Update app.py

app.py

@@ -1,43 +1,44 @@
-from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel
+import streamlit as st
 import cv2
+import numpy as np
 from PIL import Image, ImageDraw
+from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel
 import torch
-import streamlit as st
 
-# Load Hugging Face Models
+# Load Models
 detr_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
 detr_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 trocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
 trocr_model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
 
-# Detect license plates
+# Streamlit App Configuration
+st.title("Real-Time Car Number Plate Recognition")
+st.write("This app uses Hugging Face Transformers, OpenCV, and Streamlit for detecting and recognizing car number plates in real-time.")
+
+# Authorized Car Database
+authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090"}  # Dummy data for verification
+
+
+# Detect License Plates
 def detect_license_plate(frame):
     pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
     inputs = detr_processor(images=pil_image, return_tensors="pt")
     outputs = detr_model(**inputs)
 
+    # Post-process outputs to get bounding boxes
     target_sizes = torch.tensor([pil_image.size[::-1]])
     results = detr_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)
-
     return results[0]["boxes"], pil_image
 
-# Recognize text
+
+# Recognize Text from Plates
 def recognize_text_from_plate(cropped_plate):
     inputs = trocr_processor(images=cropped_plate, return_tensors="pt")
     outputs = trocr_model.generate(**inputs)
     return trocr_processor.batch_decode(outputs, skip_special_tokens=True)[0]
 
 
-
-
-# Streamlit configuration
-st.title("Real-Time Car Number Plate Recognition")
-st.text("This application uses Hugging Face Transformers to detect and recognize car plates.")
-
-# Authorized car database
-authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090"}
-
-# Verification function
+# Verify Plate Text
 def verify_plate(plate_text):
     if plate_text in authorized_cars:
         return f"✅ Access Granted: {plate_text}"
@@ -45,46 +46,45 @@ def verify_plate(plate_text):
         return f"❌ Access Denied: {plate_text}"
 
 
-# Live video feed and processing
+# Real-Time Video Processing with OpenCV
 def live_feed():
-    cap = cv2.VideoCapture(0)  # Open the webcam
-    stframe = st.empty()  # Streamlit frame for displaying video
+    cap = cv2.VideoCapture(0)  # Open webcam
+    stframe = st.empty()  # Placeholder for video stream
 
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
 
-        # Detect license plates
+        # Detect plates
         boxes, pil_image = detect_license_plate(frame)
         draw = ImageDraw.Draw(pil_image)
 
         recognized_plates = []
         for box in boxes:
-            # Crop the detected plate
+            # Crop and recognize plate
             cropped_plate = pil_image.crop((box[0], box[1], box[2], box[3]))
-
-            # Recognize text
             plate_text = recognize_text_from_plate(cropped_plate)
             recognized_plates.append(plate_text)
 
-            # Draw bounding box and text
-            draw.rectangle(box.tolist(), outline="red", width=2)
+            # Draw box and label
+            draw.rectangle(box.tolist(), outline="red", width=3)
             draw.text((box[0], box[1]), plate_text, fill="red")
 
-        # Convert PIL image back to OpenCV format
+        # Convert back to OpenCV format
         processed_frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
 
-        # Stream the video to Streamlit
-        stframe.image(processed_frame, channels="BGR")
+        # Stream video to Streamlit
+        stframe.image(processed_frame, channels="BGR", use_column_width=True)
 
-        # Show results
+        # Display results
         for plate_text in recognized_plates:
             st.write(verify_plate(plate_text))
 
     cap.release()
     cv2.destroyAllWindows()
 
-if st.button("Start Camera"):
-    live_feed()
 
+# Streamlit UI
+if st.button("Start Camera"):
+    live_feed()