SEGSPreview (CNET Image)

Documentation

• Class name: SEGSPreviewCNet
• Category: ImpactPack/Util
• Output node: True

SEGSPreviewCNet is designed to generate previews of segmentation results, specifically focusing on the control networks associated with each segmentation. It saves these previews as images, facilitating a visual inspection of how control networks influence the segmentation process.

Input types

Required

• segs
  • The 'segs' parameter represents the segmentation results to be previewed. It is crucial for generating the control network-based previews, as it contains the segmentation data along with associated control network information.
  • Comfy dtype: SEGS
  • Python dtype: Tuple[Tuple[Any, Any], List[Any]]

Output types

• image
  • Comfy dtype: IMAGE
  • unknown
  • Python dtype: unknown
• ui
  • The 'ui' parameter contains the generated preview images of the segmentation results, specifically highlighting the control networks' impact.

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SEGSPreviewCNet:
    def __init__(self):
        self.output_dir = folder_paths.get_temp_directory()
        self.type = "temp"

    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"segs": ("SEGS", ),}, }

    RETURN_TYPES = ("IMAGE", )
    OUTPUT_IS_LIST = (True, )
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Util"

    OUTPUT_NODE = True

    def doit(self, segs):
        full_output_folder, filename, counter, subfolder, filename_prefix = \
            folder_paths.get_save_image_path("impact_seg_preview", self.output_dir, segs[0][1], segs[0][0])

        results = list()
        result_image_list = []

        for seg in segs[1]:
            file = f"{filename}_{counter:05}_.webp"

            if seg.control_net_wrapper is not None and seg.control_net_wrapper.control_image is not None:
                cnet_image = seg.control_net_wrapper.control_image
                result_image_list.append(cnet_image)
            else:
                cnet_image = empty_pil_tensor(64, 64)

            cnet_pil = utils.tensor2pil(cnet_image)
            cnet_pil.save(os.path.join(full_output_folder, file))

            results.append({
                "filename": file,
                "subfolder": subfolder,
                "type": self.type
            })

            counter += 1

        return {"ui": {"images": results}, "result": (result_image_list,)}