99 lines
3.5 KiB
Markdown
99 lines
3.5 KiB
Markdown
|
|
# Documentation
|
|
- Class name: JWImageSequenceExtractFromBatch
|
|
- Category: jamesWalker55
|
|
- Output node: False
|
|
|
|
JWImageSequenceExtractFromBatch节点旨在从图像批次中提取指定范围的图像序列。通过设定起始和结束索引，用户可以灵活地从大型图像集合中选择性地获取特定子集，从而便于对特定图像数据进行后续操作和分析。该节点的设计增强了图像批处理的灵活性和精确控制能力。
|
|
|
|
# Input types
|
|
## Required
|
|
- images
|
|
- 该参数是图像提取操作的核心输入，代表待处理的图像批次。它直接决定了可供选择的图像范围，对节点的功能实现至关重要。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
- i_start
|
|
- i_start参数指定了序列提取的起始位置，决定了输出序列中的第一张图像。它在控制提取范围方面起着关键作用。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
- i_stop
|
|
- i_stop参数标记了序列提取的结束位置，影响输出序列的最后一张图像。它与i_start共同定义了提取的图像范围。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
- inclusive
|
|
- inclusive参数是一个布尔标志，决定是否将结束索引对应的图像包含在提取结果中。这为用户提供了更精细的控制选项。
|
|
- Comfy dtype: ['false', 'true']
|
|
- Python dtype: str
|
|
|
|
# Output types
|
|
- image
|
|
- 输出的image是一个张量，包含了根据指定索引范围从原始批次中提取的图像序列。这个输出为后续的图像处理和分析提供了基础。
|
|
- Comfy dtype: IMAGE
|
|
- Python dtype: torch.Tensor
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `GPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
@register_node("JWLoadImageSequence", "Batch Load Image Sequence")
|
|
class _:
|
|
CATEGORY = "jamesWalker55"
|
|
INPUT_TYPES = lambda: {
|
|
"required": {
|
|
"path_pattern": (
|
|
"STRING",
|
|
{"default": "./frame{:06d}.png", "multiline": False},
|
|
),
|
|
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
|
|
"frame_count": ("INT", {"default": 16, "min": 1, "step": 1}),
|
|
"ignore_missing_images": (("false", "true"), {"default": "false"}),
|
|
}
|
|
}
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "execute"
|
|
|
|
def execute(
|
|
self,
|
|
path_pattern: str,
|
|
start_index: int,
|
|
frame_count: int,
|
|
ignore_missing_images: str,
|
|
):
|
|
ignore_missing_images: bool = ignore_missing_images == "true"
|
|
|
|
# generate image paths to load
|
|
image_paths = []
|
|
for i in range(start_index, start_index + frame_count):
|
|
try:
|
|
image_paths.append(path_pattern.format(i))
|
|
except KeyError:
|
|
image_paths.append(path_pattern.format(i=i))
|
|
|
|
if ignore_missing_images:
|
|
# remove missing images
|
|
image_paths = [p for p in image_paths if os.path.exists(p)]
|
|
else:
|
|
# early check for missing images
|
|
for path in image_paths:
|
|
if not os.path.exists(path):
|
|
raise FileNotFoundError(f"Image does not exist: {path}")
|
|
|
|
if len(image_paths) == 0:
|
|
raise RuntimeError("Image sequence empty - no images to load")
|
|
|
|
imgs = []
|
|
for path in image_paths:
|
|
img = load_image(path)
|
|
# img.shape => torch.Size([1, 768, 768, 3])
|
|
imgs.append(img)
|
|
|
|
imgs = torch.cat(imgs, dim=0)
|
|
|
|
return (imgs,)
|
|
|
|
```
|