111 lines
3.8 KiB
Markdown
111 lines
3.8 KiB
Markdown
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# Documentation
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- Class name: FindThreshold
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- Category: Bmad/CV/Thresholding
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- Output node: False
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FindThreshold节点旨在基于特定条件识别图像分割或二值化的最佳阈值。它通过动态调整阈值参数以满足所需条件，从而提高图像处理任务的适应性和精确度。
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# Input types
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## Required
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- src
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- 需要应用阈值处理的源图像。对于确定分割或二值化的最佳阈值至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- start_at
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- 阈值范围的起始点。定义了寻找最佳阈值的搜索空间的下限。
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- Comfy dtype: INT
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- Python dtype: int
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- end_at
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- 阈值范围的结束点。设置了最佳阈值确定的搜索空间的上限。
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- Comfy dtype: INT
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- Python dtype: int
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- thresh_type
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- 指定要应用的阈值处理类型。影响图像上阈值操作的执行方式。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- downscale_factor
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- 图像缩小的比例因子。有助于在阈值搜索过程中减少计算负荷。
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- Comfy dtype: INT
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- Python dtype: int
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- condition
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- 阈值处理后的图像必须满足的条件。用于评估和选择最佳阈值。
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- Comfy dtype: STRING
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- Python dtype: str
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# Output types
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- image
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- 应用找到的最佳阈值后的图像。代表源图像的分割或二值化版本。
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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 FindThreshold:
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"""
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simple cond examples:
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cv.countNonZero(t) > 100 # the number of non black pixels (white when using binary thresh type)
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(t.size - cv.countNonZero(t)) / t.size > .50 # the percentage of black pixels is higher than 50%
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# TODO can detect some shape(s) (requires optional inputs, and for current output maybe not that useful
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if none is found, returns the last
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"""
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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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"src": ("IMAGE",),
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"start_at": ("INT", {"default": 1, "min": 1, "max": 255, "step": 1}),
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"end_at": ("INT", {"default": 255, "min": 1, "max": 255, "step": 1}),
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"thresh_type": (thresh_types, {"default": thresh_types[0]}),
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"downscale_factor": ("INT", {"default": 2, "min": 1, "step": 1}),
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"condition": ("STRING", {"multiline": True, "default":
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"# Some expression that returns True or False\n"}),
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},
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} # TODO optional inputs
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "search"
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CATEGORY = "Bmad/CV/Thresholding"
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def search(self, src, start_at, end_at, thresh_type, downscale_factor, condition):
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import cv2
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import numpy
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import math
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o_img = tensor2opencv(src, 1)
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height, width = tuple(o_img.shape)
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img = cv.resize(o_img, (height // downscale_factor, width // downscale_factor), interpolation=cv.INTER_AREA)
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max_v = max(start_at, end_at)
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min_v = min(start_at, end_at)
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range_to_check = range(min_v, max_v + 1)
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if end_at < start_at:
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range_to_check = range_to_check.__reversed__()
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condition = prepare_text_for_eval(condition)
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cond_check = eval(f"lambda t: {condition}", {
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"__builtins__": {},
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"tuple": tuple, "list": list,
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'm': math, 'cv': cv2, 'np': numpy
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}, {})
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thresh_value = end_at
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for i in range_to_check:
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_, thresh = cv.threshold(img, i, 255, thresh_types_map[thresh_type])
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if cond_check(thresh):
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thresh_value = i
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break
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_, img = cv.threshold(o_img, thresh_value, 255, thresh_types_map[thresh_type])
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img = opencv2tensor(img)
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return (img,)
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```
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