import os from dataclasses import dataclass, asdict import yaml @dataclass class OpenSfMConfig: ################################## # Params for metadata ################################## use_exif_size: bool = True # Treat images from unknown camera models as comming from different cameras unknown_camera_models_are_different: bool = False default_focal_prior: float = 0.85 ################################## # Params for features ################################## # Feature type (AKAZE, SURF, SIFT, HAHOG, ORB) feature_type: str = "HAHOG" # If true, apply square root mapping to features feature_root: bool = True # If fewer frames are detected, sift_peak_threshold/surf_hessian_threshold is reduced. feature_min_frames: int = 4000 # Same as above but for panorama images feature_min_frames_panorama: int = 16000 # Resize the image if its size is larger than specified. Set to -1 for original size feature_process_size: int = 2048 # Same as above but for panorama images feature_process_size_panorama: int = 4096 feature_use_adaptive_suppression: bool = False # Bake segmentation info (class and instance) in the feature data. Thus it is done once for all at extraction time. features_bake_segmentation: bool = False ################################## # Params for SIFT ################################## # Smaller value -> more features sift_peak_threshold: float = 0.1 # See OpenCV doc sift_edge_threshold: int = 10 ################################## # Params for SURF ################################## # Smaller value -> more features surf_hessian_threshold: float = 3000 # See OpenCV doc surf_n_octaves: int = 4 # See OpenCV doc surf_n_octavelayers: int = 2 # See OpenCV doc surf_upright: int = 0 ################################## # Params for AKAZE (See details in lib/src/third_party/akaze/AKAZEConfig.h) ################################## # Maximum octave evolution of the image 2^sigma (coarsest scale sigma units) akaze_omax: int = 4 # Detector response threshold to accept point akaze_dthreshold: float = 0.001 # Feature type akaze_descriptor: str = "MSURF" # Size of the descriptor in bits. 0->Full size akaze_descriptor_size: int = 0 # Number of feature channels (1,2,3) akaze_descriptor_channels: int = 3 akaze_kcontrast_percentile: float = 0.7 akaze_use_isotropic_diffusion: bool = False ################################## # Params for HAHOG ################################## hahog_peak_threshold: float = 0.00001 hahog_edge_threshold: float = 10 hahog_normalize_to_uchar: bool = True ################################## # Params for general matching ################################## # Ratio test for matches lowes_ratio: float = 0.8 # FLANN, BRUTEFORCE, or WORDS matcher_type: str = "FLANN" # Match symmetricly or one-way symmetric_matching: bool = True ################################## # Params for FLANN matching ################################## # Algorithm type (KMEANS, KDTREE) flann_algorithm: str = "KMEANS" # See OpenCV doc flann_branching: int = 8 # See OpenCV doc flann_iterations: int = 10 # See OpenCV doc flann_tree: int = 8 # Smaller -> Faster (but might lose good matches) flann_checks: int = 20 ################################## # Params for BoW matching ################################## bow_file: str = "bow_hahog_root_uchar_10000.npz" # Number of words to explore per feature. bow_words_to_match: int = 50 # Number of matching features to check. bow_num_checks: int = 20 # Matcher type to assign words to features bow_matcher_type: str = "FLANN" ################################## # Params for VLAD matching ################################## vlad_file: str = "bow_hahog_root_uchar_64.npz" ################################## # Params for guided matching ################################## # Number of randomized spanning-trees to samples over the tracks-graph guided_spanning_trees: int = 5 # Random ratio higher bound edges are multiplied with guided_spanning_trees_random: float = 0.5 # Threshold for epipolar distance for accepting a match in radians guided_matching_threshold: float = 0.006 # Minimum track length for initial triangulation guided_min_length_initial: int = 3 # Minimum track length for final triangulation guided_min_length_final: int = 3 # Threshold of reprojection for extending a track within a new image (in radians) guided_extend_threshold: float = 0.002 # Number of images considered as neighbors of another one guided_extend_image_neighbors: int = 50 # Maximum number of reprojected neighbors (in the tracks-graph) to check when extending a track within a new image guided_extend_feature_neighbors: int = 10 ################################## # Params for matching ################################## # Maximum gps distance between two images for matching matching_gps_distance: float = 150 # Number of images to match selected by GPS distance. Set to 0 to use no limit (or disable if matching_gps_distance is also 0) matching_gps_neighbors: int = 0 # Number of images to match selected by time taken. Set to 0 to disable matching_time_neighbors: int = 0 # Number of images to match selected by image name. Set to 0 to disable matching_order_neighbors: int = 0 # Number of images to match selected by BoW distance. Set to 0 to disable matching_bow_neighbors: int = 0 # Maximum GPS distance for preempting images before using selection by BoW distance. Set to 0 to disable matching_bow_gps_distance: float = 0 # Number of images (selected by GPS distance) to preempt before using selection by BoW distance. Set to 0 to use no limit (or disable if matching_bow_gps_distance is also 0) matching_bow_gps_neighbors: int = 0 # If True, BoW image selection will use N neighbors from the same camera + N neighbors from any different camera. If False, the selection will take the nearest neighbors from all cameras. matching_bow_other_cameras: bool = False # Number of images to match selected by VLAD distance. Set to 0 to disable matching_vlad_neighbors: int = 0 # Maximum GPS distance for preempting images before using selection by VLAD distance. Set to 0 to disable matching_vlad_gps_distance: float = 0 # Number of images (selected by GPS distance) to preempt before using selection by VLAD distance. Set to 0 to use no limit (or disable if matching_vlad_gps_distance is also 0) matching_vlad_gps_neighbors: int = 0 # If True, VLAD image selection will use N neighbors from the same camera + N neighbors from any different camera. If False, the selection will take the nearest neighbors from all cameras. matching_vlad_other_cameras: bool = False # Number of rounds to run when running triangulation-based pair selection matching_graph_rounds: int = 0 # If True, removes static matches using ad-hoc heuristics matching_use_filters: bool = False # Use segmentation information (if available) to improve matching matching_use_segmentation: bool = False ################################## # Params for geometric estimation ################################## # Outlier threshold for fundamental matrix estimation as portion of image width robust_matching_threshold: float = 0.004 # Outlier threshold for essential matrix estimation during matching in radians robust_matching_calib_threshold: float = 0.004 # Minimum number of matches to accept matches between two images robust_matching_min_match: int = 20 # Outlier threshold for essential matrix estimation during incremental reconstruction in radians five_point_algo_threshold: float = 0.004 # Minimum number of inliers for considering a two view reconstruction valid five_point_algo_min_inliers: int = 20 # Number of LM iterations to run when refining relative pose during matching five_point_refine_match_iterations: int = 10 # Number of LM iterations to run when refining relative pose during reconstruction five_point_refine_rec_iterations: int = 1000 # Check for Necker reversal ambiguities. Useful for long focal length with long distance capture (aerial manned) five_point_reversal_check: bool = False # Ratio of triangulated points non-reversed/reversed when checking for Necker reversal ambiguities five_point_reversal_ratio: float = 0.95 # Outlier threshold for accepting a triangulated point in radians triangulation_threshold: float = 0.006 # Minimum angle between views to accept a triangulated point triangulation_min_ray_angle: float = 1.0 # Triangulation type : either considering all rays (FULL), or sing a RANSAC variant (ROBUST) triangulation_type: str = "FULL" # Number of LM iterations to run when refining a point triangulation_refinement_iterations: int = 10 # Outlier threshold for resection in radians resection_threshold: float = 0.004 # Minimum number of resection inliers to accept it resection_min_inliers: int = 10 ################################## # Params for track creation ################################## # Minimum number of features/images per track min_track_length: int = 2 ################################## # Params for bundle adjustment ################################## # Loss function for the ceres problem (see: http://ceres-solver.org/modeling.html#lossfunction) loss_function: str = "SoftLOneLoss" # Threshold on the squared residuals. Usually cost is quadratic for smaller residuals and sub-quadratic above. loss_function_threshold: float = 1 # The standard deviation of the reprojection error reprojection_error_sd: float = 0.004 # The standard deviation of the exif focal length in log-scale exif_focal_sd: float = 0.01 # The standard deviation of the principal point coordinates principal_point_sd: float = 0.01 # The standard deviation of the first radial distortion parameter radial_distortion_k1_sd: float = 0.01 # The standard deviation of the second radial distortion parameter radial_distortion_k2_sd: float = 0.01 # The standard deviation of the third radial distortion parameter radial_distortion_k3_sd: float = 0.01 # The standard deviation of the fourth radial distortion parameter radial_distortion_k4_sd: float = 0.01 # The standard deviation of the first tangential distortion parameter tangential_distortion_p1_sd: float = 0.01 # The standard deviation of the second tangential distortion parameter tangential_distortion_p2_sd: float = 0.01 # The default horizontal standard deviation of the GCPs (in meters) gcp_horizontal_sd: float = 0.01 # The default vertical standard deviation of the GCPs (in meters) gcp_vertical_sd: float = 0.1 # The standard deviation of the rig translation rig_translation_sd: float = 0.1 # The standard deviation of the rig rotation rig_rotation_sd: float = 0.1 # Type of threshold for filtering outlier : either fixed value (FIXED) or based on actual distribution (AUTO) bundle_outlier_filtering_type: str = "FIXED" # For AUTO filtering type, projections with larger reprojection than ratio-times-mean, are removed bundle_outlier_auto_ratio: float = 3.0 # For FIXED filtering type, projections with larger reprojection error after bundle adjustment are removed bundle_outlier_fixed_threshold: float = 0.006 # Optimize internal camera parameters during bundle optimize_camera_parameters: bool = True # Maximum optimizer iterations. bundle_max_iterations: int = 100 # Retriangulate all points from time to time retriangulation: bool = True # Retriangulate when the number of points grows by this ratio retriangulation_ratio: float = 1.2 # Use analytic derivatives or auto-differentiated ones during bundle adjustment bundle_analytic_derivatives: bool = True # Bundle after adding 'bundle_interval' cameras bundle_interval: int = 999999 # Bundle when the number of points grows by this ratio bundle_new_points_ratio: float = 1.2 # Max image graph distance for images to be included in local bundle adjustment local_bundle_radius: int = 3 # Minimum number of common points betwenn images to be considered neighbors local_bundle_min_common_points: int = 20 # Max number of shots to optimize during local bundle adjustment local_bundle_max_shots: int = 30 # Save reconstructions at every iteration save_partial_reconstructions: bool = False ################################## # Params for GPS alignment ################################## # Use or ignore EXIF altitude tag use_altitude_tag: bool = False # orientation_prior or naive align_method: str = "auto" # horizontal, vertical or no_roll align_orientation_prior: str = "horizontal" # Enforce GPS position in bundle adjustment bundle_use_gps: bool = True # Enforce Ground Control Point position in bundle adjustment bundle_use_gcp: bool = False # Compensate GPS with a per-camera similarity transform bundle_compensate_gps_bias: bool = False ################################## # Params for rigs ################################## # Number of rig instances to use when calibration rigs rig_calibration_subset_size: int = 15 # Ratio of reconstructed images needed to consider a reconstruction for rig calibration rig_calibration_completeness: float = 0.85 # Number of SfM tentatives to run until we get a satisfying reconstruction rig_calibration_max_rounds: int = 10 ################################## # Params for image undistortion ################################## # Format in which to save the undistorted images undistorted_image_format: str = "jpg" # Max width and height of the undistorted image undistorted_image_max_size: int = 100000 ################################## # Params for depth estimation ################################## # Raw depthmap computation algorithm (PATCH_MATCH, BRUTE_FORCE, PATCH_MATCH_SAMPLE) depthmap_method: str = "PATCH_MATCH_SAMPLE" # Resolution of the depth maps depthmap_resolution: int = 640 # Number of neighboring views depthmap_num_neighbors: int = 10 # Number of neighboring views used for each depthmaps depthmap_num_matching_views: int = 6 # Minimum depth in meters. Set to 0 to auto-infer from the reconstruction. depthmap_min_depth: float = 0 # Maximum depth in meters. Set to 0 to auto-infer from the reconstruction. depthmap_max_depth: float = 0 # Number of PatchMatch iterations to run depthmap_patchmatch_iterations: int = 3 # Size of the correlation patch depthmap_patch_size: int = 7 # Patches with lower standard deviation are ignored depthmap_min_patch_sd: float = 1.0 # Minimum correlation score to accept a depth value depthmap_min_correlation_score: float = 0.1 # Threshold to measure depth closeness depthmap_same_depth_threshold: float = 0.01 # Min number of views that should reconstruct a point for it to be valid depthmap_min_consistent_views: int = 3 # Save debug files with partial reconstruction results depthmap_save_debug_files: bool = False ################################## # Params for multi-processing/threading ################################## # Number of threads to use processes: int = 1 # When processes > 1, number of threads used for reading images read_processes: int = 4 ################################## # Params for submodel split and merge ################################## # Average number of images per submodel submodel_size: int = 80 # Radius of the overlapping region between submodels submodel_overlap: float = 30.0 # Relative path to the submodels directory submodels_relpath: str = "submodels" # Template to generate the relative path to a submodel directory submodel_relpath_template: str = "submodels/submodel_%04d" # Template to generate the relative path to a submodel images directory submodel_images_relpath_template: str = "submodels/submodel_%04d/images" def default_config(): """Return default configuration""" return asdict(OpenSfMConfig()) def load_config(filepath): """DEPRECATED: = Load config from a config.yaml filepath""" if not os.path.isfile(filepath): return default_config() with open(filepath) as fin: return load_config_from_fileobject(fin) def load_config_from_fileobject(f): """Load config from a config.yaml fileobject""" config = default_config() new_config = yaml.safe_load(f) if new_config: for k, v in new_config.items(): config[k] = v return config