import streamlit as st import degirum as dg from PIL import Image import degirum_tools # hw_location: Where you want to run inference. # Use "@cloud" to use DeGirum cloud. # Use "@local" to run on local machine. # Use an IP address for AI server inference. hw_location = "@cloud" # model_zoo_url: URL/path for the model zoo. # Use cloud_zoo_url for @cloud, @local, and AI server inference options. # Use '' for an AI server serving models from a local folder. # Use a path to a JSON file for a single model zoo in case of @local inference. model_zoo_url = "https://cs.degirum.com/degirum/public" # lp_det_model_name: Name of the model for license plate detection. lp_det_model_name = "yolo_v5s_lp_det--512x512_quant_n2x_orca1_1" # lp_ocr_model_name: Name of the model for license plate OCR. lp_ocr_model_name = "yolo_v5s_lp_ocr--256x256_quant_n2x_orca1_1" # Connect to AI inference engine model_zoo = dg.connect(hw_location, model_zoo_url, token=st.secrets["DG_TOKEN"]) # Load models lp_det_model = model_zoo.load_model(lp_det_model_name, image_backend='pil', overlay_color=(255,0,0), overlay_line_width=2, overlay_font_scale=1.5 ) lp_ocr_model= model_zoo.load_model(lp_ocr_model_name, image_backend='pil') # Create a compound cropping model with 5% crop extent crop_model = degirum_tools.CroppingAndClassifyingCompoundModel( lp_det_model, lp_ocr_model, 5.0 ) st.title('DeGirum Cloud Platform Demo of License Plate Detection and Recognition Models') st.text('Upload an image. Then click on the submit button') with st.form("model_form"): uploaded_file=st.file_uploader('input image') submitted = st.form_submit_button("Submit") if submitted: image = Image.open(uploaded_file) inference_results=crop_model(image) st.image(inference_results.image_overlay,caption='Image with Bounding Boxes')