krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version2

Nepal Vehicle License Plates Detection

# Example Code: You can test this model on colab

# Install required libraries
!pip install ultralytics
!pip install PIL

# Import necessary libraries
from ultralytics import YOLO
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
from google.colab import files
import requests

# Step 1: Download the model from Hugging Face
model_url = "https://huggingface.co/krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version2/resolve/main/best.pt"
model_path = "best.pt"

# Download the model
print("Downloading the model...")
response = requests.get(model_url)
with open(model_path, 'wb') as f:
    f.write(response.content)
print("Model downloaded!")

# Step 2: Load the model
model = YOLO(model_path)

# Step 3: Upload an image
print("Please upload an image to test:")
uploaded = files.upload()
image_path = list(uploaded.keys())[0]

# Step 4: Run inference
results = model(image_path)

# Step 5: Open the image and draw bounding boxes
img = Image.open(image_path)
draw = ImageDraw.Draw(img)

for box in results[0].boxes:
    # Extract bounding box coordinates and class information
    x_min, y_min, x_max, y_max = box.xyxy[0].tolist()
    label = int(box.cls)  # Class ID
    confidence = float(box.conf)  # Confidence score

    # Draw bounding box
    draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)

    # Add label and confidence
    text = f"Class {label}, {confidence:.2f}"
    draw.text((x_min, y_min - 10), text, fill="red")

# Step 6: Display the image with bounding boxes
plt.figure(figsize=(10, 10))
plt.imshow(img)
plt.axis('off')
plt.show()