9.1 KiB
9.1 KiB
Documentation
- Class name: ImageScaleByAspectRatio
- Category: 😺dzNodes/LayerUtility
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
将图像或遮罩按宽高比缩放。可设置将缩放后的尺寸按8或者16的倍数取整，可按长边尺寸缩放。
Input types
Required
-
aspect_ratio
- 宽高比。此处提供了几个常见画幅比例。也可选"original"保持原图比例或者"custom"自定义比例。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- original
- custom
- 1:1
- 3:2
- 4:3
- 16:9
- 2:3
- 3:4
- 9:16
-
proportional_width
- 比例宽。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
- Comfy dtype: INT
- Python dtype: int
-
proportional_height
- 比例高。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
- Comfy dtype: INT
- Python dtype: int
-
fit
- 缩放画幅宽高比模式。有3种模式可以选择, letterbox模式保留完整的画幅，空白处用黑色补足；crop模式保留完整的短边，长边超出部分将被切除；fill模式不保持画幅比例，宽高各自填满画面。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- letterbox
- crop
- fill
-
method
- 缩放的采样方法，包括lanczos、bicubic、hamming、bilinear、box和nearest。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- lanczos
- bicubic
- hamming
- bilinear
- box
- nearest
-
round_to_multiple
- 倍数取整。例如设置为8，宽和高将强制设置为8的倍数。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- 8
- 16
- 32
- 64
- None
-
scale_to_longest_side
- 是否按长边尺寸缩放。
- Comfy dtype: BOOLEAN
- Python dtype: bool
-
longest_side
- scale_to_longest_side被设置为True时，此项将作为是图像长边的长度。
- Comfy dtype: INT
- Python dtype: int
Optional
-
image
- 图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
mask
- 蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
Output types
-
image
- 缩放后的图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
mask
- 缩放后的蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
original_size
- 原始尺寸。
- Comfy dtype: BOX
- Python dtype: List[int]
-
width
- 目标宽度。
- Comfy dtype: INT
- Python dtype: int
-
height
- 目标高度。
- Comfy dtype: INT
- Python dtype: int
Usage tips
- Infra type: CPU
Source code
class ImageScaleByAspectRatio:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
ratio_list = ['original', 'custom', '1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16']
fit_mode = ['letterbox', 'crop', 'fill']
method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
multiple_list = ['8', '16', '32', '64', 'None']
return {
"required": {
"aspect_ratio": (ratio_list,),
"proportional_width": ("INT", {"default": 2, "min": 1, "max": 999, "step": 1}),
"proportional_height": ("INT", {"default": 1, "min": 1, "max": 999, "step": 1}),
"fit": (fit_mode,),
"method": (method_mode,),
"round_to_multiple": (multiple_list,),
"scale_to_longest_side": ("BOOLEAN", {"default": False}), # 是否按长边缩放
"longest_side": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}),
},
"optional": {
"image": ("IMAGE",), #
"mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE", "MASK", "BOX", "INT", "INT",)
RETURN_NAMES = ("image", "mask", "original_size", "width", "height",)
FUNCTION = 'image_scale_by_aspect_ratio'
CATEGORY = '😺dzNodes/LayerUtility'
def image_scale_by_aspect_ratio(self, aspect_ratio, proportional_width, proportional_height,
fit, method, round_to_multiple, scale_to_longest_side, longest_side,
image=None, mask = None,
):
orig_images = []
orig_masks = []
orig_width = 0
orig_height = 0
target_width = 0
target_height = 0
ratio = 1.0
ret_images = []
ret_masks = []
if image is not None:
for i in image:
i = torch.unsqueeze(i, 0)
orig_images.append(i)
orig_width, orig_height = tensor2pil(orig_images[0]).size
if mask is not None:
if mask.dim() == 2:
mask = torch.unsqueeze(mask, 0)
for m in mask:
m = torch.unsqueeze(m, 0)
orig_masks.append(m)
_width, _height = tensor2pil(orig_masks[0]).size
if (orig_width > 0 and orig_width != _width) or (orig_height > 0 and orig_height != _height):
log(f"Error: {NODE_NAME} skipped, because the mask is does'nt match image.", message_type='error')
return (None, None, None, 0, 0,)
elif orig_width + orig_height == 0:
orig_width = _width
orig_height = _height
if orig_width + orig_height == 0:
log(f"Error: {NODE_NAME} skipped, because the image or mask at least one must be input.", message_type='error')
return (None, None, None, 0, 0,)
if aspect_ratio == 'original':
ratio = orig_width / orig_height
elif aspect_ratio == 'custom':
ratio = proportional_width / proportional_height
else:
s = aspect_ratio.split(":")
ratio = int(s[0]) / int(s[1])
# calculate target width and height
if orig_width > orig_height:
if scale_to_longest_side:
target_width = longest_side
else:
target_width = orig_width
target_height = int(target_width / ratio)
else:
if scale_to_longest_side:
target_height = longest_side
else:
target_height = orig_height
target_width = int(target_height * ratio)
if ratio < 1:
if scale_to_longest_side:
_r = longest_side / target_height
target_height = longest_side
else:
_r = orig_height / target_height
target_height = orig_height
target_width = int(target_width * _r)
if round_to_multiple != 'None':
multiple = int(round_to_multiple)
target_width = num_round_to_multiple(target_width, multiple)
target_height = num_round_to_multiple(target_height, multiple)
_mask = Image.new('L', size=(target_width, target_height), color='black')
_image = Image.new('RGB', size=(target_width, target_height), color='black')
resize_sampler = Image.LANCZOS
if method == "bicubic":
resize_sampler = Image.BICUBIC
elif method == "hamming":
resize_sampler = Image.HAMMING
elif method == "bilinear":
resize_sampler = Image.BILINEAR
elif method == "box":
resize_sampler = Image.BOX
elif method == "nearest":
resize_sampler = Image.NEAREST
if len(orig_images) > 0:
for i in orig_images:
_image = tensor2pil(i).convert('RGB')
_image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler)
ret_images.append(pil2tensor(_image))
if len(orig_masks) > 0:
for m in orig_masks:
_mask = tensor2pil(m).convert('L')
_mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
ret_masks.append(image2mask(_mask))
if len(ret_images) > 0 and len(ret_masks) >0:
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),[orig_width, orig_height], target_width, target_height,)
elif len(ret_images) > 0 and len(ret_masks) == 0:
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), None,[orig_width, orig_height], target_width, target_height,)
elif len(ret_images) == 0 and len(ret_masks) > 0:
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
return (None, torch.cat(ret_masks, dim=0),[orig_width, orig_height], target_width, target_height,)
else:
log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
return (None, None, None, 0, 0,)