Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing)¶
Perform mean shift filtering
Detailed description¶
This application performs mean shift fitlering (multi-threaded).
Parameters¶
This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is MeanShiftSmoothing .
| [1] | Table: Parameters table for Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing). |
| Parameter Key | Parameter Type | Parameter Description |
|---|---|---|
| in | Input image | Input image |
| fout | Output image | Output image |
| foutpos | Output image | Output image |
| ram | Int | Int |
| spatialr | Int | Int |
| ranger | Float | Float |
| thres | Float | Float |
| maxiter | Int | Int |
| rangeramp | Float | Float |
| modesearch | Boolean | Boolean |
| inxml | XML input parameters file | XML input parameters file |
| outxml | XML output parameters file | XML output parameters file |
- Input Image: The input image.
- Filtered output: The filtered output image.
- Spatial image: The spatial image output. Spatial image output is a displacement map (pixel position after convergence).
- Available RAM (Mb): Available memory for processing (in MB).
- Spatial radius: Spatial radius of the neighborhood.
- Range radius: Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space.
- Mode convergence threshold: Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations.
- Maximum number of iterations: Algorithm iterative scheme will stop if convergence hasn’t been reached after the maximum number of iterations.
- Range radius coefficient: This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger.
- Mode search.: If activated pixel iterative convergence is stopped if the path crosses an already converged pixel. Be careful, with this option, the result will slightly depend on thread number.
- Load otb application from xml file: Load otb application from xml file.
- Save otb application to xml file: Save otb application to xml file.
Example¶
To run this example in command-line, use the following:
otbcli_MeanShiftSmoothing -in maur_rgb.png -fout MeanShift_FilterOutput.tif -foutpos MeanShift_SpatialOutput.tif -spatialr 16 -ranger 16 -thres 0.1 -maxiter 100
To run this example from Python, use the following code snippet:
#!/usr/bin/python
# Import the otb applications package
import otbApplication
# The following line creates an instance of the MeanShiftSmoothing application
MeanShiftSmoothing = otbApplication.Registry.CreateApplication("MeanShiftSmoothing")
# The following lines set all the application parameters:
MeanShiftSmoothing.SetParameterString("in", "maur_rgb.png")
MeanShiftSmoothing.SetParameterString("fout", "MeanShift_FilterOutput.tif")
MeanShiftSmoothing.SetParameterString("foutpos", "MeanShift_SpatialOutput.tif")
MeanShiftSmoothing.SetParameterInt("spatialr", 16)
MeanShiftSmoothing.SetParameterFloat("ranger", 16)
MeanShiftSmoothing.SetParameterFloat("thres", 0.1)
MeanShiftSmoothing.SetParameterInt("maxiter", 100)
# The following line execute the application
MeanShiftSmoothing.ExecuteAndWriteOutput()
Limitations¶
With mode search option, the result will slightly depend on thread number.
Authors¶
This application has been written by OTB-Team.