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from typing import Union
from pathlib import Path
import argparse
import os
import subprocess
import numpy as np
import glob
import cv2
import torch
import shutil
from PIL import Image
from sklearn.neighbors import NearestNeighbors
import supervision as sv
import open3d as o3d
import time
import yaml
import json
from dgl.geometry import farthest_point_sampler
import sys
sys.path.append(str(Path(__file__).parent.parent))
sys.path.append(str(Path(__file__).parent.parent.parent))
from pgnd.utils import get_root
from pgnd.ffmpeg import make_video
root: Path = get_root(__file__)
from utils.pcd_utils import visualize_o3d, depth2fgpcd
from utils.env_utils import get_bounding_box
from gs.trainer import GSTrainer
from gs.convert import save_to_splat
def load_camera(episode_data_dir):
intr = np.load(episode_data_dir / 'calibration' / 'intrinsics.npy').astype(np.float32)
rvec = np.load(episode_data_dir / 'calibration' / 'rvecs.npy')
tvec = np.load(episode_data_dir / 'calibration' / 'tvecs.npy')
R = [cv2.Rodrigues(rvec[i])[0] for i in range(rvec.shape[0])]
T = [tvec[i, :, 0] for i in range(tvec.shape[0])]
extrs = np.zeros((len(R), 4, 4)).astype(np.float32)
for i in range(len(R)):
extrs[i, :3, :3] = R[i]
extrs[i, :3, 3] = T[i]
extrs[i, 3, 3] = 1
return intr, extrs
def project(points, intr, extr):
# extr: (4, 4)
# intr: (3, 3)
# points: (n_points, 3)
points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1)
points = points @ extr.T # (n_points, 4)
points = points[:, :3] / points[:, 2:3] # (n_points, 3)
points = points @ intr.T
points = points[:, :2] / points[:, 2:3] # (n_points, 2)
return points
def reproject(depth, intr, extr):
xx, yy = np.meshgrid(np.arange(depth.shape[1]), np.arange(depth.shape[0]))
xx = xx.flatten()
yy = yy.flatten()
points = np.stack([xx, yy, depth.flatten()], axis=1)
mask = depth.flatten() > 0
mask = np.logical_and(mask, depth.flatten() < 2)
points = points[mask]
fx = intr[0, 0]
fy = intr[1, 1]
cx = intr[0, 2]
cy = intr[1, 2]
points[:, 0] = (points[:, 0] - cx) / fx * points[:, 2]
points[:, 1] = (points[:, 1] - cy) / fy * points[:, 2]
points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1)
inv_extr = np.linalg.inv(extr)
points = points @ inv_extr.T
return points[:, :3]
class GSProcessor:
def __init__(self, name, n_his_frames, device='cuda', episode_range=None):
self.name = name
self.n_his_frames = n_his_frames
self.data_dir = root / "log" / "data" / name
self.device = device
if 'merged' in name:
self.is_merged = True
assert os.path.exists(self.data_dir)
info = json.load(open(self.data_dir / 'sub_episodes_v' / 'info.json'))
source_eval_list_all = []
for source_dir, episode_list in info.items():
if source_dir in ['n_train', 'n_eval']:
continue
source_name = Path(source_dir).parent.name
eval_episode_start, eval_episode_end = episode_list[2], episode_list[3]
source_eval_list = [] # (episode_id, start_frame, end_frame)
source_data_dir = root.parent / Path(source_dir).parent
if not os.path.exists(root.parent / source_dir):
source_data_dir = Path('/data/meta-material/data') / source_name
for eval_episode in range(eval_episode_start, eval_episode_end):
assert os.path.exists(source_data_dir / 'sub_episodes_v' / f'episode_{eval_episode:04d}' / 'meta.txt')
meta = np.loadtxt(source_data_dir / 'sub_episodes_v' / f'episode_{eval_episode:04d}' / 'meta.txt')
source_eval_list.append((source_data_dir, int(meta[0]), int(meta[1]), int(meta[2])))
source_eval_list_all.append(source_eval_list)
episode_range = source_eval_list_all
else:
self.is_merged = False
if episode_range is None:
n_episodes = len(glob.glob(str(self.data_dir / "episode*")))
episode_range = np.arange(n_episodes)
self.episodes = episode_range
n_cameras = 4
self.cameras = np.arange(n_cameras)
self.max_frames = 10000
self.H, self.W = 480, 848
self.bbox = get_bounding_box() # 3D bounding box of the scene
with open(root / "real_world" / "gs" / "config" / "default.yaml", 'r') as f:
gs_config = yaml.load(f, Loader=yaml.CLoader)
self.gs_trainer = GSTrainer(gs_config, device=self.device)
def get_gaussian(self):
if self.is_merged:
for source_eval_list in self.episodes:
self.get_gaussian_single_source(source_eval_list)
else:
self.get_gaussian_single_source(self.episodes)
def get_gaussian_single_source(self, episodes):
for episode in episodes:
if isinstance(episode, tuple):
data_dir = episode[0]
episode_id = episode[1]
start_frame = episode[2]
end_frame = episode[3]
start_frame = start_frame + self.n_his_frames
episode_data_dir = data_dir / f"episode_{episode_id:04d}"
os.makedirs(episode_data_dir / "gs", exist_ok=True)
intrs, extrs = load_camera(episode_data_dir)
pcd_paths = sorted(glob.glob(str(episode_data_dir / "pcd_clean_new" / "*.npz")))
n_frames = min(len(pcd_paths), self.max_frames)
assert start_frame < n_frames, f"episode {episode}"
assert end_frame <= n_frames
else:
episode = int(episode)
data_dir = self.data_dir
episode_id = episode
start_frame = 0
episode_data_dir = data_dir / f"episode_{episode_id:04d}"
os.makedirs(episode_data_dir / "gs", exist_ok=True)
intrs, extrs = load_camera(episode_data_dir)
pcd_paths = sorted(glob.glob(str(episode_data_dir / "pcd_clean_new" / "*.npz")))
n_frames = min(len(pcd_paths), self.max_frames)
end_frame = n_frames
pivot_skip = 120
for frame_id in range(start_frame, end_frame, pivot_skip):
print(f'[get_gaussian] processing episode {episode_id}, frame {frame_id}')
if os.path.exists(os.path.join(episode_data_dir / 'gs' / f'{frame_id:06d}.splat')):
print(f'[get_gaussian] already processed, skipping')
continue
pcd_npz = np.load(episode_data_dir / "pcd_clean_new" / f"{frame_id:06d}.npz")
pts = pcd_npz['pts']
colors = pcd_npz['colors']
# downsample
n_points = 3000
particle_tensor = torch.from_numpy(pts).float().cuda()[None]
fps_idx_tensor = farthest_point_sampler(particle_tensor, n_points, start_idx=0)[0]
pts = pts[fps_idx_tensor.cpu().numpy()]
colors = colors[fps_idx_tensor.cpu().numpy()]
if colors.max() > 1:
colors = colors.astype(np.float32) / 255
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pts)
pcd.colors = o3d.utility.Vector3dVector(colors)
imgs = []
masks = []
R_list = []
t_list = []
intr_list = []
for camera in self.cameras:
rgb_path = str(episode_data_dir / f'camera_{camera}' / 'rgb' / f'{frame_id:06d}.jpg')
mask_path = str(episode_data_dir / f'camera_{camera}' / 'mask' / f'{frame_id:06d}.png')
img = cv2.imread(rgb_path) # bgr
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)
mask = mask > 0 # binary mask
imgs.append(img)
masks.append(mask)
w2c = extrs[camera]
c2w = np.linalg.inv(w2c)
R = c2w[:3, :3]
t = c2w[:3, 3]
R_list.append(R)
t_list.append(t)
intr_list.append(intrs[camera])
# save
debug_vis = False
if debug_vis:
for i in range(len(imgs)):
# project points
points = project(pts, intrs[i], extrs[i])
points = points.astype(np.int32)
img = imgs[i].copy()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
for j in range(points.shape[0]):
if points[j, 0] < 0 or points[j, 0] >= self.W or points[j, 1] < 0 or points[j, 1] >= self.H:
continue
if not masks[i][points[j, 1], points[j, 0]]:
continue
cv2.circle(img, (points[j, 0], points[j, 1]), 2, (255, 0, 0), -1)
cv2.imwrite(f"{frame_id:06d}_{i}_proj.png", img)
cv2.imwrite(f"{frame_id:06d}_{i}_rgb.png", cv2.cvtColor(imgs[i], cv2.COLOR_RGB2BGR))
cv2.imwrite(f"{frame_id:06d}_{i}_mask.png", (masks[i] * 1).astype(np.uint8))
self.gs_trainer.clear(clear_params=True)
self.gs_trainer.update_state_no_env(pcd, imgs, masks, R_list, t_list, intr_list, n_cameras=4)
os.makedirs(root / "log/gs/train", exist_ok=True)
self.gs_trainer.train(vis_dir=str(root / "log/gs/train"))
self.gs_dir = os.path.join(episode_data_dir / "gs" / f'{frame_id:06d}.splat')
save_to_splat(
pts=self.gs_trainer.params['means3D'].detach().cpu().numpy(),
colors=self.gs_trainer.params['rgb_colors'].detach().cpu().numpy(),
scales=torch.exp(self.gs_trainer.params['log_scales']).detach().cpu().numpy(),
quats=torch.nn.functional.normalize(self.gs_trainer.params['unnorm_rotations'], dim=-1).detach().cpu().numpy(),
opacities=torch.sigmoid(self.gs_trainer.params['logit_opacities']).detach().cpu().numpy(),
output_file=self.gs_dir,
center=False, # do not center the points
rotate=False, # do not rotate the points to swap z and y
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--name', type=str, default='rope_merged')
parser.add_argument('--n_his_frames', type=int, default=6)
parser.add_argument('--device', type=int, default=0)
args = parser.parse_args()
device = f"cuda:{args.device}"
pp = GSProcessor(args.name, args.n_his_frames, device)
pp.get_gaussian()
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