| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
| import cv2 |
| import numpy as np |
| from tqdm import tqdm |
| from ultralytics import YOLO |
|
|
| from common import ROOT, load_config |
| from dynafall.data import save_pickle |
| from dynafall.features import infer_label_from_path |
|
|
|
|
| VIDEO_EXTS = {".avi", ".mp4", ".mov", ".mkv", ".mpg", ".mpeg"} |
| IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".bmp"} |
|
|
|
|
| def largest_person(result) -> np.ndarray: |
| if result.keypoints is None or result.boxes is None or len(result.boxes) == 0: |
| return np.zeros((17, 3), dtype=np.float32) |
| boxes = result.boxes.xyxy.detach().cpu().numpy() |
| areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) |
| idx = int(np.argmax(areas)) |
| xy = result.keypoints.xy[idx].detach().cpu().numpy() |
| conf = result.keypoints.conf[idx].detach().cpu().numpy() |
| return np.concatenate([xy, conf[:, None]], axis=1).astype(np.float32) |
|
|
|
|
| def extract_video(path: Path, model: YOLO, conf: float) -> np.ndarray: |
| cap = cv2.VideoCapture(str(path)) |
| frames = [] |
| ok, frame = cap.read() |
| while ok: |
| result = model.predict(frame, conf=conf, verbose=False)[0] |
| frames.append(largest_person(result)) |
| ok, frame = cap.read() |
| cap.release() |
| return np.asarray(frames, dtype=np.float32) |
|
|
|
|
| def extract_image_dir(path: Path, model: YOLO, conf: float) -> np.ndarray: |
| frames = [] |
| images = sorted(p for p in path.iterdir() if p.suffix.lower() in IMAGE_EXTS) |
| for image_path in images: |
| frame = cv2.imread(str(image_path)) |
| if frame is None: |
| continue |
| result = model.predict(frame, conf=conf, verbose=False)[0] |
| frames.append(largest_person(result)) |
| return np.asarray(frames, dtype=np.float32) |
|
|
|
|
| def image_sequence_dirs(raw_dir: Path) -> list[Path]: |
| dirs = [] |
| for path in raw_dir.rglob("*"): |
| if path.is_dir() and any(child.suffix.lower() in IMAGE_EXTS for child in path.iterdir()): |
| dirs.append(path) |
| return sorted(dirs) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--dataset", required=True) |
| ap.add_argument("--config", default="configs/default.yaml") |
| args = ap.parse_args() |
| cfg = load_config(args.config) |
| raw_dir = ROOT / "data/raw" / args.dataset |
| videos = [p for p in raw_dir.rglob("*") if p.suffix.lower() in VIDEO_EXTS] |
| image_dirs = image_sequence_dirs(raw_dir) if not videos else [] |
| if not videos and not image_dirs: |
| raise SystemExit(f"No videos or image sequence directories found under {raw_dir}") |
| model = YOLO(cfg["pose_model"]) |
| records = [] |
| for path in tqdm(videos, desc=f"Extracting videos {args.dataset}"): |
| records.append({ |
| "video_id": path.relative_to(raw_dir).with_suffix("").as_posix(), |
| "path": str(path), |
| "label": infer_label_from_path(str(path.relative_to(raw_dir))), |
| "keypoints": extract_video(path, model, cfg["conf_threshold"]), |
| }) |
| for path in tqdm(image_dirs, desc=f"Extracting image dirs {args.dataset}"): |
| records.append({ |
| "video_id": path.relative_to(raw_dir).as_posix(), |
| "path": str(path), |
| "label": infer_label_from_path(str(path.relative_to(raw_dir))), |
| "keypoints": extract_image_dir(path, model, cfg["conf_threshold"]), |
| }) |
| out = ROOT / "data/poses" / f"{args.dataset}_keypoints.pkl" |
| save_pickle(records, out) |
| print(f"Wrote {out} ({len(records)} videos)") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
