120 lines
3.9 KiB
Markdown
120 lines
3.9 KiB
Markdown
# Documentation
|
|
- Class name: MaskByDifferent
|
|
- Category: 😺dzNodes/LayerMask
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
|
|
|
|
计算两张图像不同之处，并输出为遮罩。
|
|
|
|
# Input types
|
|
|
|
## Required
|
|
|
|
- image_1
|
|
- 第一张输入图像。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
- image_2
|
|
- 第二张输入图像。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
- gain
|
|
- 差异增益。调高此值，微弱的差异将更显著的呈现。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
- fix_gap
|
|
- 修正间隙。更高的值将修补更大的缝隙。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- fix_threshold
|
|
- 修正阈值。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
- main_subject_detect
|
|
- 此项设为True将开启主体侦测，忽略主体之外的差异。
|
|
- Comfy dtype: BOOLEAN
|
|
- Python dtype: bool
|
|
|
|
# Output types
|
|
|
|
- mask
|
|
- 输出mask。
|
|
- Comfy dtype: MASK
|
|
- Python dtype: torch.Tensor
|
|
|
|
# Usage tips
|
|
- Infra type: CPU
|
|
|
|
# Source code
|
|
```python
|
|
class MaskByDifferent:
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(self):
|
|
|
|
return {
|
|
"required": {
|
|
"image_1": ("IMAGE", ), #
|
|
"image_2": ("IMAGE",), #
|
|
"gain": ("FLOAT", {"default": 1.5, "min": 0.1, "max": 100, "step": 0.1}),
|
|
"fix_gap": ("INT", {"default": 4, "min": 0, "max": 32, "step": 1}),
|
|
"fix_threshold": ("FLOAT", {"default": 0.75, "min": 0.01, "max": 0.99, "step": 0.01}),
|
|
"main_subject_detect": ("BOOLEAN", {"default": False}),
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ( "MASK",)
|
|
RETURN_NAMES = ("mask",)
|
|
FUNCTION = 'mask_by_different'
|
|
CATEGORY = '😺dzNodes/LayerMask'
|
|
|
|
def mask_by_different(self, image_1, image_2, gain, fix_gap, fix_threshold, main_subject_detect):
|
|
|
|
image1s = []
|
|
image2s = []
|
|
ret_masks = []
|
|
for i in image_1:
|
|
image1s.append(torch.unsqueeze(i, 0))
|
|
for i in image_2:
|
|
image2s.append(torch.unsqueeze(i, 0))
|
|
max_batch = max(len(image1s), len(image2s))
|
|
blank_mask = image2mask(Image.new('L', size=tensor2pil(image1s[0]).size, color='black'))
|
|
if tensor2pil(image1s[0]).size != tensor2pil(image2s[0]).size:
|
|
log(f"Error: {NODE_NAME} skipped, because the image size is not match.", message_type='error')
|
|
return (torch.cat([blank_mask], dim=0))
|
|
for i in range(max_batch):
|
|
t1 = image1s[i] if i < len(image1s) else image1s[-1]
|
|
t2 = image2s[i] if i < len(image2s) else image2s[-1]
|
|
t1 = pil2tensor(tensor2pil(t1).convert('RGB'))
|
|
t2 = pil2tensor(tensor2pil(t2).convert('RGB'))
|
|
t = torch.abs(t1 - t2) * gain
|
|
_mask = mask_fix(t, 1, fix_gap, fix_threshold, fix_threshold)
|
|
_mask = tensor2pil(_mask)
|
|
if main_subject_detect:
|
|
subject_mask1 = RMBG(tensor2pil(t1))
|
|
subject_mask2 = RMBG(tensor2pil(t2))
|
|
subject_mask = chop_image(subject_mask1, subject_mask2, blend_mode='add', opacity=100)
|
|
grow = (subject_mask.width + subject_mask.height) // 100
|
|
subject_mask = mask2image(expand_mask(image2mask(subject_mask), grow * 2, grow))
|
|
black = Image.new('L', size=_mask.size, color='black')
|
|
white = Image.new('L', size=_mask.size, color='white')
|
|
black.paste(_mask, mask=subject_mask.convert('L'))
|
|
black.paste(white, mask=subject_mask1.convert('L'))
|
|
black.paste(white, mask=subject_mask2.convert('L'))
|
|
_mask = black
|
|
|
|
ret_masks.append(image2mask(_mask))
|
|
|
|
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
|
|
return (torch.cat(ret_masks, dim=0),)
|
|
``` |