Update app.py

app.py

@@ -3,62 +3,97 @@ from streamlit_webrtc import webrtc_streamer, VideoProcessorBase
 import av
 from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image, ImageDraw
-import torch
 import numpy as np
+import torch
 
-# Load Models
+# Step 1: Load Models
+# DETR for object detection
 detr_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
 detr_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+# TrOCR for text recognition
 trocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
 trocr_model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
 
-# Authorized car database
-authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090"}
+# Authorized car database for verification
+authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090"}  # Example data
 
-# Detect License Plates
+
+# Step 2: Define Helper Functions
 def detect_license_plate(frame):
+    """
+    Detect license plates in the frame using DETR.
+    """
     pil_image = Image.fromarray(frame)
     inputs = detr_processor(images=pil_image, return_tensors="pt")
     outputs = detr_model(**inputs)
+    
+    # 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 from Plates
+
 def recognize_text_from_plate(cropped_plate):
+    """
+    Recognize text from the cropped license plate image using TrOCR.
+    """
     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]
 
-# Verify Plate
+
 def verify_plate(plate_text):
+    """
+    Check if the recognized plate text exists in the authorized cars database.
+    """
     if plate_text in authorized_cars:
         return f"✅ Access Granted: {plate_text}"
     else:
         return f"❌ Access Denied: {plate_text}"
 
-# Custom Video Processor
+
+# Step 3: Custom Video Processor for WebRTC
 class LicensePlateProcessor(VideoProcessorBase):
+    """
+    Custom video processor to handle video frames in real-time.
+    """
     def recv(self, frame: av.VideoFrame):
-        frame = frame.to_ndarray(format="bgr24")
+        frame = frame.to_ndarray(format="bgr24")  # Convert frame to NumPy array
         boxes, pil_image = detect_license_plate(frame)
         draw = ImageDraw.Draw(pil_image)
 
         recognized_plates = []
         for box in boxes:
+            # Crop detected license plate
             cropped_plate = pil_image.crop((box[0], box[1], box[2], box[3]))
             plate_text = recognize_text_from_plate(cropped_plate)
             recognized_plates.append(plate_text)
+
+            # Draw bounding box and label on the image
             draw.rectangle(box.tolist(), outline="red", width=3)
             draw.text((box[0], box[1]), plate_text, fill="red")
 
-        # Return processed frame
+        # Convert back to OpenCV format
         processed_frame = np.array(pil_image)
+
+        # Log results in Streamlit UI
         for plate_text in recognized_plates:
             st.write(verify_plate(plate_text))
+
         return av.VideoFrame.from_ndarray(processed_frame, format="bgr24")
 
-# Streamlit UI
+
+# Step 4: Streamlit Interface
 st.title("Real-Time Car Number Plate Recognition")
-st.write("Streamlit with WebRTC for camera streaming.")
-webrtc_streamer(key="plate-recognition", video_processor_factory=LicensePlateProcessor)
+st.write("This app uses Hugging Face Transformers and WebRTC for real-time processing.")
+
+# Start WebRTC Streamer
+webrtc_streamer(
+    key="plate-recognition",
+    video_processor_factory=LicensePlateProcessor,
+    rtc_configuration={
+        # Required to ensure WebRTC works across networks
+        "iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
+    }
+)