#================================================================================
#Example: thermocouples on a 2d plate
#
#pyvale: the python validation engine
#License: MIT
#Copyright (C) 2024 The Computer Aided Validation Team
#================================================================================

"""
Selecting a Region of Interest (ROI)
---------------------------------------------

This example looks at the current core functionality of the Region of Interest
(ROI) Selection Firsly we'll need to import `pyvale` itself.
"""

from pathlib import Path

# pyvale modules
import pyvale.data as dataset
import pyvale.dic as dic

# %% 
# We'll begin by selecting our Region of Interest (ROI) using the interactive selection tool.
# First, we create an instance of the ROI class. We pass a reference image to it, which is
# displayed as the underlay during ROI selection.
ref_img = dataset.dic_plate_with_hole_cam0_ref()
roi = dic.RegionOfInterest(ref_image=ref_img)
roi.interactive_selection()

# create a directory for the the different outputs
output_path = Path.cwd() / "pyvale-output" / "dic_ex01"
if not output_path.is_dir():
    output_path.mkdir(parents=True, exist_ok=True)

# %%
# .. image:: ../../../../_static/roi_tool.gif
#    :alt: ROI selection GUI (animated)
#    :width: 600px
#    :align: center

# %%
# After closing the interactive tool, a mask and a set of seed coordinates will be generated.
# These can be used directly in the DIC engine. If you plan to reuse the ROI, it’s a good idea
# to save it. For very large images, set `binary=True` to reduce file size and speed up saving.
roi_file = output_path / "roi.dat"
roi.save_array(filename=roi_file,binary=False)

# %%
# To reuse the saved ROI mask in the future, load it using:
roi.read_array(filename=roi_file,binary=False)

# %%
# If you are loading a previously saved ROI, you may want to visualize it
# overlaid on the reference image to verify it before proceeding with correlation.
roi.show_image()

# %%
# There are also programmatic ways to define an ROI.
# For example, to exclude a boundary region and keep only the central part:
roi.reset_mask()
roi.rect_boundary(left=50,right=50,bottom=50,top=50)
boundary_img = output_path / "rect_boundary.tiff"
roi.save_image(boundary_img)

# %%
# This excludes 50 pixels along each edge of the image from the ROI.
# Alternatively, to define a specific rectangular region:
roi.reset_mask()
roi.rect_region(x=200,y=200,size_x=200,size_y=200)
region_img = output_path / "rect_region.tiff"
roi.save_image(region_img)

# %%
# .. list-table::
#    :widths: 50 50
#    :align: center
#    :header-rows: 0
#
#    * - .. figure:: ../../../../_static/rect_boundary.png
#          :width: 300px
#          :align: center
#
#          ``roi.rect_boundary(left=200, right=200, bottom=200, top=200)``
#
#      - .. figure:: ../../../../_static/rect_region.png
#          :width: 300px
#          :align: center
#
#          ``roi.rect_region(x=200, y=200, size_x=200, size_y=200)``

# %%
# The `rect_region` example above creates an ROI starting at pixel coordinates (200, 200)
# with a size of 200×200 pixels.
#
# You can also manually modify the ROI mask. A good starting point is:
# `roi.rect_boundary(0, 0, 0, 0)` — this sets the ROI to include the full image.
# From there, you can manipulate `roi.mask` as you would any other 2D NumPy array.