# ================================================================================
# Example: DIC Challenge 2.0 Comparison
# 
# pyvale: the python validation engine
# License: MIT
# Copyright (C) 2024 The Computer Aided Validation Team
# ================================================================================

"""
Comparison to the 2.0 2D DIC Challenge
---------------------------------------------

The DIC challenge provides a framework for comparison and validation of existing
DIC Codes. More information can be found at `https://idics.org/challenge/
<https://idics.org/challenge/>`_. In this example we look at a comparison to a
image generated with ±0.5 pixel sinusoidal vertical displacement amplitude “star” 
pattern with varying spatial frequency. More information on the image creation
can be found in the `original paper
<https://link.springer.com/article/10.1007/s11340-021-00806-6>`_. In this
example we'll look at the convergence to the peak vertical discplacement value
of 0.5 pixels along a particular correlation direction. 

As always, we'll start with importing the required libraries:
"""

import matplotlib.pyplot as plt
from pathlib import Path

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



# %%
# There's a pair of DIC challenge images that come as part of the Pyvale install. 
# We can preload them with:
ref_pattern = dataset.dic_chal_2d_ref()
def_pattern = dataset.dic_chal_2d_def()
subset_size = 17


# %%
# we need to select our region of interest. For this example, we are only
# interested in the subsets along the mid horizontal line.
# We can use :func:`roi.rect_boundary` to exclude a large border region so we
# only correlate along the horizontal at the midpoint for our selected subset
# size
roi = dic.RegionOfInterest(ref_image=ref_pattern)
subset_radius = subset_size // 2
roi.rect_boundary(left=50,right=50,top=250-subset_radius,bottom=250-subset_radius)
roi.show_image()

# create a directory for the the different outputs
output_path = Path.cwd() / "pyvale-output" / "dic_ex05"
if not output_path.is_dir():
    output_path.mkdir(parents=True, exist_ok=True)

# %%
# .. image:: ../../../../_static/dic_challenge_roi.png
#    :alt: Selected roi
#    :width: 800px
#    :align: center 


# %% 
# To perform the correlation we need to select a seed point. Ideally, this is
# somewhere in the region where the displacements are small with low noise. 
# Here we'll select it to be [3500,250], which is close to the right hand
# boundary where the spatial frequency is lower. The results will be saved in
# the current working directory with a filename prefix of subset_size_19_*.txt
# If you are feeling adventorous you could investigate the effect of varying the
# subset size by placing the above and below sections in a loop.
dic.calculate_2d(reference=ref_pattern,
                 deformed=def_pattern,
                 roi_mask=roi.mask,
                 subset_size=subset_size,
                 subset_step=1,
                 seed=[3500,250],
                 max_displacement=10,
                 output_basepath=output_path)

# %% 
# We can import the results in the standard way
data_path = output_path / "dic_results_DIC_Challenge*.csv"
dicdata = dic.import_2d(data=data_path, layout='column', 
                                 binary=False, delimiter=",")

# %%
# Finally a simple plot of the calculated displacements at y=2500. This could be
# extended and compared to other DIC engines used in the 2.0 DIC challenge. A
# link to the dataset can be found under '2D-DIC Challenge 2.0' can be found
# at `https://idics.org/challenge/ <https://idics.org/challenge/>`_
plt.figure()
plt.xlabel("subset x location [px]")
plt.ylabel("Displacement [px]")
plt.grid(True)
plt.axhline(y=0.5, color='red', linestyle='--', linewidth=4)
plt.plot(dicdata.ss_x[0], dicdata.v_px[0, 0])
plt.tight_layout()
plt.show()

# %%
# .. image:: ../../../../_static/dic_challenge_plot.png
#    :alt: DIC Comparison with ground truth
#    :width: 600px
#    :align: center