diff --git a/HISTORY.rst b/HISTORY.rst
index 77f7fd821f6fda8b9ff7209e5d1f1c735ce2f6d8..ff2cd121a3157d02deb7726c46f00111c6d22e2f 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,12 @@
History
=======
+1.7.0 (2021-09-30)
+------------------
+
+* Added method Tie_Point_Grid.calc_overall_stats() + test to compute overall statistics from all tie points found.
+
+
1.6.2 (2021-09-29)
------------------
diff --git a/arosics/Tie_Point_Grid.py b/arosics/Tie_Point_Grid.py
index 6d3594656334dcb0b67e43ffd12cd7bb8e80d72b..f2680068b60eb1bc14a55fad1f6358b77036554b 100755
--- a/arosics/Tie_Point_Grid.py
+++ b/arosics/Tie_Point_Grid.py
@@ -492,6 +492,100 @@ class Tie_Point_Grid(object):
return float(np.median(ssim_col))
+ def calc_overall_stats(self, include_outliers: bool = False) -> dict:
+ """Calculate statistics like RMSE, MSE, MAE, ... from the tie point grid.
+
+ Full list of returned statistics:
+
+ - N_TP: number of tie points
+ - N_VALID_TP: number of valid tie points
+ - N_INVALID_TP: number of invalid tie points (false-positives)
+ - PERC_VALID_TP: percentage of valid tie points
+ - RMSE_M: root mean squared error of absolute shift vector length in map units
+ - RMSE_X_M: root mean squared error of shift vector length in x-direction in map units
+ - RMSE_Y_M: root mean squared error of shift vector length in y-direction in map units
+ - RMSE_X_PX: root mean squared error of shift vector length in x-direction in pixel units
+ - RMSE_Y_PX: root mean squared error of shift vector length in y-direction in pixel units
+ - MSE_M: mean squared error of absolute shift vector length in map units
+ - MSE_X_M: mean squared error of shift vector length in x-direction in map units
+ - MSE_Y_M: mean squared error of shift vector length in y-direction in map units
+ - MSE_X_PX: mean squared error of shift vector length in x-direction in pixel units
+ - MSE_Y_PX: mean squared error of shift vector length in y-direction in pixel units
+ - MAE_M: mean absolute error of absolute shift vector length in map units
+ - MAE_X_M: mean absolute error of shift vector length in x-direction in map units
+ - 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_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
+
+ :param include_outliers: whether to include tie points that have been marked as false-positives (if present)
+ """
+ if self.CoRegPoints_table.empty:
+ raise RuntimeError('Cannot compute overall statistics because no tie points were found at all.')
+
+ tbl = self.CoRegPoints_table
+
+ n_tiepoints = sum(tbl['ABS_SHIFT'] != self.outFillVal)
+ n_outliers = sum(tbl['OUTLIER'] == 1)
+
+ 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 MSE(vals):
+ vals_sq = vals ** 2
+ return sum(vals_sq) / len(vals_sq)
+
+ def MAE(vals):
+ vals_abs = np.abs(vals)
+ return sum(vals_abs) / len(vals_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',
+ 'ANGLE', 'SSIM_BEFORE', 'SSIM_AFTER', 'RELIABILITY']]
+
+ stats = dict(
+ N_TP=n_tiepoints,
+ 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)
+ )
+
+ return stats
+
def plot_shift_distribution(self,
include_outliers: bool = True,
unit: str = 'm',
@@ -703,7 +797,7 @@ class Tie_Point_Grid(object):
skip_nodata: bool = True,
skip_nodata_col: str = 'ABS_SHIFT'
) -> None:
- """Write the calculated tie points grid to a point shapefile (e.g., for visualization by a GIS software).
+ """Write the calculated tie point grid to a point shapefile (e.g., for visualization by a GIS software).
NOTE: The shapefile uses Tie_Point_Grid.CoRegPoints_table as attribute table.
diff --git a/tests/test_tie_point_grid.py b/tests/test_tie_point_grid.py
index 180cc1038fefcd3184c20b5e89bf1ffc5f34f2eb..d435bf4f35d503f3146f807d9f92200e79019c10 100644
--- a/tests/test_tie_point_grid.py
+++ b/tests/test_tie_point_grid.py
@@ -80,6 +80,16 @@ class Test_Tie_Point_Grid(unittest.TestCase):
self.TPG.calc_overall_ssim(include_outliers=False, after_correction=True)
self.TPG.calc_overall_ssim(include_outliers=True, after_correction=False)
+ def test_calc_overall_stats(self):
+ stats_noOL = self.TPG.calc_overall_stats(include_outliers=False)
+ stats_OL = self.TPG.calc_overall_stats(include_outliers=True)
+
+ self.assertTrue(stats_noOL)
+ self.assertTrue(stats_OL)
+ self.assertIsInstance(stats_noOL, dict)
+ self.assertIsInstance(stats_OL, dict)
+ self.assertNotEqual(stats_noOL, stats_OL)
+
def test_plot_shift_distribution(self):
with warnings.catch_warnings():
warnings.filterwarnings(