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import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import easyocr
# Load YOLOv8 model for license plate detection
model = YOLO("best (1).pt") # Replace with your trained YOLOv8 weights
# Initialize EasyOCR reader
reader = easyocr.Reader(['en'])
def detect_and_recognize_license_plate(image):
"""
Detects and recognizes license plates in an image.
"""
# Convert Gradio image input to OpenCV format
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# Detect license plates using YOLOv8
results = model(image)
license_plates = []
# Iterate through detected objects
for result in results:
for box in result.boxes:
# Get bounding box coordinates
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
# Crop the license plate region
cropped_plate = image[y1:y2, x1:x2]
# Use EasyOCR to extract text from the cropped plate
ocr_results = reader.readtext(cropped_plate)
plate_text = " ".join([res[1] for res in ocr_results]) # Combine all detected text
# Draw bounding box and text on the original image
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, plate_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# Append detected text to the list
license_plates.append(plate_text)
# Convert the image back to RGB for Gradio display
output_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Return the output image and detected license plate text
return output_image, ", ".join(license_plates)
# Gradio interface
interface = gr.Interface(
fn=detect_and_recognize_license_plate,
inputs=gr.Image(label="Upload Image"),
outputs=[gr.Image(label="Detected License Plate"), gr.Textbox(label="Extracted Text")],
title="License Plate Detection and Recognition",
description="Upload an image to detect and recognize license plates."
)
# Launch the app
interface.launch() |