nan
835225d264
- Implemented surface generation functionality in `surface.py`, including geometry estimation and texture writing. - Introduced media input handling in `input.py`, supporting image and video frame extraction. - Added model management for geometry adapters in `models/__init__.py`. - Created BEN2 and DA3 model inference logic in `ben2.py` and `da3.py`, respectively. - Developed ONNX model inference utilities for BEN2 and DA3 in `onnx_ben2.py` and `onnx_da3.py`. - Added support for MoGe-2 model inference in `moge2.py` with necessary ONNX utilities. - Implemented output handling for depth and surface data in `depth.py` and `surface.py`. - Created debug output utilities for DA3 and MoGe-2 predictions in `debug_da3.py` and `debug_moge2.py`. - Enhanced ONNX runtime session management in `onnx_runtime.py`. - Updated `__init__.py` files to include new modules and functions for better organization.
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import sys
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import tempfile
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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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/ "snapform"
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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_da3
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class FakeSession:
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def get_inputs(self):
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return [SimpleNamespace(name="images"), SimpleNamespace(name="strategy")]
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def run(self, output_names, feeds):
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self.feeds = feeds
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_, views, _, height, width = feeds["images"].shape
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depth = np.ones((1, views, height, width), dtype=np.float32)
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confidence = np.full_like(depth, 0.75)
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pose = np.zeros((1, views, 9), dtype=np.float32)
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pose[..., 6] = 1.0
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pose[..., 7:] = np.pi / 2.0
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return depth, confidence, pose
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class Da3OnnxTest(unittest.TestCase):
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def test_target_size_uses_patch_multiple(self):
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self.assertEqual(
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onnx_da3.target_size((800, 600), 504, "upper_bound_resize"),
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(504, 378),
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)
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def test_infer_returns_camera_and_image_arrays(self):
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with tempfile.TemporaryDirectory() as directory:
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image_path = Path(directory) / "input.png"
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Image.new("RGB", (80, 60), (10, 20, 30)).save(image_path)
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session = FakeSession()
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result = onnx_da3.infer(
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session,
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[image_path],
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process_res=56,
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ref_view_strategy="middle",
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)
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self.assertEqual(result.depth.shape, (1, 42, 56))
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self.assertEqual(result.conf.shape, result.depth.shape)
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self.assertEqual(result.processed_images.shape, (1, 42, 56, 3))
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self.assertEqual(result.extrinsics.shape, (1, 3, 4))
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self.assertEqual(result.intrinsics.shape, (1, 3, 3))
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self.assertEqual(int(session.feeds["strategy"][0]), 1)
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np.testing.assert_allclose(result.extrinsics[0, :3, :3], np.eye(3))
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if __name__ == "__main__":
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unittest.main()
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