PaddleOCR/tests/pipelines/test_table_recognition_v2.py

65 lines
1.7 KiB
Python

import pytest
from paddleocr import TableRecognitionPipelineV2
from ..testing_utils import (
TEST_DATA_DIR,
check_simple_inference_result,
check_wrapper_simple_inference_param_forwarding,
)
@pytest.fixture(scope="module")
def table_recognition_v2_pipeline():
return TableRecognitionPipelineV2()
@pytest.mark.parametrize(
"image_path",
[
TEST_DATA_DIR / "table.jpg",
],
)
def test_visual_predict(table_recognition_v2_pipeline, image_path):
result = table_recognition_v2_pipeline.predict(
str(image_path), use_doc_orientation_classify=False, use_doc_unwarping=False
)
check_simple_inference_result(result)
res = result[0]
assert len(res["table_res_list"]) > 0
assert isinstance(res["table_res_list"][0], dict)
assert len(res["table_res_list"][0]["cell_box_list"]) > 0
assert isinstance(res["table_res_list"][0]["pred_html"], str)
assert isinstance(res["table_res_list"][0]["table_ocr_pred"], dict)
@pytest.mark.parametrize(
"params",
[
{"use_doc_orientation_classify": False},
{"use_doc_unwarping": False},
{"use_layout_detection": False},
{"use_ocr_model": False},
{"text_det_limit_side_len": 640, "text_det_limit_type": "min"},
{"text_det_thresh": 0.5},
{"text_det_box_thresh": 0.3},
{"text_det_unclip_ratio": 3.0},
{"text_rec_score_thresh": 0.5},
],
)
def test_predict_params(
monkeypatch,
table_recognition_v2_pipeline,
params,
):
check_wrapper_simple_inference_param_forwarding(
monkeypatch,
table_recognition_v2_pipeline,
"paddlex_pipeline",
"dummy_path",
params,
)
# TODO: Test constructor and other methods