nan
591e62085a
- Introduced a new `Boundary Padding` preference in the Cutout Tool with a default of 2 px and a configurable range of 1-4 px. - Updated the Cutout client to establish inward mapping from the final Mask, sampling in a fixed direction and processing independent material RGBA and Depth. - Enhanced the handling of RGBA and Depth during cutout operations, ensuring proper mapping and preservation of source data. - Added tests to validate the new boundary padding functionality and its integration with existing cutout processes. feat(geometry): remove overall mesh smoothing from geometry nodes - Removed the `Smooth` input from several geometry node groups to eliminate unnecessary overall mesh smoothing. - Retained `Smooth Weight` for depth nodes while removing it from Cutout and Relief Plane nodes. - Ensured that boundary, normal, and fill smoothing behaviors remain intact and consistent. - Updated tests and documentation to reflect the changes in node interfaces and behaviors. feat(normals): scale tangent normals with depth - Reused the existing `o_normal_reduction` attribute to adjust Tangent Normal strength based on the geometry's `Depth Scale`. - Ensured that Tangent Normal strength is consistent with the geometry depth, preserving full strength for materials that do not provide the attribute. - Updated tests to verify the correct behavior of normal strength adjustments in both Tangent and Object Space.
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import Mock, patch
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from tests.support.blender import BlenderTestCase
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class AiImageOperatorsTest(BlenderTestCase):
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def test_upscale_operator_sends_the_selected_model(self):
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operator_type = self.upscale.UpscaleImage
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self.assertIn(operator_type, self.anyimage.CLASSES)
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self.assertIn(self.runtime.JobOperatorBase, operator_type.__mro__)
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self.assertEqual(operator_type.bl_options, {"UNDO"})
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operator = operator_type()
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operator.input_path = "input.png"
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operator._image_target = SimpleNamespace()
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operator.model = "HAT_GAN_X4_SHARPER"
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operator.max_input_size = 1024
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context = SimpleNamespace(
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scene=SimpleNamespace(anyimage_settings=SimpleNamespace())
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)
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with (
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patch.object(self.upscale, "require_environment"),
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patch.object(self.upscale, "require_model") as require_model,
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patch.object(
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self.upscale, "require_input_path", return_value=Path("input.png")
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),
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patch.object(self.upscale, "production_device", return_value="CUDA"),
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):
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request = operator.request(context)
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require_model.assert_called_once_with("HAT_GAN_X4_SHARPER")
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self.assertEqual(
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request,
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{
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"input": "input.png",
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"model": "HAT_GAN_X4_SHARPER",
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"device": "cuda",
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"max_input_size": 1024,
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},
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)
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def test_upscale_operator_opens_setup_when_required_ai_is_missing(self):
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expected = {"CANCELLED"}
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operator = self.upscale.UpscaleImage()
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with patch.object(
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self.upscale,
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"invoke_ai_setup_if_needed",
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return_value=expected,
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) as setup:
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result = operator.invoke(None, None)
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setup.assert_called_once_with(operator)
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self.assertIs(result, expected)
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def test_upscale_operator_runs_immediately_when_ai_is_ready(self):
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operator = self.upscale.UpscaleImage()
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context = object()
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expected = {"FINISHED"}
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with (
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patch.object(
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self.upscale,
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"invoke_ai_setup_if_needed",
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return_value=None,
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),
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patch.object(
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operator,
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"execute",
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return_value=expected,
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) as execute,
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):
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result = operator.invoke(context, None)
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execute.assert_called_once_with(context)
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self.assertIs(result, expected)
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self.assertNotIn("REGISTER", operator.bl_options)
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def test_remove_background_loads_a_plain_image_edit_result(self):
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result = SimpleNamespace(file=lambda key: Path("result") / f"{key}.png")
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operator = self.remove_background.RunBackgroundRemoval()
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operator._image_target = Mock()
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ops = SimpleNamespace(
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ed=SimpleNamespace(undo_push=Mock(return_value={"FINISHED"}))
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)
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with (
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patch.object(self.remove_background.bpy, "ops", ops, create=True),
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):
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operator.response(None, result)
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operator._image_target.apply.assert_called_once_with(Path("result/foreground.png"))
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ops.ed.undo_push.assert_called_once_with(message="Remove Background")
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def test_upscale_loads_a_plain_image_edit_result(self):
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result = SimpleNamespace(file=lambda key: Path("result") / f"{key}.png")
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operator = self.upscale.UpscaleImage()
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operator._image_target = Mock()
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operator.model = "HAT_GAN_X4_SHARPER"
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with (
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patch.object(
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self.upscale,
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"upscale_model_label",
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return_value="HAT Sharper",
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),
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):
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message = operator.response(None, result)
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operator._image_target.apply.assert_called_once_with(
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Path("result/upscale.png"),
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)
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self.assertEqual(message, "Image is upscaled with HAT Sharper")
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