Merge branch '331-disparity-in-dataset' into 'release_branch_1_6_0'

Resolve "check_dataset : Stockage des disparités dans le dataset"

See merge request 3d/PandoraBox/pandora!295

docs/source/userguide/as_an_api.rst

@@ -85,24 +85,28 @@ Example of a monoband image dataset
 
 ::
 
-    Dimensions:  (col: 450, row: 375)
+    Dimensions:   (col: 450, row: 375)
     Coordinates:
-      * col      (col) int64 0 1 2 3 4 5 6 7 8 ... 442 443 444 445 446 447 448 449
-      * row      (row) int64 0 1 2 3 4 5 6 7 8 ... 367 368 369 370 371 372 373 374
+      * col       (col) int64 0 1 2 3 4 5 6 7 8 ... 442 443 444 445 446 447 448 449
+      * row       (row) int64 0 1 2 3 4 5 6 7 8 ... 367 368 369 370 371 372 373 374
+      * band_disp (band_disp) <U3 'min' 'max'
     Data variables:
-        im       (row, col) float32 88.0 85.0 84.0 83.0 ... 176.0 180.0 165.0 172.0
-        msk      (row, col) int16 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
+        im        (row, col) float32 88.0 85.0 84.0 83.0 ... 176.0 180.0 165.0 172.0
+        msk       (row, col) int16 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
+        disparity (band_disp, row, col) int16 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0
     Attributes:
         no_data_img:   0
         crs:           None
         transform:     | 1.00, 0.00, 0.00|\n| 0.00, 1.00, 0.00|\n| 0.00, 0.00, 1.00|
         valid_pixels:  0
         no_data_mask:  1
+        disparity_source <disparity in config file>
 
 Two data variables are created in this dataset:
 
  * *im*: contains input image data
  * *msk*: contains input mask data + no_data of input image
+ * *disparity*: contains input disparity data with two bands (min and max)
 
 Example of a multiband image dataset
 
@@ -113,13 +117,16 @@ Example of a multiband image dataset
       * band_im     (band_im) <U1 'r' 'g'
       * row      (row) int64 0 1 2 3 4
       * col      (col) int64 0 1 2 3 4 5
+      * band_disp (band_disp) <U3 'min' 'max'
     Data variables:
         im       (band_im, row, col) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
+        disparity (band_disp, row, col) int16 -60.0 -60.0 -60.0 -60.0 -60.0 -60.0 ... -60.0 -60.0 -60.0 -60.0 -60.0
     Attributes:
         valid_pixels:  0
         no_data_mask:  1
         crs:           None
         transform:     | 1.00, 0.00, 0.00|\n| 0.00, 1.00, 0.00|\n| 0.00, 0.00, 1.00|
+        disparity_source <disparity in config file>
 
 
 .. note::

notebooks/advanced_examples/cost_profile_analysis.ipynb

@@ -155,7 +155,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg, img_left, img_right, disp_min, disp_max)"
+    "pandora_machine.run_prepare(user_cfg, img_left, img_right)"
    ]
   },
   {

notebooks/introduction_and_basic_usage.ipynb

@@ -474,7 +474,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "left_disparity, right_disparity = pandora.run(pandora_machine, img_left, img_right, disp_min, disp_max, user_cfg)"
+    "left_disparity, right_disparity = pandora.run(pandora_machine, img_left, img_right, user_cfg)"
    ]
   },
   {
@@ -678,7 +678,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg, img_left, img_right, disp_min, disp_max)"
+    "pandora_machine.run_prepare(user_cfg, img_left, img_right)"
    ]
   },
   {

notebooks/statistical_and_visual_analysis.ipynb

@@ -363,7 +363,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "left_disp_zncc, right_disp_zncc = pandora.run(pandora_machine, img_left, img_right, disp_min, disp_max, user_cfg_zncc)"
+    "left_disp_zncc, right_disp_zncc = pandora.run(pandora_machine, img_left, img_right, user_cfg_zncc)"
    ]
   },
   {
@@ -537,7 +537,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg_census, img_left, img_right, disp_min, disp_max)"
+    "pandora_machine.run_prepare(user_cfg_census, img_left, img_right)"
    ]
   },
   {
@@ -822,7 +822,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg_census, img_left, img_right, disp_min, disp_max)"
+    "pandora_machine.run_prepare(user_cfg_census, img_left, img_right)"
    ]
   },
   {

notebooks/usage_with_multiscale.ipynb

@@ -480,7 +480,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg, img_left, img_right, disp_min, disp_max, scale_factor, num_scales)"
+    "pandora_machine.run_prepare(user_cfg, img_left, img_right, scale_factor, num_scales)"
    ]
   },
   {
@@ -503,7 +503,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "left_disparity, right_disparity = pandora.run(pandora_machine, img_left, img_right, disp_min, disp_max, user_cfg)"
+    "left_disparity, right_disparity = pandora.run(pandora_machine, img_left, img_right, user_cfg)"
    ]
   },
   {
@@ -703,7 +703,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pandora_machine.run_prepare(user_cfg, img_left, img_right, disp_min, disp_max, scale_factor, num_scales)"
+    "pandora_machine.run_prepare(user_cfg, img_left, img_right, scale_factor, num_scales)"
    ]
   },
   {

pandora/__init__.py

@@ -34,7 +34,7 @@
 from pkg_resources import iter_entry_points
 
 from . import common
-from .img_tools import create_dataset_from_inputs, read_disp
+from .img_tools import create_dataset_from_inputs
 from .check_configuration import check_conf, read_config_file, read_multiscale_params
 from .state_machine import PandoraMachine
 
@@ -44,11 +44,7 @@ def run(
     pandora_machine: PandoraMachine,
     img_left: xr.Dataset,
     img_right: xr.Dataset,
-    disp_min: Union[np.ndarray, int],
-    disp_max: Union[np.ndarray, int],
     cfg: Dict[str, dict],
-    disp_min_right: Union[None, np.ndarray] = None,
-    disp_max_right: Union[None, np.ndarray] = None,
 ) -> Tuple[xr.Dataset, xr.Dataset]:
     """
     Run the pandora pipeline
@@ -57,24 +53,22 @@ def run(
     :type pandora_machine: PandoraMachine
     :param img_left: left Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-            - msk (optional): 2D (row, col) xarray.DataArray
+            - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+            - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+            - msk (optional): 2D (row, col) xarray.DataArray int16
+            - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+            - segm (optional): 2D (row, col) xarray.DataArray int16
     :type img_left: xarray.Dataset
     :param img_right: right Dataset image containing :
 
-            - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-            - msk (optional): 2D (row, col) xarray.DataArray
+            - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+            - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+            - msk (optional): 2D (row, col) xarray.DataArray int16
+            - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+            - segm (optional): 2D (row, col) xarray.DataArray int16
     :type img_right: xarray.Dataset
-    :param disp_min: minimal disparity
-    :type disp_min: int or np.ndarray
-    :param disp_max: maximal disparity
-    :type disp_max: int or np.ndarray
     :param cfg: pipeline configuration
     :type cfg: Dict[str, dict]
-    :param disp_min_right: minimal disparity of the right image
-    :type disp_min_right: np.ndarray or None
-    :param disp_max_right: maximal disparity of the right image
-    :type disp_max_right: np.ndarray or None
     :return: Two xarray.Dataset :
 
 
@@ -83,13 +77,17 @@ def run(
                 - confidence_measure : the confidence measure in the geometry of the left image 3D DataArray \
                     (row, col, indicator)
                 - validity_mask : the validity mask in the geometry of the left image 2D DataArray (row, col)
+                - classif_mask : information about a classification
+                - segm_mask : information about a segmentation
 
             - right : the right dataset. If there is no validation step, the right Dataset will be empty.If a \
             validation step is configured, the dataset will contain the variables :
                 - disparity_map : the disparity map in the geometry of the right image 2D DataArray (row, col)
                 - confidence_measure : the confidence measure in the geometry of the left image 3D DataArray \
                     (row, col, indicator)
                 - validity_mask : the validity mask in the geometry of the left image 2D DataArray (row, col)
+                - classif_mask : information about a classification
+                - segm_mask : information about a segmentation
 
     :rtype: tuple (xarray.Dataset, xarray.Dataset)
     """
@@ -98,21 +96,11 @@ def run(
     num_scales, scale_factor = read_multiscale_params(cfg)
 
     # Prepare the machine before running, including the image pyramid creation
-    pandora_machine.run_prepare(
-        cfg,
-        img_left,
-        img_right,
-        disp_min,
-        disp_max,
-        scale_factor,
-        num_scales,
-        disp_min_right,
-        disp_max_right,
-    )
+    pandora_machine.run_prepare(cfg, img_left, img_right, scale_factor, num_scales)
 
     # For each scale we run the whole pipeline, the scales will be executed from coarse to fine,
     # and the machine will store the necessary parameters for the following scale
-    for scale in range(pandora_machine.num_scales):  # pylint:disable=unused-variable
+    for _ in range(pandora_machine.num_scales):
         # Trigger the machine step by step
         for elem in list(cfg["pipeline"]):
             pandora_machine.run(elem, cfg)
@@ -179,24 +167,14 @@ def main(cfg_path: PathLike | str, output: str, verbose: bool) -> None:
     # Read images and masks
     input_config = common.split_inputs(cfg["input"])
     img_left = create_dataset_from_inputs(input_config=input_config["left"])
+    # If input_config["right"]["disp"] is None then "disp_right_min" = - "disp_left_max"
+    # and "disp_right_max" = - "disp_left_min"
+    if input_config["right"]["disp"] is None and not isinstance(input_config["left"]["disp"], str):
+        input_config["right"]["disp"] = [-input_config["left"]["disp"][1], -input_config["left"]["disp"][0]]
     img_right = create_dataset_from_inputs(input_config=input_config["right"])
 
-    # Read range of disparities
-    disp_min, disp_max = read_disp(cfg["input"]["disp_left"])
-    disp_right = cfg["input"]["disp_right"]
-    disp_min_right, disp_max_right = (None, None) if disp_right is None else read_disp(disp_right)
-
     # Run the Pandora pipeline
-    left, right = run(
-        pandora_machine,
-        img_left,
-        img_right,
-        disp_min,
-        disp_max,
-        cfg,
-        disp_min_right,  # type: ignore
-        disp_max_right,  # type: ignore
-    )
+    left, right = run(pandora_machine, img_left, img_right, cfg)
 
     # Save the left and right DataArray in tiff files
     common.save_results(left, right, output)

pandora/aggregation/aggregation.py

@@ -104,13 +104,18 @@ def cost_volume_aggregation(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
-        :type img_left: xarray.Dataset
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :type img_right: xarray.Dataset
         :param cv: the cost volume dataset with the data variables:
 

pandora/aggregation/cbca.py

@@ -96,13 +96,19 @@ def cost_volume_aggregation(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :type img_right: xarray.Dataset
         :param cv: cost volume dataset with the data variables:
 
@@ -196,13 +202,19 @@ def computes_cross_supports(
 
         :param img_left: left Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :type img_left: xarray.Dataset
         :param img_right: right Dataset image containing :
 
-                - im : 2D (row, col) or 3D (band_im, row, col) xarray.DataArray
-                - msk (optional): 2D (row, col) xarray.DataArray
+                - im: 2D (row, col) or 3D (band_im, row, col) xarray.DataArray float32
+                - disparity (optional): 3D (disp, row, col) xarray.DataArray float32
+                - msk (optional): 2D (row, col) xarray.DataArray int16
+                - classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
+                - segm (optional): 2D (row, col) xarray.DataArray int16
         :type img_right: xarray.Dataset
         :param cv: cost volume dataset with the data variables: