example/inference_yolo_pose.py

import os
import argparse
from ultralytics import YOLO
import cv2
from pathlib import Path

# Keypoint schema (17 body + 6 feet = 23 total)
COCO_BODY_17 = [
    "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"
]

FEET_6_LABELS = [
    "left_heel", "left_big_toe", "left_little_toe",
    "right_heel", "right_big_toe", "right_little_toe"
]

ALL_KEYPOINTS = COCO_BODY_17 + FEET_6_LABELS

def run_inference(frames_dir, labels_dir, model_path, img_width=1920, img_height=1080):
    """Run YOLO pose inference on all frames and save labels."""
    model = YOLO(model_path)
    os.makedirs(labels_dir, exist_ok=True)
    
    frame_files = sorted([f for f in os.listdir(frames_dir) if f.endswith('.jpg')])
    print(f"Running inference on {len(frame_files)} frames...")

    for idx, frame_file in enumerate(frame_files):
        frame_path = os.path.join(frames_dir, frame_file)
        results = model(frame_path, verbose=False)

        label_file = os.path.join(labels_dir, frame_file.replace('.jpg', '.txt'))

        with open(label_file, 'w') as f:
            for result in results:
                if result.keypoints is not None:
                    for kp in result.keypoints.data:
                        kp_np = kp.cpu().numpy()

                        # Get bounding box (approximate from keypoints)
                        valid_kp = kp_np[kp_np[:, 2] > 0]
                        if len(valid_kp) == 0:
                            continue

                        x_min, y_min = valid_kp[:, 0].min(), valid_kp[:, 1].min()
                        x_max, y_max = valid_kp[:, 0].max(), valid_kp[:, 1].max()
                        bbox_x = (x_min + x_max) / 2 / img_width
                        bbox_y = (y_min + y_max) / 2 / img_height
                        bbox_w = (x_max - x_min) / img_width
                        bbox_h = (y_max - y_min) / img_height

                        # Write YOLO format: class_id bbox keypoints
                        line = f"0 {bbox_x:.6f} {bbox_y:.6f} {bbox_w:.6f} {bbox_h:.6f}"
                        for kp_point in kp_np:
                            x_norm = kp_point[0] / img_width
                            y_norm = kp_point[1] / img_height
                            conf = kp_point[2]
                            line += f" {x_norm:.6f} {y_norm:.6f} {conf:.2f}"
                        f.write(line + "\n")

        if (idx + 1) % 50 == 0:
            print(f"Processed {idx + 1}/{len(frame_files)} frames")

    print(f"✅ Inference complete. Labels saved to {labels_dir}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--frames", required=True, help="Directory containing JPEG frames")
    parser.add_argument("--labels", required=True, help="Output directory for labels")
    parser.add_argument("--model", required=True, help="Path to YOLO weights (.pt)")
    parser.add_argument("--width", type=int, default=1920)
    parser.add_argument("--height", type=int, default=1080)
    args = parser.parse_args()
    
    run_inference(args.frames, args.labels, args.model, args.width, args.height)