From f99a9ce2b7447876f17137865887d88e3d90083c Mon Sep 17 00:00:00 2001 From: duzelis Date: Thu, 8 Aug 2024 18:04:48 +0200 Subject: [PATCH 1/5] test: add medicis ground truths to test pandora2d dichotomy performance --- .../data_medicis/T19KER/r+0.00c+0.25/left.tif | Bin 0 -> 97050 bytes .../data_medicis/T19KER/r+0.00c+0.25/right.tif | Bin 0 -> 97050 bytes .../gri_zncc_dicho_nappe_bco_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_bco_row_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_row_disp.tif | Bin 0 -> 48588 bytes .../data_medicis/T19KER/r+0.00c+0.50/left.tif | Bin 0 -> 97050 bytes .../data_medicis/T19KER/r+0.00c+0.50/right.tif | Bin 0 -> 97050 bytes .../gri_zncc_dicho_nappe_bco_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_bco_row_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_row_disp.tif | 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.../gri_zncc_dicho_nappe_sinc_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_row_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_col_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_row_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_col_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_row_disp.tif | Bin 0 -> 48588 bytes .../data_medicis/T50JML/r+0.00c+0.25/left.tif | Bin 0 -> 97050 bytes .../data_medicis/T50JML/r+0.00c+0.25/right.tif | Bin 0 -> 97050 bytes .../gri_zncc_dicho_nappe_bco_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_bco_row_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_row_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_col_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_row_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_col_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_row_disp.tif | Bin 0 -> 48588 bytes .../data_medicis/T50JML/r+0.00c+0.50/left.tif | Bin 0 -> 97050 bytes .../data_medicis/T50JML/r+0.00c+0.50/right.tif | Bin 0 -> 97050 bytes .../gri_zncc_dicho_nappe_bco_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_bco_row_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_col_disp.tif | Bin 0 -> 48588 bytes .../gri_zncc_dicho_nappe_sinc_row_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_col_disp.tif | Bin 0 -> 48588 bytes ...gri_zncc_dicho_nappe_surech_bco_row_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_col_disp.tif | Bin 0 -> 48588 bytes ...ri_zncc_dicho_nappe_surech_sinc_row_disp.tif | Bin 0 -> 48588 bytes .../data_medicis/T50JML/r+0.00c-0.25/left.tif | Bin 0 -> 97050 bytes .../data_medicis/T50JML/r+0.00c-0.25/right.tif | Bin 0 -> 97050 bytes 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tests/performance_tests/refinement/dichotomy/test_dichotomy_performance.py diff --git a/tests/conftest.py b/tests/conftest.py index afeca86..11e08e4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -25,12 +25,15 @@ import pathlib import re +import json import numpy as np import pytest import rasterio from pandora.common import write_data_array import xarray as xr +import pandora2d + def pytest_collection_modifyitems(config, items): """ @@ -219,3 +222,18 @@ def random_seed(): @pytest.fixture() def random_generator(random_seed): return np.random.default_rng(random_seed) + + +@pytest.fixture() +def run_pipeline(tmp_path): + """Fixture that returns a function to run a pipeline and which returns the output directory path.""" + + def run(configuration, output_dir="output"): + config_path = tmp_path / "config.json" + with config_path.open("w", encoding="utf-8") as file_: + json.dump(configuration, file_, indent=2) + + pandora2d.main(str(config_path), str(tmp_path / output_dir), verbose=False) + return tmp_path + + return run diff --git a/tests/functional_tests/conftest.py b/tests/functional_tests/conftest.py index c964077..fb9dbc0 100644 --- a/tests/functional_tests/conftest.py +++ b/tests/functional_tests/conftest.py @@ -18,27 +18,8 @@ # """Module with global test fixtures.""" -import json - import pytest -import pandora2d - - -@pytest.fixture() -def run_pipeline(tmp_path): - """Fixture that returns a function to run a pipeline and which returns the output directory path.""" - - def run(configuration, output_dir="output"): - config_path = tmp_path / "config.json" - with config_path.open("w", encoding="utf-8") as file_: - json.dump(configuration, file_, indent=2) - - pandora2d.main(str(config_path), str(tmp_path / output_dir), verbose=False) - return tmp_path - - return run - @pytest.fixture() def correct_pipeline_without_refinement(): diff --git a/tests/performance_tests/refinement/dichotomy/test_dichotomy_performance.py b/tests/performance_tests/refinement/dichotomy/test_dichotomy_performance.py new file mode 100644 index 0000000..24e0884 --- /dev/null +++ b/tests/performance_tests/refinement/dichotomy/test_dichotomy_performance.py @@ -0,0 +1,390 @@ +# Copyright (c) 2024 Centre National d'Etudes Spatiales (CNES). +# +# This file is part of PANDORA2D +# +# https://github.com/CNES/Pandora2D +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Test the refinement.dichotomy pipeline. +""" + +from typing import Tuple +from pathlib import Path + +import pytest + +import numpy as np +import rasterio + +from numpy.typing import NDArray + + +class TestComparisonMedicis: + """ + Tests that the pandora2d disparity maps after using the dichotomy + are similar to those obtained with Medicis. + """ + + def remove_edges( + self, medicis_map: NDArray[np.floating], pandora2d_map: NDArray[np.floating] + ) -> Tuple[NDArray[np.floating], NDArray[np.floating]]: + """ + Get reduced disparity maps after removing medicis edges full of nans (greater than pandora2d edges) + on both pandora2d and medicis disparity maps. + """ + + # Gets coordinates for which medicis col_map is different from nan + # i.e. points that are not within the edges + non_nan_row_indexes, non_nan_col_indexes = np.where(~np.isnan(medicis_map)) + + # Remove medicis edges + medicis_map = medicis_map[ + non_nan_row_indexes[0] : non_nan_row_indexes[-1] + 1, non_nan_col_indexes[0] : non_nan_col_indexes[-1] + 1 + ] + + # Remove pandora2d edges to get the same points as the ones in medicis disparity maps + pandora2d_map = pandora2d_map[ + non_nan_row_indexes[0] : non_nan_row_indexes[-1] + 1, non_nan_col_indexes[0] : non_nan_col_indexes[-1] + 1 + ] + + return medicis_map, pandora2d_map + + @pytest.fixture() + def data_path(self): + """ + Return path to get left and right images and medicis data + """ + return Path("tests/performance_tests/refinement/dichotomy/data_medicis/") + + @pytest.fixture() + def shift_path(self, data_path, img_path): + """ + Return path to get left and right images and medicis data + """ + return data_path / img_path + + @pytest.fixture() + def medicis_maps_path(self, shift_path, medicis_method_path): + """ + Return path to get medicis data + """ + return shift_path / medicis_method_path + + @pytest.fixture() + def cfg_dichotomy(self, shift_path, subpix, filter_method): + """ + Make user configuration for dichotomy loop + """ + + return { + "input": { + "left": {"nodata": -9999, "img": str(shift_path / "left.tif")}, + "right": {"nodata": -9999, "img": str(shift_path / "right.tif")}, + "col_disparity": {"init": 0, "range": 3}, + "row_disparity": {"init": 0, "range": 3}, + }, + "pipeline": { + "matching_cost": { + "matching_cost_method": "zncc", + "window_size": 65, + "step": [1, 1], + "subpix": subpix, + "spline_order": 3, + }, # we use spline_order=3 to get better results when subpix is different from 1 + "disparity": {"disparity_method": "wta", "invalid_disparity": np.nan}, + "refinement": {"refinement_method": "dichotomy", "iterations": 9, "filter": {"method": filter_method}}, + }, + } + + @pytest.mark.parametrize( + [ + "img_path", + "subpix", + "filter_method", + "medicis_method_path", + "row_shift", + "col_shift", + "row_map_threshold", + "col_map_threshold", + ], + [ + pytest.param( + "T19KER/r+0.00c+0.50/", + 1, + "bicubic", + "zncc_dicho_nappe_bco/gri_zncc_dicho_nappe_bco_", + 0.0, + 0.5, + 0.00001, + 0.0, + id="T19KER (Calama, Chile) shifted of 0.5 in columns with bicubic, 9 iter and subpix=1", + ), + pytest.param( + "T50JML/r+0.00c+0.50/", + 1, + "bicubic", + "zncc_dicho_nappe_bco/gri_zncc_dicho_nappe_bco_", + 0.0, + 0.5, + 0.0, + 0.0, + id="T50JML (Perth, Australia) shifted of 0.5 in columns with bicubic, 9 iter and subpix=1", + ), + pytest.param( + "T19KER/r+0.00c+0.25/", + 1, + "bicubic", + "zncc_dicho_nappe_bco/gri_zncc_dicho_nappe_bco_", + 0.0, + 0.25, + 0.00001, + 0.0, + id="T19KER (Calama, Chile) shifted of 0.25 in columns with bicubic, 9 iter and subpix=1", + ), + pytest.param( + "T50JML/r+0.00c+0.25/", + 1, + "bicubic", + "zncc_dicho_nappe_bco/gri_zncc_dicho_nappe_bco_", + 0.0, + 0.25, + 0.0, + 0.0, + id="T50JML (Perth, Australia) shifted of 0.25 in columns with bicubic, 9 iter and subpix=1", + ), + pytest.param( + "T19KER/r+0.00c-0.25/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.0, + -0.25, + 0.002, + 0.0, + id="T19KER (Calama, Chile) shifted of -0.25 in columns with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.00c-0.25/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.0, + -0.25, + 0.004, + 0.003, + id="T50JML (Perth, Australia) shifted of -0.25 in columns with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T19KER/r+0.00c+0.50/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.0, + 0.5, + 0.004, + 0.003, + id="T19KER (Calama, Chile) shifted of 0.5 in columns with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.00c+0.50/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.0, + 0.5, + 0.004, + 0.002, + id="T50JML (Perth, Australia) shifted of 0.5 in columns with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T19KER/r+0.25c+0.25/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.25, + 0.25, + 0.0, + 0.0, + id="T19KER (Calama, Chile) shifted of 0.25 in col and in rows with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.25c+0.25/", + 4, + "bicubic", + "zncc_dicho_nappe_surech_bco/gri_zncc_dicho_nappe_surech_bco_", + 0.25, + 0.25, + 0.005, + 0.005, + id="T50JML (Perth, Australia) shifted of 0.25 in col and in rows with bicubic, 9 iter and subpix=4", + ), + pytest.param( + "T19KER/r+0.00c+0.50/", + 1, + "sinc", + "zncc_dicho_nappe_sinc/gri_zncc_dicho_nappe_sinc_", + 0.0, + 0.5, + 0.0001, + 0.0001, + id="T19KER (Calama, Chile) shifted of 0.5 in columns with sinc, 9 iter and subpix=1", + ), + pytest.param( + "T50JML/r+0.00c+0.50/", + 1, + "sinc", + "zncc_dicho_nappe_sinc/gri_zncc_dicho_nappe_sinc_", + 0.0, + 0.5, + 0.0, + 0.0, + id="T50JML (Perth, Australia) shifted of 0.5 in columns with sinc, 9 iter and subpix=1", + ), + pytest.param( + "T19KER/r+0.00c+0.25/", + 1, + "sinc", + "zncc_dicho_nappe_sinc/gri_zncc_dicho_nappe_sinc_", + 0.0, + 0.25, + 0.0, + 0.0, + id="T19KER (Calama, Chile) shifted of 0.25 in columns with sinc, 9 iter and subpix=1", + ), + pytest.param( + "T50JML/r+0.00c+0.25/", + 1, + "sinc", + "zncc_dicho_nappe_sinc/gri_zncc_dicho_nappe_sinc_", + 0.0, + 0.25, + 0.00001, + 0.00001, + id="T50JML (Perth, Australia) shifted of 0.25 in columns with sinc, 9 iter and subpix=1", + ), + pytest.param( + "T19KER/r+0.00c-0.25/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.0, + -0.25, + 0.003, + 0.01, + id="T19KER (Calama, Chile) shifted of -0.25 in columns with sinc, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.00c-0.25/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.0, + -0.25, + 0.004, + 0.005, + id="T50JML (Perth, Australia) shifted of -0.25 in columns with sinc, 9 iter and subpix=4", + ), + pytest.param( + "T19KER/r+0.00c+0.50/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.0, + 0.5, + 0.003, + 0.004, + id="T19KER (Calama, Chile) shifted of 0.5 in columns with sinc, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.00c+0.50/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.0, + 0.5, + 0.003, + 0.003, + id="T50JML (Perth, Australia) shifted of 0.5 in columns with sinc, 9 iter and subpix=4", + ), + pytest.param( + "T19KER/r+0.25c+0.25/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.25, + 0.25, + 0.01, + 0.007, + id="T19KER (Calama, Chile) shifted of 0.25 in col and in rows with sinc, 9 iter and subpix=4", + ), + pytest.param( + "T50JML/r+0.25c+0.25/", + 4, + "sinc", + "zncc_dicho_nappe_surech_sinc/gri_zncc_dicho_nappe_surech_sinc_", + 0.25, + 0.25, + 0.004, + 0.005, + id="T50JML (Perth, Australia) shifted of 0.25 in col and in rows with sinc, 9 iter and subpix=4", + ), + ], + ) + def test_pandora2d_medicis_dichotomy( + self, run_pipeline, cfg_dichotomy, medicis_maps_path, row_shift, col_shift, row_map_threshold, col_map_threshold + ): + """ + Test that pandora2d mean errors are smaller + than those of medicis plus a threshold given as a parameter. + + Difference between medicis and pandora2d disparity maps may be linked to the difference + in the interpolation method used between the two tools when the subpix is greater than 1. + As pandora2d use the scipy zoom method (spline interpolation), + medicis use the same interpolation method as the one used for the dichotomy loop (bicubic or sinc). + + When the threshold is 0, pandora2d is at least as effective as medicis. + When the threshold is > 0, the mean error of medicis + is better than the one of pandora2d by about the value of the threshold. + """ + + # Run pandora2D pipeline + run_dir = run_pipeline(cfg_dichotomy) + + # Get pandora2d disparity maps + with rasterio.open(run_dir / "output" / "row_disparity.tif") as src: + row_map_pandora2d = src.read(1) + with rasterio.open(run_dir / "output" / "columns_disparity.tif") as src: + col_map_pandora2d = src.read(1) + + # Get medicis disparity maps + with rasterio.open(str(medicis_maps_path) + "row_disp.tif") as src: + row_map_medicis = src.read(1) + with rasterio.open(str(medicis_maps_path) + "col_disp.tif") as src: + col_map_medicis = src.read(1) + + # Remove medicis edges on both pandora2d and medicis disparity maps + # in order to compare the same sample of points. + row_map_medicis, row_map_pandora2d = self.remove_edges(row_map_medicis, row_map_pandora2d) + col_map_medicis, col_map_pandora2d = self.remove_edges(col_map_medicis, col_map_pandora2d) + + # Compute mean error between column disparities and real column shift + mean_error_pandora2d_col = np.nanmean(abs(col_map_pandora2d - col_shift)) + mean_error_medicis_col = np.nanmean(abs(col_map_medicis - col_shift)) + + # Compute mean error between row disparities and real row shift + mean_error_pandora2d_row = np.nanmean(abs(row_map_pandora2d - row_shift)) + mean_error_medicis_row = np.nanmean(abs(row_map_medicis - row_shift)) + + assert mean_error_pandora2d_col <= mean_error_medicis_col + col_map_threshold + assert mean_error_pandora2d_row <= mean_error_medicis_row + row_map_threshold From aa90b88860c574873c1b48b41d69fa1c7cfee39e Mon Sep 17 00:00:00 2001 From: duzelis Date: Fri, 23 Aug 2024 11:03:40 +0200 Subject: [PATCH 3/5] fix: update cost_surface precision in dichotomy loop --- pandora2d/refinement/dichotomy.py | 30 +++-- .../test_refinement/test_dichotomy.py | 117 +++++++++++++++++- 2 files changed, 137 insertions(+), 10 deletions(-) diff --git a/pandora2d/refinement/dichotomy.py b/pandora2d/refinement/dichotomy.py index 5d47b50..5132584 100644 --- a/pandora2d/refinement/dichotomy.py +++ b/pandora2d/refinement/dichotomy.py @@ -232,7 +232,7 @@ def refinement_method( # pylint: disable=too-many-locals # Syntax disp_row_init[...] is for assign value back to row_map with np.nditer (pos_disp_row_init, pos_disp_col_init), disp_row_init[...], disp_col_init[...], cost_value[...] = ( search_new_best_point( - cost_surface.data, + cost_surface, precision, (disp_row_init, disp_col_init), # type: ignore # Reason: is 0 dim array (pos_disp_row_init, pos_disp_col_init), @@ -269,6 +269,7 @@ def __init__(self, cost_volumes: xr.Dataset): :type disparity_margins: Margins """ self.cost_volumes = cost_volumes + self.cost_volumes["cost_volumes"].attrs.update({"subpixel": cost_volumes.attrs["subpixel"]}) def __getitem__(self, item): """Get cost surface of coordinates item where item is (row, col).""" @@ -295,7 +296,7 @@ def all_same(sequence): def search_new_best_point( - cost_surface: np.ndarray, + cost_surface: xr.DataArray, precision: float, initial_disparity: Union[Tuple[np.floating, np.floating], Tuple[int, int]], initial_position: Union[Tuple[np.floating, np.floating], Tuple[int, int]], @@ -307,8 +308,8 @@ def search_new_best_point( Find best position and cost after interpolation of cost surface for given precision. :param cost_surface: Disparities in rows and cols of a point - :type cost_surface: np.ndarray - :param precision: subpixellic precision to use + :type cost_surface: xr.Dataarray with subpix attribute + :param precision: subpixellic disparity precision to use :type precision: float :param initial_disparity: initial disparities (disp_row, disp_col) :type initial_disparity: Union[Tuple[np.floating, np.floating], Tuple[int, int]] @@ -336,17 +337,32 @@ def search_new_best_point( disp_row_shifts = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1], dtype=np.float32) * precision disp_col_shifts = np.array([-1, 0, 1, -1, 0, 1, -1, 0, 1], dtype=np.float32) * precision + # Whatever the cost_surface.attrs["subpixel"] value, the first precision in the cost surface is always 0.5 + # Then we multiply by cost_surface.attrs["subpixel"] to get right new_cols and new_rows + + # When there is no subpixel (it equals to 1), precision shift and index shift match: + # the precision shift between two points is 1. So shifting from 0.5 precision corresponds to shift index of 0.5. + # But when there is a subpixel, they do not match anymore: + # in this case, the precision shift between two points is 1/subpix. + # So to get the index corresponding to a given precision shift, we need to multiply this value by subpix. + # For example when subix equals 2, the precision shift between two points is 0.5 while the index shift is still 1. + # So in this case, shifting from 0.5 precision corresponds to shift index of 1 + # (`index_shift = 1 = 0.5 * 2 = precision_shift * subpix`) + # In the same way, shifting from 0.25 precision corresponds to shift index of 0.5 + # (`index_shift = 0.5 = 0.25 * 2 = precision_shift * subpix`) + # disp_row are along columns in cost_surface, then new_cols are computed from initial_pos_disp_row - new_cols = disp_row_shifts + initial_pos_disp_row + new_cols = disp_row_shifts * cost_surface.attrs["subpixel"] + initial_pos_disp_row + # disp_col are along rows in cost_surface, then new_rows are computed from initial_pos_disp_col - new_rows = disp_col_shifts + initial_pos_disp_col + new_rows = disp_col_shifts * cost_surface.attrs["subpixel"] + initial_pos_disp_col # New subpixel disparity values new_rows_disp = disp_row_shifts + initial_disp_row new_cols_disp = disp_col_shifts + initial_disp_col # Interpolate points at positions (new_rows[i], new_cols[i]) - candidates = filter_dicho.interpolate(cost_surface, (new_cols, new_rows)) + candidates = filter_dicho.interpolate(cost_surface.data, (new_cols, new_rows)) # In case a NaN is present in the kernel, candidates will be all-NaNs. Let’s restore initial_position value so # that best candidate search will be able to find it. diff --git a/tests/unit_tests/test_refinement/test_dichotomy.py b/tests/unit_tests/test_refinement/test_dichotomy.py index 86db96f..9c9c65f 100644 --- a/tests/unit_tests/test_refinement/test_dichotomy.py +++ b/tests/unit_tests/test_refinement/test_dichotomy.py @@ -758,8 +758,29 @@ def test_iteration(self, cost_volumes, disp_map): ) +@pytest.fixture() +def make_cost_surface(cost_surface_data, subpix): + """ + Creates a cost surface data array according to given data and subpix + """ + + cost_surface = xr.DataArray(cost_surface_data) + + cost_surface.attrs["subpixel"] = subpix + + return cost_surface + + @pytest.mark.parametrize( - ["cost_surface", "precision", "initial_disparity", "initial_position", "initial_value", "expected"], + [ + "cost_surface_data", + "subpix", + "precision", + "initial_disparity", + "initial_position", + "initial_value", + "expected", + ], [ pytest.param( np.array( @@ -771,6 +792,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.5, (2, 2), (2, 2), @@ -789,6 +811,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.5, (2, 2), (2, 2), @@ -806,6 +829,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.25, (1.5, 2.5), (1.5, 2.5), @@ -823,6 +847,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.5, (2, 2), (2, 2), @@ -840,6 +865,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.5, (2, 2), (2, 2), @@ -857,6 +883,7 @@ def test_iteration(self, cost_volumes, disp_map): [0, 0, 0, 0, 0], ] ), + 1, 0.5, (2, 2), (2, 2), @@ -864,9 +891,93 @@ def test_iteration(self, cost_volumes, disp_map): (refinement.dichotomy.Point(2.5, 2.5), np.float32(2.5), np.float32(2.5), np.float32(6.64453125)), id="NaN outside of kernel has no effect", ), + pytest.param( + np.array( + [ + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 20, 0, 0, 0], + [0, 0, 0, 0, 0], + ] + ), + 2, + 0.25, + (2, 2), + (2, 2), + 1.0, + (refinement.dichotomy.Point(1.5, 2.5), np.float32(1.75), np.float32(2.25), np.float32(6.64453125)), + id="Bottom left is best and subpix=2", + ), + pytest.param( + np.array( + [ + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 20, 0], + [0, 0, 0, 0, 0], + ] + ), + 2, + 0.125, + (2, 2), + (2, 2), + 1.0, + ( + refinement.dichotomy.Point(2.25, 2.25), + np.float32(2.125), + np.float32(2.125), + np.float32(1.77862548828125), + ), + id="Bottom right is best and subpix=2", + ), + pytest.param( + np.array( + [ + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 20, 0, 0, 0], + [0, 0, 0, 0, 0], + ] + ), + 4, + 0.125, + (2, 2), + (2, 2), + 1.0, + (refinement.dichotomy.Point(1.5, 2.5), np.float32(1.875), np.float32(2.125), np.float32(6.64453125)), + id="Bottom left is best and subpix=4", + ), + pytest.param( + np.array( + [ + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 20, 0], + [0, 0, 0, 0, 0], + ] + ), + 4, + 0.0625, + (2, 2), + (2, 2), + 1.0, + ( + refinement.dichotomy.Point(2.25, 2.25), + np.float32(2.0625), + np.float32(2.0625), + np.float32(1.77862548828125), + ), + id="Bottom right is best and subpix=4", + ), ], ) -def test_search_new_best_point(cost_surface, precision, initial_disparity, initial_position, initial_value, expected): +def test_search_new_best_point( + make_cost_surface, precision, initial_disparity, initial_position, initial_value, expected +): """Test we get new coordinates as expected.""" filter_dicho = Bicubic({"method": "bicubic"}) @@ -874,7 +985,7 @@ def test_search_new_best_point(cost_surface, precision, initial_disparity, initi cost_selection_method = np.nanargmax result = refinement.dichotomy.search_new_best_point( - cost_surface, + make_cost_surface, precision, initial_disparity, initial_position, From 1f7ce20ac085614ad8cfcbfbeb936778c1668bbf Mon Sep 17 00:00:00 2001 From: duzelis Date: Fri, 23 Aug 2024 16:49:41 +0200 Subject: [PATCH 4/5] feat: add spline_order parameter in matching_cost --- pandora2d/img_tools.py | 18 +++++++++++++----- pandora2d/matching_cost/matching_cost.py | 13 ++++++++++++- 2 files changed, 25 insertions(+), 6 deletions(-) diff --git a/pandora2d/img_tools.py b/pandora2d/img_tools.py index 908385a..0d3d7df 100644 --- a/pandora2d/img_tools.py +++ b/pandora2d/img_tools.py @@ -336,7 +336,7 @@ def remove_roi_margins(dataset: xr.Dataset, cfg: Dict): def row_zoom_img( - img: np.ndarray, ny: int, subpix: int, coords: Coordinates, ind: int, no_data: Union[int, str] + img: np.ndarray, ny: int, subpix: int, coords: Coordinates, ind: int, no_data: Union[int, str], order: int = 1 ) -> xr.Dataset: """ Return a list that contains the shifted right images in row @@ -355,13 +355,15 @@ def row_zoom_img( :type ind: int :param no_data: no_data value in img :type no_data: Union[int, str] + :param order: The order of the spline interpolation, default is 1. The order has to be in the range 0-5. + :type order: int, optional :return: an array that contains the shifted right images in row :rtype: array of xarray.Dataset """ shift = 1 / subpix # For each index, shift the right image for subpixel precision 1/subpix*index - data = zoom(img, ((ny * subpix - (subpix - 1)) / float(ny), 1), order=1)[ind::subpix, :] + data = zoom(img, ((ny * subpix - (subpix - 1)) / float(ny), 1), order=order)[ind::subpix, :] # Add a row full of no data at the end of data have the same shape as img # It enables to use Pandora's compute_cost_volume() methods, @@ -379,7 +381,7 @@ def row_zoom_img( def col_zoom_img( - img: np.ndarray, nx: int, subpix: int, coords: Coordinates, ind: int, no_data: Union[int, str] + img: np.ndarray, nx: int, subpix: int, coords: Coordinates, ind: int, no_data: Union[int, str], order: int = 1 ) -> xr.Dataset: """ Return a list that contains the shifted right images in col @@ -398,13 +400,15 @@ def col_zoom_img( :type ind: int :param no_data: no_data value in img :type no_data: Union[int, str] + :param order: The order of the spline interpolation, default is 1. The order has to be in the range 0-5. + :type order: int, optional :return: an array that contains the shifted right images in col :rtype: array of xarray.Dataset """ shift = 1 / subpix # For each index, shift the right image for subpixel precision 1/subpix*index - data = zoom(img, (1, (nx * subpix - (subpix - 1)) / float(nx)), order=1)[:, ind::subpix] + data = zoom(img, (1, (nx * subpix - (subpix - 1)) / float(nx)), order=order)[:, ind::subpix] # Add a col full of no data at the end of data to have the same shape as img # It enables to use Pandora's compute_cost_volume() methods, @@ -420,7 +424,7 @@ def col_zoom_img( ) -def shift_subpix_img(img_right: xr.Dataset, subpix: int, row: bool = True) -> List[xr.Dataset]: +def shift_subpix_img(img_right: xr.Dataset, subpix: int, row: bool = True, order: int = 1) -> List[xr.Dataset]: """ Return an array that contains the shifted right images @@ -430,6 +434,8 @@ def shift_subpix_img(img_right: xr.Dataset, subpix: int, row: bool = True) -> Li :type subpix: int :param column: column to shift (otherwise row) :type column: bool + :param order: The order of the spline interpolation, default is 1. The order has to be in the range 0-5. + :type order: int, optional :return: an array that contains the shifted right images :rtype: array of xarray.Dataset """ @@ -446,6 +452,7 @@ def shift_subpix_img(img_right: xr.Dataset, subpix: int, row: bool = True) -> Li img_right.coords, ind, img_right.attrs["no_data_img"], + order, ).assign_attrs(img_right.attrs) ) else: @@ -457,6 +464,7 @@ def shift_subpix_img(img_right: xr.Dataset, subpix: int, row: bool = True) -> Li img_right.coords, ind, img_right.attrs["no_data_img"], + order, ).assign_attrs(img_right.attrs) ) diff --git a/pandora2d/matching_cost/matching_cost.py b/pandora2d/matching_cost/matching_cost.py index 7e55e32..744507f 100644 --- a/pandora2d/matching_cost/matching_cost.py +++ b/pandora2d/matching_cost/matching_cost.py @@ -80,6 +80,7 @@ def cfg(self) -> Dict[str, Union[str, int, List[int]]]: "step": self._step, "window_size": self._window_size, "subpix": self._subpix, + "spline_order": self._spline_order, } @property @@ -112,6 +113,16 @@ def _subpix(self) -> int: """ return self.pandora_matching_cost_._subpix # pylint: disable=W0212 protected-access + @property + def _spline_order(self) -> int: + """ + Get spline_order, parameter specific to pandora + + :return: spline_order: spline_order used + :rtype: spline_order: int + """ + return self.pandora_matching_cost_._spline_order # pylint: disable=W0212 protected-access + @property def margins(self) -> Margins: """ @@ -326,7 +337,7 @@ def compute_cost_volumes( row_index = None # Contains the shifted right images (with subpixel) - imgs_right_shift_subpixel = img_tools.shift_subpix_img(img_right, self._subpix) + imgs_right_shift_subpixel = img_tools.shift_subpix_img(img_right, self._subpix, order=self._spline_order) for idx, disp_row in enumerate(disps_row): i_right = int((disp_row % 1) * self._subpix) From 89ff71abbf3da4acd3eabd079c96719f74fddd58 Mon Sep 17 00:00:00 2001 From: duzelis Date: Fri, 23 Aug 2024 16:51:19 +0200 Subject: [PATCH 5/5] docs: add spline_order parameter in matching cost configuration --- docs/source/userguide/step_by_step/matching_cost.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/source/userguide/step_by_step/matching_cost.rst b/docs/source/userguide/step_by_step/matching_cost.rst index 1e0c49c..0a27803 100644 --- a/docs/source/userguide/step_by_step/matching_cost.rst +++ b/docs/source/userguide/step_by_step/matching_cost.rst @@ -62,6 +62,12 @@ Configuration and parameters - 1 - [1,2,4] - No + * - spline_order + - Spline order used for interpolation when subpix > 1 + - int + - 1 + - > 0 and < 6 + - No .. note::

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