def start():
user_input = input('Enter file path for first_band: ')
# Open First image and get its only band.
# first_tiff = Open(r'51/AST_L1B_00308132001191022_20110122163617_24451_ImageData1_b30de2ff.tif')
first_tiff = Open(user_input)
first_band = first_tiff.GetRasterBand(1)
user_input = input('Enter file path for second_band of same image: ')
# Open Second image and get its only band.
# second_tiff = Open(r'51/AST_L1B_00308132001191022_20110122163617_24451_ImageData2_b30de2ff.tif')
second_tiff = Open(user_input)
second_band = second_tiff.GetRasterBand(1)
# Release from memory
user_input = None
# Get the rows and cols from one of the images (both should always be the same)
rows, cols, geotransform = first_tiff.RasterYSize, first_tiff.RasterXSize, first_tiff.GetGeoTransform()
print(geotransform)
# Set an output for a 16-bit unsigned integer (0-255)
out_tiff_int16 = r'TEST-RESULTS/NDVI_INT16.tif'
# Set the output for a 32-bit floating point (-1 to 1)
out_tiff_float32 = r'TEST-RESULTS/NDVI_FLOAT32.tif'
# Run the function for unsigned 16-bit integer
ndwi(first_band, second_band, rows, cols, geotransform, out_tiff_int16, gdal.GDT_UInt16)
# Run the function for 32-bit floating point
ndwi(first_band, second_band, rows, cols, geotransform, out_tiff_float32, gdal.GDT_Float32)
print('done')
def main():
if len(sys.argv) < 6:
print "Few arguments"
sys.exit()
elif len(sys.argv) > 6:
print "Too many arguments"
sys.exit()
#Tiff paths
red_path = str(sys.argv[1])
nir_path = str(sys.argv[2])
bqa_path = str(sys.argv[3])
mtl_path = str(sys.argv[4])
output_path = str(sys.argv[5])
d_sun_earth_path = 'd_sun_earth'
# Open red image and get its only band.
red_tiff = Open(red_path)
red_band = red_tiff.GetRasterBand(1)
# Open NIR image and get its only band.
nir_tiff = Open(nir_path)
nir_band = nir_tiff.GetRasterBand(1)
# Open bqa image and get its only band.
bqa_tiff = Open(bqa_path)
bqa_band = bqa_tiff.GetRasterBand(1)
# Get the rows and cols from one of the images (both should always be the same)
row, col, geotransform = nir_tiff.RasterYSize, nir_tiff.RasterXSize, nir_tiff.GetGeoTransform(
)
# Reading metadata
parameters = metaReader.readParameters(mtl_path, d_sun_earth_path)
number_sensor = int(parameters[0])
julian_day = int(parameters[1])
sun_elevation = float(parameters[2])
dist_sun_earth = float(parameters[3])
mask = get_mask(number_sensor)
if shadow_check(bqa_band, (col, row), mask):
print "Invalid inputs. Lots of cloud in tiff images"
sys.exit()
ndviC = NDVI(sun_elevation, red_band, nir_band, bqa_band, (col, row),
geotransform)
ndviC.processNDVI(number_sensor, dist_sun_earth, output_path)
def apply_valid_range(input_data_set_path: str, file_path: str) -> str:
"""Apply Valid Range -10000 -> 10000.
Args:
input_data_set_path (str) - Path to the input data set
file_path (str) - Target data set filename
Returns:
Path to valid_range_image
"""
src_ds = GDALOpen(input_data_set_path, GA_ReadOnly)
if src_ds is None:
raise ValueError(
'Could not open data set "{}"'.format(input_data_set_path))
driver = GetDriverByName('MEM')
src_band = src_ds.GetRasterBand(1)
data_set = driver.Create('', src_ds.RasterXSize, src_ds.RasterYSize, 1,
src_band.DataType)
data_set.SetGeoTransform(src_ds.GetGeoTransform())
data_set.SetProjection(src_ds.GetProjection())
data_set_band = data_set.GetRasterBand(1)
data_set_band.WriteArray(src_band.ReadAsArray())
band_array = data_set_band.ReadAsArray()
dummy = -9999
data_set_band.SetNoDataValue(dummy)
band_array[band_array <= -10000] = dummy
band_array[band_array >= 10000] = dummy
driver = GetDriverByName('GTiff')
data_set_band.WriteArray(band_array)
dst_ds = driver.CreateCopy(file_path, data_set, options=["COMPRESS=LZW"])
del dst_ds
del src_ds
del data_set
return file_path
from gdal import Open
from ndvi import ndvi
# Abrindo banda NIR
nir_tiff = Open(r'NIR_IMAGE.tif')
#nir_tiff = Open(r'nir.tif')
nir_band = nir_tiff.GetRasterBand(1)
# Abrindo banda RED.
red_tiff = Open(r'RED_IMAGE.tif')
#red_tiff = Open(r'red.tif')
red_band = red_tiff.GetRasterBand(1)
# Get the rows and cols from one of the images (both should always be the same)
rows, cols, geotransform = nir_tiff.RasterYSize, nir_tiff.RasterXSize, nir_tiff.GetGeoTransform()
print(geotransform)
# Arquivo de saida 16-bit (0-255)
out_tiff_int16 = r'NDVI_16.tif'
# Arquivo de saida 32-bit floating point (-1 to 1)
out_tiff_float32 = r'NDVI_32.tif'
# Gerando imagem NDVI 16-bit integer
ndvi(nir_band, red_band, rows, cols, geotransform, out_tiff_int16, gdal.GDT_UInt16)
# Gerando imagem NDVI 32-bit floating point
ndvi(nir_band, red_band, rows, cols, geotransform, out_tiff_float32, gdal.GDT_Float32)
print('done')
#plt.savefig('testResult.tif', dpi=300)
# !!!! Save GEOreferenced TIFF
pred = np.load('label_pred.npy')
pred = (pred*255).astype('uint8')
#pred = (pred > 0.9).astype('uint8')
cv2.imwrite(files, pred)
#cv2.imwrite(files, pred)
dataset = Open(os.path.join(path,files))
if dataset is None:
print('Unable to open')
sys.exit(1)
projection = dataset.GetProjection()
geotransform = dataset.GetGeoTransform()
if projection is None and geotransform is None:
print('No projection or geotransform found on file')
sys.exit(1)
dataset2 = Open(files, GA_Update)
if geotransform is not None and geotransform != (0,1,0,0,0,1):
dataset2.SetGeoTransform( geotransform )
if projection is not None and projection != '':
dataset2.SetProjection( projection )
gcp_count = dataset.GetGCPCount()
if gcp_count != 0:
import numpy as np
import matplotlib.pyplot as plt
from gdal import Open as OpenGdal
#-------------------------------------------------------------------------------
fn0 = '../data/SVDNB_npp_20150101-20151231_75N060W_{}_v10_c201701311200.avg_rade9.tif'
# vcm - viirs cloud mask
# vcm-orm = outlier removed
# vcm-ntl = background (non-lights) removed
# vcm-orm-ntl = both
gd = OpenGdal(fn0.format('vcm-orm-ntl'))
print gd.RasterXSize, gd.RasterYSize
gt = gd.GetGeoTransform()
#-------------------------------------------------------------------------------
xy2geo = lambda g, x, y: (g[3] + g[5] * y, g[0] + g[1] * x)
geo2xy = lambda g, f, l: ((l - g[0]) / g[1], (f - g[3]) / g[5])
#-------------------------------------------------------------------------------
# geo0 = 51.11, 17.03 # wroclaw
# geo0 = 52.22, 21.01 # warszawa
# geo0 = 50.816, 15.383 # Orle
# geo0 = 50.846015, 16.698650 # tapadla
geo0 = 50.995681, 16.901729
geor = 0.6
f0, l0 = geo0[0] + geor, geo0[1] - geor
