112 lines
4.0 KiB
Markdown
112 lines
4.0 KiB
Markdown
---
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tags:
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- Image
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- ImageLoad
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---
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# 🚫Load Images [DEPRECATED] 🛂🅐🅒🅝
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## Documentation
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- Class name: `LoadImagesFromDirectory`
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- Category: `Adv-ControlNet 🛂🅐🅒🅝/deprecated`
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- Output node: `False`
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This node is designed to load images from a specified directory, handling various image formats and optionally applying transformations such as EXIF orientation correction and conversion to a consistent color space. It supports loading a capped number of images, and can also generate masks for images with transparency, making it suitable for tasks that require both image and mask inputs.
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## Input types
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### Required
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- **`directory`**
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- The path to the directory from which images are to be loaded. This parameter is crucial as it determines the source of the images for processing.
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- Comfy dtype: `STRING`
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- Python dtype: `str`
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### Optional
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- **`image_load_cap`**
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- Limits the number of images to load from the directory, enabling control over the volume of data processed.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`start_index`**
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- Specifies the index of the first image to load from the sorted list of files in the directory, allowing for selective loading of images.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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## Output types
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- **`image`**
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- Comfy dtype: `IMAGE`
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- A batch of loaded images, preprocessed and converted into tensors.
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- Python dtype: `torch.Tensor`
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- **`mask`**
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- Comfy dtype: `MASK`
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- A batch of masks corresponding to the loaded images, useful for segmentation tasks.
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- Python dtype: `torch.Tensor`
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- **`int`**
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- Comfy dtype: `INT`
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- The total number of images successfully loaded from the directory.
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- Python dtype: `int`
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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 LoadImagesFromDirectory:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"directory": ("STRING", {"default": ""}),
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},
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"optional": {
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"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
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"start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "INT")
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FUNCTION = "load_images"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
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def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
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if not os.path.isdir(directory):
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raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
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dir_files = os.listdir(directory)
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if len(dir_files) == 0:
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raise FileNotFoundError(f"No files in directory '{directory}'.")
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dir_files = sorted(dir_files)
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dir_files = [os.path.join(directory, x) for x in dir_files]
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# start at start_index
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dir_files = dir_files[start_index:]
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images = []
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masks = []
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limit_images = False
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if image_load_cap > 0:
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limit_images = True
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image_count = 0
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for image_path in dir_files:
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if os.path.isdir(image_path):
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continue
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if limit_images and image_count >= image_load_cap:
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break
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
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images.append(image)
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masks.append(mask)
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image_count += 1
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if len(images) == 0:
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raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
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return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
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```
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