def DNtoReflectance(filepath, info, n_band, resultfolder):
num_band = str(n_band)
band = filepath + '_01_T1_B' + num_band + '.tif'
mtl = filepath + '_01_T1_MTL.txt'
data = getMTL(mtl)
SUN_ELEVATION = float(data['SUN_ELEVATION'])
SUN_ELEVATION_Rad = SUN_ELEVATION * math.pi / 180
EARTH_SUN_DISTANCE = float(data['EARTH_SUN_DISTANCE'])
image = tifffile.imread(band, key=0)
DN = np.array(image)
RADIANCE_MAXIMUM_BAND = float(data['RADIANCE_MAXIMUM_BAND_' + num_band])
REFLECTANCE_MAXIMUM_BAND = float(data['REFLECTANCE_MAXIMUM_BAND_' +
num_band])
RADIANCE_MULT_BAND = float(data['RADIANCE_MULT_BAND_' + num_band])
RADIANCE_ADD_BAND = float(data['RADIANCE_ADD_BAND_' + num_band])
Radiance = DN * RADIANCE_MULT_BAND + RADIANCE_ADD_BAND
Sun_radiance = (
(math.pi * EARTH_SUN_DISTANCE**2)**2 * RADIANCE_MAXIMUM_BAND *
np.sin(SUN_ELEVATION_Rad)) / REFLECTANCE_MAXIMUM_BAND
Reflectance = Radiance / Sun_radiance
b = np.array(Reflectance, dtype=np.float32)
np.save(resultfolder + 'Landsat_' + info + '_B' + num_band, b)
Reflectance = None
b = None
gc.collect()
print("save " + 'Landsat_' + info + '_B' + num_band)
def DNtoTCelsium(filepath, info, n_band, resultfolder):
num_band = str(n_band)
band = filepath + '_01_T1_B' + num_band + '.tif'
mtl = filepath + '_01_T1_MTL.txt'
data = getMTL(mtl)
# Open the file:
Band = tifffile.imread(band, key=0)
RADIANCE_MULT_BAND = float(data['RADIANCE_MULT_BAND_' + num_band])
RADIANCE_ADD_BAND = float(data['RADIANCE_ADD_BAND_' + num_band])
K2_CONSTANT_BAND = float(data['K2_CONSTANT_BAND_' + num_band])
K1_CONSTANT_BAND = float(data['K1_CONSTANT_BAND_' + num_band])
TOARadiance = RADIANCE_MULT_BAND * Band + RADIANCE_ADD_BAND
band_log = np.log(K1_CONSTANT_BAND / TOARadiance + 1)
band_t = K2_CONSTANT_BAND / band_log - 273.15
print(np.max(band_t), ' Max Temperature')
print(np.min(band_t), ' Min Temperature')
Band_T_float32 = np.array(band_t, dtype=np.float32)
np.save(resultfolder + 'Temperature_Band_' + str(n_band) + '_' + info,
Band_T_float32)
print('Temperature_Band_' + num_band + '_' + info + ' done')
def Reading_data(path_res, X, mtl):
firms = np.load(path_res + r'/Firms_' + X + '.npy')
print(firms)
result = np.load(path_res + r'/Algrorithm_' + X + '.npy')
print(result)
points_math = np.load(path_res + r'/Points_match_' + X + '.npy')
print(points_math)
b1 = np.load(path_res + r'\Landsat_' + X + '_B1.npy')
print(b1.shape, 'Shape')
data = getMTL(mtl)
return firms, result, points_math, b1, data
def FromMaskToCoords(path_res, info, firemask, mtl):
b1 = np.load(path_res + r'\Landsat_' + info + '_B1.npy')
data = getMTL(mtl)
UL_LAT = float(data['CORNER_UL_LAT_PRODUCT'])
UL_LON = float(data['CORNER_UL_LON_PRODUCT'])
UR_LAT = float(data['CORNER_UR_LAT_PRODUCT'])
UR_LON = float(data['CORNER_UR_LON_PRODUCT'])
LL_LAT = float(data['CORNER_LL_LAT_PRODUCT'])
LL_LON = float(data['CORNER_LL_LON_PRODUCT'])
LR_LAT = float(data['CORNER_LR_LAT_PRODUCT'])
LR_LON = float(data['CORNER_LR_LON_PRODUCT'])
Corners = index_corners(b1)
Max_ind_line = Corners[0]
Min_ind_line = Corners[1]
Max_ind_col = Corners[2]
Min_ind_col = Corners[3]
Max_Lat = max(UL_LAT,UR_LAT,LL_LAT,LR_LAT)
Min_lat = min(UL_LAT,UR_LAT,LL_LAT,LR_LAT)
Max_lon = max(UL_LON,UR_LON,LL_LON,LR_LON)
Min_lon = min(UL_LON,UR_LON,LL_LON,LR_LON)
result = np.load(firemask)
shape = result.shape
height = shape[0]
width = shape[1]
lonarr = []
latarr = []
for i in range(height):
for j in range(width):
if result[i][j]!=0:
lon_lat = Coordinates(i,j,Min_lat, Max_Lat, Min_lon, Max_lon, Max_ind_line, Min_ind_line, Max_ind_col, Min_ind_col)
latarr.append(lon_lat[0])
lonarr.append(lon_lat[1])
lonarr=np.array(lonarr)
latarr=np.array(latarr)
print(len(lonarr))
np.save(path_res + r'\lonarr_'+info,lonarr)
np.save(path_res + r'\latarr_'+info,latarr)
print('coords done')
def Сalculation_E_diff_corners(filepath, np_filepath, info):
b1 = np.load(np_filepath + r'Landsat_' + info + '_B1.npy')
mtl = filepath + '_01_T1_MTL.txt'
data = getMTL(mtl)
UL_LAT = float(data['CORNER_UL_LAT_PRODUCT'])
UL_LON = float(data['CORNER_UL_LON_PRODUCT'])
UR_LAT = float(data['CORNER_UR_LAT_PRODUCT'])
UR_LON = float(data['CORNER_UR_LON_PRODUCT'])
LL_LAT = float(data['CORNER_LL_LAT_PRODUCT'])
LL_LON = float(data['CORNER_LL_LON_PRODUCT'])
LR_LAT = float(data['CORNER_LR_LAT_PRODUCT'])
LR_LON = float(data['CORNER_LR_LON_PRODUCT'])
shape = b1.shape
mask = np.zeros(shape)
np.putmask(mask, b1 != b1[0][0], 1)
ind_lines = []
for i in range(shape[0]):
if np.sum(mask[i, :]) <= 5 and np.sum(mask[i, :]) != 0:
ind_lines.append(i)
ind_columns = []
for j in range(shape[1]):
if np.sum(mask[:, j]) <= 5 and np.sum(mask[:, j]) != 0:
ind_columns.append(j)
Max_ind_line = max(ind_lines)
Min_ind_line = min(ind_lines)
Max_ind_col = max(ind_columns)
Min_ind_col = min(ind_columns)
offsetX = Min_ind_col
offsetY = Min_ind_line
Max_Lat = max(UL_LAT, UR_LAT, LL_LAT, LR_LAT)
Min_lat = min(UL_LAT, UR_LAT, LL_LAT, LR_LAT)
Max_lon = max(UL_LON, UR_LON, LL_LON, LR_LON)
Min_lon = min(UL_LON, UR_LON, LL_LON, LR_LON)
p_lat = abs((Max_Lat - Min_lat) / (Max_ind_line - offsetY))
p_lon = abs((Max_lon - Min_lon) / (Max_ind_col - offsetX))
E_diff = (p_lat**2 + p_lon**2)**0.5
return (p_lat, p_lon)
def Max_Min_Lat_Lon(mtl):
data = getMTL(mtl)
UL_LAT = float(data['CORNER_UL_LAT_PRODUCT'])
UL_LON = float(data['CORNER_UL_LON_PRODUCT'])
UR_LAT = float(data['CORNER_UR_LAT_PRODUCT'])
UR_LON = float(data['CORNER_UR_LON_PRODUCT'])
LL_LAT = float(data['CORNER_LL_LAT_PRODUCT'])
LL_LON = float(data['CORNER_LL_LON_PRODUCT'])
LR_LAT = float(data['CORNER_LR_LAT_PRODUCT'])
LR_LON = float(data['CORNER_LR_LON_PRODUCT'])
UL = (UL_LAT, UL_LON)
LL = (LL_LAT, LL_LON)
UR = (UR_LAT, UR_LON)
LR = (LR_LAT, LR_LON)
Max_lat = max(UL_LAT,UR_LAT,LL_LAT,LR_LAT)
Min_lat = min(UL_LAT,UR_LAT,LL_LAT,LR_LAT)
Max_lon = max(UL_LON,UR_LON,LL_LON,LR_LON)
Min_lon = min(UL_LON,UR_LON,LL_LON,LR_LON)
return (Max_lat, Min_lat, Max_lon, Min_lon)
def DNtoReflectance(filepath, info, n_band, mtl, path_res):
num_band = str(n_band)
band = filepath + r'\LC08_L1TP_' + info + '_01_T1\LC08_L1TP_' + info + '_01_T1_B' + num_band + '.tif'
data = getMTL(mtl)
image = tifffile.imread(band, key=0)
DN = np.array(image)
REFLECTANCE_MULT_BAND = float(data['REFLECTANCE_MULT_BAND_' + num_band])
REFLECTANCE_ADD_BAND = float(data['REFLECTANCE_ADD_BAND_' + num_band])
Reflectance = DN * REFLECTANCE_MULT_BAND + REFLECTANCE_ADD_BAND
print(np.max(Reflectance), ' Max Value Band ' + num_band)
print(np.min(Reflectance), ' Min Value Band ' + num_band)
b = np.array(Reflectance, dtype=np.float32)
np.save(path_res + r'\Landsat_' + info + '_B' + num_band, b)
Reflectance = None
b = None
gc.collect()
print("save " + 'Landsat_' + info + '_B' + num_band)
def FromCoordinatesToValue_b7(filepath, np_folder, info, lat, lon):
b7 = np.load(np_folder + r'Landsat_' + info + '_B7.npy')
mtl = filepath + '_01_T1_MTL.txt'
data = getMTL(mtl)
UL_LAT = float(data['CORNER_UL_LAT_PRODUCT'])
UL_LON = float(data['CORNER_UL_LON_PRODUCT'])
UR_LAT = float(data['CORNER_UR_LAT_PRODUCT'])
UR_LON = float(data['CORNER_UR_LON_PRODUCT'])
LL_LAT = float(data['CORNER_LL_LAT_PRODUCT'])
LL_LON = float(data['CORNER_LL_LON_PRODUCT'])
LR_LAT = float(data['CORNER_LR_LAT_PRODUCT'])
LR_LON = float(data['CORNER_LR_LON_PRODUCT'])
Corners = index_corners(b7)
# (Max_ind_line, Min_ind_line, Max_ind_col, Min_ind_col)
Max_ind_line = Corners[0]
offsetY = Corners[1]
Max_ind_col = Corners[2]
offsetX = Corners[3]
Max_Lat = max(UL_LAT, UR_LAT, LL_LAT, LR_LAT)
Min_lat = min(UL_LAT, UR_LAT, LL_LAT, LR_LAT)
Max_lon = max(UL_LON, UR_LON, LL_LON, LR_LON)
Min_lon = min(UL_LON, UR_LON, LL_LON, LR_LON)
p_lat = abs((Max_Lat - Min_lat) / (Max_ind_line - offsetY))
p_lon = abs((Max_lon - Min_lon) / (Max_ind_col - offsetX))
Ind_Y = offsetY + math.ceil((Max_Lat - lat) / p_lat)
Ind_X = offsetX + math.ceil((lon - Min_lon) / p_lon)
val = b7[Ind_Y][Ind_X]
return val