,

app.py

@@ -14,8 +14,8 @@ from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
 MODEL_DIR = 'models/siamese_model-final'  
 VERIF_IMGS_DIR = 'registered_images'
 INPUT_IMG_DIR = 'input_images/cropped_face.png'
-THRESHOLD = 0.2
-VERIFICATION_THRESHOLD = 0.3
+THRESHOLD = 0.9
+VERIFICATION_THRESHOLD = 0.93
 GREEN_COLOR = (0, 255, 0)
 RED_COLOR = (0, 0, 255)
 
@@ -166,8 +166,8 @@ def detect_faces(image):
 
     for (x, y, w, h) in faces:
         # cv2.rectangle(image, (x, y), (x + w, y + h), GREEN_COLOR, 2)
-        cropped_image = crop_img(image, (x, y, w, h))
-        is_real = is_real_face(cropped_image)
+        # cropped_image = crop_img(image, (x, y, w, h))
+        is_real = is_real_face(image)
         if is_real:
             cv2.putText(image, "Real", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, GREEN_COLOR, 2)
             cv2.rectangle(image, (x, y), (x + w, y + h), GREEN_COLOR, 2)
@@ -253,10 +253,11 @@ with col2:
 
 if image_file and len(detected_faces) > 0:
 
-    cropped_image = crop_img(image_np, detected_faces[0])
+    # cropped_image = crop_img(image_np, detected_faces[0])
 
     temp_image_path = "cropped_face.jpg"
-    cv2.imwrite(temp_image_path, cv2.cvtColor(cropped_image, cv2.COLOR_RGB2BGR))
+    # cv2.imwrite(temp_image_path, cv2.cvtColor(cropped_image, cv2.COLOR_RGB2BGR))
+    cv2.imwrite(temp_image_path, cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR))
     INPUT_IMG_DIR = temp_image_path
 
     with st.spinner('Processing...'):