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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() |