def create_colortable(ct_file):
ct_entries = parse_color_table_file(ct_file)
ct = gdal.ColorTable()
for entry in ct_entries:
ct.SetColorEntry(entry[0], tuple(entry[1:]))
return ct
def load_color_table(nc_vname):
"""Load a color table for use with a palettized GeoTIFF
:param string nc_vname: name of the variable which maps to a color table. The color tables are stored in the directory color_tables with a filename extension of .ct
"""
if nc_vname in ["tmp", "tmx", "tmn"]:
ct_name = "tmp"
elif nc_vname in ["cld"]:
ct_name = "cld"
elif nc_vname in ["wet", "vap", "pre", "pet"]:
ct_name = "pre"
elif nc_vname in ["frs"]:
ct_name = "frs"
elif nc_vname in ["dtr"]:
ct_name = "dtr"
else:
raise Exception("No color table defined for variable: " + nc_vname)
fname = "color_tables/" + ct_name + ".ct"
# file is just a text file, use readlines
fh = open(fname, 'r')
lines = fh.readlines()
# create GDAL color table
ct = gdal.ColorTable()
# first line is length of color table
ct_length = int(lines[0])
for p in range(0, ct_length):
c = lines[p + 1].split(",")
ct.SetColorEntry(p, (int(c[0]), int(c[1]), int(c[2]), int(c[3])))
return ct
def CreateColorTable(fileLUT, logger_=logger):
"""
IN :
fileLUT [string] : path to the color file table
ex : for a table containing 3 classes ("8","90","21"), "8" must be represent in red, "90" in green, "21" in blue
cat /path/to/myColorTable.csv
8 255 0 0
90 0 255 0
21 0 255 0
OUT :
ct [gdalColorTable]
"""
filein = open(fileLUT)
ct = gdal.ColorTable()
for line in filein:
entry = line
classID = entry.split(" ")
codeColor = [int(i) for i in (classID[1:4])]
try:
ct.SetColorEntry(int(classID[0]), tuple(codeColor))
except:
logger_.warning(
"a color entry was not recognize, default value set. Class label 0, RGB code : 255, 255, 255"
)
ct.SetColorEntry(0, (255, 255, 255))
filein.close()
return ct
def pcidsk_7():
if gdaltest.pcidsk_new == 0:
return 'skip'
# Write out some metadata to the default and non-default domain and
# using the set and single methods.
band = gdaltest.pcidsk_ds.GetRasterBand(1)
ct = band.GetColorTable()
if ct is not None:
gdaltest.post_reason('Got color table unexpectedly.')
return 'fail'
ct = gdal.ColorTable()
ct.SetColorEntry(0, (0, 255, 0, 255))
ct.SetColorEntry(1, (255, 0, 255, 255))
ct.SetColorEntry(2, (0, 0, 255, 255))
band.SetColorTable(ct)
ct = band.GetColorTable()
if ct.GetColorEntry(1) != (255, 0, 255, 255):
gdaltest.post_reason('Got wrong color table entry immediately.')
return 'fail'
ct = None
band = None
# Close and reopen.
gdaltest.pcidsk_ds = None
gdaltest.pcidsk_ds = gdal.Open('tmp/pcidsk_5.pix', gdal.GA_Update)
band = gdaltest.pcidsk_ds.GetRasterBand(1)
ct = band.GetColorTable()
if ct.GetColorEntry(1) != (255, 0, 255, 255):
gdaltest.post_reason('Got wrong color table entry after reopen.')
return 'fail'
if band.GetColorInterpretation() != gdal.GCI_PaletteIndex:
gdaltest.post_reason('Not a palette?')
return 'fail'
if band.SetColorTable(None) != 0:
gdaltest.post_reason('SetColorTable failed.')
return 'fail'
if band.GetColorTable() is not None:
gdaltest.post_reason('color table still exists!')
return 'fail'
if band.GetColorInterpretation() != gdal.GCI_Undefined:
gdaltest.post_reason('Paletted?')
return 'fail'
return 'success'
def add_colortable(n_out, source):
""" Add colortable to output GDAL Dataset """
colorTable = gdal.ColorTable()
for color in colorDict[source]:
colorTable.SetColorEntry(color, colorDict[source][color] + (255, ))
n_out.vrt.dataset.GetRasterBand(1).SetColorTable(colorTable)
return n_out
def palette2colortable(pal):
"""
"""
import gdal
colortable = gdal.ColorTable()
for index in range(0, len(pal)/3):
entry = [int(col) for col in pal[index*3:index*3+3]]
colortable.SetColorEntry(index, tuple(entry))
return colortable
def colortable_1():
gdaltest.test_ct_data = [(255, 0, 0), (0, 255, 0), (0, 0, 255),
(255, 255, 255, 0)]
gdaltest.test_ct = gdal.ColorTable()
for i in range(len(gdaltest.test_ct_data)):
gdaltest.test_ct.SetColorEntry(i, gdaltest.test_ct_data[i])
return 'success'
def create_color_table(R, G, B):
"""Create a color table for use with a palettized GeoTIFF
:param array(byte) R: array / list of reds, length n
:param array(byte) G: array / list of greens, length n
:param array(byte) B: array / list of blues, length n
"""
assert (len(R) == len(G) and len(G) == len(B))
# create a GDAL color table
ct = gdal.ColorTable()
for p in range(0, len(R)):
ct.SetColorEntry(p, (R[p], G[p], B[p], 255))
return ct
def RasterPalette(paletteT):
PcR, AT, maxAT = FixGDALPalette(paletteT)
ct = gdal.ColorTable()
'''
For discrete colors
#ct.CreateColorRamp(0,(178,223,138),5,(255,127,0))
#ct.CreateColorRamp(Pcr)
for c in PcR:
ct.SetColorEntry(c[0],c[1])
'''
#for color ramps
for c in range(1, len(PcR)):
ct.CreateColorRamp(PcR[c - 1][0], PcR[c - 1][1], PcR[c][0], PcR[c][1])
return ct
'''
def create_colortable(ct_file_or_entries):
"""Create GDAL ColorTable object from Colormap object."""
if isinstance(ct_file_or_entries, str):
ct_entries = parse_color_table_file(ct_file_or_entries)
elif isinstance(ct_file_or_entries, Colormap):
ct_entries = enumerate(ct_file_or_entries.colors)
ct_entries = (((x, ) + tuple(int(c * 255.) for c in color))
for x, color in ct_entries)
else:
ct_entries = ct_file_or_entries
ct = gdal.ColorTable()
for entry in ct_entries:
ct.SetColorEntry(entry[0], tuple(entry[1:]))
return ct
def gen_GTiff(pgm_lines, gtiff_fname, proj4string):
pgm_header_info = parse_header_lines(pgm_lines[0:7])
pgm_data_array = gen_array(pgm_lines)
pgm_shape = pgm_data_array.shape
val_array = pgm_data_array * float(pgm_header_info['gain']) + float(
pgm_header_info['offset'])
color_idx_array = Natural_Breaks(val_array.ravel(), 8, 0).yb
color_idx_array.shape = pgm_shape
#Select driver
driver = gdal.GetDriverByName('GTiff')
#Create file
dataset = driver.Create(gtiff_fname, pgm_header_info['width'],
pgm_header_info['height'], 1, gdal.GDT_Byte)
epsg = Proj(proj4string)
logging.debug('Lower left coordinate (long, lat) : (%f, %f)' %
(pgm_header_info['lon0'], pgm_header_info['lat0']))
x_bl, y_bl = epsg(pgm_header_info['lon0'], pgm_header_info['lat0'])
logging.debug('Lower left coordinates (x, y): (%f, %f)' % (x_bl, y_bl))
x_ul = x_bl
y_ul = y_bl + pgm_header_info['pixsize'] * pgm_header_info['height']
x0 = x_ul
# Check reference
y0 = y_ul
dataset.SetGeoTransform([
x0, pgm_header_info['pixsize'], 0, y0, 0,
-1 * pgm_header_info['pixsize']
])
srs = osr.SpatialReference()
srs.ImportFromProj4(proj4string)
dataset.SetProjection(srs.ExportToWkt())
dataset.GetRasterBand(1).WriteArray(color_idx_array)
dataset.GetRasterBand(1).SetColorInterpretation(gdal.GCI_PaletteIndex)
color_table = gdal.ColorTable(gdal.GPI_RGB)
ce = gdal.ColorEntry()
ce.__dict__['this'] = (255, 255, 255, 255)
color_table.SetColorEntry(0, ce.__dict__['this'])
for i in range(7):
ce = gdal.ColorEntry()
ce.__dict__['this'] = YlOrRd[7][i]
color_table.SetColorEntry(i + 1, ce.__dict__['this'])
dataset.GetRasterBand(1).SetColorTable(color_table)
dataset = None
def colortable_3():
ct = gdal.ColorTable()
try:
ct.CreateColorRamp
except:
return 'skip'
ct.CreateColorRamp(0, (255, 0, 0), 255, (0, 0, 255))
if ct.GetColorEntry(0) != (255, 0, 0, 255):
return 'fail'
if ct.GetColorEntry(255) != (0, 0, 255, 255):
return 'fail'
return 'success'
def write_geotiff(fname,
data,
geo_transform,
projection,
classes,
COLORS,
data_type=gdal.GDT_Byte):
"""
Create a GeoTIFF file with the given data.
:param fname: Path to a directory with shapefiles
:param data: Number of rows of the result
:param geo_transform: Returned value of gdal.Dataset.GetGeoTransform (coefficients for
transforming between pixel/line (P,L) raster space, and projection
coordinates (Xp,Yp) space.
:param projection: Projection definition string (Returned by gdal.Dataset.GetProjectionRef)
"""
driver = gdal.GetDriverByName('GTiff')
rows, cols = data.shape
dataset = driver.Create(fname, cols, rows, 1, data_type)
dataset.SetGeoTransform(geo_transform)
dataset.SetProjection(projection)
band = dataset.GetRasterBand(1)
band.WriteArray(data)
ct = gdal.ColorTable()
for pixel_value in range(len(classes) + 1):
color_hex = COLORS[pixel_value]
r = int(color_hex[1:3], 16)
g = int(color_hex[3:5], 16)
b = int(color_hex[5:7], 16)
ct.SetColorEntry(pixel_value, (r, g, b, 255))
band.SetColorTable(ct)
metadata = {
'TIFFTAG_COPYRIGHT': 'CC BY 4.0, AND BEN HICKSON',
'TIFFTAG_DOCUMENTNAME': 'Land Cover Classification',
'TIFFTAG_IMAGEDESCRIPTION':
'Random Forests Supervised classification.',
'TIFFTAG_MAXSAMPLEVALUE': str(len(classes)),
'TIFFTAG_MINSAMPLEVALUE': '0',
'TIFFTAG_SOFTWARE': 'Python, GDAL, scikit-learn'
}
dataset.SetMetadata(metadata)
dataset = None # Close the file
return
def colortable(ctype):
"""
Generates a gdal-ingestible color table for a set of pre-defined options.
Can add your own colortable options. See https://gdal.org/doxygen/structGDALColorEntry.html
and https://gis.stackexchange.com/questions/158195/python-gdal-create-geotiff-from-array-with-colormapping
for guidance.
Parameters
----------
ctype : str
Specifies the type of colortable to return. Choose from
{'binary', 'skel', 'mask', 'tile', or 'GSW'}.
Returns
-------
color_table : gdal.ColorTable()
Color table that can be supplied to gdal when creating a raster.
"""
color_table = gdal.ColorTable()
if ctype == 'binary':
# Some examples / last value is alpha (transparency).
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (255, 255, 255, 100))
elif ctype == 'skel':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (255, 0, 255, 100))
elif ctype == 'mask':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 128, 0, 100))
elif ctype == 'tile':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 0, 255, 100))
elif ctype == 'GSW':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 0, 0, 0))
color_table.SetColorEntry(2, (176, 224, 230, 100))
return color_table
def reclassify():
directory_path = str(input('enter diectory path'))
os.chdir(directory_path)
fileName = str(input("file name"))
datasource = gdal.Open(fileName)
fileformat = datasource.GetDriver().ShortName
driver = gdal.GetDriverByName(str(fileformat))
ds = driver.Create('dem_class4.tif', datasource.RasterXSize,
datasource.RasterYSize, 1, gdal.GDT_Byte)
band = ds.GetRasterBand(1)
colors = gdal.ColorTable()
Number_of_class = int(input("enter number of class for classification"))
for i in range(Number_of_class):
color_tuple = []
Number_of_class = int(input("enter number of color to be entered"))
for j in range(Number_of_class):
color = int(input("enter color number"))
color_tuple.append(color)
colors.SetColorEntry(i, tuple(color_tuple))
band.SetRasterColorTable(colors)
band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex)
del band, ds
def test_gdal_translate_19():
if test_cli_utilities.get_gdal_translate_path() is None:
return 'skip'
ds = gdal.GetDriverByName('GTiff').Create(
'tmp/test_gdal_translate_19_src.tif', 1, 1, 2)
ct = gdal.ColorTable()
ct.SetColorEntry(127, (1, 2, 3, 255))
ds.GetRasterBand(1).SetRasterColorTable(ct)
ds.GetRasterBand(1).Fill(127)
ds.GetRasterBand(2).Fill(250)
ds = None
gdaltest.runexternal(
test_cli_utilities.get_gdal_translate_path() +
' -expand rgba tmp/test_gdal_translate_19_src.tif tmp/test_gdal_translate_19_dst.tif'
)
ds = gdal.Open('tmp/test_gdal_translate_19_dst.tif')
if ds is None:
return 'fail'
if ds.GetRasterBand(1).Checksum() != 1:
gdaltest.post_reason('Bad checksum for band 1')
return 'fail'
if ds.GetRasterBand(2).Checksum() != 2:
gdaltest.post_reason('Bad checksum for band 2')
return 'fail'
if ds.GetRasterBand(3).Checksum() != 3:
gdaltest.post_reason('Bad checksum for band 3')
return 'fail'
if ds.GetRasterBand(4).Checksum() != 250 % 7:
gdaltest.post_reason('Bad checksum for band 4')
return 'fail'
ds = None
return 'success'
def ehdr_4():
drv = gdal.GetDriverByName('EHdr')
ds = drv.Create('tmp/test_4.bil', 200, 100, 1, gdal.GDT_Byte)
raw_data = array.array('h', list(range(200))).tostring()
for line in range(100):
ds.WriteRaster(0, line, 200, 1, raw_data, buf_type=gdal.GDT_Int16)
ct = gdal.ColorTable()
ct.SetColorEntry(0, (255, 255, 255, 255))
ct.SetColorEntry(1, (255, 255, 0, 255))
ct.SetColorEntry(2, (255, 0, 255, 255))
ct.SetColorEntry(3, (0, 255, 255, 255))
ds.GetRasterBand(1).SetRasterColorTable(ct)
ds.GetRasterBand(1).SetNoDataValue(17)
ds = None
return 'success'
def CreateColorTable(fileLUT):
"""
IN :
fileLUT [string] : path to the color file table
ex : for a table containing 3 classes ("8","90","21"), "8" must be represent in red, "90" in green, "21" in blue
cat /path/to/myColorTable.csv
8 255 0 0
90 0 255 0
21 0 255 0
OUT :
ct [gdalColorTable]
"""
filein = open(fileLUT)
ct = gdal.ColorTable()
for line in filein:
entry = line
classID = entry.split(" ")
if len(classID) < 4:
continue
codeColor = [int(i) for i in (classID[1:4])]
ct.SetColorEntry(int(classID[0]), tuple(codeColor))
filein.close()
return ct
def format_colortable(name,
vmin=0.,
vmax=1.,
vmin_pal=0.,
vmax_pal=1.,
index_min=0,
index_max=254,
index_nodata=255):
"""
"""
colormap, nodata_color = load_colormap(name)
norm_min = (vmin - vmin_pal) / float((vmax_pal - vmin_pal))
norm_max = (vmax - vmin_pal) / float((vmax_pal - vmin_pal))
ncols = int(index_max) - int(index_min) + 1
colors = colormap(np.linspace(norm_min, norm_max, num=ncols))
colors = np.round(colors * 255)
entries = [(int(c[0]), int(c[1]), int(c[2])) for c in colors]
colortable = gdal.ColorTable()
for index in range(int(index_min), int(index_max) + 1):
colortable.SetColorEntry(index, entries[index - int(index_min)])
if index_nodata != None:
colortable.SetColorEntry(index_nodata, nodata_color)
return colortable
def colortable(ctype):
color_table = gdal.ColorTable()
if ctype == 'binary':
# Some examples / last value is alpha (transparency). See http://www.gdal.org/structGDALColorEntry.html
# and https://gis.stackexchange.com/questions/158195/python-gdal-create-geotiff-from-array-with-colormapping
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (255, 255, 255, 100))
elif ctype == 'skel':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (255, 0, 255, 100))
elif ctype == 'mask':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 128, 0, 100))
elif ctype == 'tile':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 0, 255, 100))
elif ctype == 'JRCmo':
color_table.SetColorEntry(0, (0, 0, 0, 0))
color_table.SetColorEntry(1, (0, 0, 0, 0))
color_table.SetColorEntry(2, (176, 224, 230, 100))
return color_table
def combine_class(band):
classes = band.GetCategoryNames()
color_int = band.GetRasterColorInterpretation()
attr_table = band.GetDefaultRAT()
cols = attr_table.GetColumnCount()
rows = attr_table.GetRowCount()
cat_names = band.GetCategoryNames()
#print cat_names
color_interpretation = band.GetRasterColorInterpretation()
print 'band color interpretation is %s' % color_interpretation
band.SetRasterColorInterpretation(GCI_PaletteIndex)
print 'band color interpretation is set to %s' % color_int
"""The output class image from ArcGIS has no color table"""
#color_table = band.GetColorTable()
#print color_table
#print color_table.GetColorEntryCount()
# initialize new color table for image
color_table = gdal.ColorTable(GPI_RGB)
print 'color table palette interpretation is %s' % color_table.GetPaletteInterpretation(
)
#print 'color table entry count is %s' % color_table.GetCount()
color_entry = color_table.SetColorEntry(color_table, 255)
#print color_entry(255,255,255,0)
# prepare information from the raster attribute table for setting up color table
class_value = []
class_name = []
rgb = [] # to be used for setting up class colors
# get column headings
#for col in range(cols):
#print attr_table.GetNameOfCol(col)
#print "\n"
for row in range(rows):
class_value.append(attr_table.GetValueAsString(row, 2))
class_name.append(attr_table.GetValueAsString(row, 3))
rgb.append((attr_table.GetValueAsString(row, 4),
attr_table.GetValueAsString(row, 5),
attr_table.GetValueAsString(row, 6)))
#print class_name
band.SetRasterCategoryNames(class_name)
#for i in range(len(rgb)):
# set color palette
#print rgb[i][0]
#red = color_table.GetColorEntry(int(rgb[i][0]))
#print red
#green = color_table.GetPaletteInterpretation(rgb[i][1])
#blue = color_table.GetPaletteInterpretation(rgb[i][2])
#color_table.SetColorEntry(i, red, green, blue)
#print class_value[i], class_name[i], rgb[i]
#band.FlushCache()
return
def gdal_api_proxy_sub():
src_ds = gdal.Open('data/byte.tif')
src_cs = src_ds.GetRasterBand(1).Checksum()
src_gt = src_ds.GetGeoTransform()
src_prj = src_ds.GetProjectionRef()
src_data = src_ds.ReadRaster(0, 0, 20, 20)
src_md = src_ds.GetMetadata()
src_ds = None
drv = gdal.IdentifyDriver('data/byte.tif')
if drv.GetDescription() != 'API_PROXY':
gdaltest.post_reason('fail')
return 'fail'
ds = gdal.GetDriverByName('GTiff').Create('tmp/byte.tif', 1, 1, 3)
ds = None
src_ds = gdal.Open('data/byte.tif')
ds = gdal.GetDriverByName('GTiff').CreateCopy('tmp/byte.tif',
src_ds,
options=['TILED=YES'])
got_cs = ds.GetRasterBand(1).Checksum()
if src_cs != got_cs:
gdaltest.post_reason('fail')
return 'fail'
ds = None
ds = gdal.Open('tmp/byte.tif', gdal.GA_Update)
ds.SetGeoTransform([1, 2, 3, 4, 5, 6])
got_gt = ds.GetGeoTransform()
if src_gt == got_gt:
gdaltest.post_reason('fail')
return 'fail'
ds.SetGeoTransform(src_gt)
got_gt = ds.GetGeoTransform()
if src_gt != got_gt:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetGCPCount() != 0:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetGCPProjection() != '':
print(ds.GetGCPProjection())
gdaltest.post_reason('fail')
return 'fail'
if len(ds.GetGCPs()) != 0:
gdaltest.post_reason('fail')
return 'fail'
gcps = [gdal.GCP(0, 1, 2, 3, 4)]
ds.SetGCPs(gcps, "foo")
got_gcps = ds.GetGCPs()
if len(got_gcps) != 1:
gdaltest.post_reason('fail')
return 'fail'
if got_gcps[0].GCPLine != gcps[0].GCPLine or \
got_gcps[0].GCPPixel != gcps[0].GCPPixel or \
got_gcps[0].GCPX != gcps[0].GCPX or \
got_gcps[0].GCPY != gcps[0].GCPY:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetGCPProjection() != 'foo':
gdaltest.post_reason('fail')
print(ds.GetGCPProjection())
return 'fail'
ds.SetGCPs([], "")
if len(ds.GetGCPs()) != 0:
gdaltest.post_reason('fail')
return 'fail'
ds.SetProjection('')
got_prj = ds.GetProjectionRef()
if src_prj == got_prj:
gdaltest.post_reason('fail')
return 'fail'
ds.SetProjection(src_prj)
got_prj = ds.GetProjectionRef()
if src_prj != got_prj:
gdaltest.post_reason('fail')
print(src_prj)
print(got_prj)
return 'fail'
ds.GetRasterBand(1).Fill(0)
got_cs = ds.GetRasterBand(1).Checksum()
if 0 != got_cs:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).WriteRaster(0, 0, 20, 20, src_data)
got_cs = ds.GetRasterBand(1).Checksum()
if src_cs != got_cs:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).Fill(0)
got_cs = ds.GetRasterBand(1).Checksum()
if 0 != got_cs:
gdaltest.post_reason('fail')
return 'fail'
ds.WriteRaster(0, 0, 20, 20, src_data)
got_cs = ds.GetRasterBand(1).Checksum()
if src_cs != got_cs:
gdaltest.post_reason('fail')
return 'fail'
# Not bound to SWIG
# ds.AdviseRead(0,0,20,20,20,20)
got_data = ds.ReadRaster(0, 0, 20, 20)
if src_data != got_data:
gdaltest.post_reason('fail')
return 'fail'
got_data = ds.GetRasterBand(1).ReadRaster(0, 0, 20, 20)
if src_data != got_data:
gdaltest.post_reason('fail')
return 'fail'
got_data_weird_spacing = ds.ReadRaster(0,
0,
20,
20,
buf_pixel_space=1,
buf_line_space=32)
if len(got_data_weird_spacing) != 32 * (20 - 1) + 20:
gdaltest.post_reason('fail')
print(len(got_data_weird_spacing))
return 'fail'
if got_data[20:20 + 20] != got_data_weird_spacing[32:32 + 20]:
gdaltest.post_reason('fail')
return 'fail'
got_data_weird_spacing = ds.GetRasterBand(1).ReadRaster(0,
0,
20,
20,
buf_pixel_space=1,
buf_line_space=32)
if len(got_data_weird_spacing) != 32 * (20 - 1) + 20:
gdaltest.post_reason('fail')
print(len(got_data_weird_spacing))
return 'fail'
if got_data[20:20 + 20] != got_data_weird_spacing[32:32 + 20]:
gdaltest.post_reason('fail')
return 'fail'
got_block = ds.GetRasterBand(1).ReadBlock(0, 0)
if len(got_block) != 256 * 256:
gdaltest.post_reason('fail')
return 'fail'
if got_data[20:20 + 20] != got_block[256:256 + 20]:
gdaltest.post_reason('fail')
return 'fail'
ds.FlushCache()
ds.GetRasterBand(1).FlushCache()
got_data = ds.GetRasterBand(1).ReadRaster(0, 0, 20, 20)
if src_data != got_data:
gdaltest.post_reason('fail')
return 'fail'
if len(ds.GetFileList()) != 1:
gdaltest.post_reason('fail')
return 'fail'
if ds.AddBand(gdal.GDT_Byte) == 0:
gdaltest.post_reason('fail')
return 'fail'
got_md = ds.GetMetadata()
if src_md != got_md:
gdaltest.post_reason('fail')
print(src_md)
print(got_md)
return 'fail'
if ds.GetMetadataItem('AREA_OR_POINT') != 'Area':
gdaltest.post_reason('fail')
return 'fail'
if ds.GetMetadataItem('foo') is not None:
gdaltest.post_reason('fail')
return 'fail'
ds.SetMetadataItem('foo', 'bar')
if ds.GetMetadataItem('foo') != 'bar':
gdaltest.post_reason('fail')
return 'fail'
ds.SetMetadata({'foo': 'baz'}, 'OTHER')
if ds.GetMetadataItem('foo', 'OTHER') != 'baz':
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetMetadata({'foo': 'baw'}, 'OTHER')
if ds.GetRasterBand(1).GetMetadataItem('foo', 'OTHER') != 'baw':
gdaltest.post_reason('fail')
return 'fail'
if ds.GetMetadataItem('INTERLEAVE', 'IMAGE_STRUCTURE') != 'BAND':
gdaltest.post_reason('fail')
return 'fail'
if len(ds.GetRasterBand(1).GetMetadata()) != 0:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMetadataItem('foo') is not None:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetMetadataItem('foo', 'baz')
if ds.GetRasterBand(1).GetMetadataItem('foo') != 'baz':
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetMetadata({'foo': 'baw'})
if ds.GetRasterBand(1).GetMetadataItem('foo') != 'baw':
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetColorInterpretation() != gdal.GCI_GrayIndex:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetColorInterpretation(gdal.GCI_Undefined)
ct = ds.GetRasterBand(1).GetColorTable()
if ct is not None:
gdaltest.post_reason('fail')
return 'fail'
ct = gdal.ColorTable()
ct.SetColorEntry(0, (1, 2, 3))
if ds.GetRasterBand(1).SetColorTable(ct) != 0:
gdaltest.post_reason('fail')
return 'fail'
ct = ds.GetRasterBand(1).GetColorTable()
if ct is None:
gdaltest.post_reason('fail')
return 'fail'
if ct.GetColorEntry(0) != (1, 2, 3, 255):
gdaltest.post_reason('fail')
return 'fail'
ct = ds.GetRasterBand(1).GetColorTable()
if ct is None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).SetColorTable(None) != 0:
gdaltest.post_reason('fail')
return 'fail'
ct = ds.GetRasterBand(1).GetColorTable()
if ct is not None:
gdaltest.post_reason('fail')
return 'fail'
rat = ds.GetRasterBand(1).GetDefaultRAT()
if rat is not None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).SetDefaultRAT(None) != 0:
gdaltest.post_reason('fail')
return 'fail'
ref_rat = gdal.RasterAttributeTable()
if ds.GetRasterBand(1).SetDefaultRAT(ref_rat) != 0:
gdaltest.post_reason('fail')
return 'fail'
rat = ds.GetRasterBand(1).GetDefaultRAT()
if rat is None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).SetDefaultRAT(None) != 0:
gdaltest.post_reason('fail')
return 'fail'
rat = ds.GetRasterBand(1).GetDefaultRAT()
if rat is not None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMinimum() is not None:
gdaltest.post_reason('fail')
return 'fail'
got_stats = ds.GetRasterBand(1).GetStatistics(0, 0)
if got_stats[3] >= 0.0:
gdaltest.post_reason('fail')
return 'fail'
got_stats = ds.GetRasterBand(1).GetStatistics(1, 1)
if got_stats[0] != 74.0:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMinimum() != 74.0:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMaximum() != 255.0:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetStatistics(1, 2, 3, 4)
got_stats = ds.GetRasterBand(1).GetStatistics(1, 1)
if got_stats != [1, 2, 3, 4]:
print(got_stats)
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).ComputeStatistics(0)
got_stats = ds.GetRasterBand(1).GetStatistics(1, 1)
if got_stats[0] != 74.0:
gdaltest.post_reason('fail')
return 'fail'
minmax = ds.GetRasterBand(1).ComputeRasterMinMax()
if minmax != (74.0, 255.0):
gdaltest.post_reason('fail')
print(minmax)
return 'fail'
if ds.GetRasterBand(1).GetOffset() != 0.0:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetScale() != 1.0:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetOffset(10.0)
if ds.GetRasterBand(1).GetOffset() != 10.0:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetScale(2.0)
if ds.GetRasterBand(1).GetScale() != 2.0:
gdaltest.post_reason('fail')
return 'fail'
ds.BuildOverviews('NEAR', [2])
if ds.GetRasterBand(1).GetOverviewCount() != 1:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetOverview(-1) is not None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetOverview(0) is None:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetOverview(0) is None:
gdaltest.post_reason('fail')
return 'fail'
got_hist = ds.GetRasterBand(1).GetHistogram()
if len(got_hist) != 256:
gdaltest.post_reason('fail')
return 'fail'
(minval, maxval, nitems,
got_hist2) = ds.GetRasterBand(1).GetDefaultHistogram()
if minval != -0.5:
gdaltest.post_reason('fail')
return 'fail'
if maxval != 255.5:
gdaltest.post_reason('fail')
return 'fail'
if nitems != 256:
gdaltest.post_reason('fail')
return 'fail'
if got_hist != got_hist2:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetDefaultHistogram(1, 2, [3])
(minval, maxval, nitems,
got_hist3) = ds.GetRasterBand(1).GetDefaultHistogram()
if minval != 1:
gdaltest.post_reason('fail')
return 'fail'
if maxval != 2:
gdaltest.post_reason('fail')
return 'fail'
if nitems != 1:
gdaltest.post_reason('fail')
return 'fail'
if got_hist3[0] != 3:
gdaltest.post_reason('fail')
return 'fail'
got_nodatavalue = ds.GetRasterBand(1).GetNoDataValue()
if got_nodatavalue is not None:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetNoDataValue(123)
got_nodatavalue = ds.GetRasterBand(1).GetNoDataValue()
if got_nodatavalue != 123:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMaskFlags() != 8:
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMaskBand() is None:
gdaltest.post_reason('fail')
return 'fail'
ds.CreateMaskBand(0)
if ds.GetRasterBand(1).GetMaskFlags() != 2:
print(ds.GetRasterBand(1).GetMaskFlags())
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetMaskBand() is None:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).CreateMaskBand(0)
if ds.GetRasterBand(1).HasArbitraryOverviews() != 0:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetUnitType('foo')
if ds.GetRasterBand(1).GetUnitType() != 'foo':
gdaltest.post_reason('fail')
return 'fail'
if ds.GetRasterBand(1).GetCategoryNames() is not None:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetCategoryNames(['foo'])
if ds.GetRasterBand(1).GetCategoryNames() != ['foo']:
gdaltest.post_reason('fail')
return 'fail'
ds.GetRasterBand(1).SetDescription('bar')
ds = None
gdal.GetDriverByName('GTiff').Delete('tmp/byte.tif')
return 'success'
def PiNo_classifier(infile, outfile, sat, detect_clouds, sun_azi,
fnorm_delta_val):
try:
print "INTO pino classifier"
start = time.time()
print infile
src_ds = gdal.Open(infile)
num_bands = src_ds.RasterCount
print infile
print "Bands:"
print num_bands
if (sat == "S2A_L1C" and (not num_bands == 13)):
src_ds = None
print "Input image does not have 13 bands" + "<br>"
return (-1)
DataType = src_ds.GetRasterBand(1).DataType
DataType = gdal.GetDataTypeName(DataType)
print "Type: " + DataType
if (DataType == "Byte"):
TOTmem = src_ds.RasterXSize * src_ds.RasterYSize * 8 / 1024 * 6 * 20
elif (DataType == "UInt16"):
TOTmem = src_ds.RasterXSize * src_ds.RasterYSize * 16 / 1024 * 6 * 20
else:
TOTmem = src_ds.RasterXSize * src_ds.RasterYSize * 32 / 1024 * 6 * 20
print TOTmem
res = UnixMemory()
FREEmem = res['free']
print FREEmem
if (FREEmem < TOTmem + (TOTmem / 100 * 20)):
print "Processing using tiling ..... " + "<br>"
ratio = int(round(TOTmem * Memory_Buffer / FREEmem * 1., ))
else:
ratio = 1
print "Ratio: " + str(ratio) + "<br>"
MaxX = src_ds.RasterXSize
MaxY = src_ds.RasterYSize
LenX = int(src_ds.RasterXSize / ratio)
LenY = int(src_ds.RasterYSize / ratio)
#print MaxX,MaxY
#print LenX,LenY
Xval = []
Yval = []
for x in range(1, ratio):
Xval.append(LenX * x)
Xval.append(MaxX)
for x in range(1, ratio):
Yval.append(LenY * x)
Yval.append(MaxY)
#print Xval
#print Yval
driver = gdal.GetDriverByName("GTiff")
print outfile
dst_ds = driver.Create(outfile,
src_ds.RasterXSize,
src_ds.RasterYSize,
1,
gdal.GDT_Byte,
options=['COMPRESS=LZW'])
dst_ds.GetRasterBand(1).SetRasterColorInterpretation(
gdal.GCI_PaletteIndex)
c = gdal.ColorTable()
ctable = [[0, (0, 0, 0)], [1, (255, 255, 255)], [2, (192, 242, 255)],
[3, (1, 255, 255)], [4, (0, 0, 0)], [5, (1, 1, 255)],
[6, (1, 123, 255)], [7,
(110, 150, 255)], [8, (168, 180, 255)],
[9, (160, 255, 90)], [10, (1, 80, 1)], [11, (12, 113, 1)],
[12, (1, 155, 1)],
[13, (100, 190, 90)], [14, (146, 255, 165)], [15, (0, 0, 0)],
[16, (210, 255, 153)], [17, (0, 0, 0)], [18, (0, 0, 0)],
[19, (0, 0, 0)], [20, (0, 0, 0)], [21, (237, 255, 193)],
[22, (200, 230, 200)], [23, (0, 0, 0)], [24, (0, 0, 0)],
[25, (0, 0, 0)], [26, (0, 0, 0)], [27, (0, 0, 0)],
[28, (0, 0, 0)], [29, (0, 0, 0)], [30, (200, 200, 150)],
[31, (227, 225, 170)], [32, (0, 0, 0)], [33, (0, 0, 0)],
[34, (255, 225, 255)], [35, (140, 5, 190)],
[36, (255, 1, 1)], [37, (0, 0, 0)], [38, (0, 0, 0)],
[39, (0, 0, 0)], [40, (20, 40, 10)], [41, (145, 1, 110)],
[42, (100, 100, 100)]]
for cid in range(0, 43):
c.SetColorEntry(cid, ctable[cid][1])
dst_ds.GetRasterBand(1).SetColorTable(c)
dst_ds.SetGeoTransform(src_ds.GetGeoTransform())
dst_ds.SetProjection(src_ds.GetProjectionRef())
print 'Setting out file ....'
OUTCLASS = dst_ds.GetRasterBand(1).ReadAsArray(0, 0,
dst_ds.RasterXSize,
dst_ds.RasterYSize)
print 'Processing <br>'
mytry = 0
#try:
if (1 == 1):
MinX = 0
MinY = 0
for x in range(0, len(Xval)):
for y in range(0, len(Yval)):
MaxX = Xval[x]
MaxY = Yval[y]
OUTCLASS[MinY:MaxY, MinX:MaxX] = classify_tile(
src_ds, MinX, MinY, MaxX, MaxY, sat, fnorm_delta_val,
DataType)
MinY = MaxY
MinX = MaxX
MinY = 0
dst_ds.GetRasterBand(1).WriteArray(OUTCLASS)
if (detect_clouds == 0):
OUTCLASS[(OUTCLASS == 1)] = 34
OUTCLASS[(OUTCLASS == 2)] = 34
else:
try:
dst_ds.GetRasterBand(1).WriteArray(
OUTCLASS
) # save temporary out so class is saved even on mask failure
filter = numpy.ndarray(shape=(17, 17), dtype=bool)
filter[:] = False
filter[0, 8] = True
filter[1, 7:9] = True
filter[2, 6:9] = True
filter[3, 5:10] = True
filter[4, 4:11] = True
filter[5, 3:12] = True
filter[6, 2:14] = True
filter[7, 2:14] = True
filter[8, 0:16] = True
filter[9, 2:14] = True
filter[10, 2:14] = True
filter[11, 3:12] = True
filter[12, 4:11] = True
filter[13, 5:10] = True
filter[14, 6:9] = True
filter[15, 7:9] = True
filter[16, 8] = True
BOOL_MATRIX = numpy.zeros(
(dst_ds.RasterXSize, dst_ds.RasterYSize)).astype(bool)
BOOL_MATRIX = (OUTCLASS == 1) + (OUTCLASS == 2)
#OUTCLASS[mmorph.close(BOOL_MATRIX,filter)]=1 # 3x3
#------------------------------------------------------------------------------------------
#-------------------------USE 3D filter for CL - SH detection -----------------------------
#------------------------------------------------------------------------------------------
if (int(sun_azi) > 0):
sft = getend([100, 100], 270 - int(sun_azi), 20)
shiftX = 100 - int(sft[0])
shiftY = 100 - int(sft[1])
BOOL_MATRIX = numpy.roll(BOOL_MATRIX, shiftX, axis=0)
BOOL_MATRIX = numpy.roll(BOOL_MATRIX, shiftY, axis=1)
print "Cloud masking step 1" + "<br>"
SHDW_MATRIX = ((OUTCLASS == 10) + (OUTCLASS == 40) +
(OUTCLASS == 41) + (OUTCLASS == 42) +
(OUTCLASS == 34) + (OUTCLASS == 35) +
(OUTCLASS == 36) + (OUTCLASS == 4) +
(OUTCLASS == 5) + (OUTCLASS == 6) +
(OUTCLASS == 7) +
(OUTCLASS == 8)).astype(bool)
SHDW_MATRIX *= BOOL_MATRIX
print "Cloud masking step 2" + "<br>"
#OUTCLASS[mmorph.close(SHDW_MATRIX,filter)]=42
print "Cloud masking step 3" + "<br>"
except:
print "Image is TOO BIG for morphological filters <br>"
dst_ds.GetRasterBand(1).WriteArray(OUTCLASS)
#close roperly the dataset
dst_ds = None
src_ds = None
print "Execution time: " + str(time.time() - start) + "<br>"
return 'Complete' + "<br>"
except Exception, e:
print "Error :"
print str(e)
if src_ds.RasterCount < 3:
print '%s has %d bands, need 3 for inputs red, green and blue.' \
% src_ds.RasterCount
sys.exit(1)
# Ensure we recognise the driver.
dst_driver = gdal.GetDriverByName(format)
if dst_driver is None:
print '"%s" driver not registered.' % format
sys.exit(1)
# Generate the median cut PCT
ct = gdal.ColorTable()
err = gdal.ComputeMedianCutPCT(src_ds.GetRasterBand(1),
src_ds.GetRasterBand(2),
src_ds.GetRasterBand(3),
color_count,
ct,
callback=gdal.TermProgress,
callback_data='Generate PCT')
# Create the working file. We have to use TIFF since there are few formats
# that allow setting the color table after creation.
if format == 'GTiff':
tif_filename = dst_filename
else:
import gdal, glob
# specify GeoTIFF file name, open it using GDAL and get the first band
listFiles=glob.glob('/Users/Andy/Documents/Rwanda/Data/TropWet_Outputs/TW_v7.2/Classified*')
for fn in listFiles:
#fn = '/Users/Andy/Documents/Rwanda/Data/TropWet_Outputs/TW_v7.2/Classified_Output_April_to_June_2016_to_2020.tif'
ds = gdal.Open(fn, 1)
band = ds.GetRasterBand(1)
# create color table
colors = gdal.ColorTable()
# set color for each value
colors.SetColorEntry(0, (209, 209, 204))
colors.SetColorEntry(1, (69, 174, 144))
colors.SetColorEntry(2, (221, 154, 162))
colors.SetColorEntry(3, (229, 206, 113))
colors.SetColorEntry(4, (34, 80, 202))
colors.SetColorEntry(5, (50, 133, 54))
colors.SetColorEntry(6, (203, 191, 124))
# set color table and color interpretation
band.SetRasterColorTable(colors)
band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex)
# close and save file
del band, ds
