Update app.py

app.py

@@ -27,6 +27,48 @@ female_background_image_paths = [
     "Data/AdobeColorFunko/Outfits/WomenOutfits/WomenThree.png"
 ]
 
+def parse_args():
+    parser = argparse.ArgumentParser(description='Funko Demo')
+    parser.add_argument(
+        '--device', type=str, default='cuda:0', help='CPU/CUDA device option.')
+    parser.add_argument(
+        '--camera-id', type=int, default=0, help='Camera device id.')
+    args = parser.parse_args()
+    return args
+    
+def capture_frame_from_webcam(duration=7):
+    data_transforms = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    args = parse_args()
+    device = torch.device(args.device)
+    
+    cap = cv2.VideoCapture(args.camera_id)  # Open the default webcam (usually ID 0)
+
+    frames = []
+    start_time = time.time()
+
+    while (time.time() - start_time) < duration:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Preprocess the frame and store it in the list
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert to RGB format
+        frame_pil = Image.fromarray(frame)  # Convert to PIL Image
+        frame_tensor = data_transforms(frame_pil)  # Preprocess
+        frames.append(frame_tensor)
+
+        # Display the video stream to the user
+        cv2.imshow("Video Capture", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+        cv2.waitKey(1)  # Adjust the delay (milliseconds) as needed for display
+
+    cap.release()
+    cv2.destroyAllWindows()  # Close the video stream window
+
+    return frames
 
 class GenderClassifier:
     def __init__(self, model_path, class_names):
@@ -467,49 +509,6 @@ def process_image_menHair(background_image, x, y, placeholder_image_path, x_coor
     background_array = np.array(background_image)
     placeholder_alpha = placeholder_image.split()[3] if placeholder_image.mode == 'RGBA' else None
 
-def parse_args():
-    parser = argparse.ArgumentParser(description='Funko Demo')
-    parser.add_argument(
-        '--device', type=str, default='cuda:0', help='CPU/CUDA device option.')
-    parser.add_argument(
-        '--camera-id', type=int, default=0, help='Camera device id.')
-    args = parser.parse_args()
-    return args
-    
-def capture_frame_from_webcam(duration=7):
-    data_transforms = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
-    args = parse_args()
-    device = torch.device(args.device)
-    
-    cap = cv2.VideoCapture(args.camera_id)  # Open the default webcam (usually ID 0)
-
-    frames = []
-    start_time = time.time()
-
-    while (time.time() - start_time) < duration:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Preprocess the frame and store it in the list
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert to RGB format
-        frame_pil = Image.fromarray(frame)  # Convert to PIL Image
-        frame_tensor = data_transforms(frame_pil)  # Preprocess
-        frames.append(frame_tensor)
-
-        # Display the video stream to the user
-        cv2.imshow("Video Capture", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-        cv2.waitKey(1)  # Adjust the delay (milliseconds) as needed for display
-
-    cap.release()
-    cv2.destroyAllWindows()  # Close the video stream window
-
-    return frames
-
 
 
 # Function to generate Funko figurines