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### What problem does this PR solve? Table structure recognition rows, columns, headers, and spans are produced in cropped table image coordinates, while OCR boxes are matched later in page-cumulative coordinates. Comparing those boxes without normalization can skip or misassign table row and column metadata. Closes #16992. ### What is changed? - Map TSR components from cropped or rotated table-image coordinates back into page-cumulative coordinates before matching OCR boxes. - Reuse one inverse rotation transform for rotated OCR boxes and TSR components. - Keep TSR layout ids in the same `table-N` form used by table OCR boxes. - Sort columns by mapped page x-coordinate after coordinate normalization. - Add focused unit coverage for page offsets, zoom scaling, and 90/180/270 degree rotated tables. ### Type of change - [x] Bug fix - [x] Test coverage ### How has this been tested? - `uv run --group test pytest test/unit_test/deepdoc/parser/test_pdf_parser_table_coordinates.py -q` - `uv run --no-sync --group test pytest --confcutdir=test/unit_test/deepdoc/parser test/unit_test/deepdoc/parser/test_pdf_parser_table_coordinates.py -q` - `uv run ruff check deepdoc/parser/pdf_parser.py test/unit_test/deepdoc/parser/test_pdf_parser_table_coordinates.py` - `uv run --no-sync python -m py_compile deepdoc/parser/pdf_parser.py test/unit_test/deepdoc/parser/test_pdf_parser_table_coordinates.py` - `git diff --check` A later dependency-sync attempt was blocked while resolving the `en-core-web-sm` wheel from GitHub, and the repository-level unit-test conftest can try to download missing NLTK `wordnet` data when it is not already present locally. The focused parser test above does not require that data fixture. --------- Co-authored-by: zq <zhouquan1511@163.com>
(1). Deploy RAGFlow services and images
https://ragflow.io/docs/build_docker_image
(2). Configure the required environment for testing
Install Python dependencies (including test dependencies):
uv sync --python 3.13 --only-group test --no-default-groups --frozen
Activate the environment:
source .venv/bin/activate
Install SDK:
uv pip install sdk/python
Modify the .env file: Add the following code:
COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.26.4 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Elasticsearch:
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
(4). Test Infinity
Modify the .env file:
DOC_ENGINE=${DOC_ENGINE:-infinity}
Start the container:
docker compose -f docker/docker-compose.yml down -v
docker compose -f docker/docker-compose.yml up -d
a) Run sdk tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api