posenet eval on videos

A10/cv_results/cv_dense_20260421_120738.json

@@ -0,0 +1,46 @@
+{
+  "model_type": "dense",
+  "model_config": {
+    "n_layers": 2,
+    "n_units": 64,
+    "dropout_rate": 0.2
+  },
+  "optimizer": "adam",
+  "learning_rate": 0.001,
+  "loss": "mse",
+  "epochs": 50,
+  "batch_size": 32,
+  "n_folds": 3,
+  "fold_results": [
+    {
+      "mse": 0.0032802510540932417,
+      "rmse": 0.057273476008473954,
+      "mae": 0.03510827198624611,
+      "r2": 0.616958498954773,
+      "fold": 1,
+      "epochs_trained": 24
+    },
+    {
+      "mse": 0.003166707931086421,
+      "rmse": 0.05627351002991035,
+      "mae": 0.036439381539821625,
+      "r2": 0.6341696381568909,
+      "fold": 2,
+      "epochs_trained": 32
+    },
+    {
+      "mse": 0.0038959148805588484,
+      "rmse": 0.062417264282879685,
+      "mae": 0.03941710665822029,
+      "r2": 0.5685998201370239,
+      "fold": 3,
+      "epochs_trained": 30
+    }
+  ],
+  "mean_mse": 0.003447624621912837,
+  "std_mse": 0.0003203603462698594,
+  "mean_mae": 0.03698825339476267,
+  "std_mae": 0.0018013808208263528,
+  "mean_r2": 0.6065759857495626,
+  "std_r2": 0.027757253636144584
+}

A10/data_loader.py

@@ -78,9 +78,11 @@ def load_kinect_csv(filepath: Union[str, bytes]) -> Dict[str, np.ndarray]:
 
     Returns:
         Dict with:
-          'xy'  : (N, 26) Kinect x,y   (Issue #40 target)
-          'z'   : (N, 13) Kinect z
-          'xyz' : (N, 39) Kinect x,y,z (Issue #41 target)
+          'xy'     : (N, 26) Kinect x,y   (Issue #40 target)
+          'z'      : (N, 13) Kinect z
+          'xyz'    : (N, 39) Kinect x,y,z (Issue #41 target)
+          'frames' : (N,)    FrameNo values (int) if the column exists, else
+                     np.arange(N) as a fallback.
     """
     if isinstance(filepath, (str, os.PathLike)):
         df = pd.read_csv(filepath)
@@ -94,20 +96,35 @@ def load_kinect_csv(filepath: Union[str, bytes]) -> Dict[str, np.ndarray]:
         z_cols.append(f"{joint}_z")
         xyz_cols.extend([f"{joint}_x", f"{joint}_y", f"{joint}_z"])
 
+    if 'FrameNo' in df.columns:
+        frames = df['FrameNo'].values.astype(np.int64)
+    else:
+        frames = np.arange(len(df), dtype=np.int64)
+
     return {
         'xy':  df[xy_cols].values.astype(np.float32),
         'z':   df[z_cols].values.astype(np.float32),
         'xyz': df[xyz_cols].values.astype(np.float32),
+        'frames': frames,
     }
 
 
-def load_posenet_csv(filepath: str) -> np.ndarray:
+def load_posenet_csv(
+    filepath: str,
+    frame_filter: Optional[np.ndarray] = None,
+) -> np.ndarray:
     """
     Load a PoseNet/MoveNet CSV already aligned to Kinect joint order.
 
     Expected columns (per slide spec):
         FrameNo, head_x, head_y, left_shoulder_x, left_shoulder_y, ...
 
+    Args:
+        filepath: PoseNet CSV path.
+        frame_filter: Optional array of FrameNo values to select in order.
+            Used to temporally align PoseNet frames to the corresponding
+            Kinect frames (Kinect CSVs may start at FrameNo != 0).
+
     Returns:
         (N, 26) PoseNet x,y for 13 joints.
     """
@@ -116,6 +133,17 @@ def load_posenet_csv(filepath: str) -> np.ndarray:
     xy_cols = []
     for joint in KINECT_JOINTS:
         xy_cols.extend([f"{joint}_x", f"{joint}_y"])
+
+    if frame_filter is not None and 'FrameNo' in df.columns:
+        df = df.set_index('FrameNo')
+        missing = [f for f in frame_filter if f not in df.index]
+        if missing:
+            raise ValueError(
+                f"{len(missing)} FrameNo(s) missing from {filepath} "
+                f"(first missing: {missing[:5]})"
+            )
+        df = df.loc[frame_filter]
+
     return df[xy_cols].values.astype(np.float32)
 
 
@@ -149,10 +177,8 @@ def load_paired_sequence(
     kinect = load_kinect_csv(kinect_path)
 
     if posenet_path is not None:
-        X = load_posenet_csv(posenet_path)
-        n = min(len(X), len(kinect['xy']))
-        X = X[:n]
-        kinect = {k: v[:n] for k, v in kinect.items()}
+        # Align PoseNet to Kinect by FrameNo when both CSVs carry that column.
+        X = load_posenet_csv(posenet_path, frame_filter=kinect['frames'])
     elif simulate_posenet:
         rng = np.random.default_rng(random_state)
         X = kinect['xy'] + rng.normal(0.0, noise_std, kinect['xy'].shape).astype(np.float32)

A10/generate_posenet_data.py

@@ -0,0 +1,196 @@
+"""
+Generate PoseNet/MoveNet (xpn, ypn) CSVs from video files.
+
+For each .avi video in --videos-dir, runs MoveNet on every frame, projects the
+17 COCO keypoints to the 13 Kinect-aligned joints used by Issue #40, and writes
+one CSV per video to --out-dir with columns:
+
+    FrameNo, head_x, head_y, left_shoulder_x, left_shoulder_y, ...
+
+The FrameNo column preserves the video frame index (0-based), so temporal
+alignment with the corresponding Kinect CSV (which may start at a later
+FrameNo such as 68) can be done later in the data loader.
+
+Usage:
+    python3 generate_posenet_data.py \
+        --videos-dir /Users/amol/Desktop/LNU/LNU_Masters/intensive/all_videos \
+        --out-dir   /Users/amol/Desktop/LNU/LNU_Masters/intensive/second_github/Data-intensive-systems/posenet_preprocessed \
+        --model lightning
+
+    # limit to first N videos (useful for a quick test):
+    python3 generate_posenet_data.py ... --limit 3
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import os
+import sys
+import time
+from pathlib import Path
+from typing import List
+
+import cv2
+import numpy as np
+
+# Make the A8 pose_estimator importable (MoveNetPoseEstimator, KEYPOINT_NAMES)
+A8_PATH = Path(
+    "/Users/amol/Desktop/LNU/LNU_Masters/intensive/github/Data-intensive-systems/A8"
+)
+if str(A8_PATH) not in sys.path:
+    sys.path.insert(0, str(A8_PATH))
+
+from pose_estimator import MoveNetPoseEstimator, KEYPOINT_NAMES  # noqa: E402
+
+
+def _load_movenet_local(model_name: str = 'lightning') -> MoveNetPoseEstimator:
+    """
+    Build a MoveNetPoseEstimator using a locally cached Kaggle-hub SavedModel.
+
+    tfhub.dev is deprecated and currently returns 404 for MoveNet; we fetch
+    the model from Kaggle Hub instead (kagglehub.model_download) and wire it
+    into the existing A8 estimator class.
+    """
+    import kagglehub
+    import tensorflow as tf
+
+    kaggle_handle = {
+        'lightning': 'google/movenet/tensorFlow2/singlepose-lightning',
+        'thunder':   'google/movenet/tensorFlow2/singlepose-thunder',
+    }[model_name]
+    model_dir = kagglehub.model_download(kaggle_handle)
+
+    est = MoveNetPoseEstimator.__new__(MoveNetPoseEstimator)
+    est.model_name = model_name
+    est.input_size = MoveNetPoseEstimator.INPUT_SIZES[model_name]
+    est.model = tf.saved_model.load(model_dir)
+    est.movenet = est.model.signatures['serving_default']
+    print(f"Loaded MoveNet '{model_name}' from {model_dir}")
+    return est
+
+# Kinect-aligned joint order (must match data_loader.KINECT_JOINTS).
+KINECT_JOINTS = [
+    'head', 'left_shoulder', 'left_elbow', 'right_shoulder', 'right_elbow',
+    'left_hand', 'right_hand', 'left_hip', 'right_hip',
+    'left_knee', 'right_knee', 'left_foot', 'right_foot',
+]
+
+# Kinect joint -> MoveNet COCO keypoint.
+KINECT_TO_MOVENET = {
+    'head': 'nose',
+    'left_shoulder': 'left_shoulder',
+    'right_shoulder': 'right_shoulder',
+    'left_elbow': 'left_elbow',
+    'right_elbow': 'right_elbow',
+    'left_hand': 'left_wrist',
+    'right_hand': 'right_wrist',
+    'left_hip': 'left_hip',
+    'right_hip': 'right_hip',
+    'left_knee': 'left_knee',
+    'right_knee': 'right_knee',
+    'left_foot': 'left_ankle',
+    'right_foot': 'right_ankle',
+}
+
+# Pre-computed COCO indices in Kinect order.
+KEYPOINT_INDEX = {name: i for i, name in enumerate(KEYPOINT_NAMES)}
+COCO_IDX_IN_KINECT_ORDER = [
+    KEYPOINT_INDEX[KINECT_TO_MOVENET[j]] for j in KINECT_JOINTS
+]
+
+CSV_HEADER = ['FrameNo'] + [
+    f"{j}_{axis}" for j in KINECT_JOINTS for axis in ('x', 'y')
+]
+
+
+def video_id_from_filename(video_path: Path) -> str:
+    """A1.avi -> A1_kinect (matches Kinect CSV base name)."""
+    return f"{video_path.stem}_kinect"
+
+
+def process_video(
+    estimator: MoveNetPoseEstimator,
+    video_path: Path,
+    out_csv: Path,
+) -> int:
+    cap = cv2.VideoCapture(str(video_path))
+    if not cap.isOpened():
+        raise RuntimeError(f"Could not open video: {video_path}")
+
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_idx = 0
+    written = 0
+
+    out_csv.parent.mkdir(parents=True, exist_ok=True)
+    with out_csv.open('w', newline='') as fh:
+        writer = csv.writer(fh)
+        writer.writerow(CSV_HEADER)
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            raw = estimator.detect_pose_raw(frame)  # (17, 3) -> [y, x, conf]
+            row: List[float] = [frame_idx]
+            for coco_idx in COCO_IDX_IN_KINECT_ORDER:
+                y, x, _conf = raw[coco_idx]
+                row.extend([float(x), float(y)])
+            writer.writerow(row)
+            written += 1
+            frame_idx += 1
+
+            if frame_idx % 50 == 0:
+                print(f"  {video_path.name}: {frame_idx}/{total_frames}", flush=True)
+
+    cap.release()
+    return written
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--videos-dir', required=True)
+    parser.add_argument('--out-dir', required=True)
+    parser.add_argument('--model', choices=['lightning', 'thunder'], default='lightning')
+    parser.add_argument('--limit', type=int, default=0,
+                        help="Process only the first N videos (0 = all).")
+    parser.add_argument('--overwrite', action='store_true',
+                        help="Re-generate CSVs even if they already exist.")
+    parser.add_argument('--pattern', default='*.avi',
+                        help="Glob pattern to find videos (default *.avi).")
+    args = parser.parse_args()
+
+    videos_dir = Path(args.videos_dir)
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    videos = sorted(videos_dir.glob(args.pattern))
+    if args.limit:
+        videos = videos[: args.limit]
+    if not videos:
+        print(f"No videos found in {videos_dir} matching {args.pattern}")
+        return
+
+    print(f"Found {len(videos)} video(s). Output -> {out_dir}")
+    print(f"Loading MoveNet '{args.model}' ...")
+    estimator = _load_movenet_local(args.model)
+
+    t0 = time.time()
+    total_frames = 0
+    for i, v in enumerate(videos, 1):
+        out_csv = out_dir / f"{video_id_from_filename(v)}.csv"
+        if out_csv.exists() and not args.overwrite:
+            print(f"[{i}/{len(videos)}] skip existing {out_csv.name}")
+            continue
+        print(f"[{i}/{len(videos)}] {v.name} -> {out_csv.name}")
+        n = process_video(estimator, v, out_csv)
+        total_frames += n
+
+    dt = time.time() - t0
+    print(f"\nDone. Processed {total_frames} frames in {dt:.1f}s "
+          f"({total_frames / dt if dt else 0:.1f} fps)")
+
+
+if __name__ == '__main__':
+    main()

A10/train.py

@@ -599,6 +599,7 @@ def main():
     # Paths
     REPO_ROOT = Path(__file__).parent.parent
     KINECT_PATH = REPO_ROOT / 'kinect_good_preprocessed'
+    POSENET_PATH = REPO_ROOT / 'posenet_preprocessed'
     RESULTS_DIR = Path(__file__).parent / 'cv_results'
     MODELS_DIR = Path(__file__).parent / 'models'
     
@@ -611,13 +612,20 @@ def main():
         print("Please ensure the kinect_good_preprocessed folder exists.")
         return
     
+    use_real_posenet = POSENET_PATH.exists()
+    
     # Load data
     print(f"\nLoading data from: {KINECT_PATH}")
     print("Issue #40: PoseNet 2D (26) -> Kinect 2D (26)")
+    if use_real_posenet:
+        print(f"Using REAL PoseNet inputs from: {POSENET_PATH}")
+    else:
+        print("PoseNet folder not found; falling back to simulated PoseNet "
+              "input (Kinect xy + noise).")
     sequences, file_names = load_all_paired_sequences(
         str(KINECT_PATH),
-        posenet_folder=None,          # real PoseNet CSVs go here when available
-        simulate_posenet=True,        # synthesise PoseNet input for now
+        posenet_folder=str(POSENET_PATH) if use_real_posenet else None,
+        simulate_posenet=not use_real_posenet,
         noise_std=0.02,
     )
     print(f"Loaded {len(sequences)} paired sequences")

posenet_preprocessed/A112_kinect.csv

@@ -0,0 +1,189 @@
+FrameNo,head_x,head_y,left_shoulder_x,left_shoulder_y,left_elbow_x,left_elbow_y,right_shoulder_x,right_shoulder_y,right_elbow_x,right_elbow_y,left_hand_x,left_hand_y,right_hand_x,right_hand_y,left_hip_x,left_hip_y,right_hip_x,right_hip_y,left_knee_x,left_knee_y,right_knee_x,right_knee_y,left_foot_x,left_foot_y,right_foot_x,right_foot_y
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posenet_preprocessed/A119_kinect.csv

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posenet_preprocessed/A123_kinect.csv

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posenet_preprocessed/A124_kinect.csv

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posenet_preprocessed/A129_kinect.csv

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posenet_preprocessed/A133_kinect.csv

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posenet_preprocessed/A135_kinect.csv

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