| """ |
| 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 ===") |
| |
| |
| config = self.load_config() |
| if config: |
| print(f"Classes: {self.class_names}") |
| print(f"Total classes: {len(self.class_names)}") |
| |
| |
| 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 |
| |
| |
| 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 |
| |
| |
| img = cv2.imread(str(img_path)) |
| img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| |
| |
| 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]) |
| |
| |
| 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) |
| |
| |
| cv2.rectangle(img_rgb, (x1, y1), (x2, y2), (255, 0, 0), 2) |
| |
| |
| 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() |
|
|
