feat(face_compare): Add face comparison functionality (NO FHE)

face_compare.py

@@ -0,0 +1,52 @@
+from facenet_pytorch import MTCNN, InceptionResnetV1
+import torchvision.transforms as transforms
+from PIL import Image
+import torch
+
+mtcnn = MTCNN(keep_all=False, device='cpu')
+
+model = InceptionResnetV1(pretrained='vggface2').eval()
+
+def preprocess_face(base64_img):
+        img = Image.open(base64_img).convert('RGB')
+
+        preprocess = transforms.Compose([
+            transforms.Resize((160, 160)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        ])
+
+        # Detect face and get the bounding box
+        box, _ = mtcnn.detect(img)
+
+        if box is not None:
+            # Crop the image using the bounding box
+            img_cropped = img.crop(box[0])  # box[0] contains (x1, y1, x2, y2)
+
+            # Apply the rest of preprocessing (resize, tensor, normalize)
+            img_preprocessed = preprocess(img_cropped).unsqueeze(0)
+            return img_preprocessed
+        else:
+            print("No face detected")
+            return None
+
+def compare_faces(base64_img1, base64_img2):
+    # Load and detect face, then crop the image
+
+    # Preprocess both images
+    img1 = preprocess_face(base64_img1)  # Replace with your image path
+    img2 = preprocess_face(base64_img2)  # Replace with your image path
+
+    # Check if faces were detected in both images
+    if img1 is not None and img2 is not None:
+        # Get the embeddings from the FaceNet model
+        with torch.no_grad():
+            emb1 = model(img1)
+            emb2 = model(img2)
+
+        # Calculate cosine similarity
+        cosine_similarity = torch.nn.functional.cosine_similarity(emb1, emb2)
+        return cosine_similarity.item()
+    else:
+        print("Face detection failed on one or both images.")
+        return None

main.py

@@ -5,6 +5,15 @@ from fastapi.middleware.cors import CORSMiddleware
 import base64
 from pydantic import BaseModel
 import time
+from facenet_pytorch import InceptionResnetV1, MTCNN
+import warnings
+import face_compare
+
+warnings.filterwarnings('ignore', category=FutureWarning, module='facenet_pytorch')
+
+mtcnn = MTCNN(keep_all=False, device='cpu')
+
+model = InceptionResnetV1(pretrained='vggface2').eval()
 
 app = FastAPI()
 router = APIRouter()
@@ -17,7 +26,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-pdf = 0 
+pdf = 0
 
 class ImageData(BaseModel):
     image: str
@@ -37,16 +46,27 @@ async def verif() -> FileResponse:
 async def upload_pdf(data: ImageData):
     header, encoded = data.image.split(',', 1)
     binary_data = base64.b64decode(encoded)
-    # Call
-    pdf = binary_data;
-    time.sleep(10)
+
+    # Save the pdf
+    pdf = binary_data
     return {"message": "Image reçue et sauvegardée"}
 
 @router.post("/uploadids")
 async def upload_ids(data: ImagesData):
-    # Call To The model
-    return {"message": "Images reçues et sauvegardées"}
+    header, encoded1 = data.idCard.split(',', 1)
+    binary_data1 = base64.b64decode(encoded1)
+    header, encoded2 = data.profileImage.split(',', 1)
+    binary_data2 = base64.b64decode(encoded2)
+    output = face_compare.compare_faces(binary_data1, binary_data2)
+    if output > 0.6:
+        return {"message": "Les images correspondent"}
+    else:
+        return {"message": "Les images ne correspondent pas"}
 
 app.include_router(router)
 
 app.mount("/", StaticFiles(directory="front/dist", html=True), name="static")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -4,4 +4,4 @@ sentencepiece==0.1.*
 torch==1.11.*
 transformers==4.*
 uvicorn[standard]==0.17.*
-numpy<2.0
+numpy<2.0