mirror of
https://github.com/RapidAI/RapidOCR.git
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Cursor AgentandDaniel
3622c4598e
Expose the PP-OCRv6 engine through a stable C ABI and installable shared and static libraries, plus a ctypes Python package that follows the RapidOCR call shape. Keep AVX-512 out of the AVX2 object file and speed up GEMM accumulate and the AArch64 GEMM tile. Co-authored-by: Daniel <znsoft@163.com>
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import os
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from pathlib import Path
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import numpy as np
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import pytest
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from rapidocr import EngineType, LoadImageError, RapidOCR
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from rapidocr.output import RapidOCROutput, filter_by_score
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def test_filter_and_json():
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boxes, txts, scores = filter_by_score(
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[[[0, 0], [10, 0], [10, 5], [0, 5]], [[1, 1], [2, 1], [2, 2], [1, 2]]],
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["Hello", " "],
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[0.9, 0.99],
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0.5,
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)
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assert txts == ("Hello",)
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result = RapidOCROutput(boxes=boxes, txts=txts, scores=scores, elapse_list=[None, None, 0.2])
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assert len(result) == 1
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assert result.elapse == pytest.approx(0.2)
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payload = result.to_json()
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assert payload[0]["txt"] == "Hello"
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assert "Hello" in result.to_markdown()
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def test_unknown_param_rejected():
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with pytest.raises(ValueError):
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RapidOCR(params={"NotASection.thresh": 0.2})
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def test_unsupported_engine():
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with pytest.raises(NotImplementedError):
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RapidOCR(params={"Det.engine_type": EngineType.OPENVINO, "Rec.engine_type": EngineType.OPENVINO})
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def test_bad_image_type():
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engine = RapidOCR(params={"Det.model_path": "a", "Rec.model_path": "b", "Rec.rec_keys_path": "c"})
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with pytest.raises(LoadImageError):
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engine(12345)
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def test_cpu_info_and_ocr():
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det = os.environ.get("PPOCR_TEST_DET")
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rec = os.environ.get("PPOCR_TEST_REC")
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dictionary = os.environ.get("PPOCR_TEST_DICT")
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image = os.environ.get("PPOCR_TEST_IMAGE")
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if not all((det, rec, dictionary, image, os.environ.get("PPOCR_LIBRARY"))):
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pytest.skip("set PPOCR_LIBRARY and PPOCR_TEST_* to run native OCR")
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from rapidocr import cpu_info
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info = cpu_info()
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assert info["active_isa"] in {"scalar", "avx2", "avx512", "neon"}
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if info["avx512"]:
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assert info["avx2"]
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engine = RapidOCR(
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params={
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"Det.model_path": det,
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"Rec.model_path": rec,
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"Rec.rec_keys_path": dictionary,
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"Global.use_cls": False,
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"Global.text_score": 0.3,
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}
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)
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result = engine(image)
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assert isinstance(result, RapidOCROutput)
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assert result.elapse >= 0
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array = np.asarray(result.img)
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again = engine(array)
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assert isinstance(again, RapidOCROutput)
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assert Path(image).is_file()
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