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error_metrics.py
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error_metrics.py
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import numpy as np
from skimage.measure import compare_ssim as ssim
from skimage.measure import compare_psnr as psnr
def ErrorMetrics(vol_s, vol_t):
# calculate various error metrics.
# vol_s should be the synthesized volume (a 3d numpy array) or an array of these volumes
# vol_t should be the ground truth volume (a 3d numpy array) or an array of these volumes
vol_s = np.squeeze(vol_s)
vol_t = np.squeeze(vol_t)
assert len(vol_s.shape) == len(vol_t.shape) == 3
assert vol_s.shape[0] == vol_t.shape[0]
assert vol_s.shape[1] == vol_t.shape[1]
assert vol_s.shape[2] == vol_t.shape[2]
vol_s[vol_t == 0] = 0
vol_s[vol_s < 0] = 0
errors = {}
errors['MSE'] = np.mean((vol_s - vol_t) ** 2.)
errors['SSIM'] = ssim(vol_t, vol_s)
dr = np.max([vol_s.max(), vol_t.max()]) - np.min([vol_s.min(), vol_t.min()])
errors['PSNR'] = psnr(vol_t, vol_s, dynamic_range=dr)
# non background in both
non_bg = (vol_t != vol_t[0, 0, 0])
errors['SSIM_NBG'] = ssim(vol_t[non_bg], vol_s[non_bg])
dr = np.max([vol_t[non_bg].max(), vol_s[non_bg].max()]) - np.min([vol_t[non_bg].min(), vol_s[non_bg].min()])
errors['PSNR_NBG'] = psnr(vol_t[non_bg], vol_s[non_bg], dynamic_range=dr)
vol_s_non_bg = vol_s[non_bg].flatten()
vol_t_non_bg = vol_t[non_bg].flatten()
errors['MSE_NBG'] = np.mean((vol_s_non_bg - vol_t_non_bg) ** 2.)
return errors