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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
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 export_scene(scene_id, scene_params, processing_args):
data = process_scene(scene_params)
out_path = processing_args["out_dir"] / scene_id
K_color = data[0]["image_K"]
K_depth = data[0]["depth_K"]
def proc_k(K):
res = np.eye(4)
res[:3, :3] = K[:3, :3]
return res
K_color = proc_k(K_color)
K_depth = proc_k(K_depth)
intrinsics_path = out_path / "intrinsic"
intrinsics_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))
all_pts = []
all_colors = []
for i, item in enumerate(data):
img_name = Path(item["image_path"]).stem
image_path = out_path / "color" / f"{img_name}.jpg"
image_path.parent.mkdir(parents=True, exist_ok=True)
try:
os.symlink(item["image_path"], image_path)
except FileExistsError:
pass
image = cv2.imread(item["image_path"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, item['shape_original'][::-1]) / 255.
depth_path = out_path / "depth" / f"{img_name}.png"
depth_path.parent.mkdir(parents=True, exist_ok=True)
cv2.imwrite(depth_path, (item["depth"] * 1000).astype(np.uint16))
pose_path = out_path / "pose" / f"{img_name}.txt"
pose_path.parent.mkdir(parents=True, exist_ok=True)
np.savetxt(pose_path, item["pose"])
pts = item["pts3d"][item["im_conf"] > processing_args["confidence_threshold"]]
image = image[item["im_conf"] > processing_args["confidence_threshold"]]
all_pts.append(pts.view(-1, 3))
all_colors.append(image.reshape(-1, 3))
all_pts = np.concatenate(all_pts, axis=0)
all_colors = np.concatenate(all_colors, axis=0)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(all_pts)
pcd.colors = o3d.utility.Vector3dVector(all_colors)
pcd = pcd.voxel_down_sample(voxel_size=processing_args["voxel_size"])
o3d.io.write_point_cloud(out_path / f"{scene_id}_vh_clean_2.ply", pcd)
processing_args = {
"confidence_threshold": 1,
"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_dust3r_posed/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]
dut3r_path = Path("/home/jovyan/users/lemeshko/Indoor/DUSt3R/res/arkit_posed")
for scene in tqdm(val_scenes):
scene_path = dut3r_path / scene
scene_params = torch.load(scene_path / "scene_params.pt")
export_scene(scene, scene_params, processing_args) |