90 lines
2.8 KiB
Markdown
90 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: BboxVisualize
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- Category: KJNodes/masking
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- Output node: False
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BboxVisualize节点旨在将边界框叠加到图像上，通过清晰地标记出特定线宽和颜色的感兴趣区域，从而增强视觉分析效果。
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# Input types
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## Required
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- images
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- 一批将绘制边界框的图像。这些图像作为可视化过程的画布。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- bboxes
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- 一个边界框坐标列表，用于指定图像上需要高亮显示的区域。这些坐标在确定需要可视化的精确区域时起着至关重要的作用。
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- Comfy dtype: BBOX
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- Python dtype: List[Tuple[int, int, int, int]]
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- line_width
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- 指定用于绘制边界框的线条粗细，影响高亮区域的可见度和显著性。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- images
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- 带有绘制边界框的修改后的图像批次，可用于视觉检查或进一步处理。
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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: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class BboxVisualize:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"bboxes": ("BBOX",),
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"line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "visualizebbox"
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DESCRIPTION = """
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Visualizes the specified bbox on the image.
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"""
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CATEGORY = "KJNodes/masking"
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def visualizebbox(self, bboxes, images, line_width):
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image_list = []
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for image, bbox in zip(images, bboxes):
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x_min, y_min, width, height = bbox
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image = image.permute(2, 0, 1)
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img_with_bbox = image.clone()
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# Define the color for the bbox, e.g., red
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color = torch.tensor([1, 0, 0], dtype=torch.float32)
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# Draw lines for each side of the bbox with the specified line width
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for lw in range(line_width):
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# Top horizontal line
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img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
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# Bottom horizontal line
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img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
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# Left vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
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# Right vertical line
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img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
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img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
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image_list.append(img_with_bbox)
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return (torch.cat(image_list, dim=0),)
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```
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