126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
from __future__ import annotations
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import numpy as np
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from backend.node_registry import register_node
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from backend.execution_context import emit_warning
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from backend.data_types import (
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COLORMAPS,
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DataField,
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ImageData,
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_apply_annotation_overlay_from_context,
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_annotation_context_from_image,
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image_to_uint8,
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normalize_font_spec,
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resolve_colormap_input,
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)
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@register_node(display_name="Annotations")
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class Annotations:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input": ("ANNOTATION_SOURCE", {
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"label": "Input",
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"accepted_types": ["DATA_FIELD", "IMAGE"],
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}),
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"colormap": (["auto"] + list(COLORMAPS), {"hide_when_input_connected": "colormap_map"}),
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"show_scale_bar": ("BOOLEAN", {"default": True}),
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"show_color_map": ("BOOLEAN", {"default": True}),
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"text_size": ("FLOAT", {
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"default": 14.0,
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"min": 6.0,
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"max": 96.0,
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"step": 1.0,
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}),
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},
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"optional": {
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"colormap_map": ("COLORMAP", {"label": "colormap"}),
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"font": ("FONT",),
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},
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}
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OUTPUTS = (
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('ANNOTATION_SOURCE', 'output'),
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)
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FUNCTION = "render"
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DESCRIPTION = (
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"Attach optional publication-style annotations to a DATA_FIELD without flattening the raw data, "
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"or annotate an IMAGE that carries viewport metadata from View3D."
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)
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def render(
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self,
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input,
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colormap: str,
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show_scale_bar: bool,
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show_color_map: bool,
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text_size: float = 1.0,
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colormap_map=None,
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font=None,
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) -> tuple:
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annotation_spec = {
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"kind": "annotation",
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"show_scale_bar": bool(show_scale_bar),
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"show_color_map": bool(show_color_map),
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"text_size": float(np.clip(text_size, 6.0, 96.0)) if np.isfinite(text_size) else 14.0,
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"font": normalize_font_spec(font),
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}
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if isinstance(input, DataField):
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resolved_colormap = resolve_colormap_input(
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colormap,
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colormap_input=colormap_map,
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inherited=input.colormap,
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default="gray",
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)
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out = input.replace(
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colormap=resolved_colormap,
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overlays=[
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*input.overlays,
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annotation_spec,
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],
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)
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return (out,)
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context = _annotation_context_from_image(input)
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if context is None:
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self._send_warning(
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"Annotations image input has no scale metadata, so scale bar and color-map legend cannot be added."
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)
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return (ImageData(image_to_uint8(input)),)
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resolved_colormap = resolve_colormap_input(
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colormap,
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colormap_input=colormap_map,
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inherited=context.get("colormap"),
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default="gray",
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)
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context["colormap"] = resolved_colormap
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missing_features = []
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xreal = context.get("xreal")
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if bool(show_scale_bar) and not (isinstance(xreal, (int, float)) and np.isfinite(float(xreal)) and float(xreal) > 0 and str(context.get("si_unit_xy", "")).strip()):
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missing_features.append("scale bar")
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if bool(show_color_map):
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legend_values = (context.get("legend_min"), context.get("legend_mid"), context.get("legend_max"))
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has_legend_values = all(
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isinstance(value, (int, float)) and np.isfinite(float(value))
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for value in legend_values
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)
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if not (has_legend_values and str(context.get("legend_unit", "")).strip()):
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missing_features.append("color-map legend")
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if missing_features:
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self._send_warning(
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f"Annotations image input is missing metadata for: {', '.join(missing_features)}."
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)
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annotated = _apply_annotation_overlay_from_context(
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image_to_uint8(input),
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context,
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annotation_spec,
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)
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return (ImageData(annotated, metadata={"annotation_context": context}),)
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def _send_warning(self, message: str):
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emit_warning(message)
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