169 lines
6.0 KiB
Markdown
169 lines
6.0 KiB
Markdown
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# Documentation
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- Class name: ChameleonMask
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- Category: Bmad/CV/C.Photography
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- Output node: False
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ChameleonMask节点旨在基于特定标准或输入动态调整或修改图像蒙版，实现灵活且上下文敏感的蒙版处理。
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# Input types
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## Required
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- dst
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- 目标图像，即蒙版将应用的位置，作为最终输出的画布。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- src
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- 源图像，用于生成蒙版，是蒙版创建的主要输入。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- thresh_blur
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- 定义蒙版模糊的阈值，影响蒙版边缘的平滑程度。
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- Comfy dtype: INT
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- Python dtype: int
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- close_dist
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- 指定蒙版闭合操作的距离，影响蒙版的紧凑性。
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- Comfy dtype: INT
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- Python dtype: int
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- open_dist
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- 确定开放操作的距离，影响蒙版的粒度。
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- Comfy dtype: INT
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- Python dtype: int
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- size_dist
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- 控制蒙版的尺寸分布调整，影响整体蒙版结构。
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- Comfy dtype: INT
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- Python dtype: int
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- mask_blur
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- 设置应用于蒙版的模糊程度，修改其柔和度和透明度。
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- Comfy dtype: INT
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- Python dtype: int
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- contrast_adjust
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- 调整蒙版的对比度，增强或减弱蒙版的视觉强度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- mode
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- 指定蒙版生成的操作模式，提供不同的蒙版创建策略。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- output_format
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- 确定输出图像的格式，影响蒙版的视觉呈现。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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- optional_roi_mask
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- 可选的感兴趣区域蒙版，可用于进一步优化蒙版生成过程。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 输出是一个应用了蒙版的图像，展示蒙版操作的结果。
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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: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class ChameleonMask: # wtf would I name this node as?
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mode_func_map = {
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"GRAY": lambda i: cv.cvtColor(i, cv.COLOR_BGR2GRAY),
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"VALUE": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 2],
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"LIGHTNESS": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HLS)[:, :, 1],
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# not sure if these would be useful, but costs nothing to leave them here
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"HUE": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 0],
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"SATURATION (HSV)": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HSV)[:, :, 1],
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"SATURATION (HSL)": lambda i: cv.cvtColor(i, cv.COLOR_RGB2HLS)[:, :, 2],
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}
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modes = list(mode_func_map.keys())
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"dst": ("IMAGE",),
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"src": ("IMAGE",),
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"thresh_blur": ("INT", {"default": 30, "min": 2, "step": 2}),
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"close_dist": ("INT", {"default": 32, "min": 0, "step": 1}),
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"open_dist": ("INT", {"default": 32, "min": 0, "step": 1}),
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"size_dist": ("INT", {"default": 8, "min": -99999, "step": 1}),
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"mask_blur": ("INT", {"default": 64, "min": 0, "step": 2}),
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"contrast_adjust": ("FLOAT", {"default": 2.4, "min": 0, "max": 20, "step": .5}),
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"mode": (s.modes, {"default": s.modes[0]}),
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"output_format": (image_output_formats_options, {
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"default": image_output_formats_options[0]
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}),
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},
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"optional": {
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"optional_roi_mask": ("IMAGE",)
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE",)
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FUNCTION = "create_mask"
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CATEGORY = "Bmad/CV/C.Photography"
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def create_mask(self, src, dst, thresh_blur, close_dist, open_dist, size_dist, mask_blur,
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contrast_adjust, mode, output_format, optional_roi_mask=None):
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src = tensor2opencv(src)
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dst = tensor2opencv(dst)
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thresh_blur += 1
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if mask_blur > 0:
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mask_blur += 1
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# compute the difference between images based on mode
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src = self.mode_func_map[mode](src)
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dst = self.mode_func_map[mode](dst)
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diff = cv.absdiff(src, dst)
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if mode == "HUE":
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diff = np.minimum(diff, 180 - diff)
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# binary thresholding
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# _, mask = cv.threshold(diff, threshold, 255, cv.THRESH_BINARY)
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diff = cv.GaussianBlur(diff, (thresh_blur, thresh_blur), 0)
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ret3, mask = cv.threshold(diff, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
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if optional_roi_mask is not None:
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optional_roi_mask = tensor2opencv(optional_roi_mask, 1)
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mask[optional_roi_mask < 127] = 0
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# morphological closing > closing > dilate/erode
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if close_dist > 0:
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close_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (close_dist, close_dist))
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mask = cv.morphologyEx(mask, cv.MORPH_CLOSE, close_kernel)
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if open_dist > 0:
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open_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (open_dist, open_dist))
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mask = cv.morphologyEx(mask, cv.MORPH_OPEN, open_kernel)
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if size_dist > 0:
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size_op = cv.MORPH_DILATE
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size = size_dist
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else:
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size_op = cv.MORPH_ERODE
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size = abs(size_dist)
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if size_dist != 0:
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size_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (size, size))
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mask = cv.morphologyEx(mask, size_op, size_kernel)
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# gaussian blur + contrast adjust
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if mask_blur > 0:
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mask = cv.GaussianBlur(mask, (mask_blur, mask_blur), 0)
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mask = cv.convertScaleAbs(mask, alpha=1 + contrast_adjust, beta=0) # / 100, beta=0)
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# convert to target format and output as tensor
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# note: diff is only meant to be used for debug purposes
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mask = maybe_convert_img(mask, 1, image_output_formats_options_map[output_format])
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diff = opencv2tensor(diff)
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mask = opencv2tensor(mask)
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return (mask, diff,)
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```
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