face recognition

app/Hackathon_setup/face_recognition.py

@@ -120,15 +120,15 @@ def get_face_class(img1):
     ckpt = torch.load(ckpt_path, map_location=device)
     myModel.load_state_dict(ckpt['net_dict'])
     myModel.eval()
-    myModel = myModel.float()
+    # myModel = myModel.float()
     # 3 Face detection (if available)
-    det_img1 = detected_face(img1)   # returns cropped face or 0 if not detected
-    if det_img1 == 0:
-        # fallback: use original image
-        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+    # det_img1 = detected_face(img1)   # returns cropped face or 0 if not detected
+    # if det_img1 == 0:
+    #     # fallback: use original image
+    #     det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
 
     # 4 Transform the face
-    img_tensor = trnscm(det_img1).unsqueeze(0).float()
+    img_tensor = transform1(img1).unsqueeze(0).to(device)
 
     # 5 Extract embeddings
     with torch.no_grad():

app/main.py

@@ -91,10 +91,12 @@ async def create_upload_files(request: Request, file3: UploadFile = File(...)):
         face_rec_filename = 'app/static/' + file3.filename
         with open(face_rec_filename, 'wb') as f:
             f.write(contents)
+
     print(face_rec_filename)
     img1 = Image.open(face_rec_filename)
-    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
-        
+    # img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+
+
     result = face_recognition.get_face_class(img1)
     print(result)