102 lines
4.5 KiB
Markdown
102 lines
4.5 KiB
Markdown
# Documentation
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- Class name: PreviewBridge
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- Category: ImpactPack/Util
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- Output node: True
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- Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git
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PreviewBridge节点旨在处理和管理图像数据，提供输入图像与后续处理阶段之间的桥梁。它负责加载、转换和缓存图像，以优化工作流程并确保系统内视觉数据的有效处理。
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# Input types
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## Required
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- images
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- ‘images’参数对于节点至关重要，因为它代表了需要处理的输入图像数据。它在节点的执行中扮演着关键角色，因为它直接影响输出和随后的图像操作。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- unique_id
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- ‘unique_id’参数对于节点的功能至关重要，因为它唯一地标识节点处理的每个图像或数据集。它确保节点能够准确跟踪和管图像，这对于节点的运行和结果完整性至关重要。
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- Comfy dtype: UNIQUE_ID
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- Python dtype: str
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## Optional
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- image
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- ‘image’参数作为节点操作中特定图像的可选标识符。它可以用来根据提供的标识符引用或检索特定的图像数据，因此在定制节点行为中扮演着重要角色。
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- Comfy dtype: STRING
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- Python dtype: str
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# Output types
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- ui
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- ‘ui’输出参数非常重要，因为它包含与节点操作相关的用户界面元素。它通常包括可以显示或用于系统UI内进一步交互的图像数据。
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- Comfy dtype: DICT
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- Python dtype: Dict[str, Any]
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- result
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- ‘result’输出参数封装了处理后的图像数据和相应的掩码。它是节点功能的关键组成部分，因为它提供了可以用于下游任务或分析的最终输出。
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- Comfy dtype: TUPLE[torch.Tensor, torch.Tensor]
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- Python dtype: Tuple[torch.Tensor, 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 PreviewBridge:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'images': ('IMAGE',), 'image': ('STRING', {'default': ''})}, 'hidden': {'unique_id': 'UNIQUE_ID'}}
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RETURN_TYPES = ('IMAGE', 'MASK')
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FUNCTION = 'doit'
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OUTPUT_NODE = True
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CATEGORY = 'ImpactPack/Util'
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def __init__(self):
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super().__init__()
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self.output_dir = folder_paths.get_temp_directory()
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self.type = 'temp'
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self.prev_hash = None
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@staticmethod
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def load_image(pb_id):
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is_fail = False
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if pb_id not in core.preview_bridge_image_id_map:
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is_fail = True
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(image_path, ui_item) = core.preview_bridge_image_id_map[pb_id]
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if not os.path.isfile(image_path):
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is_fail = True
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if not is_fail:
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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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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else:
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image = empty_pil_tensor()
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mask = torch.zeros((64, 64), dtype=torch.float32, device='cpu')
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ui_item = {'filename': 'empty.png', 'subfolder': '', 'type': 'temp'}
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return (image, mask.unsqueeze(0), ui_item)
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def doit(self, images, image, unique_id):
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need_refresh = False
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if unique_id not in core.preview_bridge_cache:
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need_refresh = True
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elif core.preview_bridge_cache[unique_id][0] is not images:
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need_refresh = True
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if not need_refresh:
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(pixels, mask, path_item) = PreviewBridge.load_image(image)
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image = [path_item]
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else:
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res = nodes.PreviewImage().save_images(images, filename_prefix='PreviewBridge/PB-')
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image2 = res['ui']['images']
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pixels = images
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mask = torch.zeros((64, 64), dtype=torch.float32, device='cpu')
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path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
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core.set_previewbridge_image(unique_id, path, image2[0])
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core.preview_bridge_image_id_map[image] = (path, image2[0])
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core.preview_bridge_image_name_map[unique_id, path] = (image, image2[0])
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core.preview_bridge_cache[unique_id] = (images, image2)
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image = image2
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return {'ui': {'images': image}, 'result': (pixels, mask)}
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``` |