| import streamlit as st | |
| import degirum as dg | |
| from PIL import Image | |
| zoo=dg.connect(dg.CLOUD,zoo_url='https://cs.degirum.com/degirum/ultralytics_v6',token=st.secrets["DG_TOKEN"]) | |
| st.title('DeGirum Cloud Platform Demo') | |
| with st.sidebar: | |
| st.header('Specify Model Options Below') | |
| runtime_agent_device=st.radio("Choose runtime agent device combo",("N2X-ORCA1","TFLite-EdgeTPU","OpenVINO-CPU"),index=0,horizontal=True) | |
| activation_option=st.radio( 'Select activation function', ['relu6', 'silu'],horizontal=True) | |
| dataset_option=st.radio( 'Select a dataset option', ['coco', 'face','lp','car','hand'],horizontal=True) | |
| show_labels=st.toggle('Show labels in output',value=True) | |
| show_probabilities=st.toggle('Show probabilities in output',value=False) | |
| runtime_agent,device=runtime_agent_device.split('-')[0],runtime_agent_device.split('-')[1] | |
| model_options=zoo.list_models(device=device,runtime=runtime_agent) | |
| st.header('Choose and Run a Model') | |
| st.text('Select a model and upload an image. Then click on the submit button') | |
| with st.form("model_form"): | |
| filtered_model_list=[] | |
| for model in model_options: | |
| if activation_option in model and dataset_option in model: | |
| filtered_model_list.append(model) | |
| st.write('Number of models found = ', len(filtered_model_list)) | |
| model_name=st.selectbox("Choose a Model from the list", filtered_model_list) | |
| uploaded_file=st.file_uploader('input image') | |
| submitted = st.form_submit_button("Submit") | |
| if submitted: | |
| model=zoo.load_model(model_name, | |
| overlay_show_labels=show_labels, | |
| overlay_show_probabilities=show_probabilities, | |
| overlay_font_scale=3, | |
| overlay_line_width=6, | |
| image_backend='pil' | |
| ) | |
| if model.output_postprocess_type=='PoseDetection': | |
| model.overlay_show_labels=False | |
| st.write("Model loaded successfully") | |
| image = Image.open(uploaded_file) | |
| predictions=model(image) | |
| if model.output_postprocess_type=='Classification' or model.output_postprocess_type=='DetectionYoloPlates': | |
| st.image(predictions.image,caption='Original Image') | |
| st.write(predictions.results) | |
| else: | |
| st.image(predictions.image_overlay,caption='Image with Bounding Boxes/Keypoints') | |
| model.measure_time=True | |
| predictions=model(image) | |
| stats=model.time_stats() | |
| st.write('Expected Frames per second for the model= ', 1000.0/stats["CoreInferenceDuration_ms"].avg) |