64 lines
2.2 KiB
Markdown
64 lines
2.2 KiB
Markdown
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# Documentation
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- Class name: Inference_Core_MaskOptFlow
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- Category: ControlNet Preprocessors/Optical Flow
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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Inference_Core_MaskOptFlow节点专为处理光流数据而设计，通过对光流应用蒙版来实现。这一预处理步骤对于需要聚焦分析光流特定区域的任务至关重要，能够提升后续处理阶段中光流数据的准确性和相关性。
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# Input types
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## Required
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- optical_flow
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- optical_flow参数代表待处理的光流数据。它对于确定两幅图像或帧之间的运动至关重要。
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- Comfy dtype: OPTICAL_FLOW
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- Python dtype: torch.Tensor
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- mask
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- mask参数用于指定光流数据中的感兴趣区域。它在过滤和优化用于目标分析的流数据方面发挥着关键作用。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Output types
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- OPTICAL_FLOW
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- 经过蒙版过滤的修改后的光流数据，可用于进一步分析或处理。
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- Comfy dtype: OPTICAL_FLOW
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- Python dtype: torch.Tensor
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- PREVIEW_IMAGE
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- 蒙版光流的可视化表示，提供了蒙版对流数据影响的预览。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class MaskOptFlow:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": dict(optical_flow=("OPTICAL_FLOW",), mask=("MASK",))
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}
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RETURN_TYPES = ("OPTICAL_FLOW", "IMAGE")
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RETURN_NAMES = ("OPTICAL_FLOW", "PREVIEW_IMAGE")
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FUNCTION = "mask_opt_flow"
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CATEGORY = "ControlNet Preprocessors/Optical Flow"
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def mask_opt_flow(self, optical_flow, mask):
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from controlnet_aux.unimatch import flow_to_image
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assert len(mask) >= len(optical_flow), f"Not enough masks to mask optical flow: {len(mask)} vs {len(optical_flow)}"
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mask = mask[:optical_flow.shape[0]]
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mask = F.interpolate(mask, optical_flow.shape[1:3])
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mask = rearrange(mask, "n 1 h w -> n h w 1")
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vis_flows = torch.stack([torch.from_numpy(flow_to_image(flow)).float() / 255. for flow in optical_flow.numpy()], dim=0)
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vis_flows *= mask
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optical_flow *= mask
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return (optical_flow, vis_flows)
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```
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