nan
1613bd3194
- Implemented the Image Perspective tool for editing image corners in Blender. - Created a new operator `EditImagePerspective` to handle the perspective transformation. - Added a preview feature for the perspective adjustment. - Introduced the `ImagePolylineTool` for editing image masks with polylines. - Developed backend support for Real-ESRGAN model inference, including ONNX integration. - Added functionality for image masking and background removal. - Created unit tests for the Real-ESRGAN ONNX model to ensure correct preprocessing and inference.
76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
import sys
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import unittest
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from pathlib import Path
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from types import SimpleNamespace
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import numpy as np
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from PIL import Image
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SOURCE_DIR = (
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Path(__file__).resolve().parents[1]
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/ "src"
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/ "anyimage"
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/ "server"
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/ "models"
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)
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sys.path.insert(0, str(SOURCE_DIR))
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import onnx_realesrgan
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class FakeSession:
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def get_inputs(self):
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return [SimpleNamespace(name="input")]
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def get_outputs(self):
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return [SimpleNamespace(name="output")]
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def run(self, output_names, feeds):
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self.output_names = output_names
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self.feeds = feeds
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source = feeds["input"]
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return [np.repeat(np.repeat(source, 4, axis=2), 4, axis=3)]
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class RealesrganOnnxTest(unittest.TestCase):
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def test_preprocess_matches_dynamic_rgb_layout(self):
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image = Image.new("RGB", (7, 5), (64, 128, 192))
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result = onnx_realesrgan.preprocess(image)
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self.assertEqual(result.shape, (1, 3, 5, 7))
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self.assertEqual(result.dtype, np.float32)
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self.assertTrue(result.flags.c_contiguous)
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np.testing.assert_allclose(
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result[0, :, 0, 0],
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np.array((64, 128, 192), dtype=np.float32) / 255.0,
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)
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def test_infer_returns_direct_four_x_rgb_prediction(self):
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session = FakeSession()
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image = Image.new("RGB", (7, 5), (10, 20, 30))
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result = onnx_realesrgan.infer(session, image)
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self.assertEqual(result.mode, "RGB")
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self.assertEqual(result.size, (28, 20))
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self.assertEqual(session.output_names, ["output"])
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self.assertEqual(session.feeds["input"].shape, (1, 3, 5, 7))
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self.assertEqual(result.getpixel((0, 0)), (10, 20, 30))
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def test_infer_rejects_non_four_x_output(self):
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class InvalidSession(FakeSession):
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def run(self, _output_names, feeds):
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return [feeds["input"]]
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with self.assertRaisesRegex(RuntimeError, "unexpected output shape"):
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onnx_realesrgan.infer(
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InvalidSession(),
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Image.new("RGB", (7, 5)),
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)
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if __name__ == "__main__":
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unittest.main()
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