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61 lines
2.1 KiB
61 lines
2.1 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import PIL |
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import pytest |
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from ultralytics import Explorer |
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from ultralytics.utils import ASSETS |
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@pytest.mark.slow |
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def test_similarity(): |
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"""Test similarity calculations and SQL queries for correctness and response length.""" |
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exp = Explorer() |
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exp.create_embeddings_table() |
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similar = exp.get_similar(idx=1) |
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assert len(similar) == 25 |
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similar = exp.get_similar(img=ASSETS / "zidane.jpg") |
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assert len(similar) == 25 |
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similar = exp.get_similar(idx=[1, 2], limit=10) |
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assert len(similar) == 10 |
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sim_idx = exp.similarity_index() |
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assert len(sim_idx) > 0 |
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sql = exp.sql_query("WHERE labels LIKE '%person%'") |
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assert len(sql) > 0 |
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@pytest.mark.slow |
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def test_det(): |
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"""Test detection functionalities and ensure the embedding table has bounding boxes.""" |
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exp = Explorer(data="coco8.yaml", model="yolov8n.pt") |
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exp.create_embeddings_table(force=True) |
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assert len(exp.table.head()["bboxes"]) > 0 |
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similar = exp.get_similar(idx=[1, 2], limit=10) |
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assert len(similar) > 0 |
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# This is a loose test, just checks errors not correctness |
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similar = exp.plot_similar(idx=[1, 2], limit=10) |
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assert isinstance(similar, PIL.Image.Image) |
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@pytest.mark.slow |
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def test_seg(): |
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"""Test segmentation functionalities and verify the embedding table includes masks.""" |
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exp = Explorer(data="coco8-seg.yaml", model="yolov8n-seg.pt") |
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exp.create_embeddings_table(force=True) |
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assert len(exp.table.head()["masks"]) > 0 |
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similar = exp.get_similar(idx=[1, 2], limit=10) |
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assert len(similar) > 0 |
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similar = exp.plot_similar(idx=[1, 2], limit=10) |
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assert isinstance(similar, PIL.Image.Image) |
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@pytest.mark.slow |
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def test_pose(): |
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"""Test pose estimation functionalities and check the embedding table for keypoints.""" |
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exp = Explorer(data="coco8-pose.yaml", model="yolov8n-pose.pt") |
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exp.create_embeddings_table(force=True) |
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assert len(exp.table.head()["keypoints"]) > 0 |
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similar = exp.get_similar(idx=[1, 2], limit=10) |
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assert len(similar) > 0 |
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similar = exp.plot_similar(idx=[1, 2], limit=10) |
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assert isinstance(similar, PIL.Image.Image)
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