82 lines
3.2 KiB
Markdown
82 lines
3.2 KiB
Markdown
# Documentation
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- Class name: LoadImage
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- Category: image
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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LoadImage节点旨在从指定目录中摄取图像文件，处理它们，并输出图像及其对应的掩码。它能够处理图像序列并将其转换为适合进一步处理的格式，强调其在准备图像相关任务数据中的作用。
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# Input types
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## Required
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- image
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- 'image'参数是要处理的图像文件的路径。它对节点的操作至关重要，因为它决定了要加载和操作的特定图像。
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- Comfy dtype: str
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- Python dtype: str
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# Output types
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- IMAGE
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- 'IMAGE'输出是一个表示处理后的图像数据的张量，它已被转换为浮点格式并进行了归一化。这个输出很重要，因为它是用于后续图像分析或操作任务的主要数据结构。
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- Comfy dtype: torch.Tensor
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- Python dtype: torch.Tensor
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- MASK
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- 'MASK'输出是一个表示与图像相关联的二进制掩码的张量，用于区分图像中的不同区域或对象。对于需要分割或对象识别的任务来说，它是必不可少的。
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- Comfy dtype: torch.Tensor
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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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# Source code
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```
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class LoadImage:
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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return {'required': {'image': (sorted(files), {'image_upload': True})}}
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CATEGORY = 'image'
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RETURN_TYPES = ('IMAGE', 'MASK')
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FUNCTION = 'load_image'
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def load_image(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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img = Image.open(image_path)
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output_images = []
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output_masks = []
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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 = 1.0 - 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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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return 'Invalid image file: {}'.format(image)
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return True
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``` |