Create app.py

app.py

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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)