three.MF.V3.Settings.Scan
1from MF.V3.Settings.Camera import Camera as MF_V3_Settings_Camera_Camera 2from MF.V3.Settings.Capture import Capture as MF_V3_Settings_Capture_Capture 3from MF.V3.Settings.Projector import Projector as MF_V3_Settings_Projector_Projector 4from MF.V3.Settings.Turntable import Turntable as MF_V3_Settings_Turntable_Turntable 5from enum import Enum 6from typing import List 7 8 9class Scan: 10 11 """ 12 Scan settings. 13 """ 14 class Processing: 15 16 """ 17 Scan processing settings. 18 """ 19 class PhaseEdgeDetection: 20 """ 21 Phase edge detection settings. 22 23 Phase edge detection produces a binary mask indicating the edges of a horizontal/vertical pair of phase images. Since flat geometries give a constant phase image gradient, we use the second derivative (Laplacian) of the phase image to detect edges rather than the gradient. 24 25 The edge mask generated by phase edge detection is an input to both phase filtering and adaptive sampling. If neither of these are enabled, the phase edge detection settings have no effect on the output point cloud. 26 """ 27 def __init__(self, threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int): 28 # The edge detection threshold. 29 self.threshold = threshold 30 # The Laplacian kernel radius. This must be in the range [1..5]. 31 self.laplacianKernelRadius = laplacianKernelRadius 32 """ 33 Gaussian blur kernel radius. (Optional) To disable, set to 0. 34 The phase images can optionally blurred before taking the Laplacian to reduce noise. 35 However as a result, the detected edges are wider. 36 """ 37 self.gaussianBlurRadius = gaussianBlurRadius 38 """ 39 Gaussian blur kernel standard deviation. 40 This parameter is ignored if `gaussianBlurSize` is zero. 41 """ 42 self.gaussianBlurStdDev = gaussianBlurStdDev 43 """ 44 The maximum image width for processing. (Optional) To disable, set to 0. 45 If this value is greater than zero, the phase images are resized to the maximum width prior 46 to computing the Laplacian and the the detected edges are then upsampled to the original size. 47 This would be done to speed up processing or to detect edges on a larger scale. 48 """ 49 self.maximumWidthForProcessing = maximumWidthForProcessing 50 51 class PhaseFilter: 52 53 """ 54 Phase filter settings. 55 """ 56 def __init__(self, kernelRadius: int, spatialWeightStdDev: float): 57 """ 58 The filter kernel radius. 59 A neighboring value must be within this radius to be included in the filter. 60 If the kernel radius is set to zero, the phase filtering is disabled. 61 """ 62 self.kernelRadius = kernelRadius 63 """ 64 The standard deviation of the spatial weights. 65 The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance 66 to the central value and $s$ is the spatial weight standard deviation. 67 If the spatial weight standard deviation is set to zero, all the spatial 68 weights are uniformly set to 1. 69 """ 70 self.spatialWeightStdDev = spatialWeightStdDev 71 72 class AdaptiveSampling: 73 """ 74 Adaptive sampling settings 75 76 Adaptive sampling will downsample points in regions of low detail 77 and keep points in regions of high detail. 78 """ 79 class Type(Enum): 80 NONE = "NONE" # Do not use adaptive sampling. 81 REGULAR = "REGULAR" # Use a regular sampling mask in regions of low detail. 82 RANDOM = "RANDOM" # Use a random sampling mask in regions of low detail. 83 84 def __init__(self, type: 'Type', rate: float): 85 # Sampling type. 86 self.type = type 87 # The sample rate [0..1] for the regions of low detail. 88 self.rate = rate 89 90 class PointClipping: 91 92 """ 93 Point clipping settings. 94 """ 95 class Type(Enum): 96 97 """ 98 Point clipping type. 99 """ 100 OutsideCube = "OutsideCube" # Clip points outside a unit cube. 101 OutsideCylinder = "OutsideCylinder" # Clip points outside a unit cylinder. 102 OutsideSphere = "OutsideSphere" # Clip points outside a unit sphere. 103 InsideCube = "InsideCube" # Clip points inside a unit cube. 104 InsideCylinder = "InsideCylinder" # Clip points inside a unit cylinder. 105 InsideSphere = "InsideSphere" # Clip points inside a unit sphere. 106 107 def __init__(self, type: 'Type', transform: List[float] = None): 108 # Point clipping type. 109 self.type = type 110 # 4x4 transform mapping 3D points to the canonical point clipping coordinates. 111 self.transform = transform 112 113 class NormalEstimation: 114 115 """ 116 Normal estimation settings. 117 """ 118 class Method(Enum): 119 NORMAL_LLS = "NORMAL_LLS" # Linear least squares method 120 NORMAL_OPEN3D = "NORMAL_OPEN3D" # Open3D method using KD tree search for nearest neighbors 121 122 def __init__(self, method: 'Method', maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool): 123 # Normal estimation method. 124 self.method = method 125 """ 126 Maximum number of nearest neighbors used to compute the normal. 127 This value is only used with the NORMAL_OPEN3D method. 128 """ 129 self.maximumNeighbourCount = maximumNeighbourCount 130 # Maximum radius for a point to be considered a neighbour. 131 self.maximumNeighbourRadius = maximumNeighbourRadius 132 # Use the maximum neighbour count. 133 self.useMaximumNeighbourCount = useMaximumNeighbourCount 134 # Use the maximum neighbour radius. 135 self.useMaximumNeighbourRadius = useMaximumNeighbourRadius 136 137 class OutlierRemoval: 138 139 """ 140 Outlier removal settings. 141 """ 142 def __init__(self, neighbourCount: int, neighbourRadius: float): 143 # The minimum number of points within the radius for a point to be retained. 144 self.neighbourCount = neighbourCount 145 # The neighbour search radius. 146 self.neighbourRadius = neighbourRadius 147 148 def __init__(self, projectorSampleRate: float = None, imageSampleRate: float = None, edgeDetection: 'PhaseEdgeDetection' = None, phaseFilter: 'PhaseFilter' = None, adaptiveSampling: 'AdaptiveSampling' = None, pointClipping: List['PointClipping'] = None, normalEstimation: 'NormalEstimation' = None, outlierRemoval: 'OutlierRemoval' = None): 149 # Projector sample rate. 150 self.projectorSampleRate = projectorSampleRate 151 # Image sample rate. 152 self.imageSampleRate = imageSampleRate 153 # Phase edge detection settings. 154 self.edgeDetection = edgeDetection 155 # Phase filter settings. 156 self.phaseFilter = phaseFilter 157 # Adaptive sampling settings. 158 self.adaptiveSampling = adaptiveSampling 159 # Point clipping settings. 160 self.pointClipping = pointClipping 161 # Normal estimation settings. 162 self.normalEstimation = normalEstimation 163 # Outlier removal settings. 164 self.outlierRemoval = outlierRemoval 165 166 def __init__(self, camera: MF_V3_Settings_Camera_Camera = None, projector: MF_V3_Settings_Projector_Projector = None, turntable: MF_V3_Settings_Turntable_Turntable = None, capture: MF_V3_Settings_Capture_Capture = None, processing: 'Processing' = None, alignWithScanner: bool = None, centerAtOrigin: bool = None): 167 # Camera settings. 168 self.camera = camera 169 # Projector settings. 170 self.projector = projector 171 # Turntable settings. 172 self.turntable = turntable 173 # Capture settings. 174 self.capture = capture 175 # Processing settings. 176 self.processing = processing 177 # Align the scan with the scanner. 178 self.alignWithScanner = alignWithScanner 179 # Center the scan at the origin. 180 self.centerAtOrigin = centerAtOrigin
class
Scan:
10class Scan: 11 12 """ 13 Scan settings. 14 """ 15 class Processing: 16 17 """ 18 Scan processing settings. 19 """ 20 class PhaseEdgeDetection: 21 """ 22 Phase edge detection settings. 23 24 Phase edge detection produces a binary mask indicating the edges of a horizontal/vertical pair of phase images. Since flat geometries give a constant phase image gradient, we use the second derivative (Laplacian) of the phase image to detect edges rather than the gradient. 25 26 The edge mask generated by phase edge detection is an input to both phase filtering and adaptive sampling. If neither of these are enabled, the phase edge detection settings have no effect on the output point cloud. 27 """ 28 def __init__(self, threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int): 29 # The edge detection threshold. 30 self.threshold = threshold 31 # The Laplacian kernel radius. This must be in the range [1..5]. 32 self.laplacianKernelRadius = laplacianKernelRadius 33 """ 34 Gaussian blur kernel radius. (Optional) To disable, set to 0. 35 The phase images can optionally blurred before taking the Laplacian to reduce noise. 36 However as a result, the detected edges are wider. 37 """ 38 self.gaussianBlurRadius = gaussianBlurRadius 39 """ 40 Gaussian blur kernel standard deviation. 41 This parameter is ignored if `gaussianBlurSize` is zero. 42 """ 43 self.gaussianBlurStdDev = gaussianBlurStdDev 44 """ 45 The maximum image width for processing. (Optional) To disable, set to 0. 46 If this value is greater than zero, the phase images are resized to the maximum width prior 47 to computing the Laplacian and the the detected edges are then upsampled to the original size. 48 This would be done to speed up processing or to detect edges on a larger scale. 49 """ 50 self.maximumWidthForProcessing = maximumWidthForProcessing 51 52 class PhaseFilter: 53 54 """ 55 Phase filter settings. 56 """ 57 def __init__(self, kernelRadius: int, spatialWeightStdDev: float): 58 """ 59 The filter kernel radius. 60 A neighboring value must be within this radius to be included in the filter. 61 If the kernel radius is set to zero, the phase filtering is disabled. 62 """ 63 self.kernelRadius = kernelRadius 64 """ 65 The standard deviation of the spatial weights. 66 The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance 67 to the central value and $s$ is the spatial weight standard deviation. 68 If the spatial weight standard deviation is set to zero, all the spatial 69 weights are uniformly set to 1. 70 """ 71 self.spatialWeightStdDev = spatialWeightStdDev 72 73 class AdaptiveSampling: 74 """ 75 Adaptive sampling settings 76 77 Adaptive sampling will downsample points in regions of low detail 78 and keep points in regions of high detail. 79 """ 80 class Type(Enum): 81 NONE = "NONE" # Do not use adaptive sampling. 82 REGULAR = "REGULAR" # Use a regular sampling mask in regions of low detail. 83 RANDOM = "RANDOM" # Use a random sampling mask in regions of low detail. 84 85 def __init__(self, type: 'Type', rate: float): 86 # Sampling type. 87 self.type = type 88 # The sample rate [0..1] for the regions of low detail. 89 self.rate = rate 90 91 class PointClipping: 92 93 """ 94 Point clipping settings. 95 """ 96 class Type(Enum): 97 98 """ 99 Point clipping type. 100 """ 101 OutsideCube = "OutsideCube" # Clip points outside a unit cube. 102 OutsideCylinder = "OutsideCylinder" # Clip points outside a unit cylinder. 103 OutsideSphere = "OutsideSphere" # Clip points outside a unit sphere. 104 InsideCube = "InsideCube" # Clip points inside a unit cube. 105 InsideCylinder = "InsideCylinder" # Clip points inside a unit cylinder. 106 InsideSphere = "InsideSphere" # Clip points inside a unit sphere. 107 108 def __init__(self, type: 'Type', transform: List[float] = None): 109 # Point clipping type. 110 self.type = type 111 # 4x4 transform mapping 3D points to the canonical point clipping coordinates. 112 self.transform = transform 113 114 class NormalEstimation: 115 116 """ 117 Normal estimation settings. 118 """ 119 class Method(Enum): 120 NORMAL_LLS = "NORMAL_LLS" # Linear least squares method 121 NORMAL_OPEN3D = "NORMAL_OPEN3D" # Open3D method using KD tree search for nearest neighbors 122 123 def __init__(self, method: 'Method', maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool): 124 # Normal estimation method. 125 self.method = method 126 """ 127 Maximum number of nearest neighbors used to compute the normal. 128 This value is only used with the NORMAL_OPEN3D method. 129 """ 130 self.maximumNeighbourCount = maximumNeighbourCount 131 # Maximum radius for a point to be considered a neighbour. 132 self.maximumNeighbourRadius = maximumNeighbourRadius 133 # Use the maximum neighbour count. 134 self.useMaximumNeighbourCount = useMaximumNeighbourCount 135 # Use the maximum neighbour radius. 136 self.useMaximumNeighbourRadius = useMaximumNeighbourRadius 137 138 class OutlierRemoval: 139 140 """ 141 Outlier removal settings. 142 """ 143 def __init__(self, neighbourCount: int, neighbourRadius: float): 144 # The minimum number of points within the radius for a point to be retained. 145 self.neighbourCount = neighbourCount 146 # The neighbour search radius. 147 self.neighbourRadius = neighbourRadius 148 149 def __init__(self, projectorSampleRate: float = None, imageSampleRate: float = None, edgeDetection: 'PhaseEdgeDetection' = None, phaseFilter: 'PhaseFilter' = None, adaptiveSampling: 'AdaptiveSampling' = None, pointClipping: List['PointClipping'] = None, normalEstimation: 'NormalEstimation' = None, outlierRemoval: 'OutlierRemoval' = None): 150 # Projector sample rate. 151 self.projectorSampleRate = projectorSampleRate 152 # Image sample rate. 153 self.imageSampleRate = imageSampleRate 154 # Phase edge detection settings. 155 self.edgeDetection = edgeDetection 156 # Phase filter settings. 157 self.phaseFilter = phaseFilter 158 # Adaptive sampling settings. 159 self.adaptiveSampling = adaptiveSampling 160 # Point clipping settings. 161 self.pointClipping = pointClipping 162 # Normal estimation settings. 163 self.normalEstimation = normalEstimation 164 # Outlier removal settings. 165 self.outlierRemoval = outlierRemoval 166 167 def __init__(self, camera: MF_V3_Settings_Camera_Camera = None, projector: MF_V3_Settings_Projector_Projector = None, turntable: MF_V3_Settings_Turntable_Turntable = None, capture: MF_V3_Settings_Capture_Capture = None, processing: 'Processing' = None, alignWithScanner: bool = None, centerAtOrigin: bool = None): 168 # Camera settings. 169 self.camera = camera 170 # Projector settings. 171 self.projector = projector 172 # Turntable settings. 173 self.turntable = turntable 174 # Capture settings. 175 self.capture = capture 176 # Processing settings. 177 self.processing = processing 178 # Align the scan with the scanner. 179 self.alignWithScanner = alignWithScanner 180 # Center the scan at the origin. 181 self.centerAtOrigin = centerAtOrigin
Scan settings.
Scan( camera: MF.V3.Settings.Camera.Camera = None, projector: MF.V3.Settings.Projector.Projector = None, turntable: MF.V3.Settings.Turntable.Turntable = None, capture: MF.V3.Settings.Capture.Capture = None, processing: Scan.Processing = None, alignWithScanner: bool = None, centerAtOrigin: bool = None)
167 def __init__(self, camera: MF_V3_Settings_Camera_Camera = None, projector: MF_V3_Settings_Projector_Projector = None, turntable: MF_V3_Settings_Turntable_Turntable = None, capture: MF_V3_Settings_Capture_Capture = None, processing: 'Processing' = None, alignWithScanner: bool = None, centerAtOrigin: bool = None): 168 # Camera settings. 169 self.camera = camera 170 # Projector settings. 171 self.projector = projector 172 # Turntable settings. 173 self.turntable = turntable 174 # Capture settings. 175 self.capture = capture 176 # Processing settings. 177 self.processing = processing 178 # Align the scan with the scanner. 179 self.alignWithScanner = alignWithScanner 180 # Center the scan at the origin. 181 self.centerAtOrigin = centerAtOrigin
class
Scan.Processing:
15 class Processing: 16 17 """ 18 Scan processing settings. 19 """ 20 class PhaseEdgeDetection: 21 """ 22 Phase edge detection settings. 23 24 Phase edge detection produces a binary mask indicating the edges of a horizontal/vertical pair of phase images. Since flat geometries give a constant phase image gradient, we use the second derivative (Laplacian) of the phase image to detect edges rather than the gradient. 25 26 The edge mask generated by phase edge detection is an input to both phase filtering and adaptive sampling. If neither of these are enabled, the phase edge detection settings have no effect on the output point cloud. 27 """ 28 def __init__(self, threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int): 29 # The edge detection threshold. 30 self.threshold = threshold 31 # The Laplacian kernel radius. This must be in the range [1..5]. 32 self.laplacianKernelRadius = laplacianKernelRadius 33 """ 34 Gaussian blur kernel radius. (Optional) To disable, set to 0. 35 The phase images can optionally blurred before taking the Laplacian to reduce noise. 36 However as a result, the detected edges are wider. 37 """ 38 self.gaussianBlurRadius = gaussianBlurRadius 39 """ 40 Gaussian blur kernel standard deviation. 41 This parameter is ignored if `gaussianBlurSize` is zero. 42 """ 43 self.gaussianBlurStdDev = gaussianBlurStdDev 44 """ 45 The maximum image width for processing. (Optional) To disable, set to 0. 46 If this value is greater than zero, the phase images are resized to the maximum width prior 47 to computing the Laplacian and the the detected edges are then upsampled to the original size. 48 This would be done to speed up processing or to detect edges on a larger scale. 49 """ 50 self.maximumWidthForProcessing = maximumWidthForProcessing 51 52 class PhaseFilter: 53 54 """ 55 Phase filter settings. 56 """ 57 def __init__(self, kernelRadius: int, spatialWeightStdDev: float): 58 """ 59 The filter kernel radius. 60 A neighboring value must be within this radius to be included in the filter. 61 If the kernel radius is set to zero, the phase filtering is disabled. 62 """ 63 self.kernelRadius = kernelRadius 64 """ 65 The standard deviation of the spatial weights. 66 The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance 67 to the central value and $s$ is the spatial weight standard deviation. 68 If the spatial weight standard deviation is set to zero, all the spatial 69 weights are uniformly set to 1. 70 """ 71 self.spatialWeightStdDev = spatialWeightStdDev 72 73 class AdaptiveSampling: 74 """ 75 Adaptive sampling settings 76 77 Adaptive sampling will downsample points in regions of low detail 78 and keep points in regions of high detail. 79 """ 80 class Type(Enum): 81 NONE = "NONE" # Do not use adaptive sampling. 82 REGULAR = "REGULAR" # Use a regular sampling mask in regions of low detail. 83 RANDOM = "RANDOM" # Use a random sampling mask in regions of low detail. 84 85 def __init__(self, type: 'Type', rate: float): 86 # Sampling type. 87 self.type = type 88 # The sample rate [0..1] for the regions of low detail. 89 self.rate = rate 90 91 class PointClipping: 92 93 """ 94 Point clipping settings. 95 """ 96 class Type(Enum): 97 98 """ 99 Point clipping type. 100 """ 101 OutsideCube = "OutsideCube" # Clip points outside a unit cube. 102 OutsideCylinder = "OutsideCylinder" # Clip points outside a unit cylinder. 103 OutsideSphere = "OutsideSphere" # Clip points outside a unit sphere. 104 InsideCube = "InsideCube" # Clip points inside a unit cube. 105 InsideCylinder = "InsideCylinder" # Clip points inside a unit cylinder. 106 InsideSphere = "InsideSphere" # Clip points inside a unit sphere. 107 108 def __init__(self, type: 'Type', transform: List[float] = None): 109 # Point clipping type. 110 self.type = type 111 # 4x4 transform mapping 3D points to the canonical point clipping coordinates. 112 self.transform = transform 113 114 class NormalEstimation: 115 116 """ 117 Normal estimation settings. 118 """ 119 class Method(Enum): 120 NORMAL_LLS = "NORMAL_LLS" # Linear least squares method 121 NORMAL_OPEN3D = "NORMAL_OPEN3D" # Open3D method using KD tree search for nearest neighbors 122 123 def __init__(self, method: 'Method', maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool): 124 # Normal estimation method. 125 self.method = method 126 """ 127 Maximum number of nearest neighbors used to compute the normal. 128 This value is only used with the NORMAL_OPEN3D method. 129 """ 130 self.maximumNeighbourCount = maximumNeighbourCount 131 # Maximum radius for a point to be considered a neighbour. 132 self.maximumNeighbourRadius = maximumNeighbourRadius 133 # Use the maximum neighbour count. 134 self.useMaximumNeighbourCount = useMaximumNeighbourCount 135 # Use the maximum neighbour radius. 136 self.useMaximumNeighbourRadius = useMaximumNeighbourRadius 137 138 class OutlierRemoval: 139 140 """ 141 Outlier removal settings. 142 """ 143 def __init__(self, neighbourCount: int, neighbourRadius: float): 144 # The minimum number of points within the radius for a point to be retained. 145 self.neighbourCount = neighbourCount 146 # The neighbour search radius. 147 self.neighbourRadius = neighbourRadius 148 149 def __init__(self, projectorSampleRate: float = None, imageSampleRate: float = None, edgeDetection: 'PhaseEdgeDetection' = None, phaseFilter: 'PhaseFilter' = None, adaptiveSampling: 'AdaptiveSampling' = None, pointClipping: List['PointClipping'] = None, normalEstimation: 'NormalEstimation' = None, outlierRemoval: 'OutlierRemoval' = None): 150 # Projector sample rate. 151 self.projectorSampleRate = projectorSampleRate 152 # Image sample rate. 153 self.imageSampleRate = imageSampleRate 154 # Phase edge detection settings. 155 self.edgeDetection = edgeDetection 156 # Phase filter settings. 157 self.phaseFilter = phaseFilter 158 # Adaptive sampling settings. 159 self.adaptiveSampling = adaptiveSampling 160 # Point clipping settings. 161 self.pointClipping = pointClipping 162 # Normal estimation settings. 163 self.normalEstimation = normalEstimation 164 # Outlier removal settings. 165 self.outlierRemoval = outlierRemoval
Scan processing settings.
Scan.Processing( projectorSampleRate: float = None, imageSampleRate: float = None, edgeDetection: Scan.Processing.PhaseEdgeDetection = None, phaseFilter: Scan.Processing.PhaseFilter = None, adaptiveSampling: Scan.Processing.AdaptiveSampling = None, pointClipping: List[Scan.Processing.PointClipping] = None, normalEstimation: Scan.Processing.NormalEstimation = None, outlierRemoval: Scan.Processing.OutlierRemoval = None)
149 def __init__(self, projectorSampleRate: float = None, imageSampleRate: float = None, edgeDetection: 'PhaseEdgeDetection' = None, phaseFilter: 'PhaseFilter' = None, adaptiveSampling: 'AdaptiveSampling' = None, pointClipping: List['PointClipping'] = None, normalEstimation: 'NormalEstimation' = None, outlierRemoval: 'OutlierRemoval' = None): 150 # Projector sample rate. 151 self.projectorSampleRate = projectorSampleRate 152 # Image sample rate. 153 self.imageSampleRate = imageSampleRate 154 # Phase edge detection settings. 155 self.edgeDetection = edgeDetection 156 # Phase filter settings. 157 self.phaseFilter = phaseFilter 158 # Adaptive sampling settings. 159 self.adaptiveSampling = adaptiveSampling 160 # Point clipping settings. 161 self.pointClipping = pointClipping 162 # Normal estimation settings. 163 self.normalEstimation = normalEstimation 164 # Outlier removal settings. 165 self.outlierRemoval = outlierRemoval
class
Scan.Processing.PhaseEdgeDetection:
20 class PhaseEdgeDetection: 21 """ 22 Phase edge detection settings. 23 24 Phase edge detection produces a binary mask indicating the edges of a horizontal/vertical pair of phase images. Since flat geometries give a constant phase image gradient, we use the second derivative (Laplacian) of the phase image to detect edges rather than the gradient. 25 26 The edge mask generated by phase edge detection is an input to both phase filtering and adaptive sampling. If neither of these are enabled, the phase edge detection settings have no effect on the output point cloud. 27 """ 28 def __init__(self, threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int): 29 # The edge detection threshold. 30 self.threshold = threshold 31 # The Laplacian kernel radius. This must be in the range [1..5]. 32 self.laplacianKernelRadius = laplacianKernelRadius 33 """ 34 Gaussian blur kernel radius. (Optional) To disable, set to 0. 35 The phase images can optionally blurred before taking the Laplacian to reduce noise. 36 However as a result, the detected edges are wider. 37 """ 38 self.gaussianBlurRadius = gaussianBlurRadius 39 """ 40 Gaussian blur kernel standard deviation. 41 This parameter is ignored if `gaussianBlurSize` is zero. 42 """ 43 self.gaussianBlurStdDev = gaussianBlurStdDev 44 """ 45 The maximum image width for processing. (Optional) To disable, set to 0. 46 If this value is greater than zero, the phase images are resized to the maximum width prior 47 to computing the Laplacian and the the detected edges are then upsampled to the original size. 48 This would be done to speed up processing or to detect edges on a larger scale. 49 """ 50 self.maximumWidthForProcessing = maximumWidthForProcessing
Phase edge detection settings.
Phase edge detection produces a binary mask indicating the edges of a horizontal/vertical pair of phase images. Since flat geometries give a constant phase image gradient, we use the second derivative (Laplacian) of the phase image to detect edges rather than the gradient.
The edge mask generated by phase edge detection is an input to both phase filtering and adaptive sampling. If neither of these are enabled, the phase edge detection settings have no effect on the output point cloud.
Scan.Processing.PhaseEdgeDetection( threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int)
28 def __init__(self, threshold: float, laplacianKernelRadius: int, gaussianBlurRadius: int, gaussianBlurStdDev: float, maximumWidthForProcessing: int): 29 # The edge detection threshold. 30 self.threshold = threshold 31 # The Laplacian kernel radius. This must be in the range [1..5]. 32 self.laplacianKernelRadius = laplacianKernelRadius 33 """ 34 Gaussian blur kernel radius. (Optional) To disable, set to 0. 35 The phase images can optionally blurred before taking the Laplacian to reduce noise. 36 However as a result, the detected edges are wider. 37 """ 38 self.gaussianBlurRadius = gaussianBlurRadius 39 """ 40 Gaussian blur kernel standard deviation. 41 This parameter is ignored if `gaussianBlurSize` is zero. 42 """ 43 self.gaussianBlurStdDev = gaussianBlurStdDev 44 """ 45 The maximum image width for processing. (Optional) To disable, set to 0. 46 If this value is greater than zero, the phase images are resized to the maximum width prior 47 to computing the Laplacian and the the detected edges are then upsampled to the original size. 48 This would be done to speed up processing or to detect edges on a larger scale. 49 """ 50 self.maximumWidthForProcessing = maximumWidthForProcessing
laplacianKernelRadius
Gaussian blur kernel radius. (Optional) To disable, set to 0. The phase images can optionally blurred before taking the Laplacian to reduce noise. However as a result, the detected edges are wider.
gaussianBlurRadius
Gaussian blur kernel standard deviation.
This parameter is ignored if gaussianBlurSize is zero.
gaussianBlurStdDev
The maximum image width for processing. (Optional) To disable, set to 0. If this value is greater than zero, the phase images are resized to the maximum width prior to computing the Laplacian and the the detected edges are then upsampled to the original size. This would be done to speed up processing or to detect edges on a larger scale.
class
Scan.Processing.PhaseFilter:
52 class PhaseFilter: 53 54 """ 55 Phase filter settings. 56 """ 57 def __init__(self, kernelRadius: int, spatialWeightStdDev: float): 58 """ 59 The filter kernel radius. 60 A neighboring value must be within this radius to be included in the filter. 61 If the kernel radius is set to zero, the phase filtering is disabled. 62 """ 63 self.kernelRadius = kernelRadius 64 """ 65 The standard deviation of the spatial weights. 66 The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance 67 to the central value and $s$ is the spatial weight standard deviation. 68 If the spatial weight standard deviation is set to zero, all the spatial 69 weights are uniformly set to 1. 70 """ 71 self.spatialWeightStdDev = spatialWeightStdDev
Phase filter settings.
Scan.Processing.PhaseFilter(kernelRadius: int, spatialWeightStdDev: float)
57 def __init__(self, kernelRadius: int, spatialWeightStdDev: float): 58 """ 59 The filter kernel radius. 60 A neighboring value must be within this radius to be included in the filter. 61 If the kernel radius is set to zero, the phase filtering is disabled. 62 """ 63 self.kernelRadius = kernelRadius 64 """ 65 The standard deviation of the spatial weights. 66 The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance 67 to the central value and $s$ is the spatial weight standard deviation. 68 If the spatial weight standard deviation is set to zero, all the spatial 69 weights are uniformly set to 1. 70 """ 71 self.spatialWeightStdDev = spatialWeightStdDev
The filter kernel radius. A neighboring value must be within this radius to be included in the filter. If the kernel radius is set to zero, the phase filtering is disabled.
kernelRadius
The standard deviation of the spatial weights. The weight of a neighboring value is $\exp(-(r/s)^2)$ where $r$ is the distance to the central value and $s$ is the spatial weight standard deviation. If the spatial weight standard deviation is set to zero, all the spatial weights are uniformly set to 1.
class
Scan.Processing.AdaptiveSampling:
73 class AdaptiveSampling: 74 """ 75 Adaptive sampling settings 76 77 Adaptive sampling will downsample points in regions of low detail 78 and keep points in regions of high detail. 79 """ 80 class Type(Enum): 81 NONE = "NONE" # Do not use adaptive sampling. 82 REGULAR = "REGULAR" # Use a regular sampling mask in regions of low detail. 83 RANDOM = "RANDOM" # Use a random sampling mask in regions of low detail. 84 85 def __init__(self, type: 'Type', rate: float): 86 # Sampling type. 87 self.type = type 88 # The sample rate [0..1] for the regions of low detail. 89 self.rate = rate
Adaptive sampling settings
Adaptive sampling will downsample points in regions of low detail and keep points in regions of high detail.
Scan.Processing.AdaptiveSampling( type: Scan.Processing.AdaptiveSampling.Type, rate: float)
class
Scan.Processing.AdaptiveSampling.Type(enum.Enum):
class
Scan.Processing.PointClipping:
91 class PointClipping: 92 93 """ 94 Point clipping settings. 95 """ 96 class Type(Enum): 97 98 """ 99 Point clipping type. 100 """ 101 OutsideCube = "OutsideCube" # Clip points outside a unit cube. 102 OutsideCylinder = "OutsideCylinder" # Clip points outside a unit cylinder. 103 OutsideSphere = "OutsideSphere" # Clip points outside a unit sphere. 104 InsideCube = "InsideCube" # Clip points inside a unit cube. 105 InsideCylinder = "InsideCylinder" # Clip points inside a unit cylinder. 106 InsideSphere = "InsideSphere" # Clip points inside a unit sphere. 107 108 def __init__(self, type: 'Type', transform: List[float] = None): 109 # Point clipping type. 110 self.type = type 111 # 4x4 transform mapping 3D points to the canonical point clipping coordinates. 112 self.transform = transform
Point clipping settings.
Scan.Processing.PointClipping( type: Scan.Processing.PointClipping.Type, transform: List[float] = None)
class
Scan.Processing.PointClipping.Type(enum.Enum):
96 class Type(Enum): 97 98 """ 99 Point clipping type. 100 """ 101 OutsideCube = "OutsideCube" # Clip points outside a unit cube. 102 OutsideCylinder = "OutsideCylinder" # Clip points outside a unit cylinder. 103 OutsideSphere = "OutsideSphere" # Clip points outside a unit sphere. 104 InsideCube = "InsideCube" # Clip points inside a unit cube. 105 InsideCylinder = "InsideCylinder" # Clip points inside a unit cylinder. 106 InsideSphere = "InsideSphere" # Clip points inside a unit sphere.
Point clipping type.
class
Scan.Processing.NormalEstimation:
114 class NormalEstimation: 115 116 """ 117 Normal estimation settings. 118 """ 119 class Method(Enum): 120 NORMAL_LLS = "NORMAL_LLS" # Linear least squares method 121 NORMAL_OPEN3D = "NORMAL_OPEN3D" # Open3D method using KD tree search for nearest neighbors 122 123 def __init__(self, method: 'Method', maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool): 124 # Normal estimation method. 125 self.method = method 126 """ 127 Maximum number of nearest neighbors used to compute the normal. 128 This value is only used with the NORMAL_OPEN3D method. 129 """ 130 self.maximumNeighbourCount = maximumNeighbourCount 131 # Maximum radius for a point to be considered a neighbour. 132 self.maximumNeighbourRadius = maximumNeighbourRadius 133 # Use the maximum neighbour count. 134 self.useMaximumNeighbourCount = useMaximumNeighbourCount 135 # Use the maximum neighbour radius. 136 self.useMaximumNeighbourRadius = useMaximumNeighbourRadius
Normal estimation settings.
Scan.Processing.NormalEstimation( method: Scan.Processing.NormalEstimation.Method, maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool)
123 def __init__(self, method: 'Method', maximumNeighbourCount: int, maximumNeighbourRadius: float, useMaximumNeighbourCount: bool, useMaximumNeighbourRadius: bool): 124 # Normal estimation method. 125 self.method = method 126 """ 127 Maximum number of nearest neighbors used to compute the normal. 128 This value is only used with the NORMAL_OPEN3D method. 129 """ 130 self.maximumNeighbourCount = maximumNeighbourCount 131 # Maximum radius for a point to be considered a neighbour. 132 self.maximumNeighbourRadius = maximumNeighbourRadius 133 # Use the maximum neighbour count. 134 self.useMaximumNeighbourCount = useMaximumNeighbourCount 135 # Use the maximum neighbour radius. 136 self.useMaximumNeighbourRadius = useMaximumNeighbourRadius
class
Scan.Processing.NormalEstimation.Method(enum.Enum):
class
Scan.Processing.OutlierRemoval:
138 class OutlierRemoval: 139 140 """ 141 Outlier removal settings. 142 """ 143 def __init__(self, neighbourCount: int, neighbourRadius: float): 144 # The minimum number of points within the radius for a point to be retained. 145 self.neighbourCount = neighbourCount 146 # The neighbour search radius. 147 self.neighbourRadius = neighbourRadius
Outlier removal settings.