def test_libsgm_zncc(self):
"""
Test pandora + plugin_libsgm if ZNCC measure is used
"""
# Prepare the configuration
user_cfg = pandora.read_config_file("tests/conf/sgm_zncc.json")
# Import pandora plugins
pandora.import_plugin()
# Instantiate machine
pandora_machine = PandoraMachine()
# Run the pandora pipeline
left, right = pandora.run(pandora_machine, self.left, self.right, -60,
0, user_cfg["pipeline"])
# Compares the calculated left disparity map with the ground truth
# If the disparity maps are not equal, raise an error
if common.strict_error(left["disparity_map"].data[61:-61, 61:-61],
self.disp_left_zncc[61:-61, 61:-61]) > 0:
raise AssertionError
# Compares the calculated right disparity map with the ground truth
# If the disparity maps are not equal, raise an error
if common.strict_error(right["disparity_map"].data,
self.disp_right_zncc) > 0:
raise AssertionError
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_libsgm_zncc(self):
"""
Test pandora + plugin_libsgm if ZNCC measure is used
"""
# Prepare the configuration
user_cfg = pandora.read_config_file("tests/conf/sgm_zncc_python.json")
# Import pandora plugins
pandora.import_plugin()
# Instantiate machine
pandora_machine = PandoraMachine()
# Run the pandora pipeline
left, right = pandora.run(pandora_machine, self.left, self.right, -60,
0, user_cfg["pipeline"])
# Compares the calculated left disparity map with the ground truth
# If the disparity maps are not equal, raise an error
np.testing.assert_allclose(left["disparity_map"].data,
self.disp_left_zncc,
rtol=1e-04)
# Compares the calculated right disparity map with the ground truth
# If the disparity maps are not equal, raise an error
np.testing.assert_allclose(right["disparity_map"].data,
self.disp_right_zncc,
rtol=1e-04)
def test_libsgm(self):
"""
Test pandora + plugin_libsgm
"""
user_cfg = pandora.read_config_file(
"tests/conf/sgm_python_parall.json")
# Instantiate machine
pandora_machine = PandoraMachine()
# Import pandora plugins
pandora.import_plugin()
# Run the pandora pipeline
left, right = pandora.run(pandora_machine, self.left, self.right, -60,
0, user_cfg)
# Compares the calculated left disparity map with the ground truth
# If the percentage of pixel errors is > 0.20, raise an error
if common.error(left["disparity_map"].data, self.disp_left, 1) > 0.20:
raise AssertionError
# Compares the calculated left disparity map with the ground truth
# If the percentage of pixel errors ( error if ground truth - calculate > 2) is > 0.15, raise an error
if common.error(left["disparity_map"].data, self.disp_left, 2) > 0.15:
raise AssertionError
# Check the left validity mask cross checking ( bit 8 and 9 )
# Compares the calculated validity mask with the ground truth ( occlusion mask )
occlusion = np.ones(
(left["validity_mask"].shape[0], left["validity_mask"].shape[1]))
occlusion[left["validity_mask"].data >= 512] = 0
# If the percentage of errors is > 0.15, raise an error
if common.error_mask(occlusion, self.occlusion) > 0.15:
raise AssertionError
# Compares the calculated right disparity map with the ground truth
# If the percentage of pixel errors is > 0.20, raise an error
if common.error(-1 * right["disparity_map"].data, self.disp_right,
1) > 0.20:
raise AssertionError
# Compares the calculated right disparity map with the ground truth
# If the percentage of pixel errors ( error if ground truth - calculate > 2) is > 0.15, raise an error
if common.error(-1 * right["disparity_map"].data, self.disp_right,
2) > 0.15:
raise AssertionError
def create_corr_conf():
"""
Create correlator configuration for stereo testing
"""
user_cfg = dict()
user_cfg['image'] = {}
user_cfg['image']['valid_pixels'] = 0
user_cfg['image']['no_data'] = 255
user_cfg["stereo"] = {}
user_cfg["stereo"]["stereo_method"] = "census"
user_cfg["stereo"]["window_size"] = 5
user_cfg["stereo"]["subpix"] = 1
user_cfg["aggregation"] = {}
user_cfg["aggregation"]["aggregation_method"] = "none"
user_cfg["optimization"] = {}
user_cfg["optimization"]["optimization_method"] = "sgm"
user_cfg["optimization"]["P1"] = 8
user_cfg["optimization"]["P2"] = 32
user_cfg["optimization"]["p2_method"] = "constant"
user_cfg["optimization"]["penalty_method"] = "sgm_penalty"
user_cfg["optimization"]["overcounting"] = False
user_cfg["optimization"]["min_cost_paths"] = False
user_cfg["refinement"] = {}
user_cfg["refinement"]["refinement_method"] = "vfit"
user_cfg["filter"] = {}
user_cfg["filter"]["filter_method"] = "median"
user_cfg["filter"]["filter_size"] = 3
user_cfg["validation"] = {}
user_cfg["validation"]["validation_method"] = "cross_checking"
user_cfg["validation"]["cross_checking_threshold"] = 1.0
user_cfg["validation"]["right_left_mode"] = "accurate"
user_cfg["validation"]["interpolated_disparity"] = "none"
# Import plugins before checking configuration
pandora.import_plugin()
# Check configuration and update the configuration with default values
# check pipeline
user_cfg_pipeline = get_config_pipeline(user_cfg)
cfg_pipeline = check_pipeline_section(user_cfg_pipeline)
# check image
user_cfg_image = get_config_image(user_cfg)
cfg_image = check_image_section(user_cfg_image)
# concatenate updated config
cfg = concat_conf([cfg_image, cfg_pipeline])
return cfg
def test_libsgm_positive_disparities(self):
"""
Test pandora + plugin_libsgm, with positive disparities
"""
user_cfg = pandora.read_config_file(
"tests/conf/sgm_python_parall.json")
# Import pandora plugins
pandora.import_plugin()
# Instantiate machine
pandora_machine = PandoraMachine()
right, left = pandora.run(pandora_machine, self.right, self.left, 1,
60, user_cfg)
# Compares the calculated left disparity map with the ground truth
# If the percentage of pixel errors is > 0.20, raise an error
if common.error(left["disparity_map"].data, self.disp_left, 1) > 0.20:
raise AssertionError
# Compares the calculated left disparity map with the ground truth
# If the percentage of pixel errors ( error if ground truth - calculate > 2) is > 0.15, raise an error
if common.error(left["disparity_map"].data, self.disp_left, 2) > 0.15:
raise AssertionError
# Compares the calculated right disparity map with the ground truth
# If the percentage of pixel errors is > 0.20, raise an error
if common.error(-1 * right["disparity_map"].data, self.disp_right,
1) > 0.20:
raise AssertionError
# Compares the calculated right disparity map with the ground truth
# If the percentage of pixel errors ( error if ground truth - calculate > 2) is > 0.15, raise an error
if common.error(-1 * right["disparity_map"].data, self.disp_right,
2) > 0.15:
raise AssertionError
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)
def configure_correlator(corr_file_path=None):
"""
Provide correlator configuration through dictionary format.
If a correlator file path is provided, read correlator
configuration file to upgrade correlator configuration.
Otherwise, use default configuration depending of the
correlator type used.
Relative paths will be made absolute.
:param corr_file_path: Path to correlation confuiguration json file
:type corr_file_path: str
:returns: The dictionary with correlator configuration
:rtype: dict
"""
# Configure correlator
# Configure pandora
# Read the user configuration file
user_cfg = None
if corr_file_path is None:
# Defaut Pandora configuration:
# * Census with 5 X 5 window
# * SGM
user_cfg = {}
user_cfg['image'] = {}
user_cfg['image']['valid_pixels'] = 0
user_cfg['image']['no_data'] = 255
user_cfg["stereo"] = {}
user_cfg["stereo"]["stereo_method"] = "census"
user_cfg["stereo"]["window_size"] = 5
user_cfg["stereo"]["subpix"] = 1
user_cfg["aggregation"] = {}
user_cfg["aggregation"]["aggregation_method"] = "none"
user_cfg["optimization"] = {}
user_cfg["optimization"]["optimization_method"] = "sgm"
user_cfg["optimization"]["P1"] = 8
user_cfg["optimization"]["P2"] = 32
user_cfg["optimization"]["p2_method"] = "constant"
user_cfg["optimization"]["penalty_method"] = "sgm_penalty"
user_cfg["optimization"]["overcounting"] = False
user_cfg["optimization"]["min_cost_paths"] = False
user_cfg["refinement"] = {}
user_cfg["refinement"]["refinement_method"] = "vfit"
user_cfg["filter"] = {}
user_cfg["filter"]["filter_method"] = "median"
user_cfg["filter"]["filter_size"] = 3
user_cfg["validation"] = {}
user_cfg["validation"]["validation_method"] = "cross_checking"
user_cfg["validation"]["cross_checking_threshold"] = 1.0
user_cfg["validation"]["right_left_mode"] = "accurate"
user_cfg["validation"]["interpolated_disparity"] = "none"
else:
user_cfg = pandora.read_config_file(corr_file_path)
# Import plugins before checking confifuration
pandora.import_plugin()
# Check configuration and update the configuration with default values
# check pipeline
user_cfg_pipeline = get_config_pipeline(user_cfg)
cfg_pipeline = check_pipeline_section(user_cfg_pipeline)
# check image
user_cfg_image = get_config_image(user_cfg)
cfg_image = check_image_section(user_cfg_image)
# concatenate updated config
cfg = concat_conf([cfg_image, cfg_pipeline])
return cfg
