260 lines
11 KiB
Python
260 lines
11 KiB
Python
import tempfile
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import unittest
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from pathlib import Path
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from unittest.mock import patch
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import numpy as np
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from tests.anyimage.server.support import load_server_module, FakeJobContext
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class ServerJobsTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.resolution = load_server_module("media.resolution")
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cls.background = load_server_module("jobs.remove_background")
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cls.background_model = load_server_module("models.background")
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cls.onnx_upscale = load_server_module("models.onnx_upscale")
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cls.depth_plane_job = load_server_module("jobs.depth_plane")
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cls.upscale_job = load_server_module("jobs.upscale")
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def test_plane_job_requests_depth_and_matching_normal_space(self):
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for plane_type, normal_space in (("DEPTH", "OBJECT"), ("RELIEF", "TANGENT")):
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normal_key = f"{normal_space.lower()}_normal"
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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input_path = root / "input.png"
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input_path.touch()
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context = FakeJobContext(root / "result", "generate-depth-plane-geometry")
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depth_texture = context.directory / "depth.exr"
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metadata = context.directory / "depth.json"
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normal_texture = context.directory / f"{normal_space.lower()}-normal.png"
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parameters = {"input": str(input_path), "plane_type": plane_type}
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with (
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patch.object(
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self.depth_plane_job,
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"validate_input_path",
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return_value=input_path,
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),
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patch.object(
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self.depth_plane_job,
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"generate_moge_artifacts",
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return_value={
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"depth": depth_texture,
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"depth_metadata": metadata,
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normal_key: normal_texture,
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},
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) as generate,
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):
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result = self.depth_plane_job.run(context, parameters)
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self.assertEqual(
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result,
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{
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"depth": "depth.exr",
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"depth_metadata": "depth.json",
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normal_key: f"{normal_space.lower()}-normal.png",
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},
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)
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generate.assert_called_once_with(
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context,
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parameters,
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input_path,
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generate_depth=True,
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normal_mode=normal_space,
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)
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def test_ben2_background_removal_writes_fast_transparent_png(self):
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from PIL import Image
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onnx_ben2 = load_server_module("models.onnx_ben2")
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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input_path = root / "input.png"
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Image.new("RGBA", (2, 1), (255, 64, 32, 128)).save(input_path)
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context = FakeJobContext(root / "result", "remove-background")
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with (
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patch.object(onnx_ben2, "create_session", return_value=object()),
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patch.object(onnx_ben2, "infer_alpha", return_value=np.full((1, 2), 128 / 255, np.float32)) as infer,
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patch.object(Image.Image, "save", autospec=True, side_effect=Image.Image.save) as save,
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):
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result = self.background.run(context, {"input": str(input_path), "model": "BEN2_BASE", "device": "cpu"})
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self.assertEqual(result, {"foreground": "foreground.png"})
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self.assertEqual([p.name for p in context.directory.glob("*.png")], ["foreground.png"])
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with Image.open(root / "result" / "foreground.png") as image:
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self.assertEqual(image.getpixel((0, 0)), (255, 64, 32, 64))
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self.assertEqual(infer.call_args.kwargs, {"release_memory": True})
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self.assertEqual(save.call_args.kwargs, {"format": "PNG", "compress_level": 1})
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self.assertIn("Background removed in", context.status["message"])
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def test_ben2_preserves_input_alpha_and_hides_transparent_rgb(self):
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from PIL import Image
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onnx_ben2 = load_server_module("models.onnx_ben2")
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source_pixels = np.asarray(
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(((255, 0, 0, 0), (0, 255, 0, 128)),),
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dtype=np.uint8,
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)
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model_result = np.full((1, 2), 128 / 255, np.float32)
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received = []
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def infer(_model, image, **_options):
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received.append(np.asarray(image.convert("RGB"), dtype=np.uint8))
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return model_result
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with tempfile.TemporaryDirectory() as directory:
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input_path = Path(directory) / "input.png"
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Image.fromarray(source_pixels, mode="RGBA").save(input_path)
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with (
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patch.object(onnx_ben2, "create_session", return_value=object()),
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patch.object(onnx_ben2, "infer_alpha", side_effect=infer),
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):
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result, _load_ms, _inference_ms = self.background_model.infer_foreground(
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input_path,
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"BEN2-ONNX",
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"cpu",
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preserve_input_alpha=True,
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)
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raw_result, _load_ms, _inference_ms = self.background_model.infer_foreground(
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input_path,
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"BEN2-ONNX",
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"cpu",
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)
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inference_pixels = received[0]
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result_pixels = np.asarray(result.convert("RGBA"), dtype=np.uint8)
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self.assertEqual(tuple(inference_pixels[0, 0]), (0, 0, 0))
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self.assertEqual(tuple(inference_pixels[0, 1]), (0, 128, 0))
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self.assertEqual(tuple(received[1][0, 0]), (0, 0, 0))
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self.assertEqual(tuple(received[1][0, 1]), (0, 128, 0))
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self.assertEqual(tuple(result_pixels[0, 0]), (255, 0, 0, 0))
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self.assertEqual(tuple(result_pixels[0, 1]), (0, 255, 0, 64))
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self.assertEqual(
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tuple(np.asarray(raw_result.convert("RGBA"))[0, 0]),
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(0, 0, 0, 128),
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)
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def test_background_alpha_job_preserves_float_precision(self):
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from PIL import Image
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onnx_ben2 = load_server_module("models.onnx_ben2")
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alpha = np.array([[0.123456, 0.987654]], dtype=np.float32)
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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path = root / "input.png"
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Image.new("RGB", (2, 1)).save(path)
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context = FakeJobContext(root / "result", "remove-background")
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with (
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patch.object(onnx_ben2, "create_session", return_value=object()),
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patch.object(onnx_ben2, "infer_alpha", return_value=alpha),
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):
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result = self.background.run(context, {"input": str(path), "model": "BEN2_BASE", "device": "cpu", "output_kind": "alpha"})
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self.assertEqual(result, {"alpha": "alpha.npy"})
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np.testing.assert_array_equal(np.load(context.directory / result["alpha"], allow_pickle=False), alpha)
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self.assertFalse(list(context.directory.glob("*.png")))
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def test_upscale_job_returns_one_image_file(self):
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from PIL import Image
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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input_path = root / "input.png"
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input_path.touch()
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context = FakeJobContext(root / "result", "upscale-image")
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with patch.object(
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self.upscale_job,
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"infer_one",
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return_value=Image.new("RGB", (8, 8), (10, 20, 30)),
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) as infer_one:
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result = self.upscale_job.run(
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context,
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{
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"input": str(input_path),
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"model": "REALESRGAN_GENERAL_WDN_X4V3",
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"device": "cpu",
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},
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)
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self.assertEqual(infer_one.call_count, 1)
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self.assertEqual(result, {"upscale": "upscale.png"})
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colors = sorted(context.directory.glob("*.png"))
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self.assertEqual(
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[path.name for path in colors],
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["upscale.png"],
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)
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self.assertIn(
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(
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"INFO",
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"Upscale model: REALESRGAN_GENERAL_WDN_X4V3; "
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"maximum AI input size: 2048 px",
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),
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context.logs,
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)
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def test_upscale_job_preserves_source_alpha(self):
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from PIL import Image
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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source_path = root / "source.png"
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pixels = np.zeros((3, 5, 4), dtype=np.uint8)
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pixels[..., :3] = (10, 20, 30)
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pixels[..., 3] = np.arange(5, dtype=np.uint8) * 60
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Image.fromarray(pixels, mode="RGBA").save(source_path)
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context = FakeJobContext(root / "result", "upscale-image")
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prediction = Image.new("RGB", (20, 12), (10, 20, 30))
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with patch.object(
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self.onnx_upscale,
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"infer",
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return_value=prediction,
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):
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self.upscale_job.run(
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context,
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{
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"input": str(source_path),
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"model": "REALESRGAN_GENERAL_WDN_X4V3",
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"device": "cpu",
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},
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)
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with Image.open(context.directory / "upscale.png") as result:
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self.assertEqual(result.mode, "RGBA")
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self.assertEqual(result.size, (10, 6))
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alpha = np.asarray(result.getchannel("A"))
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expected_alpha = np.asarray(
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Image.fromarray(pixels[:, :, 3], mode="L").resize(
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result.size,
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Image.Resampling.LANCZOS,
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)
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)
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np.testing.assert_array_equal(alpha, expected_alpha)
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self.assertLess(
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int(alpha[:, 0].max()),
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int(alpha[:, -1].min()),
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)
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def test_upscale_input_uses_the_global_long_edge_limit(self):
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from PIL import Image
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source = Image.new("RGB", (3000, 1500))
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limited = self.resolution.limit_image(source, 2048)
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self.assertEqual(limited.size, (2048, 1024))
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self.assertEqual(
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self.resolution.limit_image(Image.new("RGB", (800, 600)), 2048).size,
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(800, 600),
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)
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with self.assertRaisesRegex(ValueError, "between 1 and 8192"):
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self.resolution.limit_image(source, 9000)
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