Add tests for image layer, image plane, inventory, saved layout, commands, documentation, manifest, and packaging
- Implement tests for image layer functionality including alpha curve and depth layer behavior. - Create tests for image plane evaluation and modifier input handling. - Add tests to verify the saved asset inventory and its persistence. - Introduce tests for the documentation command to ensure correct project version deployment. - Validate the extension manifest structure and required permissions. - Develop tests for the packaging process, ensuring correct platform handling and dependency management. - Enhance image depth plane group functionality with new split threshold input and surface processing.
This commit is contained in:
439
tests/anyimage/server/test_server_jobs.py
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439
tests/anyimage/server/test_server_jobs.py
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import sys
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import tempfile
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import unittest
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from contextlib import nullcontext
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from pathlib import Path
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from types import ModuleType, SimpleNamespace
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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.ben2 = load_server_module("models.ben2")
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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_upscale_progress_omits_single_frame_count(self):
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self.assertEqual(
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self.upscale_job.frame_progress_message("Upscaling image", 0, 1),
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"Upscaling image",
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)
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self.assertEqual(
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self.upscale_job.frame_progress_message("Upscaling image", 1, 3),
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"Upscaling image (2/3)",
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)
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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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"input_frames",
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return_value=nullcontext([input_path]),
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),
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patch.object(self.depth_plane_job, "copy_color_images"),
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patch.object(
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self.depth_plane_job,
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"generate_moge2_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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inference_options = []
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save_options = []
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class FakeImage:
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size = (1, 1)
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def __enter__(self):
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return self
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def __exit__(self, *_args):
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return None
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def convert(self, mode):
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self.mode = mode
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return self
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def getchannel(self, _name):
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return self
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def putalpha(self, _alpha):
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return None
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def paste(self, *_args):
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return None
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def save(self, path, **options):
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save_options.append(options)
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Path(path).touch()
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class FakeModel:
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@classmethod
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def from_pretrained(cls, model_dir):
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self.assertEqual(model_dir, "model")
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return cls()
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def to(model_self, device):
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self.assertEqual(device.type, "cpu")
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return model_self
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def eval(self):
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return None
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def inference(self, image, **options):
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inference_options.append((image, options))
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return FakeImage()
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fake_torch = ModuleType("torch")
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fake_torch.cuda = SimpleNamespace(
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is_available=lambda: False,
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synchronize=lambda: None,
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)
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fake_torch.device = lambda device_type: SimpleNamespace(type=device_type)
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fake_torch.inference_mode = nullcontext
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fake_ben2 = ModuleType("server.models.onnx_ben2")
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fake_ben2.create_session = lambda _model_dir, _device: object()
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fake_ben2.infer = lambda _model, image, **options: (
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inference_options.append((image, options)) or FakeImage()
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)
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fake_pil = ModuleType("PIL")
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fake_pil.Image = SimpleNamespace(
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open=lambda _path: FakeImage(),
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new=lambda *_args, **_kwargs: FakeImage(),
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)
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fake_pil.ImageChops = SimpleNamespace(
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multiply=lambda _source, _foreground: FakeImage()
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)
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with tempfile.TemporaryDirectory() as directory, patch.dict(
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sys.modules,
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{
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"torch": fake_torch,
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"server.models.onnx_ben2": fake_ben2,
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"PIL": fake_pil,
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},
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):
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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", "remove-background")
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self.background.run(
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context,
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{"input": str(input_path), "model": "BEN2", "device": "cpu"},
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)
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self.assertTrue((root / "result" / "image.0001.png").is_file())
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self.assertEqual(
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inference_options[0][1],
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{"release_memory": True},
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)
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self.assertEqual(
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save_options[0],
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{"format": "PNG", "compress_level": 1},
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)
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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 = Image.new("RGBA", (2, 1), (10, 20, 30, 128))
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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", side_effect=infer),
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):
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result, _load_ms, _inference_ms = self.ben2.infer_foreground(
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input_path,
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"model",
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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.ben2.infer_foreground(
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input_path,
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"model",
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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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(10, 20, 30, 128),
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)
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def test_ben2_background_removal_processes_each_frame(self):
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inference_options = []
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class FakeImage:
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size = (1, 1)
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def __enter__(self):
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return self
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def __exit__(self, *_args):
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return None
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def convert(self, mode):
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self.mode = mode
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return self
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def getchannel(self, _name):
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return self
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def putalpha(self, _alpha):
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return None
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def paste(self, *_args):
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return None
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def save(self, path, **options):
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Path(path).touch()
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class FakeModel:
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@classmethod
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def from_pretrained(cls, model_dir):
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return cls()
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def to(model_self, device):
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return model_self
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def eval(self):
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return None
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def inference(self, image, **options):
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inference_options.append((image, options))
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return FakeImage()
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fake_torch = ModuleType("torch")
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fake_torch.cuda = SimpleNamespace(
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is_available=lambda: False,
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synchronize=lambda: None,
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)
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fake_torch.device = lambda device_type: SimpleNamespace(type=device_type)
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fake_torch.inference_mode = nullcontext
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fake_ben2 = ModuleType("server.models.onnx_ben2")
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fake_ben2.create_session = lambda _model_dir, _device: object()
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fake_ben2.infer = lambda _model, image, **options: (
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inference_options.append((image, options)) or FakeImage()
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)
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fake_pil = ModuleType("PIL")
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fake_pil.Image = SimpleNamespace(
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open=lambda _path: FakeImage(),
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new=lambda *_args, **_kwargs: FakeImage(),
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)
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fake_pil.ImageChops = SimpleNamespace(
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multiply=lambda _source, _foreground: FakeImage()
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)
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with tempfile.TemporaryDirectory() as directory, patch.dict(
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sys.modules,
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{
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"torch": fake_torch,
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"server.models.onnx_ben2": fake_ben2,
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"PIL": fake_pil,
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},
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):
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root = Path(directory)
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input_directory = root / "frames"
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input_directory.mkdir()
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(input_directory / "frame-0001.jpg").touch()
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(input_directory / "frame-0002.jpg").touch()
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context = FakeJobContext(root / "result", "remove-background")
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self.background.run(
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context,
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{
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"input": str(input_directory),
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"model": "BEN2",
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"device": "cpu",
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"video_frames": 8,
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},
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)
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result_directory = context.directory
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self.assertTrue(
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(result_directory / "image.0001.png").is_file()
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)
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self.assertTrue(
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(result_directory / "image.0002.png").is_file()
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)
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self.assertEqual(len(inference_options), 2)
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self.assertEqual(
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[options["release_memory"] for _image, options in inference_options],
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[False, True],
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)
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def test_upscale_job_saves_importable_upscale_sequence(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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frames = root / "frames"
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frames.mkdir()
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(frames / "a.png").touch()
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(frames / "b.png").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(frames),
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"model": "REALESRGAN_GENERAL_WDN_X4V3",
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"video_frames": 2,
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"device": "cpu",
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},
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)
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self.assertEqual(infer_one.call_count, 2)
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self.assertEqual(
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[call.kwargs["release_memory"] for call in infer_one.call_args_list],
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[False, True],
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)
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self.assertEqual(result, {"upscale": "."})
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colors = sorted(context.directory.glob("upscale.*.png"))
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self.assertEqual(
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[path.name for path in colors],
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["upscale.0001.png", "upscale.0002.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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"video_frames": 1,
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"device": "cpu",
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},
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)
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with Image.open(context.directory / "upscale.0001.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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Block a user