100 lines
3.5 KiB
Markdown
100 lines
3.5 KiB
Markdown
|
|
# Documentation
|
|
- Class name: OffsetMaskByNormalizedAmplitude
|
|
- Category: KJNodes/audio
|
|
- Output node: False
|
|
|
|
OffsetMaskByNormalizedAmplitude节点根据音频信号的归一化振幅对掩码进行变换。它调整掩码的方向和位置，利用振幅来决定旋转和位移的程度，从而实现与音频强度同步的动态视觉效果。
|
|
|
|
# Input types
|
|
## Required
|
|
- normalized_amp
|
|
- 音频信号的归一化振幅值数组，用于确定变换的程度。
|
|
- Comfy dtype: NORMALIZED_AMPLITUDE
|
|
- Python dtype: numpy.ndarray
|
|
- mask
|
|
- 待变换的掩码，代表一个视觉元素，其修改由音频振幅驱动。
|
|
- Comfy dtype: MASK
|
|
- Python dtype: torch.Tensor
|
|
- x
|
|
- 水平位移因子，影响掩码响应振幅变化的横向移动。
|
|
- Comfy dtype: INT
|
|
- Python dtype: float
|
|
- y
|
|
- 垂直位移因子，影响掩码根据振幅变化的纵向位移。
|
|
- Comfy dtype: INT
|
|
- Python dtype: float
|
|
- rotate
|
|
- 布尔值，指示是否根据归一化振幅旋转掩码。
|
|
- Comfy dtype: BOOLEAN
|
|
- Python dtype: bool
|
|
- angle_multiplier
|
|
- 旋转角度的乘数，用于缩放归一化振幅对掩码旋转的影响。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
|
|
# Output types
|
|
- mask
|
|
- 根据音频归一化振幅应用旋转和位移调整后的变换掩码。
|
|
- Comfy dtype: MASK
|
|
- Python dtype: torch.Tensor
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `CPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
class OffsetMaskByNormalizedAmplitude:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"normalized_amp": ("NORMALIZED_AMPLITUDE",),
|
|
"mask": ("MASK",),
|
|
"x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
|
|
"y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
|
|
"rotate": ("BOOLEAN", { "default": False }),
|
|
"angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
RETURN_NAMES = ("mask",)
|
|
FUNCTION = "offset"
|
|
CATEGORY = "KJNodes/audio"
|
|
DESCRIPTION = """
|
|
Works as a bridge to the AudioScheduler -nodes:
|
|
https://github.com/a1lazydog/ComfyUI-AudioScheduler
|
|
Offsets masks based on the normalized amplitude.
|
|
"""
|
|
|
|
def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
|
|
|
|
# Ensure normalized_amp is an array and within the range [0, 1]
|
|
offsetmask = mask.clone()
|
|
normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
|
|
|
|
batch_size, height, width = mask.shape
|
|
|
|
if rotate:
|
|
for i in range(batch_size):
|
|
rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier))
|
|
rotation_angle = rotation_amp
|
|
offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0)
|
|
if x != 0 or y != 0:
|
|
for i in range(batch_size):
|
|
offset_amp = normalized_amp[i] * 10
|
|
shift_x = min(x*offset_amp, width-1)
|
|
shift_y = min(y*offset_amp, height-1)
|
|
if shift_x != 0:
|
|
offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1)
|
|
if shift_y != 0:
|
|
offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0)
|
|
|
|
return offsetmask,
|
|
|
|
```
|