119 lines
4.4 KiB
Markdown
119 lines
4.4 KiB
Markdown
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# Documentation
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- Class name: SaltCropImageLocation
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- Category: SALT/Image/Process
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- Output node: False
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本节点旨在根据指定位置裁剪图像，实现对图像数据集的精确调整和修改。它专注于增强或隔离图像中的特定区域，以便进行进一步处理或分析。
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# Input types
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## Required
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- images
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- 要裁剪的图像集合。该参数是节点操作的核心，它决定了将根据提供的尺寸进行裁剪的具体图像。
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- Comfy dtype: IMAGE
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- Python dtype: List[Image]
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- top
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- 指定裁剪区域的上边界。调整此值可以改变裁剪的垂直起点，从而对裁剪区域进行精细控制。
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- Comfy dtype: INT
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- Python dtype: int
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- left
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- 定义裁剪区域的左边界。该参数允许精确控制裁剪的水平位置，影响保留的图像区域。
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- Comfy dtype: INT
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- Python dtype: int
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- right
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- 设置裁剪区域的右边界，决定裁剪区域的水平范围。这个值影响最终裁剪图像的宽度。
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- Comfy dtype: INT
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- Python dtype: int
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- bottom
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- 指示裁剪区域的下边界。该参数影响裁剪的垂直范围，控制裁剪图像的高度。
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- Comfy dtype: INT
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- Python dtype: int
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## Optional
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- crop_data_batch
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- 可选的裁剪数据批次，用于为一组图像指定多个裁剪区域。该参数支持对具有不同裁剪规格的图像进行批处理。
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- Comfy dtype: CROP_DATA_BATCH
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- Python dtype: Optional[List[Tuple[int, int, int, int]]]
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# Output types
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- images
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- 裁剪操作后的结果图像。这个输出对于裁剪区域的进一步处理或分析至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: List[Image]
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- crop_data_batch
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- 对应于处理后图像的裁剪数据批次。这个输出提供了应用于每张图像的裁剪区域的详细信息，便于进行进一步的操作或分析。
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- Comfy dtype: CROP_DATA_BATCH
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- Python dtype: List[Tuple[int, int, int, int]]
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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 SaltCropImageLocation:
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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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"top": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
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"left": ("INT", {"default":0, "max": 10000000, "min":0, "step":1}),
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"right": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
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"bottom": ("INT", {"default":256, "max": 10000000, "min":0, "step":1}),
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},
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"optional": {
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"crop_data_batch": ("CROP_DATA_BATCH",)
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}
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}
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RETURN_TYPES = ("IMAGE", "CROP_DATA_BATCH")
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RETURN_NAMES = ("images", "crop_data_batch")
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FUNCTION = "crop"
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CATEGORY = f"{NAME}/Image/Process"
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def crop(self, images, top=0, left=0, right=256, bottom=256, crop_data_batch=None):
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cropped_images = []
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crop_data_list = []
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master_size = None
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for i, image in enumerate(images):
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image = tensor2pil(image)
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img_width, img_height = image.size
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if crop_data_batch is not None:
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if len(crop_data_batch) >= i:
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crop_size, (crop_left, crop_top, crop_right, crop_bottom) = crop_data_batch[i]
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else:
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cropped_images.append(pil2tensor(image))
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crop_data_list.append((image.size, (0, 0, image.size[0], image.size[1])))
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continue
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else:
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crop_top = max(top, 0)
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crop_left = max(left, 0)
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crop_bottom = min(bottom, img_height)
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crop_right = min(right, img_width)
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crop_width = crop_right - crop_left
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crop_height = crop_bottom - crop_top
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if crop_width <= 0 or crop_height <= 0:
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raise ValueError("Invalid crop dimensions.")
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crop = image.crop((crop_left, crop_top, crop_right, crop_bottom))
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crop_data = (crop.size, (crop_left, crop_top, crop_right, crop_bottom))
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if not master_size:
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master_size = (((crop.size[0] // 8) * 8), ((crop.size[1] // 8) * 8))
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crop = crop.resize(master_size)
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cropped_images.append(pil2tensor(crop))
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crop_data_list.append(crop_data)
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cropped_images_batch = torch.cat(cropped_images, dim=0)
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return (cropped_images_batch, crop_data_list)
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```
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