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import streamlit as st
from open_image_models import LicensePlateDetector
from PIL import Image
import cv2
import numpy as np
# Define the available models
PlateDetectorModel = ['yolo-v9-t-640-license-plate-end2end',
'yolo-v9-t-512-license-plate-end2end',
'yolo-v9-t-384-license-plate-end2end',
'yolo-v9-t-256-license-plate-end2end']
# Streamlit interface
st.title("License Plate Detection with Open Image Models")
st.write("Select a model and upload an image to perform license plate detection.")
# Model selection dropdown
selected_model = st.selectbox("Select a License Plate Detection Model", PlateDetectorModel)
# File uploader for images
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg", "webp"])
if uploaded_file is not None:
# Load the image
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
st.write("")
st.write("Detecting license plates...")
# Convert the image to an OpenCV format
image_np = np.array(image)
image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
# Initialize the License Plate Detector
lp_detector = LicensePlateDetector(detection_model=selected_model)
# Perform license plate detection
detections = lp_detector.predict(image_cv2)
# Display the detected plates
st.write(f"Detections: {detections}")
# Annotate and display the image with detected plates
annotated_image = lp_detector.display_predictions(image_cv2)
st.image(annotated_image, caption='Annotated Image with Detections', use_column_width=True)
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