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8d20cbd0b3
Supersedes / folds in #18305. The score-based layer-2 rotation selection from #18305 now lives here, on top of `WarpCrop` (layer 1), applied to **all three** Go OCR paths, together with the Python score plumbing the Go side depends on. #18305 is closed in favor of this PR.
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
"""Offline unit tests for score-bearing OCR recognition.
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These bypass model loading by constructing OCR / OCRAdapter via
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object.__new__ and injecting fake recognizers, so they run without the
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DeepDoc weights. They verify:
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- recognize_batch_with_score returns (text, score) tuples,
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- text below drop_score is blanked while the score is preserved,
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- the original recognize_batch (text-only) contract is unchanged,
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- OCRAdapter.recognize surfaces the real score instead of a 1.0 fill.
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"""
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import unittest
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from deepdoc.server.adapters.ocr_adapter import OCRAdapter
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from deepdoc.vision.ocr import OCR
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class FakeRecognizer:
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"""Callable stand-in for OCR.text_recognizer[device_id].
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results is a list of recognizer outputs, one per call; each output is a
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list of (text, score) tuples as produced by TextRecognizer.__call__.
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"""
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def __init__(self, results):
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self._results = results
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self.calls = 0
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def __call__(self, img_list):
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res = self._results[self.calls]
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self.calls += 1
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return res, 0.0
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def make_ocr(results, drop_score=0.5):
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ocr = object.__new__(OCR)
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ocr.text_recognizer = [FakeRecognizer(results)]
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ocr.drop_score = drop_score
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return ocr
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class TestRecognizeBatchWithScore(unittest.TestCase):
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def test_returns_score_tuples(self):
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ocr = make_ocr([[["hello", 0.92]]])
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self.assertEqual(ocr.recognize_batch_with_score(["img"]), [("hello", 0.92)])
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def test_blanks_below_drop_score_keeps_score(self):
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ocr = make_ocr([[["garble", 0.10]]], drop_score=0.5)
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# text is blanked by drop_score, but the score is preserved for
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# layer-2 selection.
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self.assertEqual(ocr.recognize_batch_with_score(["img"]), [("", 0.10)])
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def test_original_recognize_batch_unchanged(self):
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# The in-process __ocr path keeps its text-only contract.
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ocr = make_ocr([[["hello", 0.92]]])
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self.assertEqual(ocr.recognize_batch(["img"]), ["hello"])
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class TestOCRAdapterScore(unittest.TestCase):
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def test_recognize_surfaces_real_score(self):
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adapter = object.__new__(OCRAdapter)
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class FakeOCR:
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def recognize_batch_with_score(self, imgs):
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return [("world", 0.88)]
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adapter._ocr = FakeOCR()
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adapter._decode_bgr = lambda data: "img"
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out = adapter.recognize(b"fake")
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self.assertEqual(out, {"output": [[[["world", 0.88]]]]})
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if __name__ == "__main__":
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unittest.main()
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