overlap, file_type="txt"):
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mask_train_input_path = os.path.join(root,scene, "mask_idx", f"mask_train_input_{frame_idx}.{file_type}")
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mask_overlap_path = os.path.join(root,scene, "mask_idx", f"mask_overlap_{frame_idx}.{file_type}")
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if not os.path.exists(os.path.join(root,scene, "mask_idx")):
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os.makedirs(os.path.join(root,scene, "mask_idx"))
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save_np_pts(indices_path, mask_idx, file_type)
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filtered_path = os.path.join(root,scene, "mask_idx", f"{frame_idx}_filtered.{file_type}")
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save_np_pts(filtered_path, filtered_idx, file_type)
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save_np_pts(mask_train_input_path, mask_train_input, file_type)
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save_np_pts(mask_overlap_path, mask_overlap, file_type)
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# filtered_path = os.path.join(root,scene, "mask_idx", f"{frame_idx}_filtered.{file_type}")
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# save_np_pts(filtered_path, filtered_idx, file_type)
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def save_scan_points_indices(root, scene, frame_idx, scan_points_indices: np.ndarray, file_type="txt"):
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indices_path = os.path.join(root,scene, "scan_points_indices", f"{frame_idx}.{file_type}")
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@ -62,14 +64,9 @@ def get_world_points(depth, cam_intrinsic, cam_extrinsic):
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points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
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return points_camera_world
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def get_world_normals(normals, cam_extrinsic):
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normals = normals / np.linalg.norm(normals, axis=1, keepdims=True)
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normals_world = np.dot(cam_extrinsic[:3, :3], normals.T).T
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return normals_world
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def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_intrinsic, cam_extrinsic):
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scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1))))
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points_camera = np.dot(cam_extrinsic, scan_points_homogeneous.T).T[:, :3]
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points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3]
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points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T
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points_image_homogeneous /= points_image_homogeneous[:, 2:]
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pixel_x = points_image_homogeneous[:, 0].astype(int)
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@ -77,11 +74,12 @@ def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_int
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h, w = mask.shape[:2]
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valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h)
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mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]]
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selected_points_indices = mask_colors == display_table_mask_label
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selected_points_indices = np.where((mask_colors == display_table_mask_label).all(axis=-1))[0]
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selected_points_indices = np.where(valid_indices)[0][selected_points_indices]
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return selected_points_indices
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def save_scene_data(root, scene, scene_idx=0, scene_total=1):
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def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
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''' configuration '''
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target_mask_label = (0, 255, 0, 255)
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@ -99,7 +97,11 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1):
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''' read frame data(depth|mask|normal) '''
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frame_num = DataLoadUtil.get_scene_seq_length(root, scene)
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for frame_id in range(frame_num):
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print(f"[scene({scene_idx}/{scene_total})|frame({frame_id}/{frame_num})]Processing {scene} frame {frame_id}")
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if frame_id != 126:
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continue
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path = DataLoadUtil.get_path(root, scene, frame_id)
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cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
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depth_L, depth_R = DataLoadUtil.load_depth(
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@ -107,8 +109,8 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1):
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cam_info["far_plane"],
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binocular=True
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)
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mask_L = DataLoadUtil.load_seg(path, binocular=True, left_only=True)
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normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
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mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True)
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#normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
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''' scene points '''
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scene_points_L = get_world_points(depth_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"])
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@ -131,28 +133,30 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1):
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)
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''' target points '''
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mask_img = mask_L
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mask_img_L = mask_L
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mask_img_R = mask_R
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mask_L = mask_L.reshape(-1, 4)
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mask_L = (mask_L == target_mask_label).all(axis=-1)
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mask_overlap = mask_L[random_sample_idx_L][overlap_idx_L]
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scene_normals_L = normal_L.reshape(-1, 3)
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target_overlap_normals = scene_normals_L[random_sample_idx_L][overlap_idx_L][mask_overlap]
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target_normals = get_world_normals(target_overlap_normals, cam_info["cam_to_world"])
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target_points = scene_overlap_points[mask_overlap]
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filtered_target_points, filtered_idx = PtsUtil.filter_points(
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target_points, target_normals, cam_info["cam_to_world"], filter_degree, require_idx=True
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)
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''' train_input_mask '''
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mask_train_input = mask_overlap[train_input_idx]
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''' scan points indices '''
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scan_points_indices = get_scan_points_indices(scan_points, mask_img, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"])
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save_full_points(root, scene, frame_id, train_input_points)
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save_target_points(root, scene, frame_id, filtered_target_points)
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save_mask_idx(root, scene, frame_id, mask_train_input, filtered_idx=filtered_idx)
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scan_points_indices_L = get_scan_points_indices(scan_points, mask_img_L, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"])
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scan_points_indices_R = get_scan_points_indices(scan_points, mask_img_R, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"])
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scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R)
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print(scan_points_indices.shape, scan_points_indices_L.shape, scan_points_indices_R.shape)
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# np.savetxt(f"{root}/{scene}/scan_points_{frame_id}_L.txt", scan_points[scan_points_indices_L])
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np.savetxt(f"{root}/{scene}/scan_points_{frame_id}.txt", scan_points[scan_points_indices])
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save_full_points(root, scene, frame_id, train_input_points, file_type=file_type)
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save_target_points(root, scene, frame_id, target_points)
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save_mask_idx(root, scene, frame_id, mask_train_input, mask_overlap,file_type=file_type)
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save_scan_points_indices(root, scene, frame_id, scan_points_indices)
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save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess
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@ -170,8 +174,10 @@ if __name__ == "__main__":
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from_idx = 0
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to_idx = len(scene_list)
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cnt = 0
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total = to_idx - from_idx
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for scene in scene_list[from_idx:to_idx]:
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save_scene_data(root, scene, cnt, total)
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save_scene_data(root, scene, cnt, total, "txt")
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cnt+=1
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