File size: 3,631 Bytes
821e6d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import torch
import torchvision.transforms.functional as TF
from ..utils import log, hex_to_rgb, tensor2pil, pil2tensor
from math import sqrt, ceil
from typing import cast
from PIL import Image
class TransformImage:
"""Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy
it return a tensor representing the transformed images with the same shape as the input tensor
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"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": "#000000"}),
},
}
FUNCTION = "transform"
RETURN_TYPES = ("IMAGE",)
CATEGORY = "mtb/transform"
def transform(
self,
image: torch.Tensor,
x: float,
y: float,
zoom: float,
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)
pw += abs(max_padding)
ph += abs(max_padding)
padding = [max(0, pw + x), max(0, ph + y), max(0, pw - x), max(0, ph - y)]
constant_color = hex_to_rgb(constant_color)
log.debug(f"Fill Tuple: {constant_color}")
for img in tensor2pil(image):
img = TF.pad(
img, # transformed_frame,
padding=padding,
padding_mode=border_handling,
fill=constant_color or 0,
)
img = cast(
Image.Image,
TF.affine(img, angle=angle, scale=zoom, translate=[x, y], shear=shear),
)
left = abs(padding[0])
upper = abs(padding[1])
right = img.width - abs(padding[2])
bottom = img.height - abs(padding[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))
transformed_images.append(img)
return (pil2tensor(transformed_images),)
__nodes__ = [TransformImage]
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