Fixed issue with other models not working

app.py

@@ -54,7 +54,13 @@ def preprocess_image(img_array, model_name, target_size=(48, 48)):
 
     # Repeat channels if model is not base_cnn
     if model_name != "Scratch CNN Model":
-        image = np.repeat(image[..., np.newaxis], 3, axis=-1)
+        # If the image is grayscale (shape: [height, width, 1])
+        if image.ndim == 3 and image.shape[-1] == 1:
+            # Repeat the grayscale channels to create RGB
+            image = np.repeat(image, 3, axis=-1)
+        # If it's already RGB (shape: [height, width, 3])
+        elif image.ndim == 3 and image.shape[-1] == 3:
+            pass  # No change needed
 
     # Add batch dimension
     image = np.expand_dims(image, axis=0)
@@ -68,6 +74,30 @@ def predict_emotion(face_array, model, model_name, emotion_list):
     return predicted_emotion
 
 
+def draw_bounding_box(img_array):
+    # Load the Haar cascade for face detection
+    cascade_path = os.path.join(
+        cv2.data.haarcascades, "haarcascade_frontalface_default.xml")
+    face_cascade = cv2.CascadeClassifier(cascade_path)
+
+    # Convert the image to grayscale for face detection
+    gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the grayscale image
+    faces = face_cascade.detectMultiScale(
+        gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Check if a face is detected
+    if len(faces) == 0:
+        return "No face detected.", None
+
+    # Draw a bounding box around the first detected face
+    x, y, w, h = faces[0]
+    cv2.rectangle(img_array, (x, y), (x+w, y+h), (255, 0, 0), 2)
+
+    return "Face detected.", img_array
+
+
 def getResult(selected_option, image):
     # Load image in memory
     img = Image.open(BytesIO(image.read()))
@@ -77,7 +107,7 @@ def getResult(selected_option, image):
     model_path = MODEL_PATHS[selected_option]
     model = load_model(model_path)
 
-    # Process the image
+    # Process the image to extract the face
     result, face_array = extract_face(img_array)
     if result == "No face detected.":
         return result, [], None
@@ -86,11 +116,28 @@ def getResult(selected_option, image):
     predicted_emotion = predict_emotion(
         face_array, model, selected_option, emotion_list)
 
-    # Convert images to Base64 for embedding in HTML
-    bounding_box_image = convert_image_to_base64(img)
-    cropped_face_image = convert_image_to_base64(Image.fromarray(face_array))
+    # Call the draw_bounding_box function to get the image with a bounding box
+    result, bounding_box_img_array = draw_bounding_box(img_array)
+
+    if result == "No face detected.":
+        return result, [], None
+
+    # Convert the image with bounding box to Base64
+    bounding_box_image = convert_image_to_base64(
+        Image.fromarray(bounding_box_img_array))
+
+    # Convert the cropped face image to Base64
+    cropped_face_image = convert_image_to_base64(
+        Image.fromarray(cv2.cvtColor(face_array, cv2.COLOR_BGR2GRAY)))
+
+    # Convert the image to grayscale for display
+    grayscale_image = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
+    grayscale_pil_image = Image.fromarray(grayscale_image)
+    grayscale_base64 = convert_image_to_base64(
+        grayscale_pil_image)  # Convert grayscale to Base64
 
-    return "The predicted emotion is:", [bounding_box_image, cropped_face_image], predicted_emotion
+    # Return the predicted emotion, list of images (bounding box, cropped face), and grayscale image
+    return "The predicted emotion is:", [bounding_box_image, cropped_face_image, grayscale_base64], predicted_emotion
 
 
 app = Flask(__name__)