dataset_tools.py

"""
Dataset Tools for YOLO Object Detection
=====================================

This script provides tools to work with your YOLO dataset locally
and from Hugging Face Hub.
"""

from datasets import load_dataset
import yaml
from pathlib import Path
import matplotlib.pyplot as plt
import cv2
import numpy as np

class YOLODatasetTools:
    def __init__(self, dataset_path=".", hf_repo_id="Sean676/cv_proj"):
        self.dataset_path = Path(dataset_path)
        self.hf_repo_id = hf_repo_id
        self.class_names = []
        self.dataset_info = {}
        
    def load_config(self):
        """Load dataset configuration from dataset.yaml"""
        config_path = self.dataset_path / "dataset.yaml"
        if config_path.exists():
            with open(config_path, 'r') as f:
                config = yaml.safe_load(f)
                self.class_names = list(config.get('names', {}).values())
                self.dataset_info = config
                return config
        return None
    
    def inspect_local_dataset(self):
        """Inspect local dataset structure"""
        print("=== Local Dataset Inspection ===")
        
        # Load config
        config = self.load_config()
        if config:
            print(f"Classes: {self.class_names}")
            print(f"Total classes: {len(self.class_names)}")
        
        # Check images and labels
        images_dir = self.dataset_path / "images"
        labels_dir = self.dataset_path / "labels"
        
        if images_dir.exists():
            print(f"\nImages directory: {images_dir}")
            for split in ["train", "val", "test"]:
                split_dir = images_dir / split
                if split_dir.exists():
                    images = list(split_dir.glob("*.jpg")) + list(split_dir.glob("*.png"))
                    print(f"  {split}: {len(images)} images")
        
        if labels_dir.exists():
            print(f"\nLabels directory: {labels_dir}")
            for split in ["train", "val", "test"]:
                split_dir = labels_dir / split
                if split_dir.exists():
                    labels = list(split_dir.glob("*.txt"))
                    print(f"  {split}: {len(labels)} label files")
    
    def load_from_hf(self):
        """Load dataset from Hugging Face Hub"""
        try:
            print("Loading dataset from Hugging Face Hub...")
            dataset = load_dataset(self.hf_repo_id)
            print(f"Dataset loaded successfully!")
            print(dataset)
            return dataset
        except Exception as e:
            print(f"Failed to load from Hub: {e}")
            return None
    
    def visualize_sample(self, split="train", max_samples=4):
        """Visualize sample images with bounding boxes"""
        images_dir = self.dataset_path / "images" / split
        labels_dir = self.dataset_path / "labels" / split
        
        if not images_dir.exists() or not labels_dir.exists():
            print(f"Split '{split}' not found locally")
            return
        
        # Get sample images
        image_files = list(images_dir.glob("*.jpg"))[:max_samples]
        
        fig, axes = plt.subplots(2, 2, figsize=(12, 10))
        axes = axes.flatten()
        
        for i, img_path in enumerate(image_files):
            if i >= max_samples:
                break
                
            # Load image
            img = cv2.imread(str(img_path))
            img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            
            # Load and draw bounding boxes
            label_path = labels_dir / (img_path.stem + ".txt")
            if label_path.exists():
                with open(label_path, 'r') as f:
                    for line in f:
                        parts = line.strip().split()
                        if len(parts) >= 5:
                            class_id = int(parts[0])
                            x_center, y_center, width, height = map(float, parts[1:5])
                            
                            # Convert to pixel coordinates
                            h, w = img.shape[:2]
                            x1 = int((x_center - width/2) * w)
                            y1 = int((y_center - height/2) * h)
                            x2 = int((x_center + width/2) * w)
                            y2 = int((y_center + height/2) * h)
                            
                            # Draw rectangle
                            cv2.rectangle(img_rgb, (x1, y1), (x2, y2), (255, 0, 0), 2)
                            
                            # Add class label
                            if class_id < len(self.class_names):
                                label = self.class_names[class_id]
                                cv2.putText(img_rgb, label, (x1, y1-10), 
                                          cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
            
            axes[i].imshow(img_rgb)
            axes[i].set_title(f"Sample {i+1}: {img_path.name}")
            axes[i].axis('off')
        
        plt.tight_layout()
        plt.savefig(f"{split}_samples.png")
        print(f"Visualization saved as {split}_samples.png")
        plt.show()
    
    def download_from_hf(self, local_path="./downloaded_dataset"):
        """Download dataset from Hugging Face to local folder"""
        try:
            dataset = load_dataset(self.hf_repo_id)
            save_path = Path(local_path)
            save_path.mkdir(exist_ok=True)
            
            dataset.save_to_disk(str(save_path))
            print(f"Dataset downloaded to: {save_path}")
            return save_path
        except Exception as e:
            print(f"Download failed: {e}")
            return None

def main():
    tools = YOLODatasetTools()
    
    print("=== YOLO Dataset Tools ===")
    print("\n1. Inspect local dataset:")
    tools.inspect_local_dataset()
    
    print("\n2. Load from Hugging Face:")
    hf_dataset = tools.load_from_hf()
    
    print("\n3. Visualize samples (if matplotlib available):")
    try:
        tools.visualize_sample("train", max_samples=2)
    except Exception as e:
        print(f"Visualization failed: {e}")
        print("Install matplotlib: pip install matplotlib")

if __name__ == "__main__":
    main()