Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import torch
+from ultralytics import YOLO
+import easyocr
+
+# Title of the app
+st.title("License Plate Recognition 🚗")
+
+# Load the YOLO model for license plate detection
+@st.cache_resource
+def load_yolo_model():
+    model_path = "best.pt"  # Replace with your model file
+    model = YOLO(model_path)
+    if torch.cuda.is_available():
+        model.to("cuda")
+    return model
+
+# Load EasyOCR reader
+@st.cache_resource
+def load_easyocr_reader():
+    return easyocr.Reader(['en'], gpu=torch.cuda.is_available())
+
+# Initialize models
+yolo_model = load_yolo_model()
+ocr_reader = load_easyocr_reader()
+
+# Function to process the uploaded image
+def process_image(image, confidence_threshold=0.5):
+    # Perform license plate detection
+    results = yolo_model(image, conf=confidence_threshold)
+    annotated_image = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
+    st.image(annotated_image, caption="Detected License Plate(s)", use_container_width=True)
+    
+    # Loop through detections
+    for result in results:
+        boxes = result.boxes.xyxy.cpu().numpy().astype(int)
+        if len(boxes) == 0:
+            st.warning("No license plate detected!")
+            return
+        for i, box in enumerate(boxes):
+            x1, y1, x2, y2 = box
+            cropped_image = image[y1:y2, x1:x2]
+            cropped_image_rgb = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
+            st.image(cropped_image_rgb, caption=f"Cropped License Plate {i+1}", use_container_width=True)
+            
+            # Perform OCR on the cropped image
+            text_results = ocr_reader.readtext(cropped_image_rgb, detail=0)
+            detected_text = " ".join(text_results)
+            st.write(f"**Extracted Text (Plate {i+1}):** {detected_text}")
+
+# Sidebar inputs
+confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
+uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
+    process_image(image, confidence_threshold)
+
+st.markdown("---")
+st.info("**Note:** This application uses EasyOCR for text recognition. Results may vary depending on image quality and lighting conditions.")