remove shims

src/mediapipe_vision/utils/comfy_utils.py

@@ -1,7 +0,0 @@
-"""Utilities specific to ComfyUI integration."""
-
-# Import from the new consolidated utilities
-from ...utils.general import AlwaysEqualProxy
-
-# This file is maintained for backward compatibility
-# New code should import directly from src.utils 

src/mediapipe_vision/utils/image_utils.py

@@ -1,52 +0,0 @@
-"""Image utilities for MediaPipe Vision integration."""
-
-# Import from the new consolidated utilities
-from ...utils.image import convert_to_cv2, convert_to_tensor
-
-# This file is maintained for backward compatibility
-# New code should import directly from src.utils
-
-import torch
-import numpy as np
-import cv2
-
-def convert_to_cv2(tensor: torch.Tensor) -> list:
-    """Converts a ComfyUI IMAGE tensor (BHWC, float32, 0-1) to a list of cv2 images (BGR, uint8)."""
-    if tensor.ndim != 4 or tensor.shape[3] != 3:
-        raise ValueError(f"Input tensor must be BHWC with 3 channels, got shape: {tensor.shape}")
-    
-    # Convert entire batch to numpy, scale to 0-255, and convert to uint8
-    images_np = tensor.cpu().numpy()
-    images_np = (images_np * 255).clip(0, 255).astype(np.uint8)
-    
-    # Convert RGB to BGR for cv2
-    cv2_images = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in images_np]
-    return cv2_images
-
-def convert_to_tensor(cv2_images) -> torch.Tensor:
-    """Converts cv2 image(s) (BGR, uint8) to a ComfyUI IMAGE tensor (BHWC, float32, 0-1).
-    
-    Args:
-        cv2_images: A single cv2 image or a list of cv2 images
-    """
-    # Handle both single image and list of images
-    if not isinstance(cv2_images, list):
-        cv2_images = [cv2_images]
-    
-    # Process each image
-    np_images = []
-    for cv2_image in cv2_images:
-        # Convert BGR to RGB
-        rgb_image = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
-        
-        # Scale to 0-1
-        image_np = rgb_image.astype(np.float32) / 255.0
-        np_images.append(image_np)
-    
-    # Stack all images into a batch
-    batch_np = np.stack(np_images, axis=0)
-    
-    # Convert to tensor (already has batch dimension)
-    tensor = torch.from_numpy(batch_np)
-    
-    return tensor

src/mediapipe_vision/utils/mask_utils.py

@@ -1,11 +0,0 @@
-# --- Helper Function --- 
-import torch
-import numpy as np
-import cv2
-from typing import List, Tuple
-import logging
-from ...utils.image import create_mask_from_points
-
-logger = logging.getLogger(__name__)
-
-# create_mask_from_points is now imported from utils.image

src/mediapipe_vision/utils/timestamp_provider.py

@@ -1,4 +0,0 @@
-from ...utils.timing import TimestampProvider
-
-# This file is maintained for backward compatibility
-# New code should import directly from src.utils.timing 

src/utils/general_utils.py

@@ -1,4 +0,0 @@
-from .general import AlwaysEqualProxy
-
-# This file is maintained for backward compatibility
-# New code should import directly from src.utils.general