70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import os.path
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import pytest
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from image_prediction.predictor import Predictor
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@pytest.fixture
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def predictions():
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return [
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{
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"class": "signature",
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"probabilities": {
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"signature": 1.0,
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"logo": 9.150285377746546e-19,
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"other": 4.374506412383356e-19,
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"formula": 3.582569597002796e-24,
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},
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}
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]
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@pytest.fixture
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def metadata():
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return [
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{
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"page_height": 612.0,
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"page_width": 792.0,
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"height": 61.049999999999955,
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"width": 139.35000000000002,
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"page_idx": 8,
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"x1": 63.5,
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"x2": 202.85000000000002,
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"y1": 472.0,
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"y2": 533.05,
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}
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]
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@pytest.fixture
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def response():
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return [
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{
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"classification": {
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"label": "signature",
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"probabilities": {"formula": 0.0, "logo": 0.0, "other": 0.0, "signature": 1.0},
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},
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"filters": {
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"allPassed": True,
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"geometry": {
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"imageFormat": {"quotient": 2.282555282555285, "tooTall": False, "tooWide": False},
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"imageSize": {"quotient": 0.13248234868245012, "tooLarge": False, "tooSmall": False},
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},
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"probability": {"unconfident": False},
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},
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"geometry": {"height": 61.049999999999955, "width": 139.35000000000002},
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"position": {"pageNumber": 9, "x1": 63.5, "x2": 202.85000000000002, "y1": 472.0, "y2": 533.05},
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}
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]
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@pytest.fixture
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def predictor():
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return Predictor()
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@pytest.fixture
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def test_pdf():
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with open("./test/test_data/f2dc689ca794fccb8cd38b95f2bf6ba9.pdf", "rb") as f:
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return f.read() |