nbv_reconstruction/preprocess/preprocessor.py

import os
import json
import numpy as np

import sys
np.random.seed(0)
# append parent directory to sys.path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
print(sys.path)

from utils.reconstruction import ReconstructionUtil
from utils.data_load import DataLoadUtil
from utils.pts import PtsUtil

def save_np_pts(path, pts: np.ndarray, file_type="txt"):
    if file_type == "txt":
        np.savetxt(path, pts)
    else:
        np.save(path, pts)

def save_full_points(root, scene, frame_idx, full_points: np.ndarray, file_type="txt"):
    pts_path = os.path.join(root,scene, "scene_pts", f"{frame_idx}.{file_type}")
    if not os.path.exists(os.path.join(root,scene, "scene_pts")):
        os.makedirs(os.path.join(root,scene, "scene_pts"))
    save_np_pts(pts_path, full_points, file_type)

def save_target_points(root, scene, frame_idx, target_points: np.ndarray, file_type="txt"):
    pts_path = os.path.join(root,scene, "target_pts", f"{frame_idx}.{file_type}")
    if not os.path.exists(os.path.join(root,scene, "target_pts")):
        os.makedirs(os.path.join(root,scene, "target_pts"))
    save_np_pts(pts_path, target_points, file_type)
    
def save_mask_idx(root, scene, frame_idx, mask_train_input: np.ndarray, mask_overlap, file_type="txt"):
    mask_train_input_path = os.path.join(root,scene, "mask_idx", f"mask_train_input_{frame_idx}.{file_type}")
    mask_overlap_path = os.path.join(root,scene, "mask_idx", f"mask_overlap_{frame_idx}.{file_type}")
    if not os.path.exists(os.path.join(root,scene, "mask_idx")):
        os.makedirs(os.path.join(root,scene, "mask_idx"))
    save_np_pts(mask_train_input_path, mask_train_input, file_type)
    save_np_pts(mask_overlap_path, mask_overlap, file_type)
    # filtered_path = os.path.join(root,scene, "mask_idx", f"{frame_idx}_filtered.{file_type}")
    # save_np_pts(filtered_path, filtered_idx, file_type)
    
def save_scan_points_indices(root, scene, frame_idx, scan_points_indices: np.ndarray, file_type="txt"):
    indices_path = os.path.join(root,scene, "scan_points_indices", f"{frame_idx}.{file_type}")
    if not os.path.exists(os.path.join(root,scene, "scan_points_indices")):
        os.makedirs(os.path.join(root,scene, "scan_points_indices"))
    save_np_pts(indices_path, scan_points_indices, file_type)

def save_scan_points(root, scene, scan_points: np.ndarray):
    scan_points_path = os.path.join(root,scene, "scan_points.txt")
    save_np_pts(scan_points_path, scan_points)
    

def get_world_points(depth, cam_intrinsic, cam_extrinsic):
    h, w = depth.shape
    i, j = np.meshgrid(np.arange(w), np.arange(h), indexing="xy")

    z = depth
    x = (i - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
    y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]

    points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
    points_camera_aug = np.concatenate((points_camera, np.ones((points_camera.shape[0], 1))), axis=-1)
    points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
    return points_camera_world

def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_intrinsic, cam_extrinsic):
    scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1))))
    points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3]
    points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T
    points_image_homogeneous /= points_image_homogeneous[:, 2:]
    pixel_x = points_image_homogeneous[:, 0].astype(int)
    pixel_y = points_image_homogeneous[:, 1].astype(int)
    h, w = mask.shape[:2]
    valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h)
    mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]]
    selected_points_indices = np.where((mask_colors == display_table_mask_label).all(axis=-1))[0]
    selected_points_indices = np.where(valid_indices)[0][selected_points_indices]
    return selected_points_indices
    

def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
    
    ''' configuration '''
    target_mask_label = (0, 255, 0, 255)
    display_table_mask_label=(0, 0, 255, 255)
    random_downsample_N = 65536
    train_input_pts_num = 8192
    voxel_size=0.002
    filter_degree = 75
    
    ''' scan points '''
    display_table_info = DataLoadUtil.get_display_table_info(root, scene)
    radius = display_table_info["radius"]
    scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
    
    ''' read frame data(depth|mask|normal) '''
    frame_num = DataLoadUtil.get_scene_seq_length(root, scene)
    for frame_id in range(frame_num):
    
        
        print(f"[scene({scene_idx}/{scene_total})|frame({frame_id}/{frame_num})]Processing {scene} frame {frame_id}")
        if frame_id != 126:
            continue
        path = DataLoadUtil.get_path(root, scene, frame_id)
        cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
        depth_L, depth_R = DataLoadUtil.load_depth(
                path, cam_info["near_plane"], 
                cam_info["far_plane"], 
                binocular=True
            )
        mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True)
        #normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
        
        ''' scene points '''
        scene_points_L = get_world_points(depth_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"])
        scene_points_R = get_world_points(depth_R, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"])
        sampled_scene_points_L, random_sample_idx_L = PtsUtil.random_downsample_point_cloud(
                scene_points_L, random_downsample_N, require_idx=True
            )
        sampled_scene_points_R = PtsUtil.random_downsample_point_cloud(
                scene_points_R, random_downsample_N
            )
        scene_overlap_points, overlap_idx_L = PtsUtil.get_overlapping_points(
                sampled_scene_points_L, sampled_scene_points_R, voxel_size, require_idx=True
            )
        
        if scene_overlap_points.shape[0] < 1024:
            scene_overlap_points = sampled_scene_points_L
            overlap_idx_L = np.arange(sampled_scene_points_L.shape[0])
        train_input_points, train_input_idx = PtsUtil.random_downsample_point_cloud(
                scene_overlap_points, train_input_pts_num, require_idx=True
            )
        
        ''' target points '''
        mask_img_L = mask_L
        mask_img_R = mask_R
        mask_L = mask_L.reshape(-1, 4)
        mask_L = (mask_L == target_mask_label).all(axis=-1)
        mask_overlap = mask_L[random_sample_idx_L][overlap_idx_L]
        target_points = scene_overlap_points[mask_overlap]
        filtered_target_points, filtered_idx = PtsUtil.filter_points(
            target_points, target_normals, cam_info["cam_to_world"], filter_degree, require_idx=True
            )

        ''' train_input_mask '''
        mask_train_input = mask_overlap[train_input_idx]
        
        
        ''' scan points indices '''
        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"]) 
        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"])
        scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R)
        print(scan_points_indices.shape, scan_points_indices_L.shape, scan_points_indices_R.shape)
        # np.savetxt(f"{root}/{scene}/scan_points_{frame_id}_L.txt", scan_points[scan_points_indices_L])
        np.savetxt(f"{root}/{scene}/scan_points_{frame_id}.txt", scan_points[scan_points_indices])
        save_full_points(root, scene, frame_id, train_input_points, file_type=file_type)
        save_target_points(root, scene, frame_id, target_points)
        save_mask_idx(root, scene, frame_id, mask_train_input, mask_overlap,file_type=file_type)
        save_scan_points_indices(root, scene, frame_id, scan_points_indices)
    
    save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess


if __name__ == "__main__":
    #root = "/media/hofee/repository/new_data_with_normal"
    root = "/media/hofee/repository/test_sample"
    list_path = "/media/hofee/repository/test_sample/test_sample_list.txt"
    scene_list = []
    
    with open(list_path, "r") as f:
        for line in f:
            scene_list.append(line.strip())

    from_idx = 0
    to_idx = len(scene_list)


    cnt = 0
    total = to_idx - from_idx
    for scene in scene_list[from_idx:to_idx]:
        save_scene_data(root, scene, cnt, total, "txt")
        cnt+=1