def runCorrection(insigma, inlinc, inpix, outsigma, thetaref=39.0, nFactor=1.0, filterSize=None):
controls = applier.ApplierControls()
# Set up input images
infiles = applier.FilenameAssociations()
infiles.insigma = insigma
infiles.inlinc = inlinc
infiles.inpix = inpix
# Set up output image
outfiles = applier.FilenameAssociations()
outfiles.outimage = outsigma
# Set format for output image
outControls = getOutDriver(outsigma)
# Set options
controls.setOutputDriverName(outControls['gdalFormat'])
controls.setCreationOptions(outControls['gdalCOOptions'])
controls.setCalcStats(outControls['calcStats'])
# Set up parameters
otherargs = applier.OtherInputs()
otherargs.thetaref = thetaref
otherargs.nFactor = nFactor
otherargs.filterSize = filterSize
# Run correction
controls.progress = cuiprogress.CUIProgressBar()
applier.apply(castelCorrection, infiles, outfiles, otherargs, controls=controls)
def writeOutImg(inputArray, outfile, n, m, c, TLX, TLY, nulVal, proj, dType):
# Write the output DEM into an image file with GDAL
nBands = 1
drvr = gdal.GetDriverByName('HFA')
ds = drvr.Create(outfile, n, m, nBands, dType, ['COMPRESS=YES'])
band = ds.GetRasterBand(1)
band.WriteArray(inputArray)
ds.SetGeoTransform((TLX, c, 0, TLY, 0, -c))
ds.SetProjection(proj)
progress = cuiprogress.CUIProgressBar()
calcstats.calcStats(ds, progress, ignore=nulVal)
del ds
def calcAverage(file1, file2, avgfile):
"""
Use RIOS to calculate the average of two files. Allows
nearest-neighbour resampling of the second file.
"""
infiles = applier.FilenameAssociations()
outfiles = applier.FilenameAssociations()
infiles.img = [file1, file2]
outfiles.avg = avgfile
controls = applier.ApplierControls()
controls.setReferenceImage(file1)
controls.setResampleMethod('near')
controls.setProgress(cuiprogress.CUIProgressBar())
applier.apply(doAvg, infiles, outfiles, controls=controls)
def apply_rf_image(in_data_stack, out_image, rf_model, nodata_vals):
"""
Apply Random Forests model generated by scikit-learn
to an input data stack and output image
Requires:
* in_data_stack - stack of all layers
* out_image - output image
* rf_model - model produced by scikit-learn
* nodata_vals - array with a no-data value for each band
Returns the mean and standard deviation of the output (predicted)
image.
"""
# Apply to image
infiles = applier.FilenameAssociations()
infiles.inimage = in_data_stack
outfiles = applier.FilenameAssociations()
outfiles.outimage = out_image
otherargs = applier.OtherInputs()
otherargs.rf = rf_model
otherargs.predict_sm = None # Array to store output SM
# Pass in list of no data values for each layer
otherargs.nodata_vals_list = nodata_vals
controls = applier.ApplierControls()
controls.setOutputDriverName(
upscaling_utilities.get_gdal_format(out_image))
controls.setCalcStats(False)
controls.progress = cuiprogress.CUIProgressBar()
applier.apply(_rios_apply_rf_image,
infiles,
outfiles,
otherargs,
controls=controls)
average_sm_predict = otherargs.predict_sm.mean()
sd_sm_predict = otherargs.predict_sm.std()
return average_sm_predict, sd_sm_predict
def createPlots(imageA,
imageB,
outPlot,
bandA=1,
bandB=1,
labelA=None,
labelB=None,
plotMin=None,
plotMax=None,
scale=1,
independentXY=False):
controls = applier.ApplierControls()
# Set up input images
infiles = applier.FilenameAssociations()
infiles.imageA = imageA
infiles.imageB = imageB
outfiles = applier.FilenameAssociations()
# Set up parameters
otherargs = applier.OtherInputs()
otherargs.bandA = bandA - 1
otherargs.bandB = bandB - 1
otherargs.outdataA = numpy.array([], dtype=numpy.float32)
otherargs.outdataB = numpy.array([], dtype=numpy.float32)
otherargs.scale = scale
# Get data
print('Extracting data')
controls.progress = cuiprogress.CUIProgressBar()
# If necessary reproject image B to image A
controls.setReferenceImage(infiles.imageA)
controls.setResampleMethod('near')
applier.apply(getData, infiles, outfiles, otherargs, controls=controls)
# Produce plot
print('Saving plot')
plotData(otherargs.outdataA, otherargs.outdataB, outPlot, labelA, labelB,
plotMin, plotMax, independentXY)
def run_s2cloudless(input_img, out_prob_img, out_cloud_msk, gdalformat):
"""
Function which runs the S2Cloudless methods in 256 x 256 pixel blocks
across an image.
:param input_img: input sentinel-2 image with all 13 bands.
:param output_img: the output image cloud mask
:param gdalformat: the GDAL image file format of the output image file.
"""
s2_pxl_cloud_detect = S2PixelCloudDetector(all_bands=True)
infiles = applier.FilenameAssociations()
infiles.s2image = input_img
outfiles = applier.FilenameAssociations()
outfiles.out_prob_img = out_prob_img
outfiles.out_cloud_msk = out_cloud_msk
otherargs = applier.OtherInputs()
otherargs.s2_pxl_cloud_detect = s2_pxl_cloud_detect
aControls = applier.ApplierControls()
aControls.progress = cuiprogress.CUIProgressBar()
aControls.drivername = gdalformat
aControls.omitPyramids = True
aControls.calcStats = False
def _applyS2Cloudless(info, inputs, outputs, otherargs):
"""
This is an internal rios function
"""
# Current shape is: [13 x n x m]
# Image data needs to be in shape [1 x n x m x 13]
s2img_reshp = numpy.expand_dims(numpy.stack([inputs.s2image[0], inputs.s2image[1], inputs.s2image[2], inputs.s2image[3], inputs.s2image[4], inputs.s2image[5], inputs.s2image[6], inputs.s2image[7], inputs.s2image[8], inputs.s2image[9], inputs.s2image[10], inputs.s2image[11], inputs.s2image[12]], axis=2), axis=0)
s2img_reshp_toa = s2img_reshp/10000.0
outputs.out_prob_img = otherargs.s2_pxl_cloud_detect.get_cloud_probability_maps(s2img_reshp_toa)
outputs.out_cloud_msk = otherargs.s2_pxl_cloud_detect.get_mask_from_prob(outputs.out_prob_img)
applier.apply(_applyS2Cloudless, infiles, outfiles, otherargs, controls=aControls)
def converttodB(info, inputs, outputs):
"""
Converts to dB using:
10*log10(b1)
"""
outputs.outimage = np.where(inputs.inimage > 0,
10 * np.log10(inputs.inimage), 0)
if len(sys.argv) == 3:
inImage = sys.argv[1]
outImage = sys.argv[2]
else:
print('''Not enough parameters provided.
Usage:
convert2dB.py inImage outImage
''')
exit()
infiles = applier.FilenameAssociations()
infiles.inimage = inImage
outfiles = applier.FilenameAssociations()
outfiles.outimage = outImage
controls = applier.ApplierControls()
controls.progress = cuiprogress.CUIProgressBar()
controls.setCalcStats(True)
applier.apply(converttodB, infiles, outfiles, controls=controls)
def calcSolarAzimuthZenith(inputImg, inImgDateTime, outputImg, gdalformat):
"""
Function which calculate a solar azimuth (band 1) and zenith (band 2) image.
:param inputImg: input image file (can be any image with a spatial header)
:param inImgDateTime: a datatime object for the data/time of the acquasition
:param outputImg: output image file and path
:param gdalformat: output file format (e.g., KEA)
"""
if not havePysolar:
raise Exception(
"The PySolar module required for this function could not be imported."
)
if not haveRIOS:
raise Exception(
"The RIOS module required for this function could not be imported."
)
infiles = applier.FilenameAssociations()
infiles.image1 = inputImg
outfiles = applier.FilenameAssociations()
outfiles.outimage = outputImg
otherargs = applier.OtherInputs()
otherargs.dateTime = inImgDateTime
aControls = applier.ApplierControls()
aControls.progress = cuiprogress.CUIProgressBar()
aControls.drivername = gdalformat
wgs84latlonProj = osr.SpatialReference()
wgs84latlonProj.ImportFromEPSG(4326)
otherargs.wgs84latlonProj = wgs84latlonProj
def _calcSolarAzimuthZenith(info, inputs, outputs, otherargs):
"""
Internal functions used within calcSolarAzimuthZenith() - don't call independently.
"""
xBlock, yBlock = info.getBlockCoordArrays()
inProj = osr.SpatialReference()
inProj.ImportFromWkt(info.getProjection())
transform = osr.CoordinateTransformation(inProj,
otherargs.wgs84latlonProj)
xBlockF = xBlock.flatten()
yBlockF = yBlock.flatten()
pts = numpy.zeros((xBlockF.shape[0], 2), dtype=numpy.float)
pts[..., 0] = xBlockF
pts[..., 1] = yBlockF
outPts = numpy.array(transform.TransformPoints(pts))
outAz = numpy.zeros_like(xBlockF, dtype=numpy.float)
outZen = numpy.zeros_like(xBlockF, dtype=numpy.float)
for i in range(outPts.shape[0]):
outAz[i] = 90 - Pysolar.solar.GetAltitude(
outPts[i, 1], outPts[i, 0], otherargs.dateTime)
outZen[i] = ((-1) * Pysolar.solar.GetAzimuth(
outPts[i, 1], outPts[i, 0], otherargs.dateTime)) - 180
outAz = numpy.reshape(outAz, xBlock.shape)
outZen = numpy.reshape(outZen, xBlock.shape)
#print("Block End")
outputs.outimage = numpy.stack((outAz, outZen))
# Apply the multiply function.
applier.apply(_calcSolarAzimuthZenith,
infiles,
outfiles,
otherargs,
controls=aControls)
