Datasets:
File size: 6,274 Bytes
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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()
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