xiaden
315666c7b6
- Split flat node modules into categorized subpackages (tagging, detect, edge, pose, segment, restore, compare, judge, transform, utility) - Extract shared internals into _shared/ (bases, tensor, formatting) - Centralize node registration via register_nodes() helper - Clean up: remove dead is_scored return, unnecessary hasattr guard, inline imports
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
"""Crop image to each bounding box from detect/OCR JSON output."""
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import json
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from comfy_api.latest import io, ui
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from .._shared.tensor import comfy_to_pil, pil_to_comfy
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class ImgUtilsBboxCrop(io.ComfyNode):
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"""Crop an image into one sub-image per bounding box from detect/OCR results."""
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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="ImgUtilsBboxCrop",
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display_name="Imgutils Bbox Crop",
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category="imgutils/utility",
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description=(
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"Crop an image to each bounding box in a JSON bbox string "
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"(from Detect or OCR). Returns one cropped image per bbox."
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),
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search_aliases=["bbox", "crop", "detect", "region", "extract"],
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inputs=[
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io.Image.Input("image", tooltip="Source image for cropping."),
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io.String.Input(
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"bboxes",
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tooltip="JSON bbox array from Detect or OCR node. Each entry: {bbox: [x1,y1,x2,y2], ...}.",
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),
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],
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outputs=[io.Image.Output(display_name="images", is_output_list=True)],
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)
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@classmethod
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def execute(cls, image, bboxes) -> io.NodeOutput:
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pil = comfy_to_pil(image)
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w, h = pil.size
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try:
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entries = json.loads(bboxes)
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except json.JSONDecodeError:
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entries = []
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crops: list = []
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for entry in entries:
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bbox = entry.get("bbox", entry)
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if isinstance(bbox, list) and len(bbox) == 4:
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x1, y1, x2, y2 = bbox
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x1 = max(0, min(int(x1), w - 1))
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y1 = max(0, min(int(y1), h - 1))
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x2 = max(x1 + 1, min(int(x2), w))
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y2 = max(y1 + 1, min(int(y2), h))
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crop = pil.crop((x1, y1, x2, y2))
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crops.append(pil_to_comfy(crop))
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if not crops:
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return io.NodeOutput([pil_to_comfy(pil)])
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return io.NodeOutput(
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crops,
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ui=ui.PreviewImage(crops[0], cls=cls),
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)
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