Added a warning in case the input image consists of multiple patches and...

Added a warning in case the input image consists of multiple patches and AROSICS processes only the largest one. Added a warning in case the reliability filtering filters more than 70% of the tie points.

Signed-off-by: Daniel Scheffler <danschef@gfz-potsdam.de>

.gitlab-ci.yml

@@ -16,7 +16,7 @@ test_arosics:
     - conda install -c conda-forge cartopy  # FIXME remove as soon as docker container is rebuilt
 
     # update py_tools_ds and geoarray
-    # - pip install "py_tools_ds>=0.15.10"
+    - pip install "py_tools_ds>=0.15.10"
     - pip install "geoarray>=0.9.0"
 
     # run tests

arosics/CoReg.py

@@ -117,6 +117,14 @@ class GeoArray_CoReg(GeoArray):
 
             self.calc_mask_nodata(fromBand=self.band4match)  # this avoids that all bands have to be read
 
+            with warnings.catch_warnings(record=True) as w:
+                _ = self.footprint_poly  # execute getter
+
+            if len(w) > 0 and 'disjunct polygone(s) outside' in str(w[-1].message):
+                warnings.warn('The footprint of the %s contains multiple separate image parts. '
+                              'AROSICS will only process the largest image part.' % self.imName)
+                # FIXME use a convex hull as footprint poly
+
         # validate footprint poly
         if not self.footprint_poly.is_valid:
             self.footprint_poly = self.footprint_poly.buffer(0)
@@ -133,7 +141,7 @@ class GeoArray_CoReg(GeoArray):
 
 
 class COREG(object):
-    """The COREG class detects and corrects global X/Y shifts between a target and refernce image.
+    """The COREG class detects and corrects global X/Y shifts between a target and reference image.
 
     Geometric shifts are calculated at a specific (adjustable) image position. Correction performs a global shifting
     in X- or Y direction.

arosics/Tie_Point_Grid.py

@@ -848,9 +848,19 @@ class Tie_Point_Refiner(object):
             self.GDF['L1_OUTLIER'] = marked_recs
             self.new_cols.append('L1_OUTLIER')
 
+            n_flagged = len(marked_recs[marked_recs])
+            perc40 = np.percentile(self.GDF.RELIABILITY, 40)
+
+            if n_flagged / len(self.GDF) > .7:
+                warnings.warn(r"More than 70%% of the found tie points have a reliability lower than %s%% and are "
+                              r"therefore marked as false-positives. Consider relaxing the minimum reliability "
+                              r"(parameter 'min_reliability') to avoid that. For example min_reliability=%d would only "
+                              r"flag 40%% of the tie points in case of your input data."
+                              % (self.min_reliability, perc40))
+
             if not self.q:
                 print('%s tie points flagged by level 1 filtering (reliability).'
-                      % (len(marked_recs[marked_recs])))
+                      % n_flagged)
 
         # SSIM filtering
         if level > 1:

arosics/version.py

@@ -22,5 +22,5 @@
 # with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 
-__version__ = '1.0.6'
-__versionalias__ = '2020-10-27_01'
+__version__ = '1.1.0'
+__versionalias__ = '2020-10-30_01'