nan
938d95db0f
- Implemented a preview tool for rectifying images with interactive aspect controls in `preview.py`. - Added tests for depth metadata loading to ensure consistent schema usage in `test_depth_metadata.py`. - Developed comprehensive tests for cutout calibration processes in `test_cutout_calibration.py`, including stability checks and depth calibration. - Created tests for frame compositing operations in `test_frame_compositing.py`, ensuring correct handling of depth and transparency. - Introduced material slot inheritance functionality in `material_slots.py` to manage geometry material assignments. - Built shared shader nodes for material processing in `shader.py`, including roughness and bump mapping setups.
123 lines
4.3 KiB
Python
123 lines
4.3 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_defaults_to_wdn_and_sends_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.assertEqual(
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operator_type.__annotations__["model"]["default"],
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"REALESRGAN_GENERAL_WDN_X4V3",
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)
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self.assertEqual(
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operator_type.__annotations__["max_input_size"]["default"],
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2048,
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)
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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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