Biapy

Fix patch:
- Rename ``PROBLEM.NUM_CPUS`` to ``PROBLEM.NUM_WORKERS`` to clarify its usage.
- Speed up SSL workflow

Changes:
- Set `TEST.DET_EXCLUDE_BORDER` to `False` by default.
- Add `TEST.DET_PEAK_LOCAL_MAX_MIN_DISTANCE`.
- 3 int tuple for TEST.RESOLUTION in instance segmentation if `TEST.ANALIZE_2D_IMGS_AS_3D_STACK`.
- Prevent usage of EfficientNet architectures for 3D.
- Add `PROBLEM.INSTANCE_SEG.WATERSHED_BY_2D_SLICE`.

Fix:
- Prevent creating multiple processes to manage data if low samples are available.
- Solve EfficientNet issue with biapy backend as discussed [here](https://github.com/mrdbourke/pytorch-deep-learning/issues/696).
- Bug in instance seg when no labels are provided.
- Disable aug sample image generation if DA is disabled.
- Fix SSL bug during training due to recent changes.

Fixes:
- Change ``DATA.PREPROCESS.*.ACTIVATE`` to ``DATA.PREPROCESS.*.ENABLE`` as the rest of the variables in all the files (changed only in config.py by error).
- Separate per_image, full_image and as_3D_stack instance files in different folders.
- Separate instance segmentation metrics when multiple choices are selected. Before full_image and per_image metrics were mixed.
- Simplify inference by setting as default patch/merge reconstruction of the prediction. This implied to remove ``TEST.STATS`` and leave only ``FULL_IMG`` to be optional.
- ``TEST.FULL_IMG`` to ``False`` by default.

Quick patch to fix some issues:
- Move ``sys.exit()`` call to ``main.py`` to prevent errors inside jupyter notebooks
- Fix issue during BMZ export in classification
- Rename ``DATA.PREPROCESSING.*.ACTIVATE`` to ``ENABLE`` as in other variables.
- Remove ``DATA.PREPROCESS.MEDIAN_BLUR.FOOTPRINT`` as it is a Numpy array and it can not be declared through YACS

Quick patch to fix some issues:
- Fix ``FORCE_RGB`` variable usage in classification
- Adapt skimage's ``relabel_sequential()`` to be as the old function we were using so the matching metrics process doesn't get stuck anymore.

General changes
Major
- Separate instance filtering and statistical measurements with ``TEST.POST_PROCESSING.MEASURE_PROPERTIES`` and ``TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES``
- Add sphericity (3D), perimeter/surface area (2D/3D) and elongation (2D) calculations using the same formulas as described in [MorphoLibJ](https://imagej.net/plugins/morpholibj)
- Multi-GPU prediction by chunks (Zarr/H5):
* Add versatile axis order
* Fix some overlap errors
- Add data preprocessing options:
* Resize
* Gaussian blur
* Median blur
* Histogram matching
* Contrast Limited Adaptive Histogram Equalization (CLAHE)
* Canny or edge detection (only 2D - grayscale or RGB)
- Change BiaPy into a class so we can call functions individually (e.g. BMZ model exportation)
- Detection:
* Add overlap in detection during multi-GPU prediction by chunks
* Now point coords work in global position
Minor
- Add ``TEST.DET_EXCLUDE_BORDER`` option

Bugs fixed:
- 2D test time augmentation bug with ``MODEL.N_CLASSES`` solved
- Fix bug when ``TEST.BY_CHUNKS`` selected using ``TEST.BY_CHUNKS.INPUT_IMG_AXES_ORDER`` of len 4.
- Avoid dividing with zero during instance stats