Spaces:
Running
Running
| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image, ImageDraw | |
| from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel | |
| import torch | |
| # 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") | |
| # 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 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] | |
| # Verify Plate Text | |
| def verify_plate(plate_text): | |
| if plate_text in authorized_cars: | |
| return f"✅ Access Granted: {plate_text}" | |
| else: | |
| return f"❌ Access Denied: {plate_text}" | |
| # Real-Time Video Processing with OpenCV | |
| def live_feed(): | |
| cap = cv2.VideoCapture(0) # Open webcam | |
| if not cap.isOpened(): | |
| st.error("Unable to access the camera.") | |
| return | |
| stframe = st.image([]) # Placeholder for video feed | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.error("Failed to capture frame from the camera. Exiting...") | |
| break | |
| # Detect plates | |
| boxes, pil_image = detect_license_plate(frame) | |
| draw = ImageDraw.Draw(pil_image) | |
| recognized_plates = [] | |
| for box in boxes: | |
| # Crop and recognize 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 box and label | |
| 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 = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) | |
| # Stream video to Streamlit | |
| stframe.image(processed_frame, channels="BGR", use_column_width=True) | |
| # Display results | |
| for plate_text in recognized_plates: | |
| st.write(verify_plate(plate_text)) | |
| cap.release() | |
| cv2.destroyAllWindows() | |
| # Streamlit UI | |
| if st.button("Start Camera"): | |
| live_feed() |