# %%
#================================================================================
# Example: stereo calibration
#
#pyvale: the python validation engine
#License: MIT
#Copyright (C) 2024 The Computer Aided Validation Team
#================================================================================
"""
Stereo Reconstruction from the DIC Challenge 1.0
------------------------------------------------

For this exampe we'll reconstruct the bespoke sample used in the first iteration
of the `Stereo DIC challenge <https://link.springer.com/article/10.1007/s11340-024-01077-7>`_. 
To keep the amount of images/data distributed with the package to a minimum,
we've already run the calibration (but feel free to try running the calibration
yourself by downloading all the images from the Stereo DIC challenge archive)
and the intrinsic/extrinisic parameters can be found in
`dic_ex11_dic_chal_calibration.txt`. In this example we'll just use the reference
images from the left and right camera to build a 3D reconstruction.
"""

# pyvale modules
import numpy as np
from pathlib import Path

import pyvale.dic as dic
import pyvale.calib as calib
import pyvale.strain as strain
import pyvale.data as dataset

# %% 
# Import the calibration parameters:

calib_params = calib.loadtxt(dataset.dic_ex11_dic_chal_calibration(), delimiter=",")


# %%
# select the images and build the ROI:

# reference images
ref0 = dataset.dic_chal_3d_cam0()
ref1 = dataset.dic_chal_3d_cam1()

# Build ROI using cam 0 reference image.
roi = dic.RegionOfInterest(ref0)
# roi.interactive_selection() # <- you can use the interactive_selection to view the yaml
roi.read_yaml(dataset.dic_ex11_dic_chal_roi())


# %%
# .. image:: ../../../../_static/dic_ex11_roi.png
#    :alt: ROI
#    :width: 100%
#    :align: center

# %%

# create an output directory 
output_path = Path.cwd() / "pyvale-output" / "dic_ex11"
if not output_path.is_dir():
    output_path.mkdir(parents=True, exist_ok=True)

# %% 
# Perform the DIC:

dic.calculate_3d(reference=[ref0, ref1],
                 deformed=[ref0, ref1],
                 calibration=calib_params,
                 roi_mask=roi.mask,
                 seed=roi.seed,
                 subset_size=27,
                 subset_step=2,
                 output_basepath=output_path)

# %%
# import the results:
dic_files = output_path / "dic_results_*.csv"
dic_results = dic.import_3d(data=dic_files, delimiter=",", binary=False)

# %%
# plot a 3d reconstruction using pyvista, using the stereo cost value as the marker colour

import pyvista as pv

stereo = dic_results.stereo
frame = 0


points = np.column_stack((
    np.ravel(stereo.x_mm[frame]),
    np.ravel(stereo.y_mm[frame]),
    np.ravel(stereo.z_mm[frame])
))

cloud = pv.PolyData(points)
cloud["cost"] = np.ravel(stereo.cost[frame])
cloud.plot(
    scalars="cost",
    eye_dome_lighting=True,
)

# %%
# .. image:: ../../../../_static/dic_ex11_3d.png
#    :alt: ROI
#    :width: 100%
#    :align: center