Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from PIL import Image | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel, VitsModel, AutoTokenizer | |
| import torch | |
| import yolov5 | |
| # Load YOLOv5 model | |
| # @st.cache(allow_output_mutation=True) | |
| def load_model(): | |
| return yolov5.load('keremberke/yolov5m-license-plate') | |
| # Load TR-OCR model | |
| # @st.cache(allow_output_mutation=True) | |
| def load_ocr_model(): | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") | |
| return processor, model | |
| # Load TTS model | |
| # @st.cache(allow_output_mutation=True) | |
| def load_tts_model(): | |
| model = VitsModel.from_pretrained("facebook/mms-tts-eng") | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") | |
| return model, tokenizer | |
| # Main function for Streamlit app | |
| def main(): | |
| st.title("License Plate Recognition App") | |
| # Static test image | |
| test_image_path = "test_image.jpg" | |
| test_image = Image.open(test_image_path) | |
| # Upload file | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| img = Image.open(uploaded_file) | |
| else: | |
| img = test_image | |
| st.image(img, caption='Image', use_column_width=True) | |
| if st.button("Run Inference"): | |
| # Load models on startup | |
| model = load_model() | |
| processor, ocr_model = load_ocr_model() | |
| tts_model, tokenizer = load_tts_model() | |
| results = model(img, size=640) | |
| # results.show() | |
| predictions = results.pred[0] | |
| boxes = predictions[:, :4] # x1, y1, x2, y2 | |
| scores = predictions[:, 4] | |
| categories = predictions[:, 5] | |
| # Crop the image of the license plate | |
| cropped_image = img.crop(tuple(results.xyxy[0][0, :4].squeeze().tolist()[:4])) | |
| st.image(cropped_image, caption='Plate detected') | |
| # Extract text from the image | |
| pixel_values = processor(cropped_image, return_tensors="pt").pixel_values | |
| generated_ids = ocr_model.generate(pixel_values) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| st.write("Detected License Plate Text:", generated_text) | |
| # Convert the text to audio | |
| inputs = tokenizer(generated_text, return_tensors="pt") | |
| with torch.no_grad(): | |
| output = tts_model(**inputs).waveform | |
| st.audio(output.numpy(), format="audio/wav", sample_rate=tts_model.config.sampling_rate) | |
| if __name__ == "__main__": | |
| main() |