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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"
# face_model_zoo_url: URL/path for the face 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.
face_model_zoo_url = "https://cs.degirum.com/degirum/ultralytics_v6"
# face_model_name: Name of the model for face detection.
face_model_name = "yolov8n_relu6_face--640x640_quant_n2x_orca1_1"
# age_model_zoo_url: URL/path for the age model zoo.
age_model_zoo_url = "https://cs.degirum.com/degirum/sandbox"
# age_model_name: Name of the model for age detection.
age_model_name = "yolov8s_regress_age_silu_utkface--256x256_float_openvino_cpu_1"
# Connect to AI inference engine getting token from env.ini file
face_zoo = dg.connect(hw_location, face_model_zoo_url, token=st.secrets["DG_TOKEN"])
age_zoo = dg.connect(hw_location, age_model_zoo_url, token=st.secrets["DG_TOKEN"])
# Load models
face_model = face_zoo.load_model(face_model_name,
image_backend='pil',
overlay_show_probabilities=True,
overlay_color=(255,0,0),
overlay_line_width=2,
overlay_font_scale=1.5
)
age_model= age_zoo.load_model(age_model_name,
overlay_show_probabilities=True,
overlay_show_labels=True,
image_backend='pil')
# Create a compound cropping model with 50% crop extent
crop_model = degirum_tools.CroppingAndClassifyingCompoundModel(
face_model, age_model, 50.0
)
st.title('DeGirum Cloud Platform Demo of Face Detection and Age Regression 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)
image.thumbnail((640,640), Image.Resampling.LANCZOS)
inference_results=crop_model(image)
st.image(inference_results.image_overlay,caption='Image with Bounding Boxes') |