mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-06-03 21:53:39 +08:00
3d03ca5500
* Init new paddleocr * Remove unused dependency * Fix typos * Fix * Add doc understanding modules * Fix package finding * Normalize name * Fix setting bugs * Fix setting bug * Support single model inference * Add PP-ChatOCRv4-doc * Add pp_chatocrv4_doc tests * Enable MKL-DNN when available * add seal_text_detection modules * add layout_detection and table_cells_detection modules * add testing scripts * Fix desc * add text_image_unwarping and table_structure_recognition modules * add formula_recognition and doc_vlm modules * update formula_recognition default_model_name * add MKLDNN_BLOCKLIST * update MKLDNN log * add seal rec pipeline * fix sth * fix sth * add doc preprocessor pipeline * fix sth * add doc understanding * add table_rec_v2, ppstructurev3, formula_rec pipelines * move test files * forward kwargs to pipeline.predict * clean test files * Add missing kwargs * Fix typo * Fix typo * rerun CI * update mkldnn BLOCKLIST * update * update warning message * fix cli args * update PIPELINE_MKLDNN_BLOCKLIST * update of workflow * skip resource_intensive tests * update config * skip ppdocbee test_predict_params --------- Co-authored-by: zhangyue66 <zhangyue66@baidu.com> Co-authored-by: zhangzelun <zhangzelun@baidu.com>
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
import pytest
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from paddleocr import FormulaRecognitionPipeline
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from ..testing_utils import (
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TEST_DATA_DIR,
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check_simple_inference_result,
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check_wrapper_simple_inference_param_forwarding,
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)
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@pytest.fixture(scope="module")
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def formula_recognition_engine() -> FormulaRecognitionPipeline:
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return FormulaRecognitionPipeline()
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# TODO: Should we separate unit tests and integration tests?
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@pytest.mark.parametrize(
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"image_path",
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[
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TEST_DATA_DIR / "doc_with_formula.png",
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],
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)
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def test_predict(
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formula_recognition_engine: FormulaRecognitionPipeline, image_path: str
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) -> None:
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"""
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Test FormulaRecognitionPipeline's formula_recognition functionality.
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Args:
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formula_recognition_engine: An instance of `FormulaRecognitionPipeline`.
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image_path: Path to the image to be processed.
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"""
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result = formula_recognition_engine.predict(str(image_path))
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check_simple_inference_result(result)
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res = result[0]
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assert isinstance(res["formula_res_list"], list)
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assert len(res["formula_res_list"]) > 0
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# TODO: Also check passing `None`
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@pytest.mark.parametrize(
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"params",
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[
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{"use_doc_orientation_classify": False},
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{"use_doc_unwarping": False},
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{"use_layout_detection": False},
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{"layout_threshold": 0.5},
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{"layout_nms": True},
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{"layout_unclip_ratio": 1.5},
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{"layout_merge_bboxes_mode": "large"},
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],
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)
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def test_predict_params(
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monkeypatch,
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formula_recognition_engine: FormulaRecognitionPipeline,
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params: dict,
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) -> None:
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check_wrapper_simple_inference_param_forwarding(
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monkeypatch,
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formula_recognition_engine,
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"paddlex_pipeline",
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"dummy_path",
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params,
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)
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# TODO: Test init params
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