Merge branch 'issue/#326' into 'master'

refactor: rename band attribute in dataset to band_im Closes #326 See merge request 3d/PandoraBox/pandora!276
CNES · Aug 31, 2023 · 51f8344 · 51f8344
2 parents 81743c2 + 88df97c
commit 51f8344
Show file tree

Hide file tree

Showing 24 changed files with 159 additions and 127 deletions.
diff --git a/docs/source/userguide/as_an_api.rst b/docs/source/userguide/as_an_api.rst
@@ -114,11 +114,11 @@ Example of a multiband image dataset
 
     Dimensions:  (band: 2, row: 5, col: 6)
     Coordinates:
-      * band     (band) <U1 'r' 'g'
+      * band_im     (band_im) <U1 'r' 'g'
       * row      (row) int64 0 1 2 3 4
       * col      (col) int64 0 1 2 3 4 5
     Data variables:
-        im       (band, row, col) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
+        im       (band_im, row, col) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
     Attributes:
         valid_pixels:  0
         no_data_mask:  1

diff --git a/docs/source/userguide/step_by_step/matching_cost.rst b/docs/source/userguide/step_by_step/matching_cost.rst
@@ -55,7 +55,7 @@ Configuration and parameters
 - For *mc_cnn* similarity measure see :ref:`plugin_mccnn_conf` of :ref:`plugin_mccnn` for sub-parameters and configuration example.
 
 .. note::
-    - NB: Parameter *band* must be present in the left and right image's metadata.
+    - NB: Parameter *band_im* must be present in the left and right image's metadata.
     - If the input images are multiband, the band's names must be present on the image metadata. To see how to add band's names on the image's metadata, please see :ref:`faq`.
 
 **Example for mono band images**

diff --git a/pandora/__init__.py b/pandora/__init__.py
@@ -55,12 +55,12 @@ def run(
     :type pandora_machine: PandoraMachine
     :param img_left: left Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
             - msk (optional): 2D (row, col) xarray.DataArray
     :type img_left: xarray.Dataset
     :param img_right: right Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
             - msk (optional): 2D (row, col) xarray.DataArray
     :type img_right: xarray.Dataset
     :param disp_min: minimal disparity

diff --git a/pandora/aggregation/aggregation.py b/pandora/aggregation/aggregation.py
@@ -104,12 +104,12 @@ def cost_volume_aggregation(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param cv: the cost volume dataset with the data variables:

diff --git a/pandora/aggregation/cbca.py b/pandora/aggregation/cbca.py
@@ -96,12 +96,12 @@ def cost_volume_aggregation(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param cv: cost volume dataset with the data variables:
@@ -196,12 +196,12 @@ def computes_cross_supports(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param cv: cost volume dataset with the data variables:

diff --git a/pandora/check_json.py b/pandora/check_json.py
@@ -246,7 +246,7 @@ def get_metadata(
     """
     img_ds = rasterio_open(img)
     coords = {
-        "band": list(img_ds.descriptions),
+        "band_im": list(img_ds.descriptions),
         "row": img_ds.height,
         "col": img_ds.width,
     }

diff --git a/pandora/disparity/disparity.py b/pandora/disparity/disparity.py
@@ -111,12 +111,12 @@ def to_disp(self, cv: xr.Dataset, img_left: xr.Dataset = None, img_right: xr.Dat
         :type cv: xarray.Dataset,
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :return: Dataset with the disparity map and the confidence measure with the data variables :
@@ -165,7 +165,7 @@ def approximate_right_disparity(cv: xr.Dataset, img_right: xr.Dataset, invalid_v
         :type cv: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param invalid_value: disparity to assign to invalid pixels
@@ -298,12 +298,12 @@ def validity_mask(
                 - confidence_measure 3D xarray.DataArray(row, col, indicator)
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param cv: cost volume dataset with the data variables:
@@ -442,7 +442,7 @@ def allocate_left_mask(disp: xr.Dataset, img_left: xr.Dataset) -> None:
         :type disp: xarray.Dataset
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :return: None
@@ -478,7 +478,7 @@ def allocate_right_mask(disp: xr.Dataset, img_right: xr.Dataset, bit_1: Union[np
         :type disp: xarray.Dataset
         :param img_right: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param bit_1: where the disparity interval is missing in the right image ( disparity range outside the image )
@@ -601,12 +601,12 @@ def to_disp(self, cv: xr.Dataset, img_left: xr.Dataset = None, img_right: xr.Dat
         :type cv: xarray.Dataset
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :return: Dataset with the disparity map and the confidence measure  with the data variables :

diff --git a/pandora/img_tools.py b/pandora/img_tools.py
@@ -75,9 +75,9 @@ def read_img(  # pylint:disable=too-many-branches
     :type segm: string
     :return: xarray.DataSet containing the variables :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray float32
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
             - msk : 2D (row, col) xarray.DataArray int16, with the convention defined in the configuration file
-            - classif : 3D (band, row, col) xarray.DataArray float32
+            - classif : 3D (band_classif, row, col) xarray.DataArray float32
 
     :rtype: xarray.DataSet
     """
@@ -109,10 +109,10 @@ def read_img(  # pylint:disable=too-many-branches
         coords = {"row": np.arange(data.shape[0]), "col": np.arange(data.shape[1])}
     # if image is 3 dimensions we create a dataset with [band row col] dims for dataArray
     else:
-        image = {"im": (["band", "row", "col"], data.astype(np.float32))}
+        image = {"im": (["band_im", "row", "col"], data.astype(np.float32))}
         # Band names are in the image metadata
         coords = {
-            "band": list(img_ds.descriptions),  # type: ignore
+            "band_im": list(img_ds.descriptions),  # type: ignore
             "row": np.arange(data.shape[1]),
             "col": np.arange(data.shape[2]),
         }
@@ -249,11 +249,11 @@ def prepare_pyramid(
 
     :param img_left: left Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img_left: xarray.Dataset
     :param img_right: right Dataset containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img_right: xarray.Dataset
     :param num_scales: number of scales
     :type num_scales: int
@@ -314,7 +314,7 @@ def fill_nodata_image(dataset: xr.Dataset) -> Tuple[np.ndarray, np.ndarray]:
     :param dataset: Dataset image
     :type dataset: xarray.Dataset containing :
 
-        - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+        - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :return: a Tuple that contains the filled image and mask
     :rtype: Tuple of np.ndarray
     """
@@ -412,7 +412,7 @@ def convert_pyramid_to_dataset(
 
     :param img_orig: Dataset image containing :
 
-        - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+        - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img_orig: xarray.Dataset
     :param images: list of images for the pyramid
     :type images: list[np.ndarray]
@@ -442,9 +442,9 @@ def convert_pyramid_to_dataset(
             )
         else:
             dataset = xr.Dataset(
-                {"im": (["band", "row", "col"], image.astype(np.float32))},
+                {"im": (["band_im", "row", "col"], image.astype(np.float32))},
                 coords={
-                    "band": list(img_orig.band.data),
+                    "band_im": list(img_orig.band.data),
                     "row": np.arange(image.shape[1]),
                     "col": np.arange(image.shape[2]),
                 },
@@ -468,7 +468,7 @@ def shift_right_img(img_right: xr.Dataset, subpix: int, band: str = None) -> Lis
 
     :param img_right: right Dataset image containing :
 
-        - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+        - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img_right: xarray.Dataset
     :param subpix: subpixel precision = (1 or pair number)
     :type subpix: int
@@ -484,7 +484,7 @@ def shift_right_img(img_right: xr.Dataset, subpix: int, band: str = None) -> Lis
         selected_band = img_right["im"].data
     else:
         # if image has more than one band we only shift the one specified in matching_cost
-        band_index_right = list(img_right.band.data).index(band)
+        band_index_right = list(img_right.band_im.data).index(band)
         selected_band = img_right["im"].data[band_index_right, :, :]
 
     if subpix > 1:
@@ -510,7 +510,7 @@ def check_inside_image(img: xr.Dataset, row: int, col: int) -> bool:
 
     :param img: Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img: xarray.Dataset
     :param row: row coordinates
     :type row: int
@@ -541,7 +541,7 @@ def census_transform(image: xr.Dataset, window_size: int, band: str = None) -> x
     ny_, nx_ = image.dims["row"], image.dims["col"]
 
     if len(image["im"].shape) > 2:
-        band_index = list(image.band.data).index(band)
+        band_index = list(image.band_im.data).index(band)
         selected_band = image["im"].data[band_index, :, :]
     else:
         selected_band = image["im"].data
@@ -591,7 +591,7 @@ def compute_mean_raster(img: xr.Dataset, win_size: int, band: str = None) -> np.
 
     :param img: Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img: xarray.Dataset
     :param win_size: window size
     :type win_size: int
@@ -603,7 +603,7 @@ def compute_mean_raster(img: xr.Dataset, win_size: int, band: str = None) -> np.
     # Right image can have 3 dim if its from dataset or 2 if its from shift_right_image function
     ny_, nx_ = img.dims["row"], img.dims["col"]
     if len(img["im"].shape) > 2:
-        band_index = list(img.band.data).index(band)
+        band_index = list(img.band_im.data).index(band)
         # Example with win_size = 3 and the input :
         #           10 | 5  | 3
         #            2 | 10 | 5
@@ -683,7 +683,7 @@ def compute_mean_patch(img: xr.Dataset, row: int, col: int, win_size: int) -> np
 
     :param img: Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img: xarray.Dataset
     :param row: row coordinates
     :type row: int
@@ -717,7 +717,7 @@ def compute_std_raster(img: xr.Dataset, win_size: int, band: str = None) -> np.n
 
     :param img: Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
     :type img: xarray.Dataset
     :param win_size: window size
     :type win_size: int
@@ -730,7 +730,7 @@ def compute_std_raster(img: xr.Dataset, win_size: int, band: str = None) -> np.n
     mean_ = compute_mean_raster(img, win_size, band)
     # Right image can have 3 dim if its from dataset or 2 if its from shift_right_image function
     if len(img["im"].shape) > 2:
-        band_index = list(img.band.data).index(band)
+        band_index = list(img.band_im.data).index(band)
         selected_band = img["im"].data[band_index, :, :]
     else:
         selected_band = img["im"].data

diff --git a/pandora/matching_cost/census.py b/pandora/matching_cost/census.py
@@ -85,12 +85,12 @@ def compute_cost_volume(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk : 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :param disp_min: minimum disparity
@@ -206,12 +206,12 @@ def census_cost(
         :type point_q: tuple
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band, row, col) xarray.DataArray
+                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
                 - msk (optional): 2D (row, col) xarray.DataArray
         :type img_right: xarray.Dataset
         :return: the xor pixel-wise between elements in the interval