add more transform effects

app.py

@@ -70,8 +70,8 @@ def process_frame(frame):
             # Determine face shape
             # face_shape = determine_face_shape(landmarks)
 
-            # Get the nose position
-            nose_x, nose_y = face_landmarks[2].astype(int)
+            # Adjust the overlay size and position
+            # nose_x, nose_y = face_landmarks[2].astype(int)
             left_eye_x, left_eye_y = face_landmarks[0].astype(int)
             right_eye_x, right_eye_y = face_landmarks[1].astype(int)
 
@@ -134,6 +134,38 @@ def transform_cv2(frame, transform):
         # perform edge detection
         img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
         return img
+    
+    elif transform == "sepia":
+        # apply sepia effect
+        kernel = np.array([[0.272, 0.534, 0.131],
+                           [0.349, 0.686, 0.168],
+                           [0.393, 0.769, 0.189]])
+        img = cv2.transform(frame, kernel)
+        img = np.clip(img, 0, 255)  # ensure values are within byte range
+        # Convert BGR to RGB if necessary (for display purposes)
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        return img_rgb
+
+    elif transform == "negative":
+        # apply negative effect
+        img = cv2.bitwise_not(frame)
+        return img
+
+    elif transform == "sketch":
+        # apply sketch effect
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        inv_gray = cv2.bitwise_not(gray)
+        blur = cv2.GaussianBlur(inv_gray, (21, 21), 0)
+        inv_blur = cv2.bitwise_not(blur)
+        img = cv2.divide(gray, inv_blur, scale=256.0)
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        return img
+
+    elif transform == "blur":
+        # apply blur effect
+        img = cv2.GaussianBlur(frame, (15, 15), 0)
+        return img
+
     else:
         return frame
     
@@ -171,7 +203,7 @@ def webcam_input(frame, transform):
 with gr.Blocks() as demo:
     with gr.Column(elem_classes=["my-column"]):
         with gr.Group(elem_classes=["my-group"]):
-            transform = gr.Dropdown(choices=["cartoon", "edges", "none"],
+            transform = gr.Dropdown(choices=["cartoon", "edges", "sepia", "negative", "sketch", "blur", "none"],
                                     value="none", label="Transformation")
             input_img = gr.Image(sources=["webcam"], type="numpy", streaming=True)
             face_shape_output = gr.Textbox(label="Detected Face Shape")