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