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1fab053 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import csv
import sys
from pathlib import Path
from typing import Optional, Sequence
import numpy as np
from PIL import Image
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from data.instant_gaussian_nersemble_dataset import InstantGaussianNersembleDataset
from model.xnemo_pose_preprocess import FaceNotFoundError, XNemoPosePreprocessor
def main() -> int:
args = parse_args()
subjects = parse_list(args.subjects)
cameras = parse_list(args.cameras)
sequences = parse_list(args.sequences)
dataset = InstantGaussianNersembleDataset(
root=args.root,
split=args.split,
subjects=subjects,
cameras=cameras,
sequences=sequences,
frame_stride=args.frame_stride,
max_frames_per_sequence=args.max_frames_per_sequence,
pose_mode="none",
return_metadata=False,
)
records = list(dataset.records)
if args.num_shards <= 0:
raise ValueError("--num-shards must be positive.")
if not 0 <= args.shard_index < args.num_shards:
raise ValueError("--shard-index must be in [0, num_shards).")
if args.num_shards > 1:
records = [record for i, record in enumerate(records) if i % args.num_shards == args.shard_index]
if args.limit is not None:
records = records[: args.limit]
cache_root = Path(args.pose_cache_root)
preview_root = Path(args.preview_root) if args.preview_root is not None else None
failure_log = Path(args.failure_log) if args.failure_log is not None else None
if failure_log is not None:
failure_log.parent.mkdir(parents=True, exist_ok=True)
print(f"records: {len(records)}")
print(f"pose cache root: {cache_root}")
if args.dry_run:
for record in records[: min(10, len(records))]:
print(record_to_cache_path(cache_root, record))
return 0
preprocessor = XNemoPosePreprocessor(
xnemo_root=args.xnemo_root,
output_size=args.output_size,
backend=args.backend,
crop=not args.no_crop,
bbox_scale=args.bbox_scale,
bbox_param_mode=args.bbox_param_mode,
min_detection_confidence=args.min_detection_confidence,
)
failures: list[tuple[str, str]] = []
counters = {"done": 0, "skipped": 0, "failed": 0}
iterator = progress(records, enabled=not args.no_progress)
try:
for record in iterator:
cache_path = record_to_cache_path(cache_root, record)
if cache_path.exists() and not args.overwrite:
counters["skipped"] += 1
continue
try:
image = dataset._load_image(record)
result = preprocessor(image)
except FaceNotFoundError as exc:
counters["failed"] += 1
failures.append((str(record.processed_dir), f"face_not_found: {exc}"))
if args.fail_fast:
raise
continue
except Exception as exc:
counters["failed"] += 1
failures.append((str(record.processed_dir), repr(exc)))
if args.fail_fast:
raise
continue
pose_tensor = result.pose_tensor.float().cpu().numpy().astype(np.float32)
payload = {
"pose_image": result.pose_image.astype(np.uint8),
"pose_tensor": pose_tensor,
"bbox_param": result.bbox_param.cpu().numpy().astype(np.float32),
"bbox": result.bbox.astype(np.float32),
"crop_bbox": result.crop_bbox.astype(np.float32),
"landmarks": result.landmarks.astype(np.float32),
}
cache_path.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(cache_path, **payload)
counters["done"] += 1
if preview_root is not None:
preview_path = record_to_cache_path(preview_root, record).with_suffix(".png")
preview_path.parent.mkdir(parents=True, exist_ok=True)
Image.fromarray(result.pose_image).save(preview_path)
finally:
preprocessor.close()
if failure_log is not None and failures:
write_failures(failure_log, failures)
print(
"finished: "
f"done={counters['done']} "
f"skipped={counters['skipped']} "
f"failed={counters['failed']}"
)
if failures:
print(f"failure log: {failure_log if failure_log is not None else '(not written)'}")
return 1 if failures and args.fail_on_any_failure else 0
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Precompute X-NeMo face-mesh RGB pose image cache for FastAvatar NeRSemble data."
)
parser.add_argument("--root", default="/workspace/cpfs-data/data/nersemble_fastavatar")
parser.add_argument(
"--pose-cache-root",
default="/workspace/cpfs-data/data/nersemble_fastavatar_xnemo_pose_cache",
)
parser.add_argument("--xnemo-root", default="/workspace/cpfs-data/code/gha/x-nemo-inference")
parser.add_argument("--split", default="all", choices=["all", "train", "val", "test"])
parser.add_argument("--subjects", nargs="*", default=None)
parser.add_argument("--cameras", nargs="*", default=None)
parser.add_argument("--sequences", nargs="*", default=None)
parser.add_argument("--frame-stride", type=int, default=1)
parser.add_argument("--max-frames-per-sequence", type=int, default=None)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--num-shards", type=int, default=1)
parser.add_argument("--shard-index", type=int, default=0)
parser.add_argument("--backend", default="auto", choices=["auto", "xnemo_task", "mediapipe"])
parser.add_argument("--output-size", type=int, default=224)
parser.add_argument("--bbox-scale", type=float, default=1.1)
parser.add_argument("--bbox-param-mode", default="identity", choices=["identity", "computed"])
parser.add_argument("--min-detection-confidence", type=float, default=0.5)
parser.add_argument("--no-crop", action="store_true")
parser.add_argument("--overwrite", action="store_true")
parser.add_argument("--dry-run", action="store_true")
parser.add_argument("--fail-fast", action="store_true")
parser.add_argument("--fail-on-any-failure", action="store_true")
parser.add_argument("--failure-log", default="preprocess_xnemo_pose_failures.csv")
parser.add_argument("--preview-root", default=None)
parser.add_argument("--no-progress", action="store_true")
return parser.parse_args()
def parse_list(values: Optional[Sequence[str]]) -> Optional[list[str]]:
if values is None:
return None
out: list[str] = []
for value in values:
for item in str(value).split(","):
item = item.strip()
if item:
out.append(item)
return out or None
def record_to_cache_path(root: Path, record) -> Path:
return (
root
/ record.subject
/ record.camera_dir
/ record.sequence
/ f"{record.frame_index:05d}.npz"
)
def write_failures(path: Path, failures: Sequence[tuple[str, str]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(["processed_dir", "error"])
writer.writerows(failures)
def progress(records, enabled: bool):
if not enabled:
return records
try:
from tqdm import tqdm
return tqdm(records, desc="precompute pose", dynamic_ncols=True)
except Exception:
return records
if __name__ == "__main__":
raise SystemExit(main())
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