Loading autocnet/transformation/tests/test_transformations.py +12 −2 Original line number Diff line number Diff line Loading @@ -78,5 +78,15 @@ class TestFundamentalMatrix(unittest.TestCase): self.assertEqual(self.F.rank, 3) def test_f_refine(self): r = self.F.refine() self.assertIsNone(r) # This should raise an error. self.F.refine() self.assertEqual(len(self.F._action_stack), 1) # Previous error should preclude do/undo self.F.rollback() self.assertEqual(self.F._current_action_stack, 0) self.F.rollforward() self.assertEqual(self.F._current_action_stack, 0) self.F._clean_attrs() self.assertNotIn('_error', self.F.__dict__) No newline at end of file autocnet/transformation/transformations.py +56 −2 Original line number Diff line number Diff line Loading @@ -186,8 +186,62 @@ class FundamentalMatrix(TransformationMatrix): warnings.warn('F rank not equal to 2. This indicates a poor F matrix.') return rank def refine(self): pass def refine(self, method=ps.esda.mapclassify.Fisher_Jenks, values=None, bin_id=0, **kwargs): """ Refine the fundamental matrix by accepting some data classification method that accepts an ndarray and returns an object with a bins attribute, where bins are data breaks. Using the bin_id, mask all values greater than the selected bin. Then compute a new fundamental matrix. The matrix is "refined" based on the reprojection errors for each point. Parameters ---------- method : object A function that accepts and ndarray and returns an object with a bins attribute values : ndarray (n,1) of values to use used for classification bin_id : int The index into the bins object. Data classified > this id is masked kwargs : dict Keyword args supported by the data classifier Returns ------- FundamentalMatrix : object A fundamental matrix class object mask : series A bool mask with index attribute identifying the valid data in the new fundamental matrix. """ # Perform the data classification if values is None: values = self.error fj = method(values.ravel(), **kwargs) # Mask the data that falls outside the provided bins mask = values <= fj.yb[bin_id] new_x1 = self.x1.iloc[mask[mask == True].index] new_x2 = self.x2.iloc[mask[mask == True].index] fmatrix, new_mask = compute_fundamental_matrix(new_x1.values, new_x2.values) mask[mask == True] = new_mask # Update the current state self[:] = fmatrix self.mask = mask # Update the action stack try: state_package = {'arr': fmatrix.copy(), 'mask': self.mask.copy()} self._action_stack.append(state_package) self._current_action_stack = len(self._action_stack) - 1 # 0 based vs. 1 based self._clean_attrs() self._notify_subscribers(self) except: warnings.warn('Refinement outlier detection removed all observations.', UserWarning) def _clean_attrs(self): for a in ['_error', '_determinant', '_condition']: Loading Loading
autocnet/transformation/tests/test_transformations.py +12 −2 Original line number Diff line number Diff line Loading @@ -78,5 +78,15 @@ class TestFundamentalMatrix(unittest.TestCase): self.assertEqual(self.F.rank, 3) def test_f_refine(self): r = self.F.refine() self.assertIsNone(r) # This should raise an error. self.F.refine() self.assertEqual(len(self.F._action_stack), 1) # Previous error should preclude do/undo self.F.rollback() self.assertEqual(self.F._current_action_stack, 0) self.F.rollforward() self.assertEqual(self.F._current_action_stack, 0) self.F._clean_attrs() self.assertNotIn('_error', self.F.__dict__) No newline at end of file
autocnet/transformation/transformations.py +56 −2 Original line number Diff line number Diff line Loading @@ -186,8 +186,62 @@ class FundamentalMatrix(TransformationMatrix): warnings.warn('F rank not equal to 2. This indicates a poor F matrix.') return rank def refine(self): pass def refine(self, method=ps.esda.mapclassify.Fisher_Jenks, values=None, bin_id=0, **kwargs): """ Refine the fundamental matrix by accepting some data classification method that accepts an ndarray and returns an object with a bins attribute, where bins are data breaks. Using the bin_id, mask all values greater than the selected bin. Then compute a new fundamental matrix. The matrix is "refined" based on the reprojection errors for each point. Parameters ---------- method : object A function that accepts and ndarray and returns an object with a bins attribute values : ndarray (n,1) of values to use used for classification bin_id : int The index into the bins object. Data classified > this id is masked kwargs : dict Keyword args supported by the data classifier Returns ------- FundamentalMatrix : object A fundamental matrix class object mask : series A bool mask with index attribute identifying the valid data in the new fundamental matrix. """ # Perform the data classification if values is None: values = self.error fj = method(values.ravel(), **kwargs) # Mask the data that falls outside the provided bins mask = values <= fj.yb[bin_id] new_x1 = self.x1.iloc[mask[mask == True].index] new_x2 = self.x2.iloc[mask[mask == True].index] fmatrix, new_mask = compute_fundamental_matrix(new_x1.values, new_x2.values) mask[mask == True] = new_mask # Update the current state self[:] = fmatrix self.mask = mask # Update the action stack try: state_package = {'arr': fmatrix.copy(), 'mask': self.mask.copy()} self._action_stack.append(state_package) self._current_action_stack = len(self._action_stack) - 1 # 0 based vs. 1 based self._clean_attrs() self._notify_subscribers(self) except: warnings.warn('Refinement outlier detection removed all observations.', UserWarning) def _clean_attrs(self): for a in ['_error', '_determinant', '_condition']: Loading
