def test_compute_piecewise_layer_not_in_dataset():
"""
Test plugin_libsgm compute_piecewise_layer function, with user asking for piecewise optimization,
without any in dataset
"""
# Prepare the configuration
user_cfg = pandora.read_config_file("tests/conf/sgm.json")
# Import pandora plugins
pandora.import_plugin()
# Load plugins
optimization_ = optimization.AbstractOptimization(
**user_cfg["pipeline"]["optimization"])
# Create input dataset
data_img_left = np.random.rand(5, 6)
img_left = xr.Dataset(
{"im": (["row", "col"], data_img_left)},
coords={
"row": np.arange(data_img_left.shape[0]),
"col": np.arange(data_img_left.shape[1])
},
)
piecewise_optimization_layer = "toto"
classif_arr = optimization_.compute_piecewise_layer(
img_left, piecewise_optimization_layer)
gt_classif = np.ones((5, 6))
np.testing.assert_array_equal(classif_arr, gt_classif)
def test_apply_confidence_with_confidence_dataarray(self):
"""
Test plugin_libsgm apply_confidence function, with user asking for confidence usage, without any in dataser
"""
# Prepare the configuration
user_cfg = pandora.read_config_file("tests/conf/sgm.json")
# Import pandora plugins
pandora.import_plugin()
# Load plugins
optimization_ = optimization.AbstractOptimization(
**user_cfg["pipeline"]["optimization"])
# Test
use_confidence = True
data_cv = np.array(
[
[[1, 1, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3],
[1, 1, 1, 1, 1]],
[[1, 1, 2, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3],
[1, 1, 1, 1, 7]],
[[1, 4, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3],
[1, 12, 1, 1, 1]],
],
dtype=np.float32,
)
data_confidence = np.expand_dims(np.array(
[[1, 1, 1, 0.5], [1, 1, 0.5, 1], [1, 1, 1, 1]], dtype=np.float32),
axis=2)
cv_in = xr.Dataset(
{"cost_volume": (["row", "col", "disp"], data_cv)},
coords={
"row": np.arange(data_cv.shape[0]),
"col": np.arange(data_cv.shape[1]),
"disp": np.arange(data_cv.shape[2]),
"indicator": ["ambiguity_confidence"],
},
attrs={
"no_data_img": 0,
"valid_pixels": 0,
"no_data_mask": 1,
"crs": None,
"transform": None
},
)
cv_in["confidence_measure"] = xr.DataArray(
data_confidence, dims=["row", "col", "indicator"])
# apply confidence
cv_updated, confidence_is_int = optimization_.apply_confidence(
cv_in, use_confidence)
# Ground Truth
optim_cv_gt = np.array(
[
[[1, 1, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3],
[0.5, 0.5, 0.5, 0.5, 0.5]],
[[1, 1, 2, 1, 1], [1, 1, 1, 1, 2], [0.5, 0.5, 0.5, 2, 1.5],
[1, 1, 1, 1, 7]],
[[1, 4, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3],
[1, 12, 1, 1, 1]],
],
dtype=np.float32,
)
# Check if the calculated confidence_measure is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(cv_updated["cost_volume"].data[:, :, :],
optim_cv_gt)
# Check if confidence_is_int is right
self.assertEqual(confidence_is_int, False)
def test_number_of_disp_with_previous_confidence():
"""
Test plugin_libsgm number_of_disp function if min_cost_paths is activated and the confidence measure was present
"""
# Prepare the configuration
user_cfg = pandora.read_config_file("tests/conf/sgm.json")
user_cfg["pipeline"]["matching_cost"]["window_size"] = 3
user_cfg["pipeline"]["optimization"]["min_cost_paths"] = True
# Load plugins
matching_cost_ = matching_cost.AbstractMatchingCost(
**user_cfg["pipeline"]["matching_cost"])
optimization_ = optimization.AbstractOptimization(
**user_cfg["pipeline"]["optimization"])
confidence_ = cost_volume_confidence.AbstractCostVolumeConfidence(
**user_cfg["pipeline"]["cost_volume_confidence"])
# Import pandora plugins
pandora.import_plugin()
data = np.array(
([1, 1, 1, 1, 1], [1, 1, 1, 1, 2], [1, 1, 1, 4, 3
], [1, 1, 1, 1, 1]),
dtype=np.float32)
left = xr.Dataset(
{"im": (["row", "col"], data)},
coords={
"row": np.arange(data.shape[0]),
"col": np.arange(data.shape[1])
},
attrs={
"no_data_img": 0,
"valid_pixels": 0,
"no_data_mask": 1,
"crs": None,
"transform": None
},
)
data = np.array(
([1, 1, 1, 2, 2], [1, 1, 1, 4, 2], [1, 1, 1, 4, 4
], [1, 1, 1, 1, 1]),
dtype=np.float32)
right = xr.Dataset(
{"im": (["row", "col"], data)},
coords={
"row": np.arange(data.shape[0]),
"col": np.arange(data.shape[1])
},
attrs={
"no_data_img": 0,
"valid_pixels": 0,
"no_data_mask": 1,
"crs": None,
"transform": None
},
)
# Computes the cost volume dataset
cv = matching_cost_.compute_cost_volume(img_left=left,
img_right=right,
disp_min=-2,
disp_max=2)
left_disp, cv = confidence_.confidence_prediction(
None, left, right, cv) # pylint:disable=unused-variable
# Disparities which give a minimum local cost, in indices
disp_path = np.array(
[
[
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
],
[
[0, 0, 0, 0, 0, 0, 0, 0],
[1, 3, 2, 3, 1, 3, 3, 2],
[0, 1, 1, 4, 2, 2, 3, 1],
[2, 4, 2, 4, 3, 0, 3, 3],
[0, 0, 0, 0, 0, 0, 0, 0],
],
[
[0, 0, 0, 0, 0, 0, 0, 0],
[3, 3, 2, 3, 1, 0, 1, 3],
[2, 1, 1, 3, 1, 3, 1, 2],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
],
[
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
],
],
dtype=np.float32,
)
invalid_disp = np.isnan(cv["cost_volume"].data)
cv_updated = optimization_.number_of_disp(cv, disp_path, invalid_disp)
# Ground truth calculated with disp_path
gt_disp = np.array(
[
[np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, 2, 3, 0, np.nan],
[np.nan, 1, 4, 8, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan],
],
dtype=np.float32,
)
# Check if the calculated confidence_measure is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(
cv_updated["confidence_measure"].data[:, :, -1], gt_disp)