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import torch |
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import torchvision.transforms.functional as TF |
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from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor |
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from math import sqrt, ceil |
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from typing import cast |
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from PIL import Image |
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class TransformImage: |
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"""Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy |
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it return a tensor representing the transformed images with the same shape as the input tensor |
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""" |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"image": ("IMAGE",), |
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"x": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}), |
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"y": ("FLOAT", {"default": 0, "step": 1, "min": -4096, "max": 4096}), |
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"zoom": ("FLOAT", {"default": 1.0, "min": 0.001, "step": 0.01}), |
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"angle": ("FLOAT", {"default": 0, "step": 1, "min": -360, "max": 360}), |
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"shear": ( |
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"FLOAT", |
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{"default": 0, "step": 1, "min": -4096, "max": 4096}, |
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), |
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"border_handling": ( |
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["edge", "constant", "reflect", "symmetric"], |
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{"default": "edge"}, |
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), |
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"constant_color": ("COLOR", {"default": "#000000"}), |
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}, |
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} |
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FUNCTION = "transform" |
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RETURN_TYPES = ("IMAGE",) |
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CATEGORY = "mtb/transform" |
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def transform( |
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self, |
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image: torch.Tensor, |
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x: float, |
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y: float, |
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zoom: float, |
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angle: float, |
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shear: float, |
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border_handling="edge", |
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constant_color=None, |
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): |
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x = int(x) |
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y = int(y) |
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angle = int(angle) |
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log.debug(f"Zoom: {zoom} | x: {x}, y: {y}, angle: {angle}, shear: {shear}") |
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if image.size(0) == 0: |
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return (torch.zeros(0),) |
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transformed_images = [] |
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frames_count, frame_height, frame_width, frame_channel_count = image.size() |
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new_height, new_width = int(frame_height * zoom), int(frame_width * zoom) |
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log.debug(f"New height: {new_height}, New width: {new_width}") |
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diagonal = sqrt(frame_width**2 + frame_height**2) |
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max_padding = ceil(diagonal * zoom - min(frame_width, frame_height)) |
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pw = int(frame_width - new_width) |
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ph = int(frame_height - new_height) |
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pw += abs(max_padding) |
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ph += abs(max_padding) |
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padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)] |
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constant_color = hex_to_rgb(constant_color) |
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log.debug(f"Fill Tuple: {constant_color}") |
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for img in tensor2pil(image): |
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img = TF.pad( |
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img, |
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padding=padding, |
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padding_mode=border_handling, |
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fill=constant_color or 0, |
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) |
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img = cast( |
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Image.Image, |
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TF.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear), |
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) |
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left = abs(padding[0]) |
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upper = abs(padding[1]) |
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right = img.width - abs(padding[2]) |
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bottom = img.height - abs(padding[3]) |
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log.debug("crop is [left, top, right, bottom] for PIL") |
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log.debug(f"crop is {left}, {upper}, {right}, {bottom}") |
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img = img.crop((left, upper, right, bottom)) |
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transformed_images.append(img) |
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return (pil2tensor(transformed_images),) |
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__nodes__ = [TransformImage] |
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