Face Detection and Age Regression Demo

README.md

@@ -1,10 +1,10 @@
 ---
-title: Face Age
-emoji: 😻
-colorFrom: gray
-colorTo: green
+title: Face Detection and Age Regression
+emoji: 📈
+colorFrom: blue
+colorTo: red
 sdk: streamlit
-sdk_version: 1.30.0
+sdk_version: 1.28.0
 app_file: app.py
 pinned: false
 license: mit

app.py

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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_color=(255,0,0),
+                                 overlay_line_width=2,
+                                 overlay_font_scale=1.5                                
+                                )
+age_model= age_zoo.load_model(age_model_name, 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')        

requirements.txt

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+degirum
+degirum_tools
+altair<5