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import pickle
from pathlib import Path
import torch
import cv2
import numpy as np
import os
import open3d as o3d
from tqdm import tqdm
from tqdm.contrib.concurrent import process_map, thread_map
from rec_utils.datasets import ARKitDataset, VGGTDataset
from rec_utils.aligner import build_aligner_1p1d
from rec_utils.datasets.arkit.utils import rotate_image
def process_scene(scene_params, target_depth_shape=None):
images = scene_params["image_files"]
poses = scene_params["poses"]
depths = scene_params["depths"]
Ks = scene_params["Ks"]
pts3d = scene_params["pts3d"]
im_confs = scene_params["im_conf"]
print(scene_params.keys())
im_shapes = scene_params["imshapes"]
im_shape = im_shapes[0]
image_hw = cv2.imread(images[0]).shape[:2]
image_scale = np.ones((3, 3))
image_scale[0] *= image_hw[1] / im_shape[1]
image_scale[1] *= image_hw[0] / im_shape[0]
if target_depth_shape is None:
target_depth_shape = image_hw
depth_scale = np.ones((3, 3))
depth_scale[0] *= target_depth_shape[1] / im_shape[1]
depth_scale[1] *= target_depth_shape[0] / im_shape[0]
data = [
{
"image_path": image,
"pose": pose,
"depth": cv2.resize(depth.numpy(), target_depth_shape[::-1], interpolation=cv2.INTER_LINEAR),
"source_K": K,
"image_K": K * image_scale,
"depth_K": K * depth_scale,
"pts3d": pts,
"im_conf": im_conf,
"im_shape_target": image_hw,
"depth_shape_target": target_depth_shape,
"shape_original": im_shape,
} for image, pose, depth, K, pts, im_conf in zip(images, poses, depths, Ks, pts3d, im_confs)
]
return data
def es_wrap(data):
try:
return export_scene(data)
except Exception as e:
print(e)
return None
def export_scene(data):
vggt_dataset, arkit_dataset, i, out_dir, processing_args = data
vggt_scene = vggt_dataset[i]
scene_id = vggt_scene.id
arkit_scene = arkit_dataset[scene_id]
out_path = out_dir / scene_id
out_path.mkdir(parents=True, exist_ok=True)
# if os.path.exists(out_path / f'{vggt_scene.id}_vh_clean_2.ply'):
# return
arkit_scene.frames = arkit_scene.frames[-100:]
aligner = build_aligner_1p1d(source_scene=vggt_scene, target_scene=arkit_scene)
scene = aligner.align(vggt_scene, inplace=True)
# scene = arkit_scene
K_color = arkit_scene[0].image_intrinsics
K_depth = arkit_scene[0].depth_intrinsics
intrinsics_path = out_path / "intrinsic"
intrinsics_path.mkdir(parents=True, exist_ok=True)
color_path = out_path / "color"
color_path.mkdir(parents=True, exist_ok=True)
depth_path = out_path / "depth"
depth_path.mkdir(parents=True, exist_ok=True)
pose_path = out_path / "pose"
pose_path.mkdir(parents=True, exist_ok=True)
np.savetxt(intrinsics_path / "intrinsic_color.txt", K_color)
np.savetxt(intrinsics_path / "intrinsic_depth.txt", K_depth)
np.savetxt(intrinsics_path / "extrinsic_color.txt", np.eye(4))
np.savetxt(intrinsics_path / "extrinsic_depth.txt", np.eye(4))
tsdffusion = o3d.pipelines.integration.ScalableTSDFVolume(
voxel_length=0.025,
sdf_trunc=0.1,
color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8
)
print("rotation angle", arkit_scene.rotation_angle)
for frame in tqdm(scene.frames):
# image = rotate_image(frame.image, arkit_scene.rotation_angle)
# # image = frame.image
# h, w = image.shape[:2]
# depth = frame.depth.astype(np.float32)
# depth = cv2.resize(depth, (w, h), interpolation=cv2.INTER_LINEAR)
# color = o3d.geometry.Image(image)
np.savetxt(str(pose_path / f'{frame.frame_id}.txt'), frame.pose)
# cv2.imwrite(str(color_path / f'{frame.frame_id}.jpg'), image[..., ::-1])
# cv2.imwrite(str(depth_path / f'{frame.frame_id}.png'), (depth * 1000.).astype(np.uint16))
# fx, fy = K_color[0, 0], K_color[1, 1]
# cx, cy = K_color[0, 2], K_color[1, 2]
# dh, dw = depth.shape
# depth_o3d = o3d.geometry.Image(depth)
# rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
# color, depth_o3d, depth_trunc=10., convert_rgb_to_intensity=False, depth_scale=1.0
# )
# camera_o3d = o3d.camera.PinholeCameraIntrinsic(w, h, fx, fy, cx, cy)
# tsdffusion.integrate(
# rgbd, camera_o3d,
# np.linalg.inv(frame.pose),
# )
# pc = tsdffusion.extract_point_cloud()
# pc.voxel_down_sample(voxel_size=0.025)
# o3d.io.write_point_cloud(str(out_path / f'{scene.id}_vh_clean_2.ply'), pc)
vggt_dataset = VGGTDataset("/home/jovyan/users/bulat/workspace/3drec/vggt/output/arkit_new/")
arkit_dataset = ARKitDataset("/workspace-SR006.nfs2/datasets/arkitscenes/offline_prepared_data/posed_images/")
processing_args = {
"voxel_size": 0.025,
"out_dir": Path("data/arkit_dust3r_posed/processed"),
}
val_path = Path("../") / "OKNO/data/arkitscenes/arkitscenes_offline_infos_val.pkl"
out_dir = Path("data/arkit_vggt/processed")
with open(val_path, "rb") as f:
data = pickle.load(f)
data_list = data["data_list"]
val_scenes = [scene["lidar_points"]["lidar_path"] for scene in data_list]
def extract_name(item):
return item.split("_")[0]
val_scenes = [extract_name(scene) for scene in val_scenes]
data = [(vggt_dataset, arkit_dataset, i, out_dir, processing_args) for i in range(len(vggt_dataset))]
thread_map(es_wrap, data, chunksize=128)
# break
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