157 lines
5.8 KiB
Markdown
157 lines
5.8 KiB
Markdown
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# Documentation
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- Class name: AddLabel
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- Category: KJNodes/text
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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AddLabel节点用于向图像添加文本标签，允许自定义文本位置、大小、颜色以及标签的背景色。它支持单张和批量处理，能够为批量图像处理多个标题。
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# Input types
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## Required
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- image
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- 需要添加标签的输入图像。这是添加标签的主要画布。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- text_x
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- 文本在图像上的起始点x坐标。它决定了文本在水平方向上的起始位置。
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- Comfy dtype: INT
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- Python dtype: int
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- text_y
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- 文本在图像上的起始点y坐标。它决定了文本在垂直方向上的起始位置。
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- Comfy dtype: INT
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- Python dtype: int
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- height
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- 要添加到图像的标签区域的高度。它影响最终图像的整体高度。
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- Comfy dtype: INT
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- Python dtype: int
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- font_size
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- 用于标签文本的字体大小。这决定了文本在标签上显示的大小。
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- Comfy dtype: INT
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- Python dtype: int
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- font_color
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- 用于标签文本的字体颜色。它定义了文本的视觉外观。
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- Comfy dtype: STRING
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- Python dtype: str
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- label_color
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- 标签区域的背景颜色。这种颜色将填充文本标签的背景。
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- Comfy dtype: STRING
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- Python dtype: str
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- font
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- 用于标签文本的字体。该参数允许选择不同的字体样式。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- text
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- 要作为标签添加到图像上的文本内容。这是实际的标签文本。
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- Comfy dtype: STRING
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- Python dtype: str
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- direction
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- 标签将被添加到图像的方向，可以是上方（up）或下方（down）。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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- caption
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- 用于批处理的可选标题。提供时，批次中的每个图像都将有其对应的标题作为标签。
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- Comfy dtype: STRING
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- Python dtype: List[str]
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# Output types
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- image
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- 添加了标签的输出图像。这个图像包括原始内容加上新添加的标签区域。
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- Comfy dtype: IMAGE
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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 AddLabel:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image":("IMAGE",),
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"text_x": ("INT", {"default": 10, "min": 0, "max": 4096, "step": 1}),
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"text_y": ("INT", {"default": 2, "min": 0, "max": 4096, "step": 1}),
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"height": ("INT", {"default": 48, "min": 0, "max": 4096, "step": 1}),
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"font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}),
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"font_color": ("STRING", {"default": "white"}),
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"label_color": ("STRING", {"default": "black"}),
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"font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
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"text": ("STRING", {"default": "Text"}),
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"direction": (
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[ 'up',
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'down',
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],
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{
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"default": 'up'
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}),
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},
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"optional":{
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"caption": ("STRING", {"default": "", "forceInput": True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "addlabel"
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CATEGORY = "KJNodes/text"
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DESCRIPTION = """
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Creates a new with the given text, and concatenates it to
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either above or below the input image.
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Note that this changes the input image's height!
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Fonts are loaded from this folder:
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ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
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"""
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def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction, caption=""):
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batch_size = image.shape[0]
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width = image.shape[2]
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if font == "TTNorms-Black.otf":
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font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf")
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else:
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font_path = folder_paths.get_full_path("kjnodes_fonts", font)
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if caption == "":
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label_image = Image.new("RGB", (width, height), label_color)
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draw = ImageDraw.Draw(label_image)
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font = ImageFont.truetype(font_path, font_size)
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try:
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draw.text((text_x, text_y), text, font=font, fill=font_color, features=['-liga'])
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except:
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draw.text((text_x, text_y), text, font=font, fill=font_color)
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label_image = np.array(label_image).astype(np.float32) / 255.0
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label_image = torch.from_numpy(label_image)[None, :, :, :]
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# Duplicate the label image for the entire batch
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label_batch = label_image.repeat(batch_size, 1, 1, 1)
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else:
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label_list = []
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assert len(caption) == batch_size, "Number of captions does not match number of images"
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for cap in caption:
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label_image = Image.new("RGB", (width, height), label_color)
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draw = ImageDraw.Draw(label_image)
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font = ImageFont.truetype(font_path, font_size)
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try:
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draw.text((text_x, text_y), cap, font=font, fill=font_color, features=['-liga'])
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except:
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draw.text((text_x, text_y), cap, font=font, fill=font_color)
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label_image = np.array(label_image).astype(np.float32) / 255.0
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label_image = torch.from_numpy(label_image)
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label_list.append(label_image)
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label_batch = torch.stack(label_list)
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print(label_batch.shape)
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if direction == 'down':
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combined_images = torch.cat((image, label_batch), dim=1)
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elif direction == 'up':
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combined_images = torch.cat((label_batch, image), dim=1)
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return (combined_images,)
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```
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