134 lines
7.1 KiB
Markdown
134 lines
7.1 KiB
Markdown
# Documentation
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- Class name: WAS_Image_Grid_Image
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- Category: WAS Suite/Image/Process
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- Output node: False
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- Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui
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WAS_Image_Grid_Image节点旨在将一系列图片处理成单个网格图片。它智能地将图片组织成指定数量的列，将它们缩放到最大单元格大小，并可选择性地为每个图片周围添加彩色边框以区分视觉效果。该节点特别适用于从图片目录创建拼贴画或缩略图。
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# Input types
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## Required
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- images_path
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- 参数'images_path'指定了要处理的图片所在的目录路径。这个路径至关重要，因为它指导节点到将被组织成网格的图片的源位置。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- pattern_glob
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- 参数'pattern_glob'用于匹配目录中的特定文件。它允许基于某种模式过滤图片，这在目录中的所有文件并非都是图片或需要一组特定图片时非常有用。
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- Comfy dtype: STRING
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- Python dtype: str
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- include_subfolders
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- 参数'include_subfolders'决定是否在创建网格的过程中包含子目录中的图片。将其设置为'true'将使节点扫描并包含指定路径下所有子目录中的图片。
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- Comfy dtype: COMBO[false, true]
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- Python dtype: Union[str, bool]
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- number_of_columns
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- 参数'number_of_columns'定义了结果网格图片中的列数。它决定了图片将如何分布在网格的宽度上，影响整体布局。
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- Comfy dtype: INT
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- Python dtype: int
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- max_cell_size
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- 参数'max_cell_size'设置了网格中每个图片单元的最大尺寸（以像素为单位）。这确保在处理后没有图片会超出这些尺寸，保持网格中的外观一致性。
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- Comfy dtype: INT
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- Python dtype: int
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- border_width
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- 参数'border_width'指定在网格中每个图片周围添加的边框宽度，如果包含边框的话。值为0表示没有边框。
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- Comfy dtype: INT
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- Python dtype: int
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- border_red
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- 参数'border_red'连同'border_green'和'border_blue'一起，通过指定RGB颜色值的红色分量来确定每个图片周围边框的颜色。
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- Comfy dtype: INT
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- Python dtype: int
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- border_green
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- 参数'border_green'指定边框颜色的RGB颜色值中的绿色分量。
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- Comfy dtype: INT
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- Python dtype: int
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- border_blue
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- 参数'border_blue'指定边框颜色的RGB颜色值中的蓝色分量。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- grid_image
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- 'grid_image'输出是包含处理过的图片网格的结果图片。它代表了根据输入参数指定的最终视觉布局。
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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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# Source code
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```
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class WAS_Image_Grid_Image:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {'required': {'images_path': ('STRING', {'default': './ComfyUI/input/', 'multiline': False}), 'pattern_glob': ('STRING', {'default': '*', 'multiline': False}), 'include_subfolders': (['false', 'true'],), 'border_width': ('INT', {'default': 3, 'min': 0, 'max': 100, 'step': 1}), 'number_of_columns': ('INT', {'default': 6, 'min': 1, 'max': 24, 'step': 1}), 'max_cell_size': ('INT', {'default': 256, 'min': 32, 'max': 1280, 'step': 1}), 'border_red': ('INT', {'default': 0, 'min': 0, 'max': 255, 'step': 1}), 'border_green': ('INT', {'default': 0, 'min': 0, 'max': 255, 'step': 1}), 'border_blue': ('INT', {'default': 0, 'min': 0, 'max': 255, 'step': 1})}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'create_grid_image'
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CATEGORY = 'WAS Suite/Image/Process'
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def create_grid_image(self, images_path, pattern_glob='*', include_subfolders='false', number_of_columns=6, max_cell_size=256, border_width=3, border_red=0, border_green=0, border_blue=0):
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if not os.path.exists(images_path):
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cstr(f'The grid image path `{images_path}` does not exist!').error.print()
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return (pil2tensor(Image.new('RGB', (512, 512), (0, 0, 0))),)
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paths = glob.glob(os.path.join(images_path, pattern_glob), recursive=False if include_subfolders == 'false' else True)
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image_paths = []
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for path in paths:
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if path.lower().endswith(ALLOWED_EXT) and os.path.exists(path):
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image_paths.append(path)
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grid_image = self.smart_grid_image(image_paths, int(number_of_columns), (int(max_cell_size), int(max_cell_size)), False if border_width <= 0 else True, (int(border_red), int(border_green), int(border_blue)), int(border_width))
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return (pil2tensor(grid_image),)
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def smart_grid_image(self, images, cols=6, size=(256, 256), add_border=False, border_color=(0, 0, 0), border_width=3):
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(max_width, max_height) = size
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row_height = 0
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images_resized = []
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for image in images:
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img = Image.open(image).convert('RGB')
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(img_w, img_h) = img.size
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aspect_ratio = img_w / img_h
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if aspect_ratio > 1:
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thumb_w = min(max_width, img_w - border_width)
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thumb_h = thumb_w / aspect_ratio
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else:
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thumb_h = min(max_height, img_h - border_width)
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thumb_w = thumb_h * aspect_ratio
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pad_w = max_width - int(thumb_w)
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pad_h = max_height - int(thumb_h)
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left = pad_w // 2
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top = pad_h // 2
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right = pad_w - left
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bottom = pad_h - top
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padding = (left, top, right, bottom)
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img_resized = ImageOps.expand(img.resize((int(thumb_w), int(thumb_h))), padding)
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if add_border:
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img_resized_bordered = ImageOps.expand(img_resized, border=border_width // 2, fill=border_color)
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images_resized.append(img_resized)
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row_height = max(row_height, img_resized.size[1])
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row_height = int(row_height)
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total_images = len(images_resized)
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rows = math.ceil(total_images / cols)
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new_image = Image.new('RGB', (cols * size[0] + (cols - 1) * border_width, rows * row_height + (rows - 1) * border_width), border_color)
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for (i, img) in enumerate(images_resized):
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if add_border:
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border_img = ImageOps.expand(img, border=border_width // 2, fill=border_color)
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x = i % cols * (size[0] + border_width)
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y = i // cols * (row_height + border_width)
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if border_img.size == (size[0], size[1]):
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new_image.paste(border_img, (x, y, x + size[0], y + size[1]))
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else:
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border_img = border_img.resize((size[0], size[1]))
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new_image.paste(border_img, (x, y, x + size[0], y + size[1]))
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else:
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x = i % cols * (size[0] + border_width)
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y = i // cols * (row_height + border_width)
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if img.size == (size[0], size[1]):
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new_image.paste(img, (x, y, x + img.size[0], y + img.size[1]))
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else:
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img = img.resize((size[0], size[1]))
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new_image.paste(img, (x, y, x + size[0], y + size[1]))
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new_image = ImageOps.expand(new_image, border=border_width, fill=border_color)
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return new_image
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``` |