workaround for cv2.remap nan behaviour + remove convertMaps

leftfield-geospatial · Sep 26, 2023 · 81bfb7e · 81bfb7e
1 parent 9a56bdb
commit 81bfb7e
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Showing 3 changed files with 7 additions and 5 deletions.
diff --git a/simple_ortho/camera.py b/simple_ortho/camera.py
@@ -395,7 +395,7 @@ def _get_undistort_maps(self, alpha: float) -> Tuple[Tuple[np.ndarray, np.ndarra
         undistort_maps = cv2.initUndistortRectifyMap(
             self._K, self._dist_param, np.eye(3), K_undistort, im_size, cv2.CV_16SC2
         )
-        undistort_maps = cv2.convertMaps(*undistort_maps, cv2.CV_16SC2)
+        # undistort_maps = cv2.convertMaps(*undistort_maps, cv2.CV_16SC2)
         return undistort_maps, K_undistort
 
     def _world_to_pixel(self, xyz: np.ndarray) -> np.ndarray:
@@ -550,7 +550,7 @@ def _get_undistort_maps(self, alpha: float) -> Tuple[Tuple[np.ndarray, np.ndarra
         undistort_maps = cv2.fisheye.initUndistortRectifyMap(
             self._K, self._dist_param, np.eye(3), K_undistort, im_size, cv2.CV_16SC2
         )
-        undistort_maps = cv2.convertMaps(*undistort_maps, cv2.CV_16SC2)
+        # undistort_maps = cv2.convertMaps(*undistort_maps, cv2.CV_16SC2)
         return undistort_maps, K_undistort
 
     def _world_to_pixel(self, xyz: np.ndarray) -> np.ndarray:

diff --git a/simple_ortho/ortho.py b/simple_ortho/ortho.py
@@ -304,7 +304,7 @@ def inv_transform(transform: rio.Affine, xy: np.array):
             # masked / nan dem pixels)
             # TODO: deal with boundary issues (round or remap)
             dem_ji = inv_transform(dem_transform, ray_xyz[:2, :]).astype('float32', copy=False)
-            dem_ji = cv2.convertMaps(*dem_ji, cv2.CV_16SC2)
+            # dem_ji = cv2.convertMaps(*dem_ji, cv2.CV_16SC2)
             dem_z = np.squeeze(cv2.remap(
                 dem_array, *dem_ji, dem_interp.to_cv(), borderMode=cv2.BORDER_CONSTANT, borderValue=float('nan')
             ), axis=1)  # yapf: disable
@@ -427,9 +427,11 @@ def _remap_tile(
         )  # yapf: disable
 
         # separate tile_ji into (j, i) grids, converting to float32 for compatibility with cv2.remap
+        # (nans are converted to -1 as cv2.remap maps nans to the first src pixel on some packages/platforms)
+        tile_ji[np.isnan(tile_ji)] = -1
         tile_jgrid = tile_ji[0, :].reshape(tile_win.height, tile_win.width).astype('float32')
         tile_igrid = tile_ji[1, :].reshape(tile_win.height, tile_win.width).astype('float32')
-        tile_jgrid, tile_igrid = cv2.convertMaps(tile_jgrid, tile_igrid, cv2.CV_16SC2)
+        # tile_jgrid, tile_igrid = cv2.convertMaps(tile_jgrid, tile_igrid, cv2.CV_16SC2)
 
         # initialise ortho tile array
         tile_array = np.full(

diff --git a/tests/test_ortho.py b/tests/test_ortho.py
@@ -299,7 +299,7 @@ def test_mask_dem(
             src_im, dem_array.shape, dem_transform, dtype='uint8', compress=Compress.deflate
         )
         with rio.open(ortho_file, 'w', **ortho_profile) as ortho_im:
-            ortho._remap(src_im, ortho_im, dem_array, interp=Interp.bilinear, full_remap=True, write_mask=False)
+            ortho._remap(src_im, ortho_im, dem_array, full_remap=True, write_mask=False)
 
     # create the dem mask
     dem_array_mask, dem_transform_mask = ortho._mask_dem(