- unify `generate_data_from_folder_training` w/ `..._evaluation`
- instead of recreating array after every batch, just zero out
- cast image results to uint8 instead of uint16
- cast categorical results to float instead of int
- make more config_params keys dependent on each other
- re-order accordingly
- in main, initialise them (as kwarg), so sacred actually
allows overriding them by named config file
- `index_start`: re-introduce cfg key, pass to Keras `Model.fit`
as `initial_epoch`
- make config keys `index_start` and `dir_of_start_model` dependent
on `continue_training`
- improve description
- `utils.provide_patches`: split up loop into
* `utils.preprocess_img` (single img function)
* `utils.preprocess_imgs` (top-level loop)
- capture exceptions for all cases (not just some)
at top level and with informative logging
- avoid repeating / delegating config keys in several
places: only as kwargs to `preprocess_img()`
- read files into memory only once, then re-use
- improve readability (avoiding long lines, repeated code)
when parsing `PrintSpace` or `Border` from PAGE-XML,
- use `lxml` XPath instead of nested loops
- convert points to polygons directly
(instead of painting on canvas and retrieving contours)
- pass result bbox in slice notation
(instead of xywh)
when matching files in `dir_images` by XML path name stem,
* use `dict` instead of `list` to assign reliably
* filter out `.xml` files (so input directories can be mixed)
* show informative warnings for files which cannot be matched
- do not restrict TF version, but depend on tf-keras and
set `TF_USE_LEGACY_KERAS=1` to avoid Keras 3 behaviour
- relax Numpy version requirement up to v2
- relax Torch version requirement
- drop TF1 session management code
- drop TF1 config in favour of TF2 config code for memory growth
- training.*: also simplify and limit line length
- training.train: always train with TensorBoard callback
after selecting the optimum angle on the original
search range, narrow down around in the vicinity
with half the range (adding computational costs,
but gaining precision)
when passing the text region mask, do not apply erosion only
if there are more than 2 columns, but iff `not erosion_hurts`
(consistent with `find_num_col`'s expectations and making
it as easy to find the column gaps on 1 and 2-column pages
as on multi-column pages)
- `find_number_of_columns_in_document`: retain vertical separators
and pass to `find_num_col` for each vertical split
- `return_boxes_of_images_by_order_of_reading_new`: reconstruct
the vertical separators from the segmentation mask and the separator
bboxes; pass it on to `find_num_col` everywhere
- `return_boxes_of_images_by_order_of_reading_new`: no need to
try-catch `find_num_col` anymore
- `return_boxes_of_images_by_order_of_reading_new`: when a vertical
split has too few columns,
* do not raise but lower the threshold `multiplier` responsible for
allowing gaps as column boundaries
* do not pass the `num_col_classifier` (i.e. expected number of
resulting columns) of the entire page to the iterative
`find_num_col` for each existing column, but only the portion
of that span
