# cv_analysis.table_inference module — CV Analysis Service 2.5.2 documentation

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# cv_analysis.table_inference module

cv_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
  Return type:

    `ndarray`

cv_analysis.table_inference.filter_fp_col_lines(line_list: list[int], filt_sums: ndarray) → list[int]
  Return type:

    `list`[`int`]

cv_analysis.table_inference.get_lines_either(table_array: ndarray, horizontal=True) → list[int]
  Return type:

    `list`[`int`]

cv_analysis.table_inference.img_bytes_to_array(img_bytes: bytes) → ndarray
  Return type:

    `ndarray`

cv_analysis.table_inference.infer_lines(img: ndarray) → dict[str, dict[str, int] | list[dict[str, int]]]
  Return type:

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

cv_analysis.table_inference.make_gaussian_kernel(kernel_size: int, sd: float) → ndarray
  Return type:

    `ndarray`

cv_analysis.table_inference.make_gaussian_nonpositive_kernel(kernel_size: int, sd: float) → ndarray
  Return type:

    `ndarray`

cv_analysis.table_inference.make_quadratic_kernel(kernel_size: int, ratio: float) → ndarray
  Return type:

    `ndarray`

cv_analysis.table_inference.min_avg_for_interval(filtered: ndarray, interval: int) → tuple[float, int]
  Return type:

    `tuple`[`float`, `int`]

cv_analysis.table_inference.save_lines(img: ndarray, lines: list[dict[str, int]]) → None
  Return type:

    `None`

cv_analysis.table_inference.save_plot(arr: ndarray, name: str, title: str = '') → None
  Return type:

    `None`

cv_analysis.table_inference.search_intervals(filtered: ndarray, min_interval: int, max_interval: int)

cv_analysis.table_inference.show(arr: ndarray, title: str = '')

cv_analysis.table_inference.show_multiple(arrs: Tuple[ndarray], title: str = '')