feat: facial recognition (#2180)

machine-learning/src/main.py

@@ -1,9 +1,13 @@
+import os
+import numpy as np
+import cv2 as cv
+import uvicorn
+
+from insightface.app import FaceAnalysis
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 from PIL import Image
 from fastapi import FastAPI
-import uvicorn
-import os
 from pydantic import BaseModel
 
 
@@ -15,15 +19,6 @@ class ClipRequestBody(BaseModel):
     text: str
 
 
-is_dev = os.getenv('NODE_ENV') == 'development'
-server_port = os.getenv('MACHINE_LEARNING_PORT', 3003)
-server_host = os.getenv('MACHINE_LEARNING_HOST', '0.0.0.0')
-
-app = FastAPI()
-
-"""
-Model Initialization
-"""
 classification_model = os.getenv(
     'MACHINE_LEARNING_CLASSIFICATION_MODEL', 'microsoft/resnet-50')
 object_model = os.getenv('MACHINE_LEARNING_OBJECT_MODEL', 'hustvl/yolos-tiny')
@@ -31,9 +26,15 @@ clip_image_model = os.getenv(
     'MACHINE_LEARNING_CLIP_IMAGE_MODEL', 'clip-ViT-B-32')
 clip_text_model = os.getenv(
     'MACHINE_LEARNING_CLIP_TEXT_MODEL', 'clip-ViT-B-32')
+facial_recognition_model = os.getenv(
+    'MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL', 'buffalo_l')
+
+cache_folder = os.getenv('MACHINE_LEARNING_CACHE_FOLDER', '/cache')
 
 _model_cache = {}
 
+app = FastAPI()
+
 
 @app.get("/")
 async def root():
@@ -73,6 +74,36 @@ def clip_encode_text(payload: ClipRequestBody):
     return model.encode(text).tolist()
 
 
+@app.post("/facial-recognition/detect-faces", status_code=200)
+def facial_recognition(payload: MlRequestBody):
+    model = _get_model(facial_recognition_model, 'facial-recognition')
+    assetPath = payload.thumbnailPath
+    img = cv.imread(assetPath)
+    height, width, _ = img.shape
+    results = []
+    faces = model.get(img)
+    for face in faces:
+        if face.det_score < 0.7:
+            continue
+        x1, y1, x2, y2 = face.bbox
+        # min face size as percent of original image
+        # if (x2 - x1) / width < 0.03 or (y2 - y1) / height < 0.05:
+        #     continue
+        results.append({
+            "imageWidth": width,
+            "imageHeight": height,
+            "boundingBox": {
+                "x1": round(x1),
+                "y1": round(y1),
+                "x2": round(x2),
+                "y2": round(y2),
+            },
+            "score": face.det_score.item(),
+            "embedding": face.normed_embedding.tolist()
+        })
+    return results
+
+
 def run_engine(engine, path):
     result = []
     predictions = engine(path)
@@ -93,12 +124,22 @@ def _get_model(model, task=None):
     key = '|'.join([model, str(task)])
     if key not in _model_cache:
         if task:
-            _model_cache[key] = pipeline(model=model, task=task)
+            if task == 'facial-recognition':
+                face_model = FaceAnalysis(
+                    name=model, root=cache_folder, allowed_modules=["detection", "recognition"])
+                face_model.prepare(ctx_id=0, det_size=(640, 640))
+                _model_cache[key] = face_model
+            else:
+                _model_cache[key] = pipeline(model=model, task=task)
         else:
-            _model_cache[key] = SentenceTransformer(model)
+            _model_cache[key] = SentenceTransformer(
+                model, cache_folder=cache_folder)
     return _model_cache[key]
 
 
 if __name__ == "__main__":
-    uvicorn.run("main:app", host=server_host,
-                port=int(server_port), reload=is_dev, workers=1)
+    host = os.getenv('MACHINE_LEARNING_HOST', '0.0.0.0')
+    port = int(os.getenv('MACHINE_LEARNING_PORT', 3003))
+    is_dev = os.getenv('NODE_ENV') == 'development'
+
+    uvicorn.run("main:app", host=host, port=port, reload=is_dev, workers=1)