{ "body": "
\n

cv_analysis.table_inference module#

\n
\n
\ncv_analysis.table_inference.filter_array(array: ~numpy.ndarray, sum_filter: ~numpy.ndarray | None, padding: ~numpy.ndarray | None = None, pad_value_function: ~typing.Callable[[~numpy.ndarray], float] = <function <lambda>>) ndarray#
\n
\n
Return type:
\n

ndarray

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.filter_fp_col_lines(line_list: list[int], filt_sums: ndarray) list[int]#
\n
\n
Return type:
\n

list[int]

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.get_lines_either(table_array: ndarray, horizontal=True) list[int]#
\n
\n
Return type:
\n

list[int]

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.img_bytes_to_array(img_bytes: bytes) ndarray#
\n
\n
Return type:
\n

ndarray

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.infer_lines(img: ndarray) dict[str, dict[str, int] | list[dict[str, int]]]#
\n
\n
Return type:
\n

dict[str, dict[str, int] | list[dict[str, int]]]

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.make_gaussian_kernel(kernel_size: int, sd: float) ndarray#
\n
\n
Return type:
\n

ndarray

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.make_gaussian_nonpositive_kernel(kernel_size: int, sd: float) ndarray#
\n
\n
Return type:
\n

ndarray

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.make_quadratic_kernel(kernel_size: int, ratio: float) ndarray#
\n
\n
Return type:
\n

ndarray

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.min_avg_for_interval(filtered: ndarray, interval: int) tuple[float, int]#
\n
\n
Return type:
\n

tuple[float, int]

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.save_lines(img: ndarray, lines: list[dict[str, int]]) None#
\n
\n
Return type:
\n

None

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.save_plot(arr: ndarray, name: str, title: str = '') None#
\n
\n
Return type:
\n

None

\n
\n
\n
\n\n
\n
\ncv_analysis.table_inference.search_intervals(filtered: ndarray, min_interval: int, max_interval: int)#
\n
\n\n
\n
\ncv_analysis.table_inference.show(arr: ndarray, title: str = '')#
\n
\n\n
\n
\ncv_analysis.table_inference.show_multiple(arrs: Tuple[ndarray], title: str = '')#
\n
\n\n
\n", "title": "cv_analysis.table_inference module", "sourcename": "modules/cv_analysis.table_inference.rst.txt", "current_page_name": "modules/cv_analysis.table_inference", "toc": "\n", "page_source_suffix": ".rst" }