65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
import torch
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from .imagefunc import log, pil2tensor,image2mask, extract_numbers
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from PIL import Image
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class BatchSelector:
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def __init__(self):
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self.NODE_NAME = 'BatchSelector'
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pass
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"select": ("STRING", {"default": "0,"},),
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},
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"optional": {
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"images": ("IMAGE",), #
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"masks": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = 'batch_selector'
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CATEGORY = '😺dzNodes/LayerUtility/SystemIO'
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def batch_selector(self, select, images=None, masks=None
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):
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ret_images = []
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ret_masks = []
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empty_image = pil2tensor(Image.new("RGBA", (64, 64), (0, 0, 0, 0)))
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empty_mask = image2mask(Image.new("L", (64, 64), color="black"))
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indexs = extract_numbers(select)
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for i in indexs:
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if images is not None:
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if i < len(images):
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ret_images.append(images[i].unsqueeze(0))
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else:
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ret_images.append(images[-1].unsqueeze(0))
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if masks is not None:
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if i < len(masks):
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ret_masks.append(masks[i].unsqueeze(0))
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else:
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ret_masks.append(masks[-1].unsqueeze(0))
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if len(ret_images) == 0:
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ret_images.append(empty_image)
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if len(ret_masks) == 0:
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ret_masks.append(empty_mask)
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: BatchSelector": BatchSelector
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: BatchSelector": "LayerUtility: Batch Selector"
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} |