145 lines
4.3 KiB
Markdown
145 lines
4.3 KiB
Markdown
# Documentation
|
|
- Class name: MaskBoxDetect
|
|
- Category: 😺dzNodes/LayerMask
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
|
|
|
|
探测mask所在区域，并输出位置和大小。
|
|
|
|
# Input types
|
|
|
|
## Required
|
|
|
|
- mask
|
|
- 遮罩图像。
|
|
- Comfy dtype: MASK
|
|
- Python dtype: torch.Tensor
|
|
|
|
- detect
|
|
- 探测方法，min_bounding_rect是大块形状最小外接矩形, max_inscribed_rect是大块形状最大内接矩形, mask_area是遮罩像素有效区域。
|
|
- 可选值: ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
|
|
- Comfy dtype: STRING
|
|
- Python dtype: str
|
|
|
|
- x_adjust
|
|
- 修正探测之后的水平偏移。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- y_adjust
|
|
- 修正探测之后的垂直偏移。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- scale_adjust
|
|
- 修正探测之后的缩放偏移。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
# Output types
|
|
|
|
- box_preview
|
|
- 探测结果预览图。红色表示探测到的结果，绿色表示加上修正后的输出结果。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
- x_percent
|
|
- x轴百分比位置。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
- y_percent
|
|
- y轴百分比位置。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
- width
|
|
- 盒子宽度。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- height
|
|
- 盒子高度。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- x
|
|
- 左上角位置x坐标输出。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
- y
|
|
- 左上角位置y坐标输出。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
# Usage tips
|
|
- Infra type: CPU
|
|
|
|
# Source code
|
|
```python
|
|
class MaskBoxDetect:
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(self):
|
|
detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
|
|
return {
|
|
"required": {
|
|
"mask": ("MASK", ),
|
|
"detect": (detect_mode,), # 探测类型：最小外接矩形/最大内接矩形/掩码区域
|
|
"x_adjust": ("INT", {"default": 0, "min": -9999, "max": 9999, "step": 1}), # x轴修正
|
|
"y_adjust": ("INT", {"default": 0, "min": -9999, "max": 9999, "step": 1}), # y轴修正
|
|
"scale_adjust": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100, "step": 0.01}), # 比例修正
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "FLOAT", "FLOAT", "INT", "INT", "INT", "INT",)
|
|
RETURN_NAMES = ("box_preview", "x_percent", "y_percent", "width", "height", "x", "y",)
|
|
FUNCTION = 'mask_box_detect'
|
|
CATEGORY = '😺dzNodes/LayerMask'
|
|
|
|
def mask_box_detect(self, mask, detect, x_adjust, y_adjust, scale_adjust):
|
|
|
|
if mask.dim() == 2:
|
|
mask = torch.unsqueeze(mask, 0)
|
|
|
|
if mask.shape[0] > 0:
|
|
mask = torch.unsqueeze(mask[0], 0)
|
|
|
|
_mask = mask2image(mask).convert('RGB')
|
|
|
|
_mask = gaussian_blur(_mask, 20).convert('L')
|
|
x = 0
|
|
y = 0
|
|
width = 0
|
|
height = 0
|
|
|
|
if detect == "min_bounding_rect":
|
|
(x, y, width, height) = min_bounding_rect(_mask)
|
|
elif detect == "max_inscribed_rect":
|
|
(x, y, width, height) = max_inscribed_rect(_mask)
|
|
else:
|
|
(x, y, width, height) = mask_area(_mask)
|
|
log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
|
|
_width = width
|
|
_height = height
|
|
if scale_adjust != 1.0:
|
|
_width = int(width * scale_adjust)
|
|
_height = int(height * scale_adjust)
|
|
x = x - int((_width - width) / 2)
|
|
y = y - int((_height - height) / 2)
|
|
x += x_adjust
|
|
y += y_adjust
|
|
x_percent = (x + _width / 2) / _mask.width * 100
|
|
y_percent = (y + _height / 2) / _mask.height * 100
|
|
preview_image = tensor2pil(mask).convert('RGB')
|
|
preview_image = draw_rect(preview_image, x - x_adjust, y - y_adjust, width, height, line_color="#F00000", line_width=int(preview_image.height / 60))
|
|
preview_image = draw_rect(preview_image, x, y, width, height, line_color="#00F000", line_width=int(preview_image.height / 40))
|
|
log(f"{NODE_NAME} Processed.", message_type='finish')
|
|
return ( pil2tensor(preview_image), round(x_percent, 2), round(y_percent, 2), _width, _height, x, y,)
|
|
``` |