From cf292c26df3c09633374ea82ca522cc3ef410c3f Mon Sep 17 00:00:00 2001 From: John Lindsay Date: Tue, 8 Jan 2019 12:35:37 -0500 Subject: [PATCH] Updates for 0.13 release Updates for 0.13 release --- WhiteboxToolsManual.pdf | Bin 4405324 -> 4411939 bytes manual/WhiteboxToolsManual.md | 309 ++++++++++++------ readme.txt | 3 +- src/tools/data_tools/set_nodata_value.rs | 13 +- src/tools/image_analysis/mosaic.rs | 2 +- .../image_analysis/mosaic_with_feathering.rs | 2 +- whitebox_tools.py | 47 ++- 7 files changed, 255 insertions(+), 121 deletions(-) diff --git a/WhiteboxToolsManual.pdf b/WhiteboxToolsManual.pdf index 88c0932cf8ce30bc9f7af5f124d59cbb91adcf03..afc63851c12efcf8fef101fd8638e4fb1d9d835b 100644 GIT binary patch delta 367332 zcmZ5`V|*WB({5wiY}~lvKelb#Nn<-3+qTu%w$<2HgT`u{o;>e4AD;Joy7zbO%(eH< z>~D8==9;>W1}`d(0k2a~CpKOwB!(9fytu@T&*<@5dHW41>9he&VIi3K+Ju3gWUEdG zX`X9(kfyZEhmlt3#ZLurfk6EH@XjtyW=6K~9_ts`GI1LqRR61YtQQEQXWSltV-%=$ 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Lindsay © 2017-2019 \ Geomorphometry and Hydrogeomatics Research Group \ University of Guelph \ Guelph, Canada \ -January 5, 2019 \ +January 8, 2019 \ ![](./img/GHRGLogoSm.png){width=54% height=54%} @@ -1210,7 +1210,13 @@ remove_polygon_holes( #### 8.1.17 SetNodataValue -Assign a specified value in an input image to the NoData value. +This tool will re-assign a user-defined background value in an input raster image the **NoData** value. +More precisely, the NoData value will be changed to the specified background value and any existing +grid cells containing the previous NoData value, if it had been defined, will be changed to this new +value. Most WhiteboxTools tools recognize NoData grid cells and treat them specially. NoData grid cells +are also often displayed transparently by GIS software. The user must specify the names of the input and +output rasters and the background value. The default background value is zero, although any numeric +value is possible. *Parameters*: @@ -1561,46 +1567,7 @@ block_minimum_gridding( ``` -#### 8.2.4 BufferVector - -This tool - -*See Also*: - -*Parameters*: - -**Flag** **Description** -------------------- --------------- --i, -\-input Input vector file --o, -\-output Output vector file --\-dist, -\-distance Buffer distance --\-dissolve Optional flag to request the output polygons be dissolved --\-snap Snap tolerance - - -*Python function*: - -~~~~{.python} -buffer_vector( - i, - output, - distance=10.0, - dissolve=True, - snap=0.0, - callback=default_callback) -~~~~ - -*Command-line Interface*: - -``` ->>./whitebox_tools -r=BufferVector -v --wd="/path/to/data/" ^ --input=layer1.shp -o=out_file.shp --dist=25.0 --dissolve - - -``` - - -#### 8.2.5 Centroid +#### 8.2.4 Centroid This tool calculates the centroid, or average location, of raster polygon objects. For vector features, use the `CentroidVector` tool instead. @@ -1641,7 +1608,7 @@ centroid( ``` -#### 8.2.6 CentroidVector +#### 8.2.5 CentroidVector This can be used to identify the centroid point of a vector polyline or polygon feature or a group of vector points. The output is a vector shapefile of points. For multi-part polyline or polygon features, @@ -1681,7 +1648,7 @@ centroid_vector( ``` -#### 8.2.7 Clump +#### 8.2.6 Clump Groups cells that form physically discrete areas, assigning them unique identifiers. @@ -1716,7 +1683,7 @@ clump( ``` -#### 8.2.8 ConstructVectorTin +#### 8.2.7 ConstructVectorTin This tool creates a vector triangular irregular network (TIN) for a set of vector points. @@ -1754,7 +1721,7 @@ construct_vector_tin( ``` -#### 8.2.9 CreateHexagonalVectorGrid +#### 8.2.8 CreateHexagonalVectorGrid This tool can be used to create a hexagonal vector grid. The extent of the hexagonal grid is based on the extent of a user-specified base file (any supported raster format, @@ -1793,7 +1760,7 @@ create_hexagonal_vector_grid( ``` -#### 8.2.10 CreatePlane +#### 8.2.9 CreatePlane Creates a raster image based on the equation for a simple plane. @@ -1831,7 +1798,7 @@ create_plane( ``` -#### 8.2.11 CreateRectangularVectorGrid +#### 8.2.10 CreateRectangularVectorGrid This tool can be used to create a rectangular vector grid. The extent of the rectangular grid is based on the extent of a user-specified base file (any supported raster format, @@ -1874,7 +1841,7 @@ create_rectangular_vector_grid( ``` -#### 8.2.12 Dissolve +#### 8.2.11 Dissolve This tool can be used to remove the interior, or shared, boundaries within a vector polygon coverage. You can either dissolve all interior boundaries or dissolve those @@ -1919,7 +1886,7 @@ dissolve( ``` -#### 8.2.13 EliminateCoincidentPoints +#### 8.2.12 EliminateCoincidentPoints This tool can be used to remove any coincident, or nearly coincident, points from a vector points file. The user must specify the name of the input file, @@ -1958,7 +1925,7 @@ eliminate_coincident_points( ``` -#### 8.2.14 ExtendVectorLines +#### 8.2.13 ExtendVectorLines This tool can be used to extend vector lines by a specified distance. The user must input the names of the input and output shapefiles, the distance to extend features @@ -1997,7 +1964,7 @@ extend_vector_lines( ``` -#### 8.2.15 ExtractNodes +#### 8.2.14 ExtractNodes Converts vector lines or polygons into vertex points. @@ -2028,7 +1995,7 @@ extract_nodes( ``` -#### 8.2.16 ExtractRasterValuesAtPoints +#### 8.2.15 ExtractRasterValuesAtPoints Extracts the values of raster(s) at vector point locations. @@ -2060,7 +2027,7 @@ extract_raster_values_at_points( ``` -#### 8.2.17 FindLowestOrHighestPoints +#### 8.2.16 FindLowestOrHighestPoints Locates the lowest and/or highest valued cells in a raster. @@ -2094,11 +2061,8 @@ find_lowest_or_highest_points( ``` -#### 8.2.18 IdwInterpolation +#### 8.2.17 IdwInterpolation -This tool interpolates vector points into a raster surface using an inverse-distance weighted scheme. - -Most IDW tool have the option to work either based on a fixed number of neighbouring points or a fixed neighbourhood size. This tool is currently configured to perform the later only, using a FixedRadiusSearch structure. Using a fixed number of neighbours will require use of a KD-tree structure. I've been testing one Rust KD-tree library but its performance @@ -2108,6 +2072,7 @@ other options here. Another change that will need to be implemented is the use of a nodal function. The original Whitebox GAT tool allows for use of a constant or a quadratic. This tool only allows the former. +This tool interpolates vector points into a raster surface using an inverse-distance weighted scheme. *Parameters*: @@ -2158,7 +2123,7 @@ idw_interpolation( ``` -#### 8.2.19 LayerFootprint +#### 8.2.18 LayerFootprint This tool creates a vector polygon footprint of the area covered by a raster grid or vector layer. It will create a vector rectangle corresponding to the bounding box. The user must @@ -2203,7 +2168,7 @@ layer_footprint( ``` -#### 8.2.20 Medoid +#### 8.2.19 Medoid This tool calculates the medoid for a series of vector features contained in a shapefile. The medoid of a two-dimensional feature is conceptually similar its centroid, or mean position, but the medoid @@ -2249,7 +2214,7 @@ medoid( ``` -#### 8.2.21 MinimumBoundingBox +#### 8.2.20 MinimumBoundingBox This tool delineates the minimum bounding box (MBB) for a group of vectors. The MBB is the smallest box to completely enclose a feature. The algorithm works by rotating the feature, calculating the axis-aligned @@ -2294,7 +2259,7 @@ minimum_bounding_box( ``` -#### 8.2.22 MinimumBoundingCircle +#### 8.2.21 MinimumBoundingCircle This tool delineates the minimum bounding circle (MBC) for a group of vectors. The MBC is the smallest enclosing circle to completely enclose a feature. @@ -2332,7 +2297,7 @@ minimum_bounding_circle( ``` -#### 8.2.23 MinimumBoundingEnvelope +#### 8.2.22 MinimumBoundingEnvelope This tool delineates the minimum bounding axis-aligned box for a group of vector features. The is the smallest rectangle to completely enclose a feature, in which the sides of the envelope are aligned with the x and y @@ -2372,7 +2337,7 @@ minimum_bounding_envelope( ``` -#### 8.2.24 MinimumConvexHull +#### 8.2.23 MinimumConvexHull This tool creates a vector convex polygon around vector features. The convex hull is a convex closure of a set of points or polygon verticies and can be may be @@ -2414,7 +2379,7 @@ minimum_convex_hull( ``` -#### 8.2.25 NearestNeighbourGridding +#### 8.2.24 NearestNeighbourGridding Creates a raster grid based on a set of vector points and assigns grid values using the nearest neighbour. @@ -2461,7 +2426,7 @@ nearest_neighbour_gridding( ``` -#### 8.2.26 PolygonArea +#### 8.2.25 PolygonArea This tool calculates the area of vector polygons, adding the result to the vector's attribute table (AREA field). The area calculation will account @@ -2493,7 +2458,7 @@ polygon_area( ``` -#### 8.2.27 PolygonLongAxis +#### 8.2.26 PolygonLongAxis This tool can be used to map the long axis of polygon features. The long axis is the longer of the two primary axes of the minimum bounding box (MBB), i.e. the smallest box @@ -2528,7 +2493,7 @@ polygon_long_axis( ``` -#### 8.2.28 PolygonPerimeter +#### 8.2.27 PolygonPerimeter This tool calculates the perimeter of vector polygons, adding the result to the vector's attribute table (PERIMETER field). The area calculation will @@ -2560,7 +2525,7 @@ polygon_perimeter( ``` -#### 8.2.29 PolygonShortAxis +#### 8.2.28 PolygonShortAxis This tool can be used to map the short axis of polygon features. The short axis is the shorter of the two primary axes of the minimum bounding box (MBB), i.e. the smallest box @@ -2595,7 +2560,7 @@ polygon_short_axis( ``` -#### 8.2.30 RasterCellAssignment +#### 8.2.29 RasterCellAssignment Assign row or column number to cells. @@ -2630,7 +2595,7 @@ raster_cell_assignment( ``` -#### 8.2.31 Reclass +#### 8.2.30 Reclass Reclassifies the values in a raster image. @@ -2672,7 +2637,7 @@ reclass( ``` -#### 8.2.32 ReclassEqualInterval +#### 8.2.31 ReclassEqualInterval Reclassifies the values in a raster image based on equal-ranges. @@ -2710,7 +2675,7 @@ reclass_equal_interval( ``` -#### 8.2.33 ReclassFromFile +#### 8.2.32 ReclassFromFile Reclassifies the values in a raster image using reclass ranges in a text file. @@ -2744,7 +2709,7 @@ reclass_from_file( ``` -#### 8.2.34 SmoothVectors +#### 8.2.33 SmoothVectors This tool smooths a vector coverage of either a POLYLINE or POLYGON base ShapeType. The algorithm uses a simple moving average method for smoothing, where the size of the averaging window is specified @@ -2780,7 +2745,7 @@ smooth_vectors( ``` -#### 8.2.35 TinGridding +#### 8.2.34 TinGridding Creates a raster grid based on a triangular irregular network (TIN) fitted to vector points and linear interpolation within each triangular-shaped plane. @@ -2826,7 +2791,7 @@ tin_gridding( ``` -#### 8.2.36 VectorHexBinning +#### 8.2.35 VectorHexBinning The practice of binning point data to form a type of 2D histogram, density plot, or what is sometimes called a heatmap, is quite useful as an alternative for the @@ -2883,7 +2848,7 @@ vector_hex_binning( ``` -#### 8.2.37 VoronoiDiagram +#### 8.2.36 VoronoiDiagram This tool creates a vector Voronoi diagram for a set of vector points. The Voronoi diagram is the dual graph of the Delaunay triangulation. The tool @@ -3038,7 +3003,7 @@ NoData values in the input cost surface image are ignored during processing and in the outputs. The output cost accumulation raster is of the float data type and continuous data scale. *See Also*: -: + `CostAllocation`, `CostPathway`, `WeightedOverlay` *Parameters*: @@ -5142,7 +5107,7 @@ Sun, Rosin, Martin, and Langbein (2007) Fast and effective feature-preserving mesh denoising. *See Also*: -: + `DrainagePreservingSmoothing` *Parameters*: @@ -6682,7 +6647,7 @@ flow path enforcement in digital elevation models.* **Hydrological Processes**, It uses a breach-first, fill-second approach. *See Also*: -: + `FillDepressions` *Parameters*: @@ -6749,7 +6714,33 @@ breach_single_cell_pits( #### 8.7.6 D8FlowAccumulation -Calculates a D8 flow accumulation raster from an input DEM. +This tool is used to generate a flow accumulation grid (i.e. catchment area) using the +D8 (O'Callaghan and Mark, 1984) algorithm. This algorithm is an example of single-flow-direction +(SFD) method because the flow entering each grid cell is routed to only one downslope neighbour, +i.e. flow divergence is not permitted. The user must specify the name of the input digital +elevation model (DEM). The DEM must have been hydrologically corrected to remove all spurious +depressions and flat areas. DEM pre-processing is usually achieved using the `BreachDepressions` or +`FillDepressions` tools. + +In addition to the input DEM, the user must specify the output type. The output flow-accumulation +can be 1) `cells` (i.e. the number of inflowing grid cells), `catchment area` (i.e. the upslope area), +or `specific contributing area` (i.e. the catchment area divided by the flow width. The default value +is `cells`. The user must also specify whether the output flow-accumulation grid should be +log-tranformed, i.e. the output, if this option is selected, will be the natural-logarithm of the +accumulated flow value. This is a transformation that is often performed to better visualize the +contributing area distribution. Because contributing areas tend to be very high along valley bottoms +and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of +values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the +highest values. Log-transformation provides a means of compensating for this phenomenon. Importantly, +however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain +indices, such as the wetness index, or relative stream power index. + +Grid cells possessing the **NoData** value in the input flow-pointer grid are assigned the **NoData** +value in the output flow-accumulation image. + +*See Also*: +: +`DInfFlowAccumulation`, `BreachDepressions`, `FillDepressions` *Parameters*: @@ -6757,8 +6748,8 @@ Calculates a D8 flow accumulation raster from an input DEM. ------------------- --------------- -i, -\-dem Input raster DEM file -o, -\-output Output raster file --\-out_type Output type; one of 'cells', 'specific contributing area' (default), and - 'catchment area' +-\-out_type Output type; one of 'cells' (default), 'catchment area', and 'specific + contributing area' -\-log Optional flag to request the output be log-transformed -\-clip Optional flag to request clipping the display max by 1% @@ -6769,7 +6760,7 @@ Calculates a D8 flow accumulation raster from an input DEM. d8_flow_accumulation( dem, output, - out_type="specific contributing area", + out_type="cells", log=False, clip=False, callback=default_callback) @@ -6779,7 +6770,7 @@ d8_flow_accumulation( ``` >>./whitebox_tools -r=D8FlowAccumulation -v ^ ---wd="/path/to/data/" --dem=DEM.tif -o=output.dtifep ^ +--wd="/path/to/data/" --dem=DEM.tif -o=output.tif ^ --out_type='cells' >>./whitebox_tools -r=D8FlowAccumulation -v ^ --wd="/path/to/data/" --dem=DEM.tif -o=output.tif ^ @@ -8282,7 +8273,7 @@ change_vector_analysis( #### 8.8.2 Closing -A closing is a mathematical morphology operating involving an erosion (min filter) of a dilation (max filter) set. +A closing is a mathematical morphology operation involving an erosion (min filter) of a dilation (max filter) set. *Parameters*: @@ -8317,7 +8308,28 @@ closing( #### 8.8.3 CreateColourComposite -Creates a colour-composite image from three bands of multispectral imagery. +This tool can be used to create a colour-composite image from three bands of multi-spectral imagery. +The user must specify the names of the input images to enter into the red, green, and blue channels +of the resulting composite image. The output image uses the 32-bit aRGB colour model, and therefore, +in addition to red, green and blue bands, the user may optionally specify a fourth image that will +be used to determine pixel opacity (the 'a' channel). If no opacity image is specified, each pixel +will be opaque. This can be useful for cropping an image to an irregular-shaped boundary. The opacity +channel can also be used to create transparent gradients in the composite image. + +A balance contrast enchancment (BCE) can optionally be performed on the bands prior to creation of +the colour composite. While this operation will add to the runtime of `CreateColourComposite`, if +the individual input bands have not already had contrast enchancements, then it is advisable that +the BCE option be used to improve the quality of the resulting colour composite image. + +NoData values in any of the input images are assigned NoData values in the output image and are not +taken into account when performing the BCE operation. Please note, not all images have NoData values +identified. When this is the case, and when the background value is 0 (often the case with +multispectral imagery), then the `CreateColourComposite` tool can be told to ignore zero values using +the `--zeros` flag. + +*See Also*: + +`BalanceContrastEnhancment` *Parameters*: @@ -8329,6 +8341,7 @@ Creates a colour-composite image from three bands of multispectral imagery. -\-opacity Input opacity band image file (optional) -o, -\-output Output colour composite file -\-enhance Optional flag indicating whether a balance contrast enhancement is performed +-\-zeros Optional flag to indicate if zeros are nodata values *Python function*: @@ -8341,6 +8354,7 @@ create_colour_composite( output, opacity=None, enhance=True, + zeros=False, callback=default_callback) ~~~~ @@ -8353,7 +8367,7 @@ create_colour_composite( >>./whitebox_tools ^ -r=CreateColourComposite -v --wd="/path/to/data/" ^ --red=band3.tif --green=band2.tif --blue=band1.tif ^ ---opacity=a.tif -o=output.tif +--opacity=a.tif -o=output.tif --enhance --zeros ``` @@ -8638,6 +8652,10 @@ and cubic convolution. The order of the input source image files is important. G cells in the output image will be assigned the corresponding value determined from the first image found in the list to possess an overlapping coordinate. +This is the preferred mosaicing tool to use when appending multiple images with +little to no overlapping areas, e.g. tiled data. When images have significant overlap +areas, users are advised to use the `MosaicWithFeathering` tool instead. + Resample is very similar in operation to the Mosaic tool. The Resample tool should be used when there is an existing image into which you would like to dump information from one or more source images. If the source images are more extensive than the destination @@ -8650,6 +8668,10 @@ that represents the bounding rectangle of each of the two or more input images. cells in the output image that do not overlap with any of the input images will be assigned the NoData value. +*See Also*: + +`MosaicWithFeathering` + *Parameters*: **Flag** **Description** @@ -8675,13 +8697,73 @@ mosaic( ``` >>./whitebox_tools -r=Mosaic -v --wd='/path/to/data/' ^ -i='image1.tif;image2.tif;image3.tif' -o=dest.tif ^ ---method='cc +--method='cc' + + +``` + + +#### 8.8.12 MosaicWithFeathering + +This tool will create a mosaic from two input images. It is similar in operation to the `Mosaic` tool, +however, this tool is the preferred method of mosaicing images when there is significant overlap between +the images. For areas of overlap, the feathering method will calculate the output value as a weighted +combination of the two input values, where the weights are derived from the squared distance of the +pixel to the edge of the data in each of the input raster files. Therefore, less weight is assigned to +an image's pixel value where the pixel is very near the edge of the image. Note that the distance is +actually calculated to the edge of the grid and not necessarily the edge of the data, which can differ +if the image has been rotated during registration. The result of this feathering method is that the +output mosaic image should have very little evidence of the original image edges within the overlapping +area. + +Unlike the Mosaic tool, which can take multiple input images, this tool only accepts two input images. +Mosaic is therefore useful when there are many, adjacent or only slightly overlapping images, e.g. for +tiled data sets. + +Users may want to use the `HistogramMatching` tool prior to mosaicing if the two input images differ +significantly in their radiometric properties. i.e. if image contrast differences exist. + +*See Also*: + +`Mosaic`, `HistogramMatching` + +*Parameters*: + +**Flag** **Description** +------------------- --------------- +-\-i1, -\-input1 Input raster file to modify +-\-i2, -\-input2 Input reference raster file +-o, -\-output Output raster file +-\-method Resampling method; options include 'nn' (nearest neighbour), 'bilinear', and + 'cc' (cubic convolution) +-\-weight + + +*Python function*: + +~~~~{.python} +mosaic_with_feathering( + input1, + input2, + output, + method="cc", + weight=4.0, + callback=default_callback) +~~~~ + +*Command-line Interface*: + +``` +>>./whitebox_tools -r=MosaicWithFeathering -v ^ +--wd='/path/to/data/' --input1='image1.tif' ^ +--input2='image2.tif' -o='output.tif' --method='cc' ^ +--weight=4.0 ``` -#### 8.8.12 NormalizedDifferenceVegetationIndex +#### 8.8.13 NormalizedDifferenceVegetationIndex Calculates the normalized difference vegetation index (NDVI) from near-infrared and red imagery. @@ -8724,9 +8806,9 @@ normalized_difference_vegetation_index( ``` -#### 8.8.13 Opening +#### 8.8.14 Opening -An opening is a mathematical morphology operating involving a dilation (max filter) of an erosion (min filter) set. +An opening is a mathematical morphology operation involving a dilation (max filter) of an erosion (min filter) set. *Parameters*: @@ -8759,7 +8841,7 @@ opening( ``` -#### 8.8.14 RemoveSpurs +#### 8.8.15 RemoveSpurs Removes the spurs (pruning operation) from a Boolean line image.; intended to be used on the output of the LineThinning tool. @@ -8792,7 +8874,7 @@ remove_spurs( ``` -#### 8.8.15 Resample +#### 8.8.16 Resample Resample is very similar in operation to the Mosaic tool. The Resample tool should be used when there is an existing image into which you would like to dump information @@ -8837,7 +8919,7 @@ resample( ``` -#### 8.8.16 RgbToIhs +#### 8.8.17 RgbToIhs Converts red, green, and blue (RGB) images into intensity, hue, and saturation (IHS) images. @@ -8888,7 +8970,7 @@ rgb_to_ihs( ``` -#### 8.8.17 SplitColourComposite +#### 8.8.18 SplitColourComposite This tool splits an RGB colour composite image into seperate multispectral images. @@ -8919,7 +9001,7 @@ split_colour_composite( ``` -#### 8.8.18 ThickenRasterLine +#### 8.8.19 ThickenRasterLine Thickens single-cell wide lines within a raster image. @@ -8950,7 +9032,7 @@ thicken_raster_line( ``` -#### 8.8.19 TophatTransform +#### 8.8.20 TophatTransform Performs either a white or black top-hat transform on an input image. @@ -8987,7 +9069,7 @@ tophat_transform( ``` -#### 8.8.20 WriteFunctionMemoryInsertion +#### 8.8.21 WriteFunctionMemoryInsertion Performs a write function memory insertion for single-band multi-date change detection. @@ -11630,7 +11712,20 @@ lidar_join( #### 8.11.23 LidarKappaIndex -Performs a kappa index of agreement (KIA) analysis on the classifications of two LAS files. +This tool performs a kappa index of agreement (KIA) analysis on the classification values of two LiDAR +(LAS) files. The output report HTML file should be displayed automatically but can also be displayed +afterwards in any web browser. As a measure of overall classification accuracy, the KIA is more robust +than the percent agreement calculation because it takes into account the agreement occurring by random +chance. In addition to the KIA, the tool will output the producer's and user's accuracy, the overall +accuracy, and the error matrix. The KIA is often used as a means of assessing the accuracy of an image +classification analysis; however the `LidarKappaIndex` tool performs the analysis on a point-to-point +basis, comparing the class values of the points in one input LAS file with the corresponding nearest +points in the second input LAS file. + +The user must also specify the name and resolution of an output raster file, which is used to show the +spatial distribution of class accuracy. Each grid cell contains the overall accuracy, i.e. the points +correctly classified divided by the total number of points contained within the cell, expressed as a +percentage. *Parameters*: @@ -11639,6 +11734,8 @@ Performs a kappa index of agreement (KIA) analysis on the classifications of two -\-i1, -\-input1 Input LiDAR classification file -\-i2, -\-input2 Input LiDAR reference file -o, -\-output Output HTML file +-\-class_accuracy Output classification accuracy raster file +-\-resolution Output raster's grid resolution *Python function*: @@ -11648,6 +11745,8 @@ lidar_kappa_index( input1, input2, output, + class_accuracy, + resolution=1.0, callback=default_callback) ~~~~ @@ -11655,8 +11754,8 @@ lidar_kappa_index( ``` >>./whitebox_tools -r=LidarKappaIndex -v ^ ---wd="/path/to/data/" --i1=class.tif --i2=reference.tif ^ --o=kia.html +--wd="/path/to/data/" --i1=class.las --i2=reference.las ^ +-o=kia.html --class_accuracy=new_file.tif --resolution=0.5 ``` @@ -15234,8 +15333,8 @@ long_profile_from_points( #### 8.13.11 RasterStreamsToVector -This tool converts a raster stream file into a vector file. The user must specify -1) the name of the raster streams file, 2) the name of the D8 flow pointer file, +This tool converts a raster stream file into a vector file. The user must specify: 1) +the name of the raster streams file, 2) the name of the D8 flow pointer file, and 3) the name of the output vector file. Streams in the input raster streams file are denoted by cells containing any positive, non-zero integer. A field in the vector database file, called STRM_VAL, will correspond to this positive @@ -15767,6 +15866,8 @@ tributary_identifier( + + diff --git a/readme.txt b/readme.txt index d05905652..e28d8f701 100644 --- a/readme.txt +++ b/readme.txt @@ -56,11 +56,10 @@ for more details. * Release Notes: * ****************** -Version 0.13.0 (XX-01-2019) +Version 0.13.0 (08-01-2019) - The release largely focusses on bug-fixes rather than adding new features. The following tools were added to the project: MosaicWithFeathering - - Support was added for GeoTIFF MODELTRANSFORMATIONTAG (Tag 33920). - Support was added for reading GeoTIFFs that have coordinate transformations defined by multiple tiepoints contained with the ModelTiepointTag (Tag 33922). diff --git a/src/tools/data_tools/set_nodata_value.rs b/src/tools/data_tools/set_nodata_value.rs index c8fc041c5..1e9bb4a03 100644 --- a/src/tools/data_tools/set_nodata_value.rs +++ b/src/tools/data_tools/set_nodata_value.rs @@ -1,7 +1,7 @@ /* This tool is part of the WhiteboxTools geospatial analysis library. Authors: Dr. John Lindsay -Created: Sept. 10, 2017 +Created: 10/09/2017 Last Modified: 12/10/2018 License: MIT */ @@ -20,6 +20,13 @@ use std::sync::mpsc; use std::sync::Arc; use std::thread; +/// This tool will re-assign a user-defined background value in an input raster image the **NoData** value. +/// More precisely, the NoData value will be changed to the specified background value and any existing +/// grid cells containing the previous NoData value, if it had been defined, will be changed to this new +/// value. Most WhiteboxTools tools recognize NoData grid cells and treat them specially. NoData grid cells +/// are also often displayed transparently by GIS software. The user must specify the names of the input and +/// output rasters and the background value. The default background value is zero, although any numeric +/// value is possible. pub struct SetNodataValue { name: String, description: String, @@ -36,10 +43,6 @@ impl SetNodataValue { let description = "Assign a specified value in an input image to the NoData value.".to_string(); - // let mut parameters = "-i, --input Input raster file.\n".to_owned(); - // parameters.push_str("-o, --output Output raster file.\n"); - // parameters.push_str("--back_value Background value to set to nodata (default is 0.0).\n"); - let mut parameters = vec![]; parameters.push(ToolParameter { name: "Input File".to_owned(), diff --git a/src/tools/image_analysis/mosaic.rs b/src/tools/image_analysis/mosaic.rs index 6af9a757d..23006274b 100644 --- a/src/tools/image_analysis/mosaic.rs +++ b/src/tools/image_analysis/mosaic.rs @@ -39,7 +39,7 @@ use std::thread; /// cells in the output image that do not overlap with any of the input images will be /// assigned the NoData value. /// -/// # See Also: +/// # See Also /// `MosaicWithFeathering` pub struct Mosaic { name: String, diff --git a/src/tools/image_analysis/mosaic_with_feathering.rs b/src/tools/image_analysis/mosaic_with_feathering.rs index dfe5a385a..ed1edb569 100644 --- a/src/tools/image_analysis/mosaic_with_feathering.rs +++ b/src/tools/image_analysis/mosaic_with_feathering.rs @@ -36,7 +36,7 @@ use std::thread; /// Users may want to use the `HistogramMatching` tool prior to mosaicing if the two input images differ /// significantly in their radiometric properties. i.e. if image contrast differences exist. /// -/// # See Also: +/// # See Also /// `Mosaic`, `HistogramMatching` pub struct MosaicWithFeathering { name: String, diff --git a/whitebox_tools.py b/whitebox_tools.py index c8c91470c..7676acd20 100644 --- a/whitebox_tools.py +++ b/whitebox_tools.py @@ -363,6 +363,7 @@ def list_tools(self, keywords=[]): + ############## # Data Tools # ############## @@ -2085,7 +2086,7 @@ def downslope_index(self, dem, output, drop=2.0, out_type="tangent", callback=No args.append("--out_type={}".format(out_type)) return self.run_tool('downslope_index', args, callback) # returns 1 if error - def drainage_preserving_smoothing(self, dem, output, filter=11, norm_diff=8.0, num_iter=5, reduction=80.0, dfm=0.15, zfactor=1.0, callback=None): + def drainage_preserving_smoothing(self, dem, output, filter=11, norm_diff=15.0, num_iter=10, max_diff=2.0, reduction=80.0, dfm=0.15, zfactor=1.0, callback=None): """Reduces short-scale variation in an input DEM while preserving breaks-in-slope and small drainage features using a modified Sun et al. (2007) algorithm. Keyword arguments: @@ -2095,6 +2096,7 @@ def drainage_preserving_smoothing(self, dem, output, filter=11, norm_diff=8.0, n filter -- Size of the filter kernel. norm_diff -- Maximum difference in normal vectors, in degrees. num_iter -- Number of iterations. + max_diff -- Maximum allowable absolute elevation change (optional). reduction -- Maximum Amount to reduce the threshold angle by (0 = full smoothing; 100 = no smoothing). dfm -- Difference from median threshold (in z-units), determines when a location is low-lying. zfactor -- Optional multiplier for when the vertical and horizontal units are not the same. @@ -2106,6 +2108,7 @@ def drainage_preserving_smoothing(self, dem, output, filter=11, norm_diff=8.0, n args.append("--filter={}".format(filter)) args.append("--norm_diff={}".format(norm_diff)) args.append("--num_iter={}".format(num_iter)) + args.append("--max_diff={}".format(max_diff)) args.append("--reduction={}".format(reduction)) args.append("--dfm={}".format(dfm)) args.append("--zfactor={}".format(zfactor)) @@ -2175,7 +2178,7 @@ def elev_relative_to_watershed_min_max(self, dem, watersheds, output, callback=N args.append("--output='{}'".format(output)) return self.run_tool('elev_relative_to_watershed_min_max', args, callback) # returns 1 if error - def feature_preserving_denoise(self, dem, output, filter=11, norm_diff=8.0, num_iter=5, zfactor=1.0, callback=None): + def feature_preserving_denoise(self, dem, output, filter=11, norm_diff=15.0, num_iter=10, max_diff=2.0, zfactor=1.0, callback=None): """Reduces short-scale variation in an input DEM using a modified Sun et al. (2007) algorithm. Keyword arguments: @@ -2185,6 +2188,7 @@ def feature_preserving_denoise(self, dem, output, filter=11, norm_diff=8.0, num_ filter -- Size of the filter kernel. norm_diff -- Maximum difference in normal vectors, in degrees. num_iter -- Number of iterations. + max_diff -- Maximum allowable absolute elevation change (optional). zfactor -- Optional multiplier for when the vertical and horizontal units are not the same. callback -- Custom function for handling tool text outputs. """ @@ -2194,6 +2198,7 @@ def feature_preserving_denoise(self, dem, output, filter=11, norm_diff=8.0, num_ args.append("--filter={}".format(filter)) args.append("--norm_diff={}".format(norm_diff)) args.append("--num_iter={}".format(num_iter)) + args.append("--max_diff={}".format(max_diff)) args.append("--zfactor={}".format(zfactor)) return self.run_tool('feature_preserving_denoise', args, callback) # returns 1 if error @@ -2961,14 +2966,14 @@ def breach_single_cell_pits(self, dem, output, callback=None): args.append("--output='{}'".format(output)) return self.run_tool('breach_single_cell_pits', args, callback) # returns 1 if error - def d8_flow_accumulation(self, dem, output, out_type="specific contributing area", log=False, clip=False, callback=None): + def d8_flow_accumulation(self, dem, output, out_type="cells", log=False, clip=False, callback=None): """Calculates a D8 flow accumulation raster from an input DEM. Keyword arguments: dem -- Input raster DEM file. output -- Output raster file. - out_type -- Output type; one of 'cells', 'specific contributing area' (default), and 'catchment area'. + out_type -- Output type; one of 'cells' (default), 'catchment area', and 'specific contributing area'. log -- Optional flag to request the output be log-transformed. clip -- Optional flag to request clipping the display max by 1%. callback -- Custom function for handling tool text outputs. @@ -3662,7 +3667,7 @@ def change_vector_analysis(self, date1, date2, magnitude, direction, callback=No return self.run_tool('change_vector_analysis', args, callback) # returns 1 if error def closing(self, i, output, filterx=11, filtery=11, callback=None): - """A closing is a mathematical morphology operating involving an erosion (min filter) of a dilation (max filter) set. + """A closing is a mathematical morphology operation involving an erosion (min filter) of a dilation (max filter) set. Keyword arguments: @@ -3679,7 +3684,7 @@ def closing(self, i, output, filterx=11, filtery=11, callback=None): args.append("--filtery={}".format(filtery)) return self.run_tool('closing', args, callback) # returns 1 if error - def create_colour_composite(self, red, green, blue, output, opacity=None, enhance=True, callback=None): + def create_colour_composite(self, red, green, blue, output, opacity=None, enhance=True, zeros=False, callback=None): """Creates a colour-composite image from three bands of multispectral imagery. Keyword arguments: @@ -3690,6 +3695,7 @@ def create_colour_composite(self, red, green, blue, output, opacity=None, enhanc opacity -- Input opacity band image file (optional). output -- Output colour composite file. enhance -- Optional flag indicating whether a balance contrast enhancement is performed. + zeros -- Optional flag to indicate if zeros are nodata values. callback -- Custom function for handling tool text outputs. """ args = [] @@ -3699,6 +3705,7 @@ def create_colour_composite(self, red, green, blue, output, opacity=None, enhanc if opacity is not None: args.append("--opacity='{}'".format(opacity)) args.append("--output='{}'".format(output)) if enhance: args.append("--enhance") + if zeros: args.append("--zeros") return self.run_tool('create_colour_composite', args, callback) # returns 1 if error def flip_image(self, i, output, direction="vertical", callback=None): @@ -3851,6 +3858,26 @@ def mosaic(self, inputs, output, method="cc", callback=None): args.append("--method={}".format(method)) return self.run_tool('mosaic', args, callback) # returns 1 if error + def mosaic_with_feathering(self, input1, input2, output, method="cc", weight=4.0, callback=None): + """Mosaics two images together using a feathering technique in overlapping areas to reduce edge-effects. + + Keyword arguments: + + input1 -- Input raster file to modify. + input2 -- Input reference raster file. + output -- Output raster file. + method -- Resampling method; options include 'nn' (nearest neighbour), 'bilinear', and 'cc' (cubic convolution). + weight -- . + callback -- Custom function for handling tool text outputs. + """ + args = [] + args.append("--input1='{}'".format(input1)) + args.append("--input2='{}'".format(input2)) + args.append("--output='{}'".format(output)) + args.append("--method={}".format(method)) + args.append("--weight={}".format(weight)) + return self.run_tool('mosaic_with_feathering', args, callback) # returns 1 if error + def normalized_difference_vegetation_index(self, nir, red, output, clip=0.0, osavi=False, callback=None): """Calculates the normalized difference vegetation index (NDVI) from near-infrared and red imagery. @@ -3872,7 +3899,7 @@ def normalized_difference_vegetation_index(self, nir, red, output, clip=0.0, osa return self.run_tool('normalized_difference_vegetation_index', args, callback) # returns 1 if error def opening(self, i, output, filterx=11, filtery=11, callback=None): - """An opening is a mathematical morphology operating involving a dilation (max filter) of an erosion (min filter) set. + """An opening is a mathematical morphology operation involving a dilation (max filter) of an erosion (min filter) set. Keyword arguments: @@ -5249,7 +5276,7 @@ def lidar_join(self, inputs, output, callback=None): args.append("--output='{}'".format(output)) return self.run_tool('lidar_join', args, callback) # returns 1 if error - def lidar_kappa_index(self, input1, input2, output, callback=None): + def lidar_kappa_index(self, input1, input2, output, class_accuracy, resolution=1.0, callback=None): """Performs a kappa index of agreement (KIA) analysis on the classifications of two LAS files. Keyword arguments: @@ -5257,12 +5284,16 @@ def lidar_kappa_index(self, input1, input2, output, callback=None): input1 -- Input LiDAR classification file. input2 -- Input LiDAR reference file. output -- Output HTML file. + class_accuracy -- Output classification accuracy raster file. + resolution -- Output raster's grid resolution. callback -- Custom function for handling tool text outputs. """ args = [] args.append("--input1='{}'".format(input1)) args.append("--input2='{}'".format(input2)) args.append("--output='{}'".format(output)) + args.append("--class_accuracy='{}'".format(class_accuracy)) + args.append("--resolution={}".format(resolution)) return self.run_tool('lidar_kappa_index', args, callback) # returns 1 if error def lidar_nearest_neighbour_gridding(self, i=None, output=None, parameter="elevation", returns="all", resolution=1.0, radius=2.5, exclude_cls=None, minz=None, maxz=None, callback=None):