Image Dir Iterator

Documentation

• Class name: ImageDirIterator
• Category: cspnodes
• Output node: False

The ImageDirIterator node is designed to iterate through images in a specified directory, allowing for the retrieval of images by their index. This functionality is particularly useful for applications that require sequential or random access to a collection of images, such as in image processing pipelines or data loading for machine learning models.

Input types

Required

• directory_path
  • Specifies the path to the directory containing the images to be iterated over. This path is crucial for locating and accessing the image files.
  • Comfy dtype: STRING
  • Python dtype: str
• image_index
  • Determines the index of the image to retrieve from the sorted list of image files in the directory. The index is wrapped around using modulo to ensure it falls within the valid range of available images.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • Returns a tensor representation of the image at the specified index, preprocessed and ready for further processing or model input.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ImageDirIterator:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "directory_path": ("STRING", {}),
                "image_index": ("INT", {"default": 0})
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "get_image_by_index"
    CATEGORY = "cspnodes"

    def get_image_by_index(self, directory_path, image_index):
        # Get list of image files sorted by modification time (most recent first)
        image_files = sorted(
            [os.path.join(directory_path, f) for f in os.listdir(directory_path)
             if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))],
            key=lambda x: os.path.getmtime(x),
            reverse=True
        )

        # Wrap the index around using modulo
        image_index = image_index % len(image_files)

        # Load and preprocess the image
        image = Image.open(image_files[image_index])
        image = ImageOps.exif_transpose(image)  # Correct orientation
        image = image.convert("RGB")  # Ensure image is in RGB format

        # Convert image to tensor
        image_tensor = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)[None,]

        return (image_tensor,)