| | import torch |
| | import numpy as np |
| | from PIL import Image |
| |
|
| | class ConstrainImage: |
| | """ |
| | A node that constrains an image to a maximum and minimum size while maintaining aspect ratio. |
| | """ |
| |
|
| | @classmethod |
| | def INPUT_TYPES(cls): |
| | return { |
| | "required": { |
| | "images": ("IMAGE",), |
| | "max_width": ("INT", {"default": 1024, "min": 0}), |
| | "max_height": ("INT", {"default": 1024, "min": 0}), |
| | "min_width": ("INT", {"default": 0, "min": 0}), |
| | "min_height": ("INT", {"default": 0, "min": 0}), |
| | "crop_if_required": (["yes", "no"], {"default": "no"}), |
| | }, |
| | } |
| |
|
| | RETURN_TYPES = ("IMAGE",) |
| | FUNCTION = "constrain_image" |
| | CATEGORY = "image" |
| | OUTPUT_IS_LIST = (True,) |
| |
|
| | def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required): |
| | crop_if_required = crop_if_required == "yes" |
| | results = [] |
| | for image in images: |
| | i = 255. * image.cpu().numpy() |
| | img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB") |
| |
|
| | current_width, current_height = img.size |
| | aspect_ratio = current_width / current_height |
| |
|
| | constrained_width = max(min(current_width, min_width), max_width) |
| | constrained_height = max(min(current_height, min_height), max_height) |
| |
|
| | if constrained_width / constrained_height > aspect_ratio: |
| | constrained_width = max(int(constrained_height * aspect_ratio), min_width) |
| | if crop_if_required: |
| | constrained_height = int(current_height / (current_width / constrained_width)) |
| | else: |
| | constrained_height = max(int(constrained_width / aspect_ratio), min_height) |
| | if crop_if_required: |
| | constrained_width = int(current_width / (current_height / constrained_height)) |
| |
|
| | resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS) |
| |
|
| | if crop_if_required and (constrained_width > max_width or constrained_height > max_height): |
| | left = max((constrained_width - max_width) // 2, 0) |
| | top = max((constrained_height - max_height) // 2, 0) |
| | right = min(constrained_width, max_width) + left |
| | bottom = min(constrained_height, max_height) + top |
| | resized_image = resized_image.crop((left, top, right, bottom)) |
| |
|
| | resized_image = np.array(resized_image).astype(np.float32) / 255.0 |
| | resized_image = torch.from_numpy(resized_image)[None,] |
| | results.append(resized_image) |
| | |
| | return (results,) |
| |
|
| | NODE_CLASS_MAPPINGS = { |
| | "ConstrainImage|pysssss": ConstrainImage, |
| | } |
| |
|
| | NODE_DISPLAY_NAME_MAPPINGS = { |
| | "ConstrainImage|pysssss": "Constrain Image 🐍", |
| | } |
| |
|
