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Added more statistics to Tie_Point_Grid.calc_overall_stats(). Bumped …
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…version.

Signed-off-by: Daniel Scheffler <danschef@gfz-potsdam.de>
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danschef committed Oct 13, 2021
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6 changes: 6 additions & 0 deletions HISTORY.rst
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History
=======

1.7.1 (2021-10-13)
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* Added more statistics to Tie_Point_Grid.calc_overall_stats().


1.7.0 (2021-09-30)
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84 changes: 63 additions & 21 deletions arosics/Tie_Point_Grid.py
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Expand Up @@ -516,14 +516,53 @@ def calc_overall_stats(self, include_outliers: bool = False) -> dict:
- MAE_Y_M: mean absolute error of shift vector length in y-direction in map units
- MAE_X_PX: mean absolute error of shift vector length in x-direction in pixel units
- MAE_Y_PX: mean absolute error of shift vector length in y-direction in pixel units
- MEAN_ABS_SHIFT: mean absolute shift vector length in map units
- MEAN_X_SHIFT_M: mean shift vector length in x-direction in map units
- MEAN_Y_SHIFT_M: mean shift vector length in y-direction in map units
- MEAN_X_SHIFT_PX: mean shift vector length in x-direction in pixel units
- MEAN_Y_SHIFT_PX: mean shift vector length in y-direction in pixel units
- MEAN_ANGLE: mean direction of the shift vectors in degrees from north
- MEDIAN_ANGLE: median direction of the shift vectors in degrees from north
- MEAN_SSIM_BEFORE: mean structural similatity index within each matching window before co-registration
- MEDIAN_SSIM_BEFORE: median structural similatity index within each matching window before co-registration
- MEAN_SSIM_AFTER: mean structural similatity index within each matching window after co-registration
- MEDIAN_RELIABILITY: median tie point reliability in percent
- MEDIAN_SSIM_AFTER: median structural similatity index within each matching window after co-registration
- MEAN_RELIABILITY: mean tie point reliability in percent
- MEDIAN_ABS_SHIFT: median absolute shift vector length in map units
- MEDIAN_X_SHIFT_M: median shift vector length in x-direction in map units
- MEDIAN_Y_SHIFT_M: median shift vector length in y-direction in map units
- MEDIAN_X_SHIFT_PX: median shift vector length in x-direction in pixel units
- MEDIAN_Y_SHIFT_PX: median shift vector length in y-direction in pixel units
- MEDIAN_ANGLE: median direction of the shift vectors in degrees from north
- MEDIAN_SSIM_BEFORE: median structural similatity index within each matching window before co-registration
- MEDIAN_SSIM_AFTER: median structural similatity index within each matching window after co-registration
- MEDIAN_RELIABILITY: median tie point reliability in percent
- STD_ABS_SHIFT: standard deviation of absolute shift vector length in map units
- STD_X_SHIFT_M: standard deviation of shift vector length in x-direction in map units
- STD_Y_SHIFT_M: standard deviation of shift vector length in y-direction in map units
- STD_X_SHIFT_PX: standard deviation of shift vector length in x-direction in pixel units
- STD_Y_SHIFT_PX: standard deviation of shift vector length in y-direction in pixel units
- STD_ANGLE: standard deviation of direction of the shift vectors in degrees from north
- STD_SSIM_BEFORE: standard deviation of structural similatity index within each matching window before
co-registration
- STD_SSIM_AFTER: standard deviation of structural similatity index within each matching window after
co-registration
- STD_RELIABILITY: standard deviation of tie point reliability in percent
- MIN_ABS_SHIFT: minimal absolute shift vector length in map units
- MIN_X_SHIFT_M: minimal shift vector length in x-direction in map units
- MIN_Y_SHIFT_M: minimal shift vector length in y-direction in map units
- MIN_X_SHIFT_PX: minimal shift vector length in x-direction in pixel units
- MIN_Y_SHIFT_PX: minimal shift vector length in y-direction in pixel units
- MIN_ANGLE: minimal direction of the shift vectors in degrees from north
- MIN_SSIM_BEFORE: minimal structural similatity index within each matching window before co-registration
- MIN_SSIM_AFTER: minimal structural similatity index within each matching window after co-registration
- MIN_RELIABILITY: minimal tie point reliability in percent
- MIN_ABS_SHIFT: maximal absolute shift vector length in map units
- MAX_X_SHIFT_M: maximal shift vector length in x-direction in map units
- MAX_Y_SHIFT_M: maximal shift vector length in y-direction in map units
- MAX_X_SHIFT_PX: maximal shift vector length in x-direction in pixel units
- MAX_Y_SHIFT_PX: maximal shift vector length in y-direction in pixel units
- MAX_ANGLE: maximal direction of the shift vectors in degrees from north
- MAX_SSIM_BEFORE: maximal structural similatity index within each matching window before co-registration
- MAX_SSIM_AFTER: maximal structural similatity index within each matching window after co-registration
- MAX_RELIABILITY: maximal tie point reliability in percent
:param include_outliers: whether to include tie points that have been marked as false-positives (if present)
"""
Expand All @@ -538,17 +577,17 @@ def calc_overall_stats(self, include_outliers: bool = False) -> dict:
tbl = tbl if include_outliers else tbl[tbl['OUTLIER'] == 0].copy() if 'OUTLIER' in tbl.columns else tbl
tbl = tbl.copy().replace(self.outFillVal, np.nan)

def RMSE(vals):
vals_sq = vals ** 2
return np.sqrt(sum(vals_sq) / len(vals_sq))
def RMSE(shifts):
shifts_sq = shifts ** 2
return np.sqrt(sum(shifts_sq) / len(shifts_sq))

def MSE(vals):
vals_sq = vals ** 2
return sum(vals_sq) / len(vals_sq)
def MSE(shifts):
shifts_sq = shifts ** 2
return sum(shifts_sq) / len(shifts_sq)

def MAE(vals):
vals_abs = np.abs(vals)
return sum(vals_abs) / len(vals_abs)
def MAE(shifts):
shifts_abs = np.abs(shifts)
return sum(shifts_abs) / len(shifts_abs)

abs_shift, x_shift_m, y_shift_m, x_shift_px, y_shift_px, angle, ssim_before, ssim_after, reliability = \
[tbl[k].dropna().values for k in ['ABS_SHIFT', 'X_SHIFT_M', 'Y_SHIFT_M', 'X_SHIFT_PX', 'Y_SHIFT_PX',
Expand All @@ -559,31 +598,34 @@ def MAE(vals):
N_VALID_TP=len(abs_shift),
N_INVALID_TP=n_outliers,
PERC_VALID_TP=(n_tiepoints - n_outliers) / n_tiepoints * 100,

RMSE_M=RMSE(abs_shift),
RMSE_X_M=RMSE(x_shift_m),
RMSE_Y_M=RMSE(y_shift_m),
RMSE_X_PX=RMSE(x_shift_px),
RMSE_Y_PX=RMSE(y_shift_px),

MSE_M=MSE(abs_shift),
MSE_X_M=MSE(x_shift_m),
MSE_Y_M=MSE(y_shift_m),
MSE_X_PX=MSE(x_shift_px),
MSE_Y_PX=MSE(y_shift_px),

MAE_M=MAE(abs_shift),
MAE_X_M=MAE(x_shift_m),
MAE_Y_M=MAE(y_shift_m),
MAE_X_PX=MAE(x_shift_px),
MAE_Y_PX=MAE(y_shift_px),
MEAN_ANGLE=np.mean(angle),
MEDIAN_ANGLE=np.median(angle),
MEAN_SSIM_BEFORE=np.mean(ssim_before),
MEDIAN_SSIM_BEFORE=np.median(ssim_before),
MEAN_SSIM_AFTER=np.mean(ssim_after),
MEDIAN_SSIM_AFTER=np.median(ssim_after),
MEAN_RELIABILITY=np.mean(reliability),
MEDIAN_RELIABILITY=np.median(reliability)
)

for stat, func in zip(['mean', 'median', 'std', 'min', 'max'],
[np.mean, np.median, np.std, np.min, np.max]):
for n in ['abs_shift', 'x_shift_m', 'y_shift_m', 'x_shift_px', 'y_shift_px',
'angle', 'ssim_before', 'ssim_after', 'reliability']:

vals = locals()[n]
stats[f'{stat}_{n}'.upper()] = func(vals)

return stats

def plot_shift_distribution(self,
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4 changes: 2 additions & 2 deletions arosics/version.py
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# limitations under the License.


__version__ = '1.7.0'
__versionalias__ = '2021-09-30_01'
__version__ = '1.7.1'
__versionalias__ = '2021-10-13_01'

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