fix: ✨ refactor existing

nodes/graph_utils.py

@@ -10,6 +10,7 @@
 
 
 def get_image(filename, subfolder, folder_type):
+ log.debug(f"Getting image {filename} from {subfolder} of {folder_type}")
  data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
  url_values = urllib.parse.urlencode(data)
  with urllib.request.urlopen(

nodes/transform.py

@@ -1,5 +1,9 @@
 import torch
 import torchvision.transforms.functional as F
+from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor
+from math import sqrt, ceil
+from typing import cast
+from PIL import Image
 
 
 class TransformImage:
@@ -14,15 +18,19 @@ def INPUT_TYPES(cls):
  return {
  "required": {
  "image": ("IMAGE",),
- "x": ("FLOAT", {"default": 0}),
- "y": ("FLOAT", {"default": 0}),
- "zoom": ("FLOAT", {"default": 1.0, "min": 0.001}),
- "angle": ("FLOAT", {"default": 0}),
- "shear": ("FLOAT", {"default": 0}),
+ "x": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}),
+ "y": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}),
+ "zoom": ("FLOAT", {"default": 1.0, "min": 0.001, "step": 0.01}),
+ "angle": ("FLOAT", {"default": 0, "step": 1, "min": -360, "max": 360}),
+ "shear": (
+ "FLOAT",
+ {"default": 0, "step": 1, "min": -4096, "max": 4096},
+ ),
  "border_handling": (
  ["edge", "constant", "reflect", "symmetric"],
  {"default": "edge"},
  ),
+ "constant_color": ("COLOR", {"default": "black"}),
  },
  }
 
@@ -36,45 +44,67 @@ def transform(
  x: float,
  y: float,
  zoom: float,
- angle: int,
- shear,
+ angle: float,
+ shear: float,
  border_handling="edge",
+ constant_color=None,
  ):
+ x = int(x)
+ y = int(y)
+ angle = int(angle)
+
+ log.debug(f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}")
+
  if image.size(0) == 0:
  return (torch.zeros(0),)
  transformed_images = []
  frames_count, frame_height, frame_width, frame_channel_count = image.size()
 
  new_height, new_width = int(frame_height * zoom), int(frame_width * zoom)
 
+ log.debug(f"New height: {new_height}, New width: {new_width}")
+
+ # - Calculate diagonal of the original image
+ diagonal = sqrt(frame_width**2 + frame_height**2)
+ max_padding = ceil(diagonal * zoom - min(frame_width, frame_height))
+ # Calculate padding for zoom
  pw = int(frame_width - new_width)
  ph = int(frame_height - new_height)
- padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)]
 
- for img in image:
- img = img.permute(2, 0, 1)
- new_height, new_width = int(frame_height * zoom), int(frame_width * zoom)
- pw = int(frame_width - new_width)
- ph = int(frame_height - new_height)
+ pw += max_padding
+ ph += max_padding
+
+ padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)]
 
- padding = [int(i) for i in padding]
+ constant_color = hex_to_rgb(constant_color)
+ log.debug(f"Fill Tuple: {constant_color}")
 
+ for img in tensor2pil(image):
  img = F.pad(
  img, # transformed_frame,
  padding=padding,
  padding_mode=border_handling,
+ fill=constant_color or 0,
  )
 
- img = F.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear)
+ img = cast(
+ Image.Image,
+ F.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear),
+ )
 
- crop = [ph + y, -(ph - y), x + pw, -(pw - x)]
+ left = abs(padding[0])
+ upper = abs(padding[1])
+ right = img.width - abs(padding[2])
+ bottom = img.height - abs(padding[3])
 
- img = img[:, crop[0] : crop[1], crop[2] : crop[3]]
+ # log.debug("crop is [:,top:bottom, left:right] for tensors")
+ log.debug("crop is [left, top, right, bottom] for PIL")
+ log.debug(f"crop is {left}, {upper}, {right}, {bottom}")
+ img = img.crop((left, upper, right, bottom))
 
- img = img.permute(1, 2, 0)
- transformed_images.append(img.unsqueeze(0))
+ transformed_images.append(img)
 
- return (torch.cat(transformed_images, dim=0),)
+ return (pil2tensor(transformed_images),)
 
 
 __nodes__ = [TransformImage]

utils.py

@@ -22,6 +22,11 @@
  comfy_mode = "venv"
 
 
+def hex_to_rgb(hex_color):
+ hex_color = hex_color.lstrip("#")
+ return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
+
+
 # region MISC Utilities
 def add_path(path, prepend=False):
  if isinstance(path, list):