xiaden
7d711010d5
V2 io.ComfyNode wrapper for deepghs/imgutils — anime image analysis, tagging, detection, pose estimation, and segmentation. 6 nodes: - ImgUtilsValidate — safety/NSFW/content classification (18 ops) - ImgUtilsDescribe — WD14/DeepDanbooru/OCR/detection (17 ops) - ImgUtilsTransform — edge detection/restoration/upscaling (11 ops) - ImgUtilsCompare — CCIP/LPIPS image comparison (3 ops) - ImgUtilsPose — DWPose keypoint detection + visualization - ImgUtilsSegment — isnetis character segmentation (mask + RGBA)
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
"""
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Node 6: ImgUtilsSegment — "Segment character"
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Segments character from background using isnetis (anime character segmentation).
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Outputs both a mask image and the segmented RGBA image with transparent background.
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Uses:
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imgutils.segment.isnetis.segment_rgba_with_isnetis() -> (mask: PIL, rgba_image: PIL)
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"""
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from __future__ import annotations
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from comfy_api.latest import io, ui
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from .utils import comfy_to_pil, pil_to_comfy
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class ImgUtilsSegment(io.ComfyNode):
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"""Segment character from background using isnetis."""
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="ImgUtilsSegment",
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display_name="Imgutils Segment",
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category="imgutils/segment",
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description=(
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"Segment anime character from background using isnetis. "
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"Outputs the alpha mask and an RGBA image with transparent background."
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),
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search_aliases=[
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"segment", "isnetis", "mask", "character",
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"background", "remove bg", "cutout", "alpha",
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],
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inputs=[
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io.Image.Input("image", tooltip="Input image containing character to segment"),
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],
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outputs=[
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io.Mask.Output(display_name="mask"),
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io.Image.Output(display_name="segmented_rgba"),
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],
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)
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@classmethod
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def execute(cls, image) -> io.NodeOutput:
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"""
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Run character segmentation and return mask + RGBA image.
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Args:
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image: ComfyUI IMAGE tensor (B,H,W,C), float32, [0,1]
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Returns:
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NodeOutput with (mask_tensor, segmented_rgba_tensor)
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"""
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from imgutils.segment import segment_rgba_with_isnetis
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pil_image = comfy_to_pil(image.numpy() if hasattr(image, "numpy") else image)
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mask_pil, rgba_pil = segment_rgba_with_isnetis(pil_image)
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# pil_to_comfy handles grayscale "L" mode (mask) and "RGBA" mode
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mask_tensor = pil_to_comfy(mask_pil)
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rgba_tensor = pil_to_comfy(rgba_pil)
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return io.NodeOutput(mask_tensor, rgba_tensor, ui=ui.PreviewImage(rgba_tensor, cls=cls))
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