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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("🦀 Open Image Models: Pre-trained Models for Object Detection")
st.write("Leverage fast and efficient pre-trained ONNX models for various object detection tasks, starting with license plate detection.")
st.markdown("---")
# Model selection dropdown (specific to license plate detection in this example)
selected_model = st.selectbox("🔍 Select a License Plate Detection Model", PlateDetectorModel)
# File uploader for images
uploaded_file = st.file_uploader("📂 Upload an image...", type=["jpg", "png", "jpeg", "webp"])
if uploaded_file is not None:
# Load the image using PIL
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
st.write("")
st.write("🔍 **Detecting license plates...**")
# Convert the PIL image to an OpenCV format (NumPy array)
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)
# Streamlit display for detections
if detections:
st.success(f"✅ {len(detections)} License Plates Detected!")
# Use an expander to show details in a more organized way
with st.expander("See detected plates details"):
for i, detection in enumerate(detections):
# Access attributes of the DetectionResult class
bbox = detection.bounding_box
st.markdown(f"""
**Plate {i+1}:**
- **Label:** {detection.label}
- **Confidence:** {detection.confidence:.2f}
- **Bounding Box:** (x1: {bbox.x1}, y1: {bbox.y1}, x2: {bbox.x2}, y2: {bbox.y2})
""")
else:
st.warning("⚠️ No license plates detected!")
# Annotate and display the image with detected plates
annotated_image = lp_detector.display_predictions(image_cv2)
# Convert the annotated image from BGR to RGB for Streamlit display
annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
st.image(annotated_image_rgb, caption='Annotated Image with Detections', use_column_width=True)
# Add some additional style or layout to make the app more attractive
st.markdown("""
<style>
.stButton>button {
font-size: 16px;
background-color: #4CAF50;
color: white;
border-radius: 8px;
}
.stImage img {
border-radius: 10px;
padding: 10px;
}
</style>
""", unsafe_allow_html=True)
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