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| import streamlit as st | |
| from streamlit_webrtc import webrtc_streamer, VideoProcessorBase | |
| import av | |
| from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel | |
| from PIL import Image, ImageDraw | |
| import numpy as np | |
| import torch | |
| # 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 for verification | |
| authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090", "CH01AG2863"} # Example data | |
| # 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 | |
| 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] | |
| 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}" | |
| # 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") # 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") | |
| # 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") | |
| # Step 4: Streamlit Interface | |
| st.title("Real-Time Car Number Plate Recognition") | |
| 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"]}] | |
| } | |
| ) | |