import importlib.util import sys import tempfile import threading import time import unittest from pathlib import Path from types import ModuleType, SimpleNamespace from unittest.mock import patch PROJECT_ROOT = Path(__file__).resolve().parents[1] def load_server_module(): server_directory = PROJECT_ROOT / "src" / "anyimage" / "server" specification = importlib.util.spec_from_file_location( "server", server_directory / "__init__.py", submodule_search_locations=[str(server_directory)], ) module = importlib.util.module_from_spec(specification) sys.modules["server"] = module specification.loader.exec_module(module) return module class FakeCuda: class OutOfMemoryError(RuntimeError): pass @staticmethod def is_available(): return False @staticmethod def empty_cache(): return None class ServerTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.server_module = load_server_module() importlib.import_module("server.selection.polygon_mask") importlib.import_module("server.jobs.prepare_geometry_data") from blendjob import JobServer from server.model_manager import ModelManager, ModelSessionCache cls.JobServer = JobServer cls.ModelManager, cls.ModelSessionCache = ModelManager, ModelSessionCache cls.server = cls.server_module.server cls.app_module = sys.modules["server.app"] def setUp(self): self.jobs_temp = tempfile.TemporaryDirectory() self.addCleanup(self.jobs_temp.cleanup) self.storage_root = Path(self.jobs_temp.name) def test_required_model_download_skips_ready_model(self): manager = self.ModelManager(Path(".")) downloads = [] context = SimpleNamespace(progress=lambda *_args: None) with ( patch.object( manager, "ready", side_effect=lambda key: key == "BEN2_BASE", ), patch.object( manager, "_download", side_effect=lambda _context, key, _progress: downloads.append(key), ), ): result = manager.download_missing( context, ("MOGE2_VITS_NORMAL", "BEN2_BASE"), ) self.assertEqual(downloads, ["MOGE2_VITS_NORMAL"]) self.assertEqual(result, {"downloaded": ["MOGE2_VITS_NORMAL"]}) def test_models_are_owned_by_the_server_resource(self): with tempfile.TemporaryDirectory() as directory: manager = self.ModelManager(Path(directory)) snapshot = manager.snapshot() self.assertEqual( snapshot, { "loaded": { "ben2": "", "geometry": "", "upscale": "", } }, ) self.assertIn("server.model_catalog", sys.modules) self.assertNotIn("model_catalog", sys.modules) def create_cache(self): cache = self.ModelSessionCache.__new__(self.ModelSessionCache) cache.ben2_model = None cache.ben2_key = None cache.da3_model = None cache.da3_key = None cache.moge_model = None cache.moge_key = None cache.upscale_model = None cache.upscale_key = None return cache def create_server(self): source = self.server_module.server server = self.JobServer("AnyImage Job Server", storage_root=self.storage_root) server.handlers.update(source.handlers) server.resource_factories.update(source.resource_factories) server.bind(self.storage_root) return server def wait_for_job(self, context, timeout=2.0): deadline = time.monotonic() + timeout while time.monotonic() < deadline: if context._snapshot()["state"] in {"succeeded", "failed", "cancelled"}: return context._snapshot() time.sleep(0.01) self.fail(f"Job did not finish: {context._snapshot()}") def test_ben2_model_is_reused_for_matching_runtime_key(self): loads = [] ben2 = ModuleType("server.models.onnx_ben2") ben2.create_session = lambda model_dir, device: ( loads.append((model_dir, device)) or object() ) cache = self.create_cache() device = SimpleNamespace(type="cuda") with patch.dict(sys.modules, {"server.models.onnx_ben2": ben2}): first, _load_ms = cache.get_ben2("model", device) second, cached_load_ms = cache.get_ben2("model", device) self.assertIs(first, second) self.assertEqual(loads, [("model", "cuda")]) self.assertEqual(cache.snapshot()["ben2"], "model") self.assertEqual(cached_load_ms, 0.0) def test_da3_model_is_reused_for_matching_runtime_key(self): loads = [] da3 = ModuleType("server.models.onnx_da3") da3.create_session = lambda model_dir, device: ( loads.append((model_dir, device)) or object() ) cache = self.create_cache() device = SimpleNamespace(type="cuda") with patch.dict( sys.modules, { "server.models.onnx_da3": da3, }, ): first = cache.get_da3("model", device) second = cache.get_da3("model", device) self.assertIs(first, second) self.assertEqual(loads, [("model", "cuda")]) self.assertEqual(cache.snapshot()["geometry"], "model") def test_model_cache_keeps_three_categories(self): cache = self.create_cache() ben2 = object() old_moge = object() upscale = object() cache.ben2_model = ben2 cache.ben2_key = ("ben2", "cuda") cache.moge_model = old_moge cache.moge_key = ("old-moge", "cuda") cache.upscale_model = upscale cache.upscale_key = ("upscale", "cuda") device = SimpleNamespace(type="cuda") da3 = object() def load_da3(_model_dir, _device, key): cache.da3_model = da3 cache.da3_key = key return da3 with patch.object(cache, "_load_da3", side_effect=load_da3): self.assertIs(cache.get_da3("da3", device), da3) self.assertIs(cache.ben2_model, ben2) self.assertIsNone(cache.moge_model) self.assertIs(cache.upscale_model, upscale) self.assertEqual(cache.snapshot()["geometry"], "da3") new_moge = object() fake_moge = ModuleType("server.models.onnx_moge2") fake_moge.create_session = lambda _model_dir, _device: new_moge with patch.dict(sys.modules, {"server.models.onnx_moge2": fake_moge}): self.assertIs(cache.get_moge2("new-moge", "cuda"), new_moge) self.assertIs(cache.ben2_model, ben2) self.assertIsNone(cache.da3_model) self.assertIs(cache.upscale_model, upscale) self.assertEqual(cache.snapshot()["geometry"], "new-moge") def test_upscale_model_is_reused_for_matching_runtime_key(self): loads = [] upscale = ModuleType("server.models.onnx_upscale") upscale.create_session = lambda model_dir, device: ( loads.append((model_dir, device)) or object() ) cache = self.create_cache() with patch.dict( sys.modules, {"server.models.onnx_upscale": upscale}, ): first = cache.get_upscale("model", "cpu") second = cache.get_upscale("model", "cpu") self.assertIs(first, second) self.assertEqual(loads, [("model", "cpu")]) self.assertEqual(cache.snapshot()["upscale"], "model") def test_dedicated_server_runs_only_one_job_at_a_time(self): server = self.JobServer("Test", storage_root=self.storage_root) first_started = threading.Event() release_first = threading.Event() order = [] @server.job("example") def example(_context, parameters): order.append(parameters["name"]) if parameters["name"] == "first": first_started.set() release_first.wait(1.0) first = server.submit("example", {"name": "first"}, job_id="job") self.assertTrue(first_started.wait(1.0)) second = server.submit("example", {"name": "second"}, job_id="other") self.assertIsNotNone(first) self.assertIsNotNone(second) self.assertTrue(server.snapshot("instance")["busy"]) self.assertEqual(server.snapshot("instance")["queued_jobs"], 1) self.assertNotIn("clients", server.snapshot("instance")) release_first.set() self.wait_for_job(first) self.wait_for_job(second) self.assertEqual(order, ["first", "second"]) server.close() def test_server_registers_tasks_instead_of_model_names(self): server = self.create_server() self.assertEqual( set(server.handlers), { "download-model", "download-required-models", "refine-image-mask", "remove-background", "upscale-image", "prepare-geometry-data", "debug-da3", "debug-moge2", "debug-ben2", "debug-upscale", }, ) def test_queued_job_can_be_cancelled_before_it_runs(self): server = self.JobServer("Test", storage_root=self.storage_root) first_started = threading.Event() release_first = threading.Event() executed = [] @server.job("example") def example(_context, parameters): executed.append(parameters["name"]) if parameters["name"] == "first": first_started.set() release_first.wait(1.0) first = server.submit("example", {"name": "first"}) self.assertTrue(first_started.wait(1.0)) second = server.submit("example", {"name": "second"}) self.assertIs(server.cancel(second.job_id), second) release_first.set() self.wait_for_job(first) second_status = self.wait_for_job(second) self.assertEqual(second_status["state"], "cancelled") self.assertEqual(executed, ["first"]) server.close() def test_cancelling_job_keeps_server_and_model_cache_alive(self): fake_models = SimpleNamespace( snapshot=lambda: {"loaded": {"ben2": "BEN2_BASE", "da3": ""}}, clear=lambda: None, close=lambda: None, ) with patch.object(self.app_module, "ModelManager", return_value=fake_models): server = self.create_server() started = threading.Event() @server.job("cancellable") def cancellable(context, _parameters): started.set() while not context._is_cancelled(): time.sleep(0.001) context.check_cancelled() context = server.submit("cancellable", {}, job_id="job") self.assertTrue(started.wait(1.0)) self.assertIsNotNone(server.cancel("job")) self.assertTrue(context._is_cancelled()) status = self.wait_for_job(context) self.assertEqual(status["state"], "cancelled") self.assertFalse(server.shutdown_event.is_set()) self.assertEqual( server.snapshot("instance")["resources"]["model_manager"]["loaded"]["ben2"], "BEN2_BASE", ) def test_job_progress_is_kept_in_server_memory(self): server = self.JobServer("Test", storage_root=self.storage_root) @server.job("example") def example(context, _parameters): context.progress(0.5, "Half way") context = server.submit("example", {}) self.wait_for_job(context) self.assertRegex(context.job_id, r"^[0-9a-f]{32}$") self.assertEqual(context.directory.name, context.job_id) self.assertEqual(context._snapshot()["state"], "succeeded") self.assertEqual(context._snapshot()["progress"], 1.0) def test_runtime_storage_root_owns_the_jobs_directory(self): storage_root = self.storage_root server = self.JobServer("Test") server.bind(storage_root) @server.job("example") def example(_context, _parameters): return {"value": 42} context = server.submit("example", {}) self.wait_for_job(context) self.assertEqual(server.jobs_directory, storage_root / "jobs") self.assertEqual(context.storage_root, storage_root.resolve()) self.assertEqual(context._snapshot()["result"], {"value": 42}) def test_job_server_owns_and_clears_shared_resources(self): events = [] resource = SimpleNamespace( snapshot=lambda: {"loaded": "model"}, clear=lambda: events.append("clear"), close=lambda: events.append("close"), ) server = self.JobServer("Test", storage_root=self.storage_root) server.add_resource("model_manager", resource) @server.job("example") def example(context, _parameters): self.assertIs(context.resource("model_manager"), resource) release.wait(1.0) release = threading.Event() context = server.submit("example", {}, job_id="job") self.assertFalse(server.clear_resource("model_manager")) release.set() self.wait_for_job(context) self.assertTrue(server.clear_resource("model_manager")) server.close() self.assertEqual( server.snapshot("instance")["resources"]["model_manager"]["loaded"], "model", ) self.assertEqual(events, ["clear", "close"]) def test_polygon_selection_mask_uses_even_odd_fill_for_self_intersection(self): image_mask = sys.modules["server.selection.polygon_mask"] polygon = ( (1, 1), (9, 1), (9, 9), (1, 9), (1, 1), (3, 3), (3, 7), (7, 7), (7, 3), (3, 3), (1, 1), ) mask = image_mask.polygon_selection_mask((10, 10), polygon) self.assertTrue(mask[2, 2]) self.assertFalse(mask[4, 4]) self.assertEqual(int(mask.sum()), 48) def test_polygon_selection_mask_nonzero_keeps_same_direction_overlap(self): image_mask = sys.modules["server.selection.polygon_mask"] polygon = ( (1, 1), (7, 1), (7, 7), (1, 7), (1, 1), (4, 1), (10, 1), (10, 7), (4, 7), (4, 1), (1, 1), ) even_odd = image_mask.polygon_selection_mask((12, 9), polygon) nonzero = image_mask.polygon_selection_mask( (12, 9), polygon, "NONZERO" ) self.assertTrue(even_odd[3, 2]) self.assertFalse(even_odd[3, 5]) self.assertTrue(nonzero[3, 2]) self.assertTrue(nonzero[3, 5]) self.assertTrue(nonzero[3, 8]) def test_image_mask_canvas_clears_rgba_outside_the_selection(self): import numpy as np from PIL import Image image_mask = sys.modules["server.selection.polygon_mask"] source_path = self.storage_root / "source.png" pixels = np.full((2, 2, 4), (20, 40, 60, 255), dtype=np.uint8) Image.fromarray(pixels, mode="RGBA").save(source_path) canvas, _selection, bounds = image_mask.create_selection_canvas( source_path, ((-1, -1), (2, -1), (-1, 2)), ) result = np.asarray(canvas) self.assertEqual(bounds, (-1, -1, 2, 2)) self.assertEqual(result.shape, (3, 3, 4)) self.assertEqual(tuple(result[2, 2]), (0, 0, 0, 0)) self.assertEqual(tuple(result[1, 1]), (20, 40, 60, 255)) def test_image_mask_crops_tightly_to_alpha(self): import numpy as np from PIL import Image image_mask = sys.modules["server.selection.polygon_mask"] pixels = np.zeros((4, 5, 4), dtype=np.uint8) pixels[1:3, 2:4] = (20, 40, 60, 255) result, bounds = image_mask.crop_to_alpha( Image.fromarray(pixels, mode="RGBA"), (10, 20, 15, 24), ) result = np.asarray(result) self.assertEqual(bounds, (12, 21, 14, 23)) self.assertEqual(result.shape, (2, 2, 4)) self.assertTrue(np.all(result[:, :, 3] == 255)) def test_mesh_refine_selection_keeps_source_size_and_selection_alpha(self): cutout_job = importlib.import_module("server.selection.cutout_images") import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "source.png" pixels = np.full((6, 8, 4), (20, 40, 60, 255), dtype=np.uint8) Image.fromarray(pixels, mode="RGBA").save(source_path) job_directory = self.storage_root / "job" job_directory.mkdir() context = SimpleNamespace( directory=job_directory, resource=lambda _name: SimpleNamespace( directory=lambda _model: Path("model") ), check_cancelled=lambda: None, ) def passthrough(path, *_args, **_kwargs): return Image.open(path).convert("RGBA"), 0.0, 0.0 output_path = job_directory / "image.0001.png" with patch.object( cutout_job, "infer_foreground", side_effect=passthrough, ): cutout_job.refine_selection( context, {"refine_model": "BEN2_BASE", "device": "cpu"}, source_path, ((1, 1), (6, 1), (1, 5)), output_path, ) result = np.asarray(Image.open(output_path).convert("RGBA")) self.assertEqual(result.shape, (6, 8, 4)) self.assertEqual(tuple(result[1, 1]), (20, 40, 60, 255)) self.assertEqual(tuple(result[5, 7]), (0, 0, 0, 0)) def test_mesh_cutout_images_crop_color_and_preserve_source_alpha(self): cutout_job = importlib.import_module("server.selection.cutout_images") import json import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "analysis-source.png" selection_path = self.storage_root / "selection-source.png" output_directory = self.storage_root / "cutout-images" output_directory.mkdir() source = np.full((6, 8, 4), (10, 80, 160, 255), dtype=np.uint8) source[2, 3, 3] = 25 source[2, 4, 3] = 26 source[3, 5, 3] = 0 Image.fromarray(source, mode="RGBA").save(source_path) selection = np.zeros((6, 8, 4), dtype=np.uint8) selection[1:5, 2:7] = (200, 20, 40, 255) selection[1, 2] = (200, 20, 40, 0) Image.fromarray(selection, mode="RGBA").save(selection_path) color_path, mask_path, analysis_path, bounds_path = ( cutout_job.write_images( source_path, selection_path, output_directory, ) ) color = np.asarray(Image.open(color_path).convert("RGBA")) mask = np.asarray(Image.open(mask_path).convert("RGBA")) analysis = np.asarray(Image.open(analysis_path).convert("RGBA")) self.assertEqual(json.loads(bounds_path.read_text()), [2, 1, 7, 5]) self.assertEqual(color.shape, (4, 5, 4)) self.assertEqual(tuple(color[1, 1]), (10, 80, 160, 25)) self.assertEqual(int(mask[1, 1, 3]), 0) self.assertEqual(tuple(analysis[1, 1]), (0, 0, 0, 0)) self.assertEqual(tuple(color[1, 2]), (10, 80, 160, 26)) self.assertGreater(int(mask[1, 2, 3]), 25) self.assertEqual(int(mask[0, 0, 3]), 0) self.assertEqual(tuple(color[0, 0]), (10, 80, 160, 255)) self.assertEqual(tuple(analysis[0, 0]), (0, 0, 0, 0)) self.assertEqual(tuple(analysis[1, 2]), (10, 80, 160, 26)) self.assertEqual(tuple(color[2, 3]), (10, 80, 160, 0)) self.assertEqual(int(mask[2, 3, 3]), 0) self.assertEqual(tuple(analysis[2, 3]), (0, 0, 0, 0)) def test_mesh_unrefined_cutout_discards_selection_outside_the_source(self): cutout_job = importlib.import_module("server.selection.cutout_images") import json import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / 'outside-source.png' full_selection_path = self.storage_root / 'outside-selection-full.png' output_directory = self.storage_root / 'outside-cutout' output_directory.mkdir() Image.new('RGBA', (8, 6), (10, 80, 160, 255)).save(source_path) polygon = ((-2, 1), (10, 1), (10, 5), (-2, 5)) _selection_path, selection_bounds = ( cutout_job.write_selection( source_path, polygon, full_selection_path, ) ) color_path, mask_path, analysis_path, bounds_path = ( cutout_job.write_images( source_path, full_selection_path, output_directory, selection_bounds, ) ) color = np.asarray(Image.open(color_path).convert('RGBA')) mask = np.asarray(Image.open(mask_path).convert('RGBA')) analysis = np.asarray(Image.open(analysis_path).convert('RGBA')) self.assertEqual(selection_bounds, (-2, 1, 10, 5)) self.assertEqual(json.loads(bounds_path.read_text()), [0, 1, 8, 5]) self.assertEqual(color.shape, (4, 8, 4)) self.assertEqual(tuple(color[1, 0]), (10, 80, 160, 255)) self.assertEqual(tuple(color[1, 7]), (10, 80, 160, 255)) self.assertGreater(int(mask[1, 0, 3]), 127) self.assertEqual(tuple(analysis[1, 0]), (10, 80, 160, 255)) def test_mesh_job_discards_unrefined_outside_selection_with_normal(self): import json import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "mesh-source.png" pixels = np.full((6, 8, 4), (20, 40, 60, 255), dtype=np.uint8) Image.fromarray(pixels, mode="RGBA").save(source_path) job_directory = self.storage_root / "mesh-job" context = SimpleNamespace( directory=job_directory, progress=lambda *_args: None, check_cancelled=lambda: None, resource=lambda _name: object(), ) normal = np.zeros((1, 4, 12, 3), dtype=np.float32) normal[..., 0] = 1.0 prediction = SimpleNamespace(normal=normal) received = [] received_images = [] def infer(_manager, parameters, images, **_kwargs): received.append(parameters) received_images.extend(images) return prediction with patch.object(mesh_job.moge2, "infer", side_effect=infer): result = mesh_job.run( context, { "input": str(source_path), "shape": "BALLOON", "polygon": ((-2, 1), (10, 1), (10, 5), (-2, 5)), "mesh_detail": 64, "invert": False, "device": "cpu", "model": "MOGE2_VITS_NORMAL", "resolution_level": 5, "normal_analysis": True, }, ) color_path = job_directory / "image.0001.png" color_pixels = np.asarray(Image.open(color_path).convert("RGBA")) normal_pixels = np.asarray( Image.open(job_directory / result["normal"]).convert("RGB") ) self.assertEqual( result, { "selection": "selection.png", "bounds": "bounds.json", "normal": "normal.png", }, ) self.assertTrue(color_path.is_file()) self.assertFalse((job_directory / "contour.json").exists()) self.assertEqual(received[0]["model"], "MOGE2_VITS_NORMAL") self.assertEqual(received_images, [job_directory / "analysis.png"]) analysis_pixels = np.asarray( Image.open(received_images[0]).convert("RGBA") ) self.assertEqual( json.loads((job_directory / "bounds.json").read_text()), [0, 1, 8, 5], ) self.assertEqual(tuple(analysis_pixels[2, 2, :3]), (20, 40, 60)) self.assertGreater(int(analysis_pixels[2, 2, 3]), 240) self.assertEqual(tuple(analysis_pixels[2, 0, :3]), (20, 40, 60)) self.assertEqual(tuple(color_pixels[2, 0, :3]), (20, 40, 60)) self.assertGreater(int(color_pixels[2, 0, 3]), 240) self.assertEqual(tuple(color_pixels[2, 7, :3]), (20, 40, 60)) self.assertEqual(color_pixels.shape[:2], (4, 8)) self.assertEqual(analysis_pixels.shape[:2], (4, 8)) self.assertEqual(normal_pixels.shape[:2], (4, 8)) self.assertEqual(tuple(normal_pixels[0, 0]), (255, 127, 127)) self.assertEqual(tuple(normal_pixels[2, 0]), (255, 127, 127)) def test_cutout_limits_the_cropped_model_input_but_keeps_color_resolution(self): import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "limited-cutout-source.png" Image.new("RGBA", (20, 10), (20, 40, 60, 255)).save(source_path) job_directory = self.storage_root / "limited-cutout-job" context = SimpleNamespace( directory=job_directory, progress=lambda *_args: None, check_cancelled=lambda: None, resource=lambda _name: object(), ) prediction = SimpleNamespace( normal=np.zeros((1, 2, 8, 3), dtype=np.float32) ) inferred_sizes = [] def infer(_manager, _parameters, images, **_kwargs): with Image.open(images[0]) as model_input: inferred_sizes.append(model_input.size) return prediction with patch.object(mesh_job.moge2, "infer", side_effect=infer): result = mesh_job.run( context, { "input": str(source_path), "shape": "BALLOON", "polygon": ((2, 3), (18, 3), (18, 7), (2, 7)), "mesh_detail": 64, "max_input_size": 8, "invert": False, "device": "cpu", "model": "MOGE2_VITS_NORMAL", "resolution_level": 5, "normal_analysis": True, }, ) with Image.open(job_directory / "image.0001.png") as color: color_size = color.size with Image.open(job_directory / result["normal"]) as normal: normal_size = normal.size self.assertEqual(inferred_sizes, [(8, 2)]) self.assertEqual(color_size, (16, 4)) self.assertEqual(normal_size, (16, 4)) self.assertFalse((job_directory / "model-input.png").exists()) def test_mesh_job_skips_moge_normal_when_option_is_disabled(self): from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "mesh-no-normal-source.png" Image.new("RGBA", (8, 6), (20, 40, 60, 255)).save(source_path) job_directory = self.storage_root / "mesh-no-normal-job" context = SimpleNamespace( directory=job_directory, progress=lambda *_args: None, check_cancelled=lambda: None, resource=lambda _name: object(), ) with patch.object(mesh_job, "_write_camera_normal") as write_normal: result = mesh_job.run( context, { "input": str(source_path), "shape": "BALLOON", "polygon": ((0, 0), (8, 0), (8, 6), (0, 6)), "mesh_detail": 64, "normal_analysis": False, "invert": False, "device": "cpu", }, ) self.assertEqual( result, {"selection": "selection.png", "bounds": "bounds.json"}, ) write_normal.assert_not_called() self.assertFalse((job_directory / "normal.png").exists()) def test_mesh_depth_balloon_uses_metric_point_field(self): from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "mesh-depth-balloon-source.png" Image.new("RGBA", (6, 6), (20, 40, 60, 255)).save(source_path) job_directory = self.storage_root / "mesh-depth-balloon-job" context = SimpleNamespace( directory=job_directory, progress=lambda *_args: None, check_cancelled=lambda: None, ) field_path = job_directory / "field.npz" with patch.object( mesh_job, "_write_depth_field", return_value=(field_path, None), ) as write_surface: result = mesh_job.run( context, { "input": str(source_path), "shape": "DEPTH_BALLOON", "polygon": ((1, 1), (5, 1), (5, 5), (1, 5)), "normal_analysis": False, "video_frames": 1, "invert": False, }, ) self.assertEqual( result, { "selection": "selection.png", "bounds": "bounds.json", "field": "field.npz", }, ) self.assertTrue(write_surface.called) self.assertEqual( write_surface.call_args.kwargs["selection_path"], job_directory / "selection.png", ) def test_moge_depth_and_normal_share_one_prediction(self): import numpy as np mesh_job = sys.modules["server.jobs.prepare_geometry_data"] job_directory = self.storage_root / "shared-moge-prediction" job_directory.mkdir() color_path = job_directory / "analysis.png" normal_path = job_directory / "normal.png" prediction = object() valid = np.ones((2, 3), dtype=bool) frame = SimpleNamespace( depth=np.ones((2, 3), dtype=np.float32), points=np.ones((2, 3, 3), dtype=np.float32), intrinsics=np.eye(3, dtype=np.float32), valid_mask=valid, model_mask=valid, ) context = SimpleNamespace( directory=job_directory, resource=lambda _name: object(), check_cancelled=lambda: None, ) parameters = { "model": "MOGE2_VITS_NORMAL", "device": "cpu", "resolution_level": 5, } with ( patch.object( mesh_job.moge2, "infer", return_value=prediction ) as infer, patch.object( mesh_job.moge2, "geometry_frame", return_value=frame ), patch.object(mesh_job, "_write_field") as write_field, patch.object( mesh_job, "_write_camera_normal_from_prediction", return_value=normal_path, ) as write_normal, ): field_path, written_normal_path = mesh_job._write_depth_field( context, parameters, color_path, include_normal=True, ) infer.assert_called_once() write_field.assert_called_once() write_normal.assert_called_once_with( prediction, color_path, normal_path ) self.assertEqual(field_path, job_directory / "field.npz") self.assertEqual(written_normal_path, normal_path) def test_depth_calibration_mask_is_separate_from_model_validity(self): import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] selection_path = self.storage_root / "depth-selection.png" selection = np.zeros((2, 3, 4), dtype=np.uint8) selection[0, 1, 3] = 255 selection[1, 2, 3] = 255 Image.fromarray(selection, mode="RGBA").save(selection_path) frame = SimpleNamespace( valid_mask=np.asarray( ((True, True, True), (True, True, False)), dtype=bool, ) ) original_valid = frame.valid_mask.copy() result = mesh_job._selection_calibration_mask(frame, selection_path) np.testing.assert_array_equal( result, ((False, True, False), (False, False, False)), ) np.testing.assert_array_equal(frame.valid_mask, original_valid) def test_depth_field_writes_original_and_calibration_masks_separately(self): import numpy as np mesh_job = sys.modules["server.jobs.prepare_geometry_data"] output_path = self.storage_root / "field.npz" valid = np.asarray(((True, True), (True, False)), dtype=bool) calibration = np.asarray(((False, True), (False, False)), dtype=bool) depth = np.asarray(((2.0, 2.1), (2.2, 0.0)), dtype=np.float32) points = np.zeros((2, 2, 3), dtype=np.float32) points[..., 2] = depth frame = SimpleNamespace( depth=depth, points=points, intrinsics=np.eye(3, dtype=np.float32), valid_mask=valid, model_mask=valid, ) mesh_job._write_field(frame, output_path, calibration) with np.load(output_path, allow_pickle=False) as archive: np.testing.assert_array_equal(archive["valid"], valid) np.testing.assert_array_equal( archive["calibration_valid"], calibration, ) def test_mesh_job_uses_upscale_source_only_for_analysis(self): from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] display_path = self.storage_root / "upscaled.png" analysis_path = self.storage_root / "original.png" Image.new("RGBA", (12, 8), (200, 20, 40, 255)).save(display_path) Image.new("RGBA", (3, 2), (10, 80, 160, 255)).save(analysis_path) job_directory = self.storage_root / "mesh-upscale-source-job" context = SimpleNamespace( directory=job_directory, progress=lambda *_args: None, check_cancelled=lambda: None, ) field_path = job_directory / "field.npz" with patch.object( mesh_job, "_write_depth_field", return_value=(field_path, None), ) as write_surface: result = mesh_job.run( context, { "input": str(display_path), "analysis_input": str(analysis_path), "shape": "DEPTH_SURFACE", "normal_analysis": False, "video_frames": 1, }, ) self.assertEqual(result, {"field": "field.npz"}) self.assertEqual(write_surface.call_args.args[2], analysis_path) with Image.open(job_directory / "image.0001.png") as color: self.assertEqual(color.size, (12, 8)) def test_mesh_masked_color_keeps_same_direction_overlap(self): cutout_job = importlib.import_module("server.selection.cutout_images") import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "opaque-overlap.png" output_path = self.storage_root / "cutout-overlap.png" Image.new("RGBA", (12, 9), (20, 40, 60, 255)).save(source_path) polygon = ( (1, 1), (7, 1), (7, 7), (1, 7), (1, 1), (4, 1), (10, 1), (10, 7), (4, 7), (4, 1), (1, 1), ) cutout_job._write_masked_color(source_path, polygon, output_path) alpha = np.asarray(Image.open(output_path).convert("RGBA"))[:, :, 3] self.assertGreater(int(alpha[3, 2]), 200) self.assertGreater(int(alpha[3, 5]), 200) self.assertGreater(int(alpha[3, 8]), 200) def test_mesh_masked_color_preserves_nonzero_winding_hole(self): cutout_job = importlib.import_module("server.selection.cutout_images") import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "opaque-hole.png" output_path = self.storage_root / "cutout-hole.png" Image.new("RGBA", (10, 10), (20, 40, 60, 255)).save(source_path) polygon = ( (1, 1), (9, 1), (9, 9), (1, 9), (1, 1), (3, 3), (3, 7), (7, 7), (7, 3), (3, 3), (1, 1), ) cutout_job._write_masked_color(source_path, polygon, output_path) alpha = np.asarray(Image.open(output_path).convert("RGBA"))[:, :, 3] self.assertGreater(int(alpha[2, 2]), 200) self.assertLess(int(alpha[4, 4]), 32) def test_mesh_masked_color_antialiases_the_cutout_edge(self): cutout_job = importlib.import_module("server.selection.cutout_images") import numpy as np from PIL import Image mesh_job = sys.modules["server.jobs.prepare_geometry_data"] source_path = self.storage_root / "opaque.png" output_path = self.storage_root / "cutout.png" Image.new("RGBA", (16, 16), (20, 40, 60, 255)).save(source_path) cutout_job._write_masked_color( source_path, ((1, 1), (14, 1), (1, 14)), output_path, ) alpha = np.asarray(Image.open(output_path).convert("RGBA"))[:, :, 3] self.assertTrue(np.any((alpha > 0) & (alpha < 255))) if __name__ == "__main__": unittest.main()