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 = '')#