run with conda env

README.md

@@ -25,4 +25,18 @@ Dive into the world of high-performance face recognition by cloning this repo an
 ```bash
 git clone https://github.com/Atticuszz/boostface.git
 cd boostface
-# Follow the setup instructions in the documentation
+```
+
+- download arcface_onnx model
+```bash
+cd src/Demo/backend/services/inference/model_zoo
+wget https://github.com/Atticuszz/BoostFace/releases/download/dataset/models.zip
+unzip models.zip
+rm -rf models.zip
+```
+
+- install env
+```bash
+cd src/Demo
+conda env create -f environment.yml
+```

src/Demo/backend/services/db/milvus_client.py

@@ -61,7 +61,7 @@ def _create_collection(self) -> Collection:
 
         logger.debug(f"\nCollection {self._config.collection.name} is creating...")
         collection = Collection(**self._config.collection.as_dict())
-        logger.debug("collection created:", self._config.collection.name)
+        logger.debug(f"collection created:{self._config.collection.name}")
         return collection
 
     def insert(self, entities: list[ndarray, ndarray, ndarray]):

src/Demo/environment.yml

@@ -0,0 +1,28 @@
+name: BoostFace-demo
+channels:
+  - conda-forge
+  - nvidia
+  - defaults
+dependencies:
+  - python=3.11
+  - cudatoolkit=11.8
+  - cudnn=8.9.2.26
+  - numpy=1.26.4
+  - streamlit=1.31.1
+  - pydantic-settings=2.2.1
+  - uvicorn=0.27.1
+  - scikit-image=0.22.0
+  - onnx=1.15.0
+  - scalene
+  - pip
+  - pip:
+    - onnxruntime-gpu
+    - supabase-py-async
+    - pymilvus
+    - python-dotenv
+    - pygizmokit
+    - fastapi
+    - opencv-python
+    - pydantic
+    - filterpy
+    - websockets

src/Demo/web/inference/settings.py

@@ -11,7 +11,7 @@
 
 from typing import NamedTuple
 
-DATA_ROOT = Path(__file__).parent / "data"
+DATA_ROOT = Path(__file__).parents[1] / "data"
 VIDEO_ROOT = DATA_ROOT / "video"
 IMAGE_ROOT = DATA_ROOT / "image"
 MODEL_ROOT = DATA_ROOT / "model"