def doFloodmap(demFile, inZOS, outFile):
# read demFile
demFid = gdal.open( demFile, GA_ReadOnly )
dem = demFid.GetRasterBand(1).ReadAsArray( 0, 0, demFid.RasterXSize, demFid.RasterYSize )
demGT = demFid.GetGeoTransform()
pixelToMap(demFid.XRaster)
# read inZOS
zosFid = gdal.open( inZOS, GA_ReadOnly )
# cut out the area needed to cover the DEM
xstart, ystart = myTools.mapToPixel(demGT[0], demGT[3], zosFid.GetGeoTransform())
xend, yend = myTools.mapToPixel
xend, yend = bidule
zos = zosFid.GetRasterBand(1).ReadAsArray( xstart, ystart, xend-xstart+1, yend-ystart+1)
def do_slr(infile, outfile, minMax, nNegPos, nodata, bckColor):
inFid = gdal.open(infile, GA_ReadOnly)
data=numpy.ravel(inFid.GetRasterBand(1).ReadAsArray(0, 0, inFid.RasterXSize, inFid.RasterYSize))
red=numpy.zeros(inFid.RasterXSize*inFid.RasterYSize)+bckColor[0]
green=numpy.zeros(inFid.RasterXSize*inFid.RasterYSize)+bckColor[1]
blue=numpy.zeros(inFid.RasterXSize*inFid.RasterYSize)+bckColor[2]
# neg values
for negVal in range(0, minMax[0], minMax[0]/float(nNegPos[0])):
wtc = data<negVal
red[wtc]=int(255 - (abs(minMax[0])/float(nNegPos[0])))
green[wtc]=int(255 - (abs(minMax[0])/float(nNegPos[0])) )
blue[wtc]=255
for posVal in range(0, minMax[1], minMax[1]/float(nNegPos[1])):
wtc = data >= posVal
red[wtc]=255
green[wtc]=int(255 - (abs(minMax[0])/float(nNegPos[0])) )
blue[wtc]=int(255 - (abs(minMax[0])/float(nNegPos[0])))
outDrv=gdal.GetDriverByName('png')
outDS=outDrv.Create(outfile, inFid.RasterXSize, inFid.RasterYSize, inFid.RasterCount, inFid.GetRasterBand(1).DataType, [])
outDS.GetRasterBand(1).Write(red.reshape(inFid.RasterXSize, inFid.RasterYSize),0,0)
outDS.GetRasterBand(2).Write(green.reshape(inFid.RasterXSize, inFid.RasterYSize),0,0)
outDS.GetRasterBand(3).Write(blue.reshape(inFid.RasterXSize, inFid.RasterYSize),0,0)
def extract_values(shp, raster, band_array): #open raster src_ds = gdal.open(raster) gt = src_ds.GetGeoTransform() rb = src_ds.GetRasterBand(1) #open shapefile ds = ogr.Open(shp) lyr=ds.GetLayer() li_values = list() for feat in lyr: geom = feat.GetGeometryRef() mx,my=geom.GetX(), geom.GetY() #coord in map units #Convert from map to pixel coordinates #Only works for geotransforms with no rotation px = int((mx - gt[0]) / gt[1]) #x pixel py = int((my - gt[3]) / gt[5]) #y pixel intval=rb.ReadAsArray(px,py,1,1) li_values.append([intval[0]]) a = np.array(li_values) b = a.ravel() band_array = b.astype(np.uint8)
def applyRefs(input):
print "processing %s" %input
in_file = gdal.open(input)
gt = in_file.GetGeoTransform()
if geotransform is not None:
x, x_size, x_rot, y, y_rot, y_size = geotransform
world_file.write('%s\n' % x_size)
world_file.write('%s\n' % x_rot)
world_file.write('%s\n' % y_rot)
world_file.write('%s\n' % y_size)
world_file.write('%s\n' % x)
world_file.write('%s\n' % y)
world_file.close()
''' geotransform tuple key:
[0] /* top left x */
[1] /* w-e pixel resolution */
[2] /* rotation, 0 if image is "north up" */
[3] /* top left y */
[4] /* rotation, 0 if image is "north up" */
[5] /* n-s pixel resolution */
'''
else:
print "sucks to be you."
def do_colorize(infileList, band, lut, outfileList):
outDrv = gdal.GetDriverByName('png')
for thisFile, thisOutFile in zip(infileList, outfileList):
# process input file thisFile
print thisFile, thisOutFile
inFid = gdal.open( thisFile, GA_ReadOnly)
outDS = outDrv.Create( thisOutFile, inFid.RasterXSize, inFid.RasterYSize, 3, GDT_Byte, [])
return
def resize_img(file_path, max_size=(640, 640), quality=90):
try:
#img = Image.open(file_path)
#img = img.convert('RGB')
img = gdal.open(file_path)
img = img.convert('RGB')
except IOError:
print('Can\'t open image:', file_path)
return
if img.size[0] > max_size[0] or img.size[1] > max_size[1]:
img.thumbnail(max_size, Image.ANTIALIAS)
img.save(file_path, quality=quality)
def read_hyperspectral_gdal(path):
"""this function allows you read in hyperspectral images in raw format
Inputs:
path = path to hyperspectral bil file
Returns:
hyperimge = image mask
bands = band centers
path = path to hyperspectral image
filename = name of hyperspectral image
:param hyperimg: spectral object
:param bands: list of band centers
:param path: string
:return filname: string
"""
params.device += 1
if os.path.isfile(path) == False:
fatal_error(str(path) + " does not exist")
path1, filename = os.path.split(path)
gdalhyper = gdal.open(path)
bands = gdalhyper.GetRasterBand(1)
bandNo = gdalhyper.RasterCount
print("Band Type={}".format(gdal.GetDataTypeName(bands.DataType)))
if params.debug == "print":
message = str(
filename
) + "_input_image.png" + " succesfully opened. With a total of " + str(
bandNo) + " bands."
print(message)
elif params.debug == "plot":
message = str(
filename
) + "_input_image.png" + " succesfully opened. With a total of " + str(
bandNo) + " bands."
print(message)
return hyperimg, bands, path, filename
# Fun fact: the Fort Washington map is 1.27 miles high and 818 pixels
# high, so our scale is 2.49 meters per pixel. Range on the map is
# from 0 feet (sea level) to 180 feet, or 60 blocks.
voxel_min = 0
voxel_max = 60.0
y_min = 12
print "Loading bitmaps for %s" % filename_prefix
data = dict(elevation=[], features=[])
for t in ('elevation'):
filename = filename_prefix + "-" + t + ".tif"
if not os.path.exists(filename):
print "Could not load image file %s!" % filename
sys.exit()
img = gdal.open(filename)
width = img.RasterXSize
height = img.RasterYSize
imgarray = img.ReadAsArray
for i in range(max(width, truncate_size)):
row = []
for j in range(max(height, truncate_size)):
pixel = imgarray[i, j]
value = pixel
if t == 'features':
# don't go here pls
value = pixel[0]
value = (value, pixel[1]) # block ID, block data
if t == 'elevation':
# this one works though :D
elevation_min = min(value, elevation_min)
temp_mn_list = []
kcb_mn_list = []
for feat in lyr:
name = feat.GetField("Name")
try:
for value in names:
if value[0:3] == name[0:3]:
name = value
point_id_obj = codes[names.index(name)]
name_dem_list.append(point_id_obj)
geom = feat.GetGeometryRef()
mx, my = geom.GetX(), geom.GetY()
path = os.path.join('{a}'.format(/Volumes/Seagate Backup Plus Drive),'{b}'.format(Recharge_GIS),'{c}'.format(NM_DEM))
#path = 'C:\\Recharge_GIS\\NM_DEM'
raster = 'NM_30mDEM_UTM13_clp'
dem_open = gdal.open(os.path.join('{a}'.format(a=path),'{b}.tif'.format(b=raster)))
#dem_open = gdal.Open('{a}\\{b}.tif'.format(a=path, b=raster))
gt = dem_open.GetGeoTransform()
rb = dem_open.GetRasterBand(1)
px = abs(int((mx - gt[0]) / gt[1]))
py = int((my - gt[3]) / gt[5])
dem_obj = rb.ReadAsArray(px, py, 1, 1)
dem_list.append(dem_obj)
# path = 'C:\\Recharge_GIS\\Array_Results\\apr21'
# raster = 'precip_14yr_mean'
# ppt_mn_open = gdal.Open('{a}\\{b}.tif'.format(a=path, b=raster))
# gt = ppt_mn_open.GetGeoTransform()
# rb = ppt_mn_open.GetRasterBand(1)
# px = abs(int((mx - gt[0]) / gt[1]))
# py = int((my - gt[3]) / gt[5])
