"""
As-designed vs as-scanned deviation
===================================

A real reverse-engineering question: a vendor ships a nominal CAD
model of a part, and a depth camera reconstructs the part that
actually arrived. Where, and by how much, does the as-built surface
deviate from design?

This example uses two real models of the same physical object from
the T-LESS dataset (Hodaň et al., WACV 2017, CC-BY 4.0): a manually
authored CAD model and a model reconstructed from Primesense RGB-D
scans. Both are in millimetres in a shared frame. We rigidly align
the scan to the CAD nominal, then color the scan by signed distance
to the design surface, the first-cut incoming-quality metric.
"""

# %%

import numpy as np
import pandas as pd
import pyvista as pv

import pyvista_cad  # noqa: F401  # registers the .cad accessor
from pyvista_cad.examples import downloads

# %%
# Load the as-designed CAD model and the as-scanned reconstruction.

cad_path, scan_path = downloads.scan_pair_paths()
design = pv.read(cad_path).extract_surface()
design

# %%
# The scan carries vertex RGB and normals from the RGB-D
# reconstruction: real sensor data, not a perturbed copy.

scan = pv.read(scan_path)
scan

# %%
# Rigidly register the scan onto the CAD nominal. Real scans land in
# an approximate pose; the deviation is only meaningful after an ICP
# alignment removes the residual rigid offset.

scan = scan.align(design)
scan

# %%
# Compute deviation: signed distance from each scan point to the CAD
# surface.

scan = scan.compute_implicit_distance(design)
dev = scan['implicit_distance']

pd.DataFrame(
    {
        'metric': ['min', 'max', 'mean', 'RMS'],
        'deviation_mm': [
            dev.min(),
            dev.max(),
            dev.mean(),
            np.sqrt((dev**2).mean()),
        ],
    }
)

# %%
# Side by side: the as-designed CAD on the left, the registered scan
# colored by signed deviation on the right. Red is proud of nominal,
# blue is below it; the bulk of the surface is within scanner noise,
# with clear excursions where the reconstruction is incomplete.

clim = float(np.percentile(np.abs(dev), 95))

pl = pv.Plotter(shape=(1, 2))
pl.subplot(0, 0)
pl.cad.add(design, color='lightgray')
pl.subplot(0, 1)
pl.add_mesh(
    scan,
    scalars='implicit_distance',
    cmap='coolwarm',
    clim=(-clim, clim),
    scalar_bar_args={'title': 'deviation [mm]'},
)
pl.link_views()
pl.show()