def subset_image_baseimage(output_file,input_file,baseimage,same_res=False):
"""
subset a image base on the extent of another image
Args:
output_file:the result file
input_file:the image need to subset
baseimage:the base image which provide the extend for subset
same_res: if true, then will resample the output to the resolution of baseimage, otherwise, keep the resolution
Returns:True is successful, False otherwise
"""
(ulx,uly,lrx,lry) = RSImage.get_image_proj_extent(baseimage)
if ulx is False:
return False
# check the save folder is valid or not
save_dir = os.path.dirname(output_file)
if len(save_dir) < 1:
basic.outputlogMessage('output save to current folder')
else:
basic.outputlogMessage('result save to %s'%save_dir)
img_obj = RSImageclass()
if same_res:
img_obj.open(baseimage)
else:
img_obj.open(input_file) # the resolution should keep the same
xres = img_obj.GetXresolution()
yres = img_obj.GetYresolution()
img_obj=None
if subset_image_projwin(output_file,input_file,ulx,uly,lrx,lry,xres=xres,yres=yres) is False:
return False
return True
def calculate_mean_of_images(images_list):
if len(images_list)<1:
basic.outputlogMessage('No image in the list')
return False
# get band number
img_obj = RSImageclass()
first_img = get_first_path_in_line(images_list[0])
if first_img is False:
return False
img_obj.open(first_img)
band_count = img_obj.GetBandCount()
img_obj = None
mean_of_images = [] # each band has one value
for i in range(0,band_count):
mean_of_images.append(0.0)
total_pixel = 0
number = 0
for image in images_list:
(width,height,mean_of_band) = cal_the_mean_of_bands(image)
pixel_count = width*height
number = number +1
print ('%d / %d'%(number,len(images_list)))
if width is False:
return False
if len(mean_of_band) != band_count:
basic.outputlogMessage('error, band count is different')
return False
for i in range(0, band_count):
mean_of_images[i] = (mean_of_images[i]*total_pixel + mean_of_band[i]*pixel_count)
total_pixel = total_pixel + width*height
for i in range(0, band_count):
mean_of_images[i] = mean_of_images[i]/total_pixel
for i in range(0, band_count):
basic.outputlogMessage('band {}: mean {}'.format(i+1, mean_of_images[i]))
f_obj = open('mean_value.txt','w')
f_obj.writelines('total image count {} \n'.format(len(images_list)))
for i in range(0,len(mean_of_images)):
f_obj.writelines('band {} \n'.format(i + 1))
for i in range(0,len(mean_of_images)):
f_obj.writelines('mean_value: {} \n'.format(mean_of_images[i]))
f_obj.close()
return mean_of_images
def convert_pixel_xy_to_map_coordinate(pixel_x, pixel_y, ref_image):
"""
get the map x,y of a pixel point
Args:
pixel_x: pixel, column index
pixel_y: pixel, row index
ref_image: the georeference image
Returns: x_map, y_map
"""
img_obj = RSImageclass()
if img_obj.open(ref_image) is False:
raise IOError('Open %s failed' % ref_image)
# pixel to map coordiante
# https://www.gdal.org/classGDALDataset.html#a5101119705f5fa2bc1344ab26f66fd1d
# Xp = padfTransform[0] + P*padfTransform[1] + L*padfTransform[2];
# Yp = padfTransform[3] + P*padfTransform[4] + L*padfTransform[5];
padfTransform = img_obj.GetGeoTransform()
x_map = padfTransform[
0] + pixel_x * padfTransform[1] + pixel_y * padfTransform[2]
y_map = padfTransform[
3] + pixel_x * padfTransform[4] + pixel_y * padfTransform[5]
return x_map, y_map
def subset_image_by_shapefile(imagefile, shapefile, bkeepmidfile):
"""
subset an image by polygons contained in the shapefile
Args:
imagefile:input image file path
shapefile:input shapefile contains polygon
bkeepmidfile:indicate whether keep middle file
Returns:output file name if succussful, False Otherwise
"""
if io_function.is_file_exist(imagefile) is False:
return False
if io_function.is_file_exist(shapefile) is False:
return False
Outfilename = io_function.get_name_by_adding_tail(imagefile, 'vsub')
# ds = ogr.Open(shapefile)
# lyr = ds.GetLayer(0)
# lyr.ResetReading()
# ft = lyr.GetNextFeature()
# subprocess.call(['gdalwarp', imagefile, Outfilename, '-cutline', shapefile,\
# '-crop_to_cutline'])
orgimg_obj = RSImageclass()
if orgimg_obj.open(imagefile) is False:
return False
x_res = abs(orgimg_obj.GetXresolution())
y_res = abs(orgimg_obj.GetYresolution())
CommandString = 'gdalwarp ' + ' -tr ' + str(x_res) + ' ' + str(
y_res
) + ' ' + imagefile + ' ' + Outfilename + ' -cutline ' + shapefile + ' -crop_to_cutline ' + ' -overwrite '
if basic.exec_command_string_one_file(CommandString, Outfilename) is False:
return False
# while ft:
# country_name = ft.GetFieldAsString('admin')
# outraster = imagefile.replace('.tif', '_%s.tif' % country_name.replace(' ', '_'))
# subprocess.call(['gdalwarp', imagefile, Outfilename, '-cutline', shapefile,
# '-crop_to_cutline', '-cwhere', "'admin'='%s'" % country_name])
#
# ft = lyr.GetNextFeature()
if not bkeepmidfile:
io_function.delete_file_or_dir(imagefile)
os.remove(imagefile)
if io_function.is_file_exist(Outfilename):
return Outfilename
else:
# basic.outputlogMessage(result)
basic.outputlogMessage(
'The version of GDAL must be great than 2.0 in order to use the r option '
)
return False
def coregistration(exefile, parafile_inp, parafile_ini, basefile, warpfile):
#check input
if not is_file_exist(exefile):
return False
if (not is_file_exist(parafile_inp)) or (not is_file_exist(parafile_ini)):
return False
if (not is_file_exist(basefile)) or (not is_file_exist(warpfile)):
return False
baseimage = RSImageclass()
warpimage = RSImageclass()
if not baseimage.open(basefile):
return False
if not warpimage.open(warpfile):
return False
if not check_format(baseimage):
return False
if not check_format(warpimage):
return False
Outbandname = setparameters(parafile_inp, parafile_ini, baseimage,
warpimage)
CommandString = exefile + ' -r ' + parafile_inp
basic.outputlogMessage(CommandString)
(status, result) = commands.getstatusoutput(CommandString)
basic.outputlogMessage(result)
if not os.path.isfile(Outbandname):
return False
#convert the result file to tif
Outputtiff = Outbandname.split('.')[0] + '_tran.tif'
CommandString = 'gdal_translate -of GTiff ' + Outbandname + ' ' + Outputtiff
basic.outputlogMessage(CommandString)
(status, result) = commands.getstatusoutput(CommandString)
basic.outputlogMessage(result)
if not os.path.isfile(Outputtiff):
return False
return True
def cal_the_mean_of_bands(image_path):
img_obj = RSImageclass()
if img_obj.open(image_path):
width = img_obj.GetWidth()
height = img_obj.GetHeight()
mean_of_bands = RSImage.get_image_mean_value(image_path)
if mean_of_bands is False:
return (False,False,False)
return (width,height,mean_of_bands)
else:
basic.outputlogMessage('error, Open image %s failed' %image_path)
return (False,False,False)
def calculate_mean_of_images(images_list):
if len(images_list)<1:
basic.outputlogMessage('No image in the list')
return False
# get band number
img_obj = RSImageclass()
img_obj.open(images_list[0])
band_count = img_obj.GetBandCount()
img_obj = None
mean_of_images = [] # each band has one value
for i in range(0,band_count):
mean_of_images.append(0.0)
total_pixel = 0
number = 0
for image in images_list:
(width,height,mean_of_band) = cal_the_mean_of_bands(image)
pixel_count = width*height
number = number +1
print ('%d / %d'%(number,len(images_list)))
if width is False:
return False
if len(mean_of_band) != band_count:
basic.outputlogMessage('error, band count is different')
return False
for i in range(0, band_count):
mean_of_images[i] = (mean_of_images[i]*total_pixel + mean_of_band[i]*pixel_count)
total_pixel = total_pixel + width*height
for i in range(0, band_count):
mean_of_images[i] = mean_of_images[i]/total_pixel
for i in range(0, band_count):
basic.outputlogMessage('band {}: mean {}'.format(i+1,mean_of_images[i]))
return mean_of_images
def convert_image_to_gray_auto(output_image, input_image):
"""
convert inputed image to 8bit
Args:
output_image:output image file path
input_image: input imag file path
Returns:output_image if successful, False otherwise
"""
if os.path.isfile(output_image) is True:
basic.outputlogMessage('%s already exist,skip' % output_image)
return output_image
input_image_obj = RSImageclass()
if input_image_obj.open(input_image) is False:
return False
# GDT_Unknown = 0, GDT_Byte = 1, GDT_UInt16 = 2, GDT_Int16 = 3,
# GDT_UInt32 = 4, GDT_Int32 = 5, GDT_Float32 = 6, GDT_Float64 = 7,
# GDT_CInt16 = 8, GDT_CInt32 = 9, GDT_CFloat32 = 10, GDT_CFloat64 = 11,
#GDT_Byte
if input_image_obj.GetGDALDataType() == 1:
# io_function.copy_file_to_dst(input_image,output_image)
output_image = input_image
return output_image
(max_value_list,
min_value_list) = RSImage.get_image_max_min_value(input_image)
if max_value_list is False or min_value_list is False:
return False
input_image_obj = None
# CommandString = 'gdal_translate -r bilinear -ot Byte -scale ' + input_image + ' '+output_image
# return basic.exec_command_string_one_file(CommandString,output_image)
args_list = ['gdal_translate', '-r', 'bilinear', '-ot', 'Byte']
for band_index in range(0, len(max_value_list)):
args_list.append('-b')
args_list.append(str(band_index + 1))
args_list.append('-scale')
args_list.append(str(min_value_list[band_index]))
args_list.append(str(max_value_list[band_index]))
args_list.append(str(1))
args_list.append(str(254))
args_list.append(input_image)
args_list.append(output_image)
return basic.exec_command_args_list_one_file(args_list, output_image)
def cal_the_mean_of_bands(input_path):
# if multi files in one line, then only consider the first file
image_path = get_first_path_in_line(input_path)
if image_path is False:
return False
img_obj = RSImageclass()
if img_obj.open(image_path):
width = img_obj.GetWidth()
height = img_obj.GetHeight()
mean_of_bands = RSImage.get_image_mean_value(image_path)
if mean_of_bands is False:
return (False,False,False)
return (width,height,mean_of_bands)
else:
basic.outputlogMessage('error, Open image %s failed' %image_path)
return (False,False,False)
class RSImgProclass(object):
def __init__(self):
self.imgpath = ''
self.img__obj = None #RSImageclass object
def __del__(self):
# close dataset
# print 'RSImageclass__del__'
self.img__obj = None
def Read_Image_band_data_to_numpy_array_all_pixel(self, bandindex,
image_path):
if io_function.is_file_exist(image_path) is False:
return False
self.img__obj = RSImageclass()
if self.img__obj.open(image_path) is False:
return False
width = self.img__obj.GetWidth()
height = self.img__obj.GetHeight()
return self.__Read_band_data_to_numpy_array(bandindex, 0, 0, width,
height, self.img__obj)
def __Read_Image_band_data_to_numpy_array(self, bandindex, xoff, yoff,
width, height, image_path):
return True
def __Read_band_data_to_numpy_array(self,
bandindex,
xoff,
yoff,
width,
height,
image_obj=None):
if image_obj is None:
image_obj = self.img__obj
offsetvaluestr = image_obj.ReadbandData(
bandindex, xoff, yoff, width, height,
image_obj.GetGDALDataType()) #first band offset, may be xoffset
if offsetvaluestr is False:
return False
# offsetvalue = None
# print image_obj.GetGDALDataType()
if image_obj.GetGDALDataType() == 3: #GDT_Int16
offsetvalue = struct.unpack('h' * width * height, offsetvaluestr)
elif image_obj.GetGDALDataType() == 6:
offsetvalue = struct.unpack('f' * width * height, offsetvaluestr)
else:
basic.outputlogMessage('error: not support datatype currently')
return False
return numpy.asarray(offsetvalue)
def statistic_element_count(self, value, myarray):
loc_nodata = numpy.where(numpy.fabs(myarray - value) < 0.001)
loc_nodatanum = numpy.array(loc_nodata).size
return loc_nodatanum
def statistic_not_element_count(self, not_value, myarray):
loc_not_value = numpy.where(numpy.fabs(myarray - not_value) > 0.0001)
loc_not_value_num = numpy.array(loc_not_value).size
return loc_not_value_num
def statistic_pixel_count(self, pixel_value, RSImageclass_object):
return True
def compose_two_image(self, main_image, second_image, nodata):
if io_function.is_file_exist(main_image) is False:
return False
if io_function.is_file_exist(second_image) is False:
return False
main_img = RSImageclass()
if main_img.open(main_image) is False:
return False
width_main = main_img.GetWidth()
height_main = main_img.GetHeight()
bandcount_main = main_img.GetBandCount()
sec_img = RSImageclass()
if sec_img.open(second_image) is False:
return False
width_sec = sec_img.GetWidth()
height_sec = sec_img.GetHeight()
bandcount_sec = sec_img.GetBandCount()
if width_main != width_sec or height_main != height_sec or bandcount_main != bandcount_sec:
basic.outputlogMessage(
'Error: The dimension of two composed images is different')
return False
if main_img.GetGDALDataType() != sec_img.GetGDALDataType(
) or main_img.GetGDALDataType() != 6:
basic.outputlogMessage(
'Error: The Data type of two composed imagaes is different or is not float'
)
return False
outputfile = io_function.get_name_by_adding_tail(main_image, 'comp')
imagenew = RSImageclass()
width = width_main
height = height_main
if not imagenew.New(outputfile, width, height, bandcount_main,
main_img.GetGDALDataType()):
return False
for i in range(0, bandcount_main):
bandindex = i + 1
band_main_str = main_img.ReadbandData(bandindex, 0, 0, width,
height,
main_img.GetGDALDataType())
band_sec_str = sec_img.ReadbandData(bandindex, 0, 0, width, height,
sec_img.GetGDALDataType())
band_main_data = struct.unpack('f' * width * height, band_main_str)
band_main_numpy = numpy.asarray(band_main_data)
band_sec_data = struct.unpack('f' * width * height, band_sec_str)
band_sec_numpy = numpy.asarray(band_sec_data)
compose_loc = numpy.where(
(numpy.fabs(band_main_numpy - nodata) < 0.0001)
& (numpy.fabs(band_sec_numpy - nodata) > 0.0001))
band_main_numpy[compose_loc] = band_sec_numpy[compose_loc]
basic.outputlogMessage('outputfortest2: compose_loc_num = %d' %
numpy.array(compose_loc).size)
templist = band_main_numpy.tolist()
band_composed_str = struct.pack('%sf' % width * height, *templist)
if imagenew.WritebandData(bandindex, 0, 0, width, height,
band_composed_str,
imagenew.GetGDALDataType()) is False:
return False
imagenew.SetBandNoDataValue(bandindex, nodata)
imagenew.SetGeoTransform(main_img.GetGeoTransform())
imagenew.SetProjection(main_img.GetProjection())
main_img = None
sec_img = None
imagenew = None
return outputfile
def subset_image_by_shapefile(imagefile,
shapefile,
bkeepmidfile=True,
overwrite=False,
format='GTiff',
save_path=None,
resample_m='bilinear',
src_nodata=None,
dst_nondata=None,
xres=None,
yres=None,
compress=None,
tiled=None,
bigtiff=None,
thread_num=None):
"""
subset an image by polygons contained in the shapefile
the shapefile and imagefile may have different projections, the gdalwarp can handle
Args:
imagefile:input image file path
shapefile:input shapefile contains polygon
bkeepmidfile:indicate whether keep middle file
format: output format, default is GTiff, GeoTIFF File Format. Use "VRT": GDAL Virtual Format to save disk storage
Returns:output file name if succussful, False Otherwise
"""
if io_function.is_file_exist(imagefile) is False:
return False
if io_function.is_file_exist(shapefile) is False:
return False
if save_path is None:
Outfilename = io_function.get_name_by_adding_tail(imagefile, 'vsub')
else:
Outfilename = save_path
# ds = ogr.Open(shapefile)
# lyr = ds.GetLayer(0)
# lyr.ResetReading()
# ft = lyr.GetNextFeature()
# subprocess.call(['gdalwarp', imagefile, Outfilename, '-cutline', shapefile,\
# '-crop_to_cutline'])
if overwrite is False and os.path.isfile(Outfilename):
basic.outputlogMessage('warning, crop file: %s already exist, skip' %
Outfilename)
return Outfilename
orgimg_obj = RSImageclass()
if orgimg_obj.open(imagefile) is False:
return False
if xres is None or yres is None:
x_res = abs(orgimg_obj.GetXresolution())
y_res = abs(orgimg_obj.GetYresolution())
else:
x_res = xres
y_res = yres
CommandString = 'gdalwarp -r %s '% resample_m+' -tr ' + str(x_res) + ' '+ str(y_res)+ ' -of ' + format + ' ' + \
imagefile +' ' + Outfilename +' -cutline ' +shapefile +' -crop_to_cutline ' + ' -overwrite '
if src_nodata != None:
CommandString += ' -srcnodata %d ' % src_nodata
if dst_nondata != None:
CommandString += ' -dstnodata %d ' % dst_nondata
if compress != None:
CommandString += ' -co ' + 'compress=%s' % compress # lzw
if tiled != None:
CommandString += ' -co ' + 'TILED=%s' % tiled # yes
if bigtiff != None:
CommandString += ' -co ' + 'bigtiff=%s' % bigtiff # IF_SAFER
if thread_num != None:
CommandString += ' -multi -wo NUM_THREADS=%d ' % thread_num
if basic.exec_command_string_one_file(CommandString, Outfilename) is False:
return False
# while ft:
# country_name = ft.GetFieldAsString('admin')
# outraster = imagefile.replace('.tif', '_%s.tif' % country_name.replace(' ', '_'))
# subprocess.call(['gdalwarp', imagefile, Outfilename, '-cutline', shapefile,
# '-crop_to_cutline', '-cwhere', "'admin'='%s'" % country_name])
#
# ft = lyr.GetNextFeature()
if not bkeepmidfile:
io_function.delete_file_or_dir(imagefile)
os.remove(imagefile)
if io_function.is_file_exist(Outfilename):
return Outfilename
else:
# basic.outputlogMessage(result)
basic.outputlogMessage(
'The version of GDAL must be great than 2.0 in order to use the r option '
)
return False
def coregistration_siftGPU(basefile, warpfile, bkeepmidfile, xml_obj):
tiepointfile = '0_1_after.pts'
if os.path.isfile(tiepointfile):
basic.outputlogMessage(
'warning:tie points already exist in dir, skip get_tie_points_by_ZY3ImageMatch'
)
else:
tiepointfile = tiepoints.get_tie_points_by_ZY3ImageMatch(
basefile, warpfile, bkeepmidfile)
if tiepointfile is False:
basic.outputlogMessage('Get tie points by ZY3ImageMatch failed')
return False
xml_obj.add_coregistration_info('tie_points_file', tiepointfile)
#draw tie points rms vector on base image
result_rms_files = '0_1_fs.txt'
tiepoint_vector_ = 'tiepoints_vector.png'
output_tie_points_vector_on_base_image(basefile, result_rms_files,
tiepoint_vector_)
xml_obj.add_coregistration_info('tie_points_drawed_image',
os.path.abspath(tiepoint_vector_))
#check the tie points
try:
rms_files_obj = open(result_rms_files, 'r')
rms_lines = rms_files_obj.readlines()
if len(rms_lines) < 2:
basic.outputlogMessage("%s do not contain tie points information" %
os.path.abspath(result_rms_files))
return False
required_point_count = parameters.get_required_minimum_tiepoint_number(
)
acceptable_rms = parameters.get_acceptable_maximum_RMS()
xml_obj.add_coregistration_info('required_tie_point_count',
str(required_point_count))
xml_obj.add_coregistration_info('acceptable_rms', str(acceptable_rms))
try:
digit_str = re.findall('\d+', rms_lines[0])
tiepoints_count = int(digit_str[0])
xml_obj.add_coregistration_info('tie_points_count',
str(tiepoints_count))
if tiepoints_count < required_point_count:
basic.outputlogMessage(
"ERROR: tiepoints count(%d) is less than required one(%d)"
% (tiepoints_count, required_point_count))
return False
digit_str = re.findall('\d+\.?\d*', rms_lines[1])
totalrms_value = float(digit_str[2])
xml_obj.add_coregistration_info('total_rms_value',
str(totalrms_value))
if totalrms_value > acceptable_rms:
basic.outputlogMessage(
"ERROR:Total RMS(%f) exceeds the acceptable one(%f)" %
(totalrms_value, acceptable_rms))
return False
except ValueError:
return basic.outputlogMessage(str(ValueError))
return False
rms_files_obj.close()
except IOError:
syslog.outputlogMessage(str(IOError))
return False
baseimg = RSImageclass()
if not baseimg.open(basefile):
return False
proj = baseimg.GetProjection()
geotransform = baseimg.GetGeoTransform()
xres = baseimg.GetXresolution()
yres = baseimg.GetYresolution()
try:
Outputtiff = setGCPsfromptsFile(warpfile, proj, geotransform,
tiepointfile)
except RuntimeError as e:
basic.outputlogMessage('setGCPsfromptsFile failed: ')
basic.outputlogMessage(str(e))
return False
if Outputtiff is False:
return False
else:
basic.outputlogMessage('setGCPsfromptsFile completed, Out file: ' +
Outputtiff)
# if not bkeepmidfile:
# os.remove(warpfile)
xml_obj.add_coregistration_info('setted_gcps_file', Outputtiff)
#warp image
warpresultfile = Outputtiff.split('.')[0] + '_warp.tif'
#-order 1 -tps
#-tr xres yres: set output file resolution (in target georeferenced units)
# set resolution as the same as base image is important
order_number = parameters.get_gdalwarp_polynomial_order()
xml_obj.add_coregistration_info('warp_polynomial_order_number',
str(order_number))
if order_number is False:
return False
CommandString = 'gdalwarp ' + ' -order ' + str(
order_number) + ' -r bilinear -tr ' + str(xres) + ' ' + str(
yres) + ' ' + Outputtiff + ' ' + warpresultfile
basic.outputlogMessage(CommandString)
(status, result) = commands.getstatusoutput(CommandString)
basic.outputlogMessage(result)
if not os.path.isfile(warpresultfile):
return False
if not bkeepmidfile:
os.remove(Outputtiff)
return warpresultfile
def setGCPsfromptsFile(imagefile, projection, GeoTransform, ptsfile):
if not is_file_exist(imagefile):
return False
image = RSImageclass()
if not image.open(imagefile):
return False
ngcpcount = image.ds.GetGCPCount()
if ngcpcount > 0:
basic.outputlogMessage(
'warning: The file already have GCP,GCP count is ' +
str(ngcpcount))
allgcps = []
inputfile_object = open(ptsfile, 'r')
all_points = inputfile_object.readlines()
for linestr in all_points:
if linestr[0:1] == '#' or linestr[0:1] == ';' or len(linestr) < 2:
continue
if len(allgcps) >= 10000:
basic.outputlogMessage(
'warning: the count of gcps already greater than 10000, and ignore the others to make geotiff work correctly'
)
continue
tiepointXY = linestr.split()
base_x = float(tiepointXY[0])
base_y = float(tiepointXY[1])
Xp = GeoTransform[
0] + base_x * GeoTransform[1] + base_y * GeoTransform[2]
Yp = GeoTransform[
3] + base_x * GeoTransform[4] + base_y * GeoTransform[5]
warp_x = float(tiepointXY[2])
warp_y = float(tiepointXY[3])
info = 'GCPbysiftgpu_%d' % len(allgcps)
id = str(len(allgcps))
gcp1 = gdal.GCP(Xp, Yp, 0, warp_x, warp_y, info, id)
allgcps.append(gcp1)
inputfile_object.close()
Outputtiff = imagefile.split('.')[0] + '_new.tif'
format = "GTiff"
driver = gdal.GetDriverByName(format)
metadata = driver.GetMetadata()
if metadata.has_key(
gdal.DCAP_CREATE) and metadata[gdal.DCAP_CREATE] == 'YES':
basic.outputlogMessage('Driver %s supports Create() method.' % format)
else:
basic.outputlogMessage('Driver %s not supports Create() method.' %
format)
return False
# if metadata.has_key(gdal.DCAP_CREATECOPY) and metadata[gdal.DCAP_CREATECOPY] == 'YES':
# syslog.outputlogMessage('Driver %s supports CreateCopy() method.' % format)
# dst_ds = driver.CreateCopy(Outputtiff, dataset, 0)
datatype = image.GetGDALDataType()
dst_ds = driver.Create(Outputtiff, image.GetWidth(), image.GetHeight(),
image.GetBandCount(), datatype)
for bandindex in range(0, image.GetBandCount()):
bandobject = image.Getband(bandindex + 1)
banddata = bandobject.ReadRaster(0, 0, image.GetWidth(),
image.GetHeight(), image.GetWidth(),
image.GetHeight(), datatype)
#byte
# if banddata is 1:
# bandarray = struct.unpack('B'*image.GetWidth()*image.GetHeight(), banddata)
dst_ds.GetRasterBand(bandindex + 1).WriteRaster(
0, 0, image.GetWidth(), image.GetHeight(), banddata,
image.GetWidth(), image.GetHeight(), datatype)
dst_ds.SetGCPs(allgcps, projection)
# if I have set the GCPs, should not do this again, or SetGCPs will be undo
# dst_ds.SetGeoTransform(image.GetGeoTransform())
# dst_ds.SetProjection(image.GetProjection())
if not os.path.isfile(Outputtiff):
basic.outputlogMessage(
'result file not exist, the operation of create set gcp failed')
return False
dst_ds = None
image = None
return Outputtiff
def prepare_gimpdem_for_Jakobshavn(workdir):
nodata = parameters.get_nodata_value()
#mosaics gimdem files
os.chdir(workdir)
gimpdem_file = ['gimpdem1_2.tif', 'gimpdem2_2.tif']
gimpdem_output = 'dem_gimp_jako.tif'
if os.path.isfile(gimpdem_output) is False:
if RSImageProcess.mosaics_images(gimpdem_file,
gimpdem_output) is False:
return False
geoid_file = 'geoid_hegith_jako.tif'
#convert srs
img_temp = RSImageclass()
if not img_temp.open(gimpdem_output):
return False
x_res = img_temp.GetXresolution()
y_res = img_temp.GetYresolution()
srs_UTM_prj4 = '\'+proj=utm +zone=22 +datum=WGS84 +units=m +no_defs\' '
gimpdem_utm = io_function.get_name_by_adding_tail(gimpdem_output, 'utm22')
if os.path.isfile(gimpdem_utm) is False:
if RSImageProcess.transforms_raster_srs(gimpdem_output, srs_UTM_prj4,
gimpdem_utm, abs(x_res),
abs(y_res)) is False:
return False
# print img_temp.GetGDALDataType(),type(img_temp.GetGDALDataType())
# print img_temp.GetProjection(),type(img_temp.GetProjection())
# print img_temp.GetGeoTransform(),type(img_temp.GetGeoTransform())
#get geoid_height
# srs_longlat_wkt = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.01745329251994328]]"
# srs_longlat_prj4 = '\'+proj=longlat +datum=WGS84 +no_defs\' '
# image = RSImageclass(syslog)
# if not image.open(gimpdem_utm):
# return False
#
# img_pro = RSImgProclass(syslog)
# dem_data = img_pro.Read_Image_band_data_to_numpy_array_all_pixel(1,gimpdem_utm)
# if dem_data is False:
# return False
#
# # geoid_height = geoid_height + -9999
# geoid_utm_file = basic.get_name_by_adding_tail(geoid_file,'tran',syslog)
# if RSImageProcess.transforms_raster_srs_to_base_image(geoid_file,gimpdem_utm,geoid_utm_file,\
# x_res,y_res,syslog) is False:
# return False
# geoid_height = img_pro.Read_Image_band_data_to_numpy_array_all_pixel(1,geoid_utm_file)
# if geoid_height is False:
# return False
#
# #get ellipsoidal height
# Image_array = dem_data + geoid_height
# gimpdem_utm_elli = basic.get_name_by_adding_tail(gimpdem_utm,'elli',syslog)
# RSImageProcess.save_numpy_2d_array_to_image_tif(gimpdem_utm_elli,Image_array,\
# image.GetGDALDataType(),image.GetGeoTransform(),image.GetProjection(),nodata)
# image = None
# img_pro = None
return True
def convert_orthometricH_to_elliopsoidalH(output, orthometricH_file,
geoidHfile):
if io_function.is_file_exist(
orthometricH_file) is False or io_function.is_file_exist(
geoidHfile) is False:
return False
orthom_obj = RSImageclass()
if orthom_obj.open(orthometricH_file) is False:
return False
geoidH_obj = RSImageclass()
if geoidH_obj.open(geoidHfile) is False:
return False
Nodata = parameters.get_nodata_value()
x_res = orthom_obj.GetXresolution()
y_res = orthom_obj.GetYresolution()
x_res_geoid = geoidH_obj.GetXresolution()
y_res_geoid = geoidH_obj.GetYresolution()
orthom_prj = orthom_obj.GetProjection()
geoid_prj = geoidH_obj.GetProjection()
#check projection and resolution, and convert it if need
#use orthometricH_file as base image
if x_res != x_res_geoid or y_res != y_res_geoid or orthom_prj != geoid_prj:
geoid_convertfile = io_function.get_name_by_adding_tail(
geoidHfile, 'tran')
if os.path.isfile(geoid_convertfile) is False:
if RSImageProcess.transforms_raster_srs(
geoidHfile, orthom_prj, geoid_convertfile, abs(x_res),
abs(y_res)) is False:
return False
else:
basic.outputlogMessage(geoid_convertfile + ' already exist')
#sub geoidHfile base on the small one
(ulx, uly, lrx,
lry) = RSImageProcess.get_image_proj_extent(orthometricH_file)
if ulx is False:
return False
geoid_convertfile_sub = io_function.get_name_by_adding_tail(
geoid_convertfile, 'sub')
if os.path.isfile(geoid_convertfile_sub) is False:
result = RSImageProcess.subset_image_projwin(geoid_convertfile_sub,
geoid_convertfile, ulx,
uly, lrx, lry)
if result is False:
return False
else:
basic.outputlogMessage(geoid_convertfile_sub + ' already exist')
##caculate elliopsoidal height
# orthometricH_data = img_pro.Read_Image_band_data_to_numpy_array_all_pixel(1,orthometricH_file)
# img_pro = None
# img_pro = RSImgProclass(syslog)
# geoidH_data = img_pro.Read_Image_band_data_to_numpy_array_all_pixel(1,geoid_convertfile_sub)
# img_pro = None
# if orthometricH_data.shape != geoidH_data.shape:
# syslog.outputlogMessage("the shape of orthometricH_data and geoidH_data is different")
# return False
#
# nodata = parameters.get_nodata_value(syslog)
# width = orthom_obj.GetWidth()
# height = orthom_obj.GetHeight()
# orthometricH_data = orthometricH_data.astype(numpy.float32)
# geoidH_data = geoidH_data.astype(numpy.float32)
# elliopsoidalH = orthometricH_data + geoidH_data
# elliopsoidalH = elliopsoidalH.reshape(height,width)
#
# RSImageProcess.save_numpy_2d_array_to_image_tif(output,elliopsoidalH,6,\
# orthom_obj.GetGeoTransform(),orthom_obj.GetProjection(),nodata,syslog)
#
# orthom_obj = None
# geoidH_obj = None
CommandString = 'gdal_calc.py -A '+orthometricH_file + ' -B ' + geoid_convertfile_sub +\
' --NoDataValue='+str(Nodata) +' --outfile='+output + ' --calc="A+B"'
if RSImageProcess.exec_commond_string_one_file(CommandString,
output) is False:
return False
else:
basic.outputlogMessage(
"converting orthometric Height to elliopsoidal Height is completed"
)
return True
def get_geoimage_range_geoid_height(outputfile, ref_image):
#convert srs
ref_img_obj = RSImageclass()
if not ref_img_obj.open(ref_image):
return False
# x_res = ref_img_obj.GetXresolution()
# y_res = ref_img_obj.GetYresolution()
width = ref_img_obj.GetWidth()
height = ref_img_obj.GetHeight()
img_pro = RSImgProclass()
ref_image_data = img_pro.Read_Image_band_data_to_numpy_array_all_pixel(
1, ref_image)
if ref_image_data is False:
return False
nodata = parameters.get_nodata_value()
Image_array = ref_image_data.reshape(height, width)
start_x = ref_img_obj.GetStartX()
start_y = ref_img_obj.GetStartY()
resolution_x = ref_img_obj.GetXresolution()
resolution_y = ref_img_obj.GetYresolution()
ref_img_WKT = ref_img_obj.GetProjection()
# ref_img_WKT = RSImageProcess.get_raster_or_vector_srs_info_wkt(ref_image,syslog)
(i, j) = numpy.where(Image_array != nodata)
input_x = start_x + j * resolution_x
input_y = start_y + i * resolution_y
# srs_longlat_prj4 = '\'+proj=longlat +datum=WGS84 +no_defs\''
# intput_proj4 = RSImage.wkt_to_proj4(ref_img_WKT,syslog)
# intput_proj4 = RSImageProcess.get_raster_or_vector_srs_info_proj4(ref_image,syslog)
# map_projection.convert_points_coordinate_proj4(input_x,input_y,intput_proj4,srs_longlat_prj4,syslog)
srs_longlat_wkt = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.01745329251994328]]"
map_projection.convert_points_coordinate(input_x, input_y, ref_img_WKT,
srs_longlat_wkt)
tempsave_str = []
save_point_txt_file = 'TXTgeoid_' + os.path.splitext(
os.path.basename(ref_image))[0] + '.txt'
if os.path.isfile(save_point_txt_file):
file_object = open(save_point_txt_file, 'r')
savepoints = file_object.readlines()
for point in savepoints:
tempsave_str.append(point)
file_object.close()
io_function.delete_file_or_dir(save_point_txt_file)
file_object = open(save_point_txt_file, 'a')
nsize = Image_array.size
Image_array = Image_array.astype(numpy.float32)
for index in range(0, nsize):
lon_deg = input_x[index]
lat_deg = input_y[index]
if index < len(tempsave_str):
temp_point = tempsave_str[index].split()
value = float(temp_point[2])
else:
(LongitudeDeg, LongitudeMin, LongitudeSec) = degree_to_dms(lon_deg)
(LatitudeDeg, LatitudeMin, LatitudeSec) = degree_to_dms(lat_deg)
value = get_geoid_height(LatitudeDeg, LatitudeMin, LatitudeSec,
LongitudeDeg, LongitudeMin, LongitudeSec,
nodata)
if value is False:
break
saved_point = ('%f %f %f' % (lon_deg, lat_deg, value))
print saved_point
# tempsave_str.append(saved_point)
file_object.writelines(saved_point + '\n')
file_object.flush()
basic.outputlogMessage('Longitude=%f, Latitude=%f, geoid = %f' %
(lon_deg, lat_deg, value))
# Image_array[index] = value
Image_array[i[index], j[index]] = value
print(i[index], j[index], Image_array[i[index], j[index]])
file_object.close()
if index != (nsize - 1):
return False
#save geoid height
RSImageProcess.save_numpy_2d_array_to_image_tif(outputfile,Image_array,\
6,ref_img_obj.GetGeoTransform(),ref_img_WKT,nodata)
return True
def compose_two_image(self, main_image, second_image, nodata):
if io_function.is_file_exist(main_image) is False:
return False
if io_function.is_file_exist(second_image) is False:
return False
main_img = RSImageclass()
if main_img.open(main_image) is False:
return False
width_main = main_img.GetWidth()
height_main = main_img.GetHeight()
bandcount_main = main_img.GetBandCount()
sec_img = RSImageclass()
if sec_img.open(second_image) is False:
return False
width_sec = sec_img.GetWidth()
height_sec = sec_img.GetHeight()
bandcount_sec = sec_img.GetBandCount()
if width_main != width_sec or height_main != height_sec or bandcount_main != bandcount_sec:
basic.outputlogMessage(
'Error: The dimension of two composed images is different')
return False
if main_img.GetGDALDataType() != sec_img.GetGDALDataType(
) or main_img.GetGDALDataType() != 6:
basic.outputlogMessage(
'Error: The Data type of two composed imagaes is different or is not float'
)
return False
outputfile = io_function.get_name_by_adding_tail(main_image, 'comp')
imagenew = RSImageclass()
width = width_main
height = height_main
if not imagenew.New(outputfile, width, height, bandcount_main,
main_img.GetGDALDataType()):
return False
for i in range(0, bandcount_main):
bandindex = i + 1
band_main_str = main_img.ReadbandData(bandindex, 0, 0, width,
height,
main_img.GetGDALDataType())
band_sec_str = sec_img.ReadbandData(bandindex, 0, 0, width, height,
sec_img.GetGDALDataType())
band_main_data = struct.unpack('f' * width * height, band_main_str)
band_main_numpy = numpy.asarray(band_main_data)
band_sec_data = struct.unpack('f' * width * height, band_sec_str)
band_sec_numpy = numpy.asarray(band_sec_data)
compose_loc = numpy.where(
(numpy.fabs(band_main_numpy - nodata) < 0.0001)
& (numpy.fabs(band_sec_numpy - nodata) > 0.0001))
band_main_numpy[compose_loc] = band_sec_numpy[compose_loc]
basic.outputlogMessage('outputfortest2: compose_loc_num = %d' %
numpy.array(compose_loc).size)
templist = band_main_numpy.tolist()
band_composed_str = struct.pack('%sf' % width * height, *templist)
if imagenew.WritebandData(bandindex, 0, 0, width, height,
band_composed_str,
imagenew.GetGDALDataType()) is False:
return False
imagenew.SetBandNoDataValue(bandindex, nodata)
imagenew.SetGeoTransform(main_img.GetGeoTransform())
imagenew.SetProjection(main_img.GetProjection())
main_img = None
sec_img = None
imagenew = None
return outputfile
def calculate_terrain_offset(output, dem_file, image_file, exec_dir,
bkeepmidfile):
if io_function.is_file_exist(
image_file) is False or io_function.is_file_exist(
dem_file, ) is False:
return False
exefile = os.path.join(exec_dir, 'geometry_pro')
nodata = parameters.get_nodata_value()
(centre_lat, centre_lon) = RSImage.get_image_latlon_centre(image_file)
if centre_lat is False or centre_lon is False:
return False
image_obj = RSImageclass()
if image_obj.open(image_file) is False:
return False
dem_obj = RSImageclass()
if dem_obj.open(dem_file) is False:
return False
x_res = image_obj.GetXresolution()
y_res = image_obj.GetYresolution()
x_res_dem = dem_obj.GetXresolution()
y_res_dem = dem_obj.GetYresolution()
image_prj = image_obj.GetProjection()
dem_prj = dem_obj.GetProjection()
#check projection and resolution, and convert it if need
#use orthometricH_file as base image
dem_convertedfile = io_function.get_name_by_adding_tail(dem_file, 'tran')
if x_res != x_res_dem or y_res != y_res_dem or image_prj != dem_prj:
if os.path.isfile(dem_convertedfile) is False:
if map_projection.transforms_raster_srs(
dem_file, image_prj, dem_convertedfile, abs(x_res),
abs(y_res)) is False:
return False
if os.path.isfile(dem_convertedfile):
dem_file = dem_convertedfile
#sub dem file
(ulx, uly, lrx, lry) = RSImage.get_image_proj_extent(image_file)
if ulx is False:
return False
tail = os.path.splitext(os.path.basename(image_file))[0]
dem_file_sub = io_function.get_name_by_adding_tail(dem_file, tail)
if os.path.isfile(dem_file_sub) is False:
if RSImageProcess.subset_image_projwin(dem_file_sub, dem_file, ulx,
uly, lrx, lry) is False:
return False
#calculateing terrain contains a lot of I/O operations, the parallel computing will slow down it
nblockwidth = 8000
nblcckheight = 8000
njobs = 1
logfile = 'cal_terrain_offset_log.txt'
CommandString = exefile \
+ ' -i ' + image_file + ' -d ' + dem_file_sub \
+ ' -o ' + output + ' -n ' + str(nodata)\
+ ' -w ' + str(nblockwidth) + ' -h ' + str(nblcckheight) + ' -j ' +str(njobs) \
+ ' --centre_lat=' + str(centre_lat) \
+ ' --logfile=' + logfile
basic.outputlogMessage(CommandString)
(status, result) = commands.getstatusoutput(CommandString)
basic.outputlogMessage(result)
if bkeepmidfile is False:
# if os.path.isfile(dem_convertedfile):
# io_function.delete_file_or_dir(dem_convertedfile)
if os.path.isfile(dem_file_sub):
io_function.delete_file_or_dir(dem_file_sub)
if os.path.isfile(output):
if os.path.getsize(output) > 0:
return output
else:
basic.outputlogMessage('error: the size of file %s is 0' %
os.path.basename(output))
return False
else:
return False