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 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 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 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_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 Read_Image_data_to_numpy_array_all_band_pixel(self,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() bandcount = self.img__obj.GetBandCount() images = numpy.zeros((width, height, bandcount)) for band in range(0,bandcount): band_img = self.__Read_band_data_to_numpy_array(band+1,0,0,width,height,self.img__obj) if band_img is False: return False images[:,:,band] = band_img return images
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)
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 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 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 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
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 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 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