92 lines
2.9 KiB
Markdown
92 lines
2.9 KiB
Markdown
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# Documentation
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- Class name: SV_random
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- Category: Clothing - SAL-VTON
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- Output node: False
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RandomImageFromDir节点旨在从指定目录中随机选择一张图片。这个功能特别适用于需要图片选择变化性和不可预测性的应用场景，例如数据增强过程或在给定上下文中模拟多样化场景。
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# Input types
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## Required
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- folder_path
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- 指定将从中随机选择图片的目录路径。这个参数对于确定可供随机选择的图片池至关重要。
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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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- 返回从指定目录中随机选择的图片。这个输出对于在进一步处理或显示中引入变化性和随机性至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 提供与随机选择的图片相关联的蒙版（如果适用），以便于进一步的图像处理任务。
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- Comfy dtype: MASK
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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 RandomImageFromDir:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"folder_path": ("STRING", {
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"multiline": False,
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"default": "./input"
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})
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "load_image"
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CATEGORY = node_category
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def load_image(self, folder_path):
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files = os.listdir(folder_path)
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image_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".ico", ".jfif"}
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images = [file for file in files if os.path.splitext(file)[1].lower() in image_extensions]
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random_image = os.path.join(folder_path, random.choice(images))
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img = Image.open(random_image)
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output_images = []
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output_masks = []
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# this is from load_image node. Lazy but works :')
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for i in ImageSequence.Iterator(img):
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i = ImageOps.exif_transpose(i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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output_images.append(image)
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output_masks.append(mask.unsqueeze(0))
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if len(output_images) > 1:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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return (output_image, output_mask)
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@classmethod
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def IS_CHANGED(s, image):
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return float("NaN")
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```
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