LS100 — GPU Pose Estimation with YOLOv8-Pose (Colab)

# DO NOT CHANGE ANYTHING IN THIS CELL
# Quick GPU check
!nvidia-smi || echo "nvidia-smi not available (GPU runtime not enabled?)"

import torch, platform
print("PyTorch CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("GPU name:", torch.cuda.get_device_name(0))
print("Python:", platform.python_version())

# DO NOT CHANGE ANYTHING IN THIS CELL
# Install Ultralytics (YOLOv8) and supporting libs
%pip -q install ultralytics==8.3.30 opencv-python-headless pandas numpy tqdm
import ultralytics
from ultralytics import YOLO
print("Ultralytics version:", ultralytics.__version__)

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import os, sys, cv2, math, time, pathlib, json
import numpy as np
import pandas as pd
from tqdm import tqdm

import torch
from ultralytics import YOLO

# === Option A: Google Drive (Recommended for larger videos) ===
# INSTRUCTION: Uncomment this block if your video is in Drive.
# from google.colab import drive
# drive.mount('/content/drive')
# INPUT_VIDEO = "/content/drive/MyDrive/your_folder/your_video.mp4"  # <-- EDIT THIS PATH
# OUTPUT_DIR = "/content/drive/MyDrive/ls100_pose_outputs"           # <-- EDIT THIS PATH

# === Option B: Local upload ===
# INSTRUCTION: Use the UI prompt to upload a file; set INPUT_VIDEO automatically.
INPUT_VIDEO = None
OUTPUT_DIR = "/content/pose_outputs"
os.makedirs(OUTPUT_DIR, exist_ok=True)

try:
    from google.colab import files
    print("Upload a video file (mp4/mov/avi):")
    uploaded = files.upload()
    if uploaded:
        INPUT_VIDEO = list(uploaded.keys())[0]
        print("Using uploaded file as INPUT_VIDEO:", INPUT_VIDEO)
except Exception as e:
    print("Colab file upload not available in this environment:", e)

if INPUT_VIDEO is None:
    print("\n⚠️ IMPORTANT: If you did not upload a file, set INPUT_VIDEO to a valid path above (Option A).")
else:
    print("INPUT_VIDEO =", INPUT_VIDEO)
print("OUTPUT_DIR  =", OUTPUT_DIR)

# STUDENTS: You may change ONLY the model filename below.
MODEL_WEIGHTS = "yolov8s-pose.pt"  # try: 'yolov8n-pose.pt' for faster, 'yolov8m/l/x-pose.pt' for accuracy

# DO NOT CHANGE ANYTHING BELOW
assert torch.cuda.is_available(), "GPU not available. Enable GPU runtime in Colab (Runtime → Change runtime type)."
device = 0  # GPU index
model = YOLO(MODEL_WEIGHTS)
model.to('cuda')  # move model to GPU
print("Loaded model on CUDA:", MODEL_WEIGHTS)

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COCO_KP_NAMES = [
    "nose","left_eye","right_eye","left_ear","right_ear",
    "left_shoulder","right_shoulder","left_elbow","right_elbow",
    "left_wrist","right_wrist","left_hip","right_hip",
    "left_knee","right_knee","left_ankle","right_ankle"
]
NUM_KP = len(COCO_KP_NAMES)

# DO NOT CHANGE ANYTHING IN THIS CELL
def get_video_fps_frames(path):
    cap = cv2.VideoCapture(path)
    if not cap.isOpened():
        raise RuntimeError(f"Cannot open video: {path}")
    fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
    frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
    cap.release()
    return fps, frames

# DO NOT CHANGE ANYTHING IN THIS CELL
def process_video_to_csv(
    input_video: str,
    output_dir: str,
    model: YOLO,
    conf: float = 0.25,
    iou: float = 0.5,
    imgsz: int = 640,
    tracker: str = "bytetrack.yaml",
    project_name: str = "ls100_pose",
    run_name: str = "run1",
) -> dict:
    """Run YOLOv8-Pose tracking on a video and export keypoints to CSV.

    Returns a dict with paths: {'csv_path', 'annotated_video_dir', 'annotated_video' (if found)}
    """
    os.makedirs(output_dir, exist_ok=True)
    fps, total_frames = get_video_fps_frames(input_video)
    print(f"Video FPS: {fps:.2f}, frames: {total_frames}")

    # Run tracking (GPU). Ultralytics writes annotated video(s) to project/name directory.
    results = model.track(
        source=input_video,
        conf=conf,
        iou=iou,
        imgsz=imgsz,
        device=0,
        verbose=False,
        stream=False,
        save=True,
        project=output_dir,  # write directly into OUTPUT_DIR/<run_name>
        name=run_name,
        tracker=tracker,
        persist=True
    )

    # 'results' is a list of per-frame Results
    if not isinstance(results, list):
        # Some versions may return a generator if stream=True; we set stream=False above.
        results = list(results)

    rows = []
    for frame_idx, r in enumerate(tqdm(results, desc="Extracting keypoints")):
        ts_ms = int(round(frame_idx * 1000.0 / max(fps, 1e-6)))

        # No detections
        if r.keypoints is None or r.keypoints.xy is None or len(r.keypoints.xy) == 0:
            continue

        kps_xy = r.keypoints.xy  # shape: [num_people, 17, 2]
        kps_conf = getattr(r.keypoints, 'conf', None)  # may be None or [num_people, 17]
        boxes = r.boxes
        ids = None
        if boxes is not None and getattr(boxes, 'id', None) is not None:
            try:
                ids = boxes.id.cpu().numpy().astype(int)
            except Exception:
                ids = None

        det_conf = None
        if boxes is not None and getattr(boxes, 'conf', None) is not None:
            try:
                det_conf = boxes.conf.cpu().numpy()
            except Exception:
                det_conf = None

        num_people = kps_xy.shape[0]
        for i in range(num_people):
            track_id = int(ids[i]) if ids is not None and i < len(ids) else -1
            this_det_conf = float(det_conf[i]) if det_conf is not None and i < len(det_conf) else None

            row = {
                "frame_idx": frame_idx,
                "timestamp_ms": ts_ms,
                "track_id": track_id,
                "det_conf": this_det_conf
            }

            # keypoints
            kpi = kps_xy[i].cpu().numpy() if hasattr(kps_xy[i], 'cpu') else kps_xy[i]
            kpci = None
            if kps_conf is not None:
                kpci = kps_conf[i].cpu().numpy() if hasattr(kps_conf[i], 'cpu') else kps_conf[i]

            # Fill x,y,(conf) for each named keypoint
            for kp_idx, kp_name in enumerate(COCO_KP_NAMES):
                x = float(kpi[kp_idx, 0])
                y = float(kpi[kp_idx, 1])
                row[f"{kp_name}_x"] = x
                row[f"{kp_name}_y"] = y
                if kpci is not None and kp_idx < kpci.shape[0]:
                    row[f"{kp_name}_conf"] = float(kpci[kp_idx])
                else:
                    row[f"{kp_name}_conf"] = None

            rows.append(row)

    df = pd.DataFrame(rows)
    csv_path = os.path.join(output_dir, f"{run_name}_keypoints.csv")
    df.to_csv(csv_path, index=False)
    print(f"Saved CSV: {csv_path}  ({len(df):,} rows)")

    # Try to find the annotated video saved by Ultralytics
    ann_dir = os.path.join(output_dir, run_name)
    ann_video = None
    if os.path.isdir(ann_dir):
        # heuristics: find first video file in ann_dir
        for f in os.listdir(ann_dir):
            if f.lower().endswith((".mp4", ".avi", ".mov", ".mkv")):
                ann_video = os.path.join(ann_dir, f)
                break

    return {
        "csv_path": csv_path,
        "annotated_video_dir": ann_dir if os.path.isdir(ann_dir) else None,
        "annotated_video": ann_video
    }

# STUDENTS: You may change RUN_NAME. Leave other defaults unless instructed.
RUN_NAME = "demo_run"

# DO NOT CHANGE ANYTHING BELOW
assert INPUT_VIDEO is not None and os.path.exists(INPUT_VIDEO), "Set INPUT_VIDEO to a valid video path."
out = process_video_to_csv(
    input_video=INPUT_VIDEO,
    output_dir=OUTPUT_DIR,
    model=model,
    conf=0.25,
    iou=0.5,
    imgsz=640,
    tracker="bytetrack.yaml",
    project_name="ls100_pose",
    run_name=RUN_NAME
)
print("\nOutputs:")
for k, v in out.items():
    print(f" - {k}: {v}")

# DO NOT CHANGE ANYTHING IN THIS CELL
csv_path = out["csv_path"]
df = pd.read_csv(csv_path)
df.head()

# STUDENTS: You may run this to download files locally.
try:
    from google.colab import files
    files.download(out["csv_path"])
    if out["annotated_video"]:
        files.download(out["annotated_video"])
except Exception as e:
    print("Colab download not available in this environment:", e)