def random_ClipRaster(trainRaster,labelRaster,outDir,imgNums,imgHeight,imgWidth): #定义随机裁剪函数（训练原图，标签原图，输出路径，裁剪数量，图像行数，图像列数）
Min_X = arcpy.GetRasterProperties_management(trainRaster,"LEFT") #训练原图的X的最小值
Min_Y = arcpy.GetRasterProperties_management(trainRaster,"BOTTOM") #训练原图的Y的最小值
Max_X = arcpy.GetRasterProperties_management(trainRaster,"RIGHT") #训练原图的X的最大值
Max_Y = arcpy.GetRasterProperties_management(trainRaster,"TOP") #训练原图的Y的最大值
CELLSIZEX = arcpy.GetRasterProperties_management(trainRaster,"CELLSIZEX") #获取训练原图单像素的宽度
CELLSIZEY = arcpy.GetRasterProperties_management(trainRaster,"CELLSIZEY") #获取训练原图单像素的高度
COLUMNCOUNT = arcpy.GetRasterProperties_management(trainRaster,"COLUMNCOUNT") #获取训练原图列数
ROWCOUNT =arcpy.GetRasterProperties_management(trainRaster,"ROWCOUNT") #获取训练原图行数
label_Min_X = arcpy.GetRasterProperties_management(labelRaster,"LEFT") #标签原图的X的最小值
label_Min_Y = arcpy.GetRasterProperties_management(labelRaster,"BOTTOM") #标签原图的Y的最小值
label_Max_X = arcpy.GetRasterProperties_management(labelRaster,"RIGHT") #标签原图的X的最大值
label_Max_Y = arcpy.GetRasterProperties_management(labelRaster,"TOP")#标签原图的Y的最大值
label_CELLSIZEX = arcpy.GetRasterProperties_management(labelRaster,"CELLSIZEX") #获取标签原图单像素的宽度
label_CELLSIZEY = arcpy.GetRasterProperties_management(labelRaster,"CELLSIZEY") #获取标签原图单像素的高度
label_COLUMNCOUNT = arcpy.GetRasterProperties_management(labelRaster,"COLUMNCOUNT") #获取标签原图列数
label_ROWCOUNT =arcpy.GetRasterProperties_management(labelRaster,"ROWCOUNT") #获取标签原图行数
rotate_list = ["90","180","270","mirror"]
mkdir_dem = outDir + "train_dem"
mkdir_label = outDir + "train_label"
os.popen("mkdir "+mkdir_dem) #计算机先自动生成文件夹
os.popen("mkdir "+mkdir_label)
if (str(label_Min_X) == str(Min_X)) and (str(label_Min_Y) == str(Min_Y))and ( str(Max_X) == str(label_Max_X))and( str(Max_Y) == str(label_Max_Y))and (str(label_CELLSIZEX) == str(CELLSIZEX))and(str(label_CELLSIZEY)== str(CELLSIZEY))and( str(COLUMNCOUNT) == str(label_COLUMNCOUNT))and(str(ROWCOUNT)== str(label_ROWCOUNT)):
num =0
#print("test")
while num < imgNums:
random_x = random.randint(0,int(str(COLUMNCOUNT)) - imgWidth) #拟随机生成行列号来生成图像，(0,COLUMNCOUNT - imgWidth)为可选列号的范围
random_y = random.randint(0,int(str(ROWCOUNT))- imgHeight) #(0,ROWCOUNT- imgHeight)为可选行号的范围
temp_x = float(str(Min_X)) + random_x * float(str(CELLSIZEX)) #temp_x为所选图像左下角的横坐标
temp_y = float(str(Min_Y)) + random_y * float(str(CELLSIZEY)) #temp_y为所选图像左下角的纵坐标
cood_xy = str(temp_x) + " " +str(temp_y) + " " +str(temp_x + imgWidth*(float(str(CELLSIZEX)))) + " " + str(temp_y + imgHeight*(float(str(CELLSIZEY))))#所选图像的左下坐标和右上坐标
#print(cood_xy)
out_demRaster = outDir+"train_dem\\"+"dem"+"_"+ str(num) + ".tif"#dem_1.tif dem_2.tif
out_labelRaster = outDir+"train_label\\" +"label" +"_" +str(num) +".tif" #label_1.tif label_2.tif
arcpy.Clip_management(trainRaster,cood_xy,out_demRaster,"#","#","NONE","MAINTAIN_EXTENT") #裁剪输出
arcpy.Clip_management(labelRaster,cood_xy,out_labelRaster,"#","#","NONE","MAINTAIN_EXTENT") #裁剪输出
#arcpy.Clip_management(trainRaster,cood_xy,out_demRaster,"#","#","NONE","NO_MAINTAIN_EXTENT") #裁剪输出
#arcpy.Clip_management(labelRaster,cood_xy,out_labelRaster,"#","#","NONE","NO_MAINTAIN_EXTENT") #裁剪输出
dem_numpy = arcpy.RasterToNumPyArray(out_demRaster,nodata_to_value=-9999) #栅格转成numpy,目的是统一设置nodata值为-9999，用于后面的判断
dem_numpy = numpy.hstack(dem_numpy) #把numpy数组元素变成一维，用于方便循环
for value in list(dem_numpy):
if value == -9999:
noDataValue = -9999
break
else :
noDataValue = "None"
if noDataValue == "None":
random_rotate = random.randint(0,3)
out_demRotateRaster = outDir+"train_dem\\"+"dem"+"_"+ str(num) +"_"+rotate_list[random_rotate]+ ".tif" #dem_1.tif dem_2.tif
out_labelRotateRaster = outDir+"train_label\\"+"label"+"_"+ str(num) +"_"+rotate_list[random_rotate]+ ".tif" #dem_1.tif dem_2.tif
if rotate_list[random_rotate] == "mirror":
arcpy.Mirror_management(out_demRaster,out_demRotateRaster) #镜像操作
arcpy.Mirror_management(out_labelRaster,out_labelRotateRaster)
else :
rotate_x = temp_x + (imgWidth*float(str(CELLSIZEX)))/2 #计算旋转中心
rotate_y = temp_y + (imgHeight*float(str(CELLSIZEY)))/2
arcpy.Rotate_management(out_demRaster,out_demRotateRaster,rotate_list[random_rotate],str(rotate_x)+" "+str(rotate_y)) #旋转操作
arcpy.Rotate_management(out_labelRaster,out_labelRotateRaster,rotate_list[random_rotate],str(rotate_x)+" "+str(rotate_y))
print("%dth and conversion %s images are generated"%(num,rotate_list[random_rotate])) #提示图片裁剪好了
num = num+1 #图片保留，继续循环
else: #删除生成的train和label图片
remove_dem = glob.glob(outDir + "train_dem\\" + "dem" + "_" + str(num) + ".*") #每一个图片有好几个文件，都需要删除
remove_label = glob.glob(outDir + "train_label\\" + "label" + "_" + str(num) + ".*")
for tiff in remove_dem:
#print(tiff)
os.remove(tiff)
print("Delete"+ " " + tiff)
for label_tiff in remove_label:
os.remove(label_tiff)
print("Delete"+ " " + label_tiff)
num = num
else :
Error = " Image matching failed " #两张源图片格式如果大小不匹配，无法运用此函数，直接error
print(Error)
return
print("Successfully Done!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
def main(profile_line,
in_raster,
swath_width,
output_csv,
ncols=None,
nrows=None):
"""
The main processing of the data and extraction
of the profile data.
Arguments:
line feature class or feature set
raster dem
buffer distance as linar units e.g. ("5 Meters")
outputfile csv name
Optional arguments
ncols numbger of rows default is the raster height
nrows number of columns default is the raster width
The main function buffers the profile line, then clips
the raster and roates it so that it runs from north to south
witth a bearing of 0 degrees.
The raster is then found for each row and placed in a csv file
"""
# Set enviromntal variables
arcpy.env.overwriteOutput = True
# Collect data from our input layers
spatail_reference = arcpy.Describe(profile_line).spatialReference
arcpy.AddMessage("spatial reference is:{}".format(spatail_reference.name))
cursor = arcpy.da.SearchCursor(profile_line, ["SHAPE@"])
SHAPE_INDEX = 0
for feature in cursor:
for part in feature[SHAPE_INDEX]:
line = arcpy.Polyline(part, spatail_reference)
arcpy.AddMessage("first X: {}".format(line.firstPoint.X))
arcpy.AddMessage("first Y: {}".format(line.firstPoint.Y))
arcpy.AddMessage("last X: {}".format(line.lastPoint.X))
arcpy.AddMessage("last Y: {}".format(line.lastPoint.Y))
# Buffer the profile line
#buffer_fc = "{}\\buffer".format(arcpy.env.scratchGDB)
buffer_fc = "in_memory\\buffer"
# Need to halve the width to get the correct buffer distance
buffer_distance = float(swath_width.split()[0]) / 2
buffer_linear_units = "{} {}".format(buffer_distance,
swath_width.split()[1])
arcpy.AddMessage("Buffering the profile line by: {}".format(
buffer_linear_units))
arcpy.Buffer_analysis(line, buffer_fc, buffer_linear_units, "FULL",
"FLAT", "NONE", "", "GEODESIC")
# Clip the dem to the buffer
arcpy.AddMessage("Clipping the raster by the buffer")
output_clip = "{}\\clip".format(arcpy.env.scratchGDB)
arcpy.Clip_management(dem, "", output_clip, buffer_fc, "",
"ClippingGeometry")
#"MAINTAIN_EXTENT")
# Roate the dem so that it is upright
bearing = north_bearing(line.firstPoint, line.lastPoint)
rotation = rotation_angle(bearing)
if rotation <= 1:
arcpy.AddMessage("No rotation needed")
out_rotate = output_clip
else:
arcpy.AddMessage("Rotating raster by {}".format(rotation))
out_rotate = "{}\\rotate".format(arcpy.env.scratchGDB)
arcpy.Rotate_management(output_clip, out_rotate, rotation)
# Convert the dem to a numpy array and prepear it for the stats
# need to skip nodata values in array
# need to stip out lines of nodata
# This needs to be done in bloks or else we run out of memory.
arcpy.AddMessage("Converting the roated dem to a numpy array")
dem_raster = arcpy.Raster(out_rotate)
dem_cell_size = int(dem_raster.meanCellWidth)
stats_result = None
arcpy.AddMessage("Pixel Size: {} ".format(dem_cell_size))
dem_blocks = block_processing.EnumRasterToNumPyArray(
out_rotate, num_rows=nrows)
for dem_array in dem_blocks:
dem_array_skip_nd = np.ma.masked_array(dem_array,
dem_array == -9999)
mask_nd_rows = np.all(np.isnan(dem_array_skip_nd), axis=1)
dem_arr = dem_array_skip_nd[~mask_nd_rows]
# Need to flip it as the array starts at the bottom
# lefthand corner
dem_arr_flip = np.flipud(dem_arr)
try:
stats = row_stats(dem_arr_flip)
except ValueError:
continue
if stats_result is None:
stats_result = stats
else:
stats_result = np.vstack((stats_result, stats))
number_rows = np.size(stats_result, axis=0)
distance = np.arange(0, number_rows * dem_cell_size, dem_cell_size)
distance_reshape = distance.reshape(number_rows, 1)
distance_stats = np.hstack((distance_reshape, stats_result))
arcpy.AddMessage("Writing {} to disk.".format(output_csv))
np.savetxt(
output_csv,
distance_stats,
header=
"distance, max, min, mean, std, minus_1std, plus_1std, kurtosis",
fmt='%1d, %1.3f, %1.3f, %1.3f, %1.3f, %1.3f, %1.3f, %1.3f',
comments='')
# Clean up
file_list = [out_rotate, output_clip, buffer_fc]
for file_item in file_list:
if arcpy.Exists(file_item):
arcpy.Delete_management(file_item)
bandExtent = str(band.extent)
originalBottom, originalLeft, originalTop, originalRight, waste1, waste2, waste3, waste4 = bandExtent.split(" ")
originalBottomFlt = float(originalBottom)
originalLeftFlt = float(originalLeft)
originalTopFlt = float(originalTop)
originalRightFlt = float(originalRight)
bandList.append([bandExtent])
# Fixes orientation, size, and location of acolite image
arcpy.DefineProjection_management(acoRaster, proj)
#rotates acolite image if applicable
if rotateYN == 'true':
arcpy.Rotate_management(acoRaster, "intermedRot" ,"180")
arcpy.env.snapRaster = pathLandsat
arcpy.CheckOutExtension("Spatial")
#flips acolite image if appropriate
if flipYN == 'true':
if rotateYN == 'true':
arcpy.Mirror_management("intermedRot", "intermedMir")
else:
arcpy.Mirror_management(acoRaster, "intermedMir")
#rescales acolite image
cellSizeXResult = arcpy.GetRasterProperties_management(pathLandsat, "CELLSIZEX")
cellSizeYResult = arcpy.GetRasterProperties_management(pathLandsat, "CELLSIZEY")
def mosaic(dnight, sets):
'''
This module creates the mosaic of the galactic model for each data set.
'''
#set arcpy environment variables part 2/2
arcpy.env.workspace = filepath.rasters + 'scratch_galactic/'
arcpy.env.scratchWorkspace = filepath.rasters + 'scratch_galactic'
for s in sets:
#file paths
calsetp = filepath.calibdata + dnight + '/S_0%s/' % s[0]
gridsetp = filepath.griddata + dnight + '/S_0%s/gal/' % s[0]
if os.path.exists(gridsetp):
shutil.rmtree(gridsetp)
os.makedirs(gridsetp)
#read in the galactic coordinates from coordinates_%s.txt
file = filepath.calibdata + dnight + '/coordinates_%s.txt' % s[0]
Gal_ang, Gal_l, Gal_b = n.loadtxt(file, usecols=(1, 2, 3)).T
#read in the registered images coordinates
file = filepath.calibdata + dnight + '/pointerr_%s.txt' % s[0]
Obs_AZ, Obs_ALT = n.loadtxt(file, usecols=(3, 4)).T
Obs_AZ[n.where(Obs_AZ > 180)] -= 360
Obs_AZ[35] %= 360
#loop through each file in the set
for w in range(len(Obs_AZ) + 1):
v = w + 1
if w == 45:
w = 35
Obs_AZ[w] -= 360
get_galgn(Gal_l[w], Gal_b[w])
if v in range(0, 50, 5):
print 'Generating galactic image %i/45' % v
#rotate by galctic angle
arcpy.Rotate_management('galgn.tif', 'rotaterasterg.tif',
str(Gal_ang[w]), "0 0", "BILINEAR")
#re-define projection to topocentric coordinates
arcpy.DefineProjection_management('rotaterasterg.tif',
tc(Obs_AZ[w], Obs_ALT[w]))
#reproject into GCS
arcpy.ProjectRaster_management('rotaterasterg.tif',
'gal%02d.tif' % v, geogcs,
"BILINEAR", "0.05")
#clip to image boundary
rectangle = clip_envelope(Obs_AZ, Obs_ALT, w)
arcpy.Clip_management("gal%02d.tif" % v, rectangle, "gali%02d" % v)
#Mosaic to topocentric coordinate model; save in Griddata\
print "Mosaicking into all sky galactic model"
R = ';'.join(['gali%02d' % i for i in range(1, 47)])
arcpy.MosaicToNewRaster_management(R, gridsetp, 'galtopmags', geogcs,
"32_BIT_FLOAT", "0.05", "1",
"BLEND", "FIRST")
print "Creating layer files for galactic mosaic"
layerfile = filepath.griddata + dnight + '/galtopmags%s.lyr' % s[0]
arcpy.MakeRasterLayer_management(gridsetp + 'galtopmags', 'galtoplyr')
arcpy.SaveToLayerFile_management('galtoplyr', layerfile, "ABSOLUTE")
#Set layer symbology to magnitudes layer
symbologyLayer = filepath.rasters + 'magnitudes.lyr'
arcpy.ApplySymbologyFromLayer_management(layerfile, symbologyLayer)
lyrFile = arcpy.mapping.Layer(layerfile)
lyrFile.replaceDataSource(gridsetp, 'RASTER_WORKSPACE', 'galtopmags',
'FALSE')
lyrFile.save()
#Downscale the raster and save it as a fits file
file = filepath.griddata + dnight + "/S_0" + s[0] + "/gal/galtopmags"
arcpy_raster = arcpy.sa.Raster(file)
A = arcpy.RasterToNumPyArray(arcpy_raster, "#", "#", "#", -9999)
A_small = downscale_local_mean(A[:1800, :7200], (25, 25)) #72x288
fname = filepath.griddata + dnight + '/galtopmags%s.fits' % s[0]
fits.writeto(fname, A_small, overwrite=True)
originalTopFlt = float(originalTop)
originalRightFlt = float(originalRight)
widthOriginal = originalRightFlt - originalLeftFlt
heightOriginal = originalTopFlt - originalBottomFlt
print widthOriginal, heightOriginal
print "Done pulling original data"
# Fixes orientation, size, and location of acolite image
print proj
print acoRaster
arcpy.DefineProjection_management(acoRaster, proj)
#rotates acolite image if applicable
if rotateYN == 'true':
arcpy.Rotate_management(acoRaster, "intermedRot", rotateAngle)
print "rotate"
#bring snapraster back here
arcpy.CheckOutExtension("Spatial")
#flips acolite image if appropriate
if flipYN == 'true':
if rotateYN == 'true':
arcpy.Mirror_management("intermedRot", "intermedMir")
print 'flip + rotate'
else:
arcpy.Mirror_management(acoRaster, "intermedMir")
print 'flip'
arcpy.AddMessage('\n')
# Rotate grid lines based on a pivot point - using the input fc centroid
# Also requires us to convert to raster, rotate, and then convert back to vector
# (As there is no built in vector rotation tool ...)
#
arcpy.AddMessage('>> Now running rotation process...')
arcpy.AddMessage('\n')
pivot_point = '{0} {1}'.format(bdy_fc_centroid[0], bdy_fc_centroid[1]) # X Y
out_raster = os.path.join(tmp_gdb, bdy_name + '_raster')
out_raster_rotated = os.path.join(tmp_gdb, bdy_name + '_raster_r')
tmp_fishnet_rotated = os.path.join(tmp_gdb, bdy_name + '_fishnet_r')
# Convert to raster, rotate, and convert back to polyline (use 10m to keep our raster cells separate)
arcpy.PolylineToRaster_conversion(tmp_fishnet_path, 'OID', out_raster,
'MAXIMUM_LENGTH', 'NONE', 10)
arcpy.Rotate_management(out_raster, out_raster_rotated, rotation_val,
pivot_point, 'NEAREST')
arcpy.RasterToPolyline_conversion(out_raster_rotated, tmp_fishnet_rotated,
'ZERO', 0, 'SIMPLIFY')
arcpy.AddMessage(
'Rotated data by specified value: {0} degrees'.format(rotation_val))
# Perform a real simplification on the layer - to tidy up the lines
tmp_fishnet_rotated_simpl = tmp_fishnet_rotated + '_s'
arcpy.SimplifyLine_cartography(tmp_fishnet_rotated, tmp_fishnet_rotated_simpl,
'POINT_REMOVE', 10)
time.sleep(5)
arcpy.AddMessage('Simplified/cleaned up data')
arcpy.AddMessage('\n')
# Clip rotated lines to input boundary
#
tmp_fishnet_clip = os.path.join(tmp_gdb, bdy_name + '_fishnet_r_s_c')