def fmask(self):
'''-----\n
This method applies Fmask algorithm or its equivalent with the BQA band (default for Ecopotential)'''
os.chdir(self.ruta_escena)
print('Starting Cloud Mask')
try:
print('Starting Fmask')
t = time.time()
#Last value is the cloud confidence value
a = os.system(
'removethisisFmaskinstalled/usr/GERS/Fmask_4_0/application/run_Fmask_4_0.sh /usr/local/MATLAB/MATLAB_Runtime/v93 3 3 1 {}'
.format(self.umbral))
a
if a == 0:
self.cloud_mask = 'Fmask'
print('Cloud Mask (Fmask) generated in ' +
str(t - time.time()) + ' seconds')
else:
print('Starting Cloud Mask with BQA band')
outfile = os.path.join(
os.path.join(self.ruta_escena, self.escena + '_Fmask.tif'))
for i in os.listdir(self.ruta_escena):
if i.endswith('BQA.TIF'):
bqa = os.path.join(self.ruta_escena, i)
masker = LandsatMasker(bqa, collection=1)
conf = LandsatConfidence.high
cloud = masker.get_cloud_mask(conf)
cirrus = masker.get_cirrus_mask(conf)
shadow = masker.get_cloud_shadow_mask(conf)
MASCARA = cloud + cirrus + shadow
masker.save_tif(MASCARA, outfile)
self.cloud_mask = 'BQA'
print('Cloud Mask (BQA) generated in ' + str(t - time.time()) +
' seconds')
print('Calling get_water')
self.get_water()
except Exception as e:
print("Unexpected error:", type(e), e)
def img_preprocess(qa, b4, b3, b2, tdimn):
masker = LandsatMasker(qa, collection=1)
conf = LandsatConfidence.high
mask1 = masker.get_cirrus_mask(conf)
mask2 = masker.get_cloud_mask(conf)
mask = np.bitwise_or(mask1, mask2)
mask = misc.imresize(mask, tdimn)
#mask = misc.imrotate(mask,18,interp='nearest')
img1 = gdal.Open(b4)
orig1 = img1.ReadAsArray()
orig1 = misc.imresize(orig1, tdimn)
orig1 = orig1[:, :, np.newaxis]
img2 = gdal.Open(b3)
orig2 = img2.ReadAsArray()
orig2 = misc.imresize(orig2, tdimn)
orig2 = orig2[:, :, np.newaxis]
img3 = gdal.Open(b2)
orig3 = img3.ReadAsArray()
orig3 = misc.imresize(orig3, tdimn)
orig3 = orig3[:, :, np.newaxis]
orig = np.concatenate((orig1, orig2, orig3), axis=2)
#orig = misc.imrotate(orig,18,interp='nearest')
#io.imsave(os.path.join(path,'rgb2.png'),orig)
#img = io.imread(os.path.join(path,'rgb2.png'))
# =============================================================================
# plt.figure(figsize=(40,20))
# plt.subplot(131)
# plt.imshow(orig)
# plt.title('Cloud-1')
#
# plt.figure(figsize=(40,20))
# plt.subplot(131)
# plt.imshow(np.squeeze(orig1,axis=2),cmap = plt.get_cmap('gist_gray'))
# plt.imshow(mask, cmap = plt.get_cmap('Reds'), alpha=0.5)
# plt.title('Cloud-2')
# =============================================================================
orig = np.reshape(orig, (1, mask.shape[0], mask.shape[1], 3))
img_m = np.reshape(mask, (1, mask.shape[0], mask.shape[1], 1))
return (orig, img_m)
def img_preprocess(qa,b4,b3,b2, pt):
masker = LandsatMasker(qa,collection=1)
conf = LandsatConfidence.high
mask1 = masker.get_cirrus_mask(conf)
mask2 = masker.get_cloud_mask(conf)
mask = np.bitwise_or(mask1,mask2)
mask = misc.imresize(mask,tdimn)
img1 = gdal.Open(b4)
orig1 = img1.ReadAsArray()
orig1 = misc.imresize(orig1,tdimn)
orig1 = orig1[:,:,np.newaxis]
img2 = gdal.Open(b3)
orig2 = img2.ReadAsArray()
orig2 = misc.imresize(orig2,tdimn)
orig2 = orig2[:,:,np.newaxis]
img3 = gdal.Open(b2)
orig3 = img3.ReadAsArray()
orig3 = misc.imresize(orig3,tdimn)
orig3 = orig3[:,:,np.newaxis]
orig = np.concatenate((orig1,orig2,orig3),axis=2)
# =============================================================================
# io.imsave(os.path.join(pt,'rgb2.png'),orig)
# img_t = load_img(os.path.join(pt,'rgb2.png'),grayscale=True)
# img_t = img_to_array(img_t)
# =============================================================================
# =============================================================================
# plt.figure(figsize=(40,20))
# plt.subplot(131)
# plt.imshow(orig,cmap = plt.get_cmap('gist_gray'))
# plt.imshow(mask, cmap = plt.get_cmap('Reds'), alpha=0.3)
# plt.title('Cloud-2')
# plt.show()
# =============================================================================
img_m = np.reshape(mask,(mask.shape[0],mask.shape[1],1))
#print(img_t.shape,img_m.shape)
return (orig, img_m)
def img_preprocess(i, qa):
masker = LandsatMasker(qa, collection=1)
conf = LandsatConfidence.high
mask1 = masker.get_cirrus_mask(conf)
mask2 = masker.get_cloud_mask(conf)
mask = np.bitwise_or(mask1, mask2)
mask = misc.imresize(mask, tdimn)
orig = misc.imread(os.path.join(dir1, "All_Imgs/orig_{}.png".format(i)))
# =============================================================================
# plt.figure(figsize=(40,20))
# plt.subplot(131)
# plt.imshow(orig)
# plt.imshow(mask, cmap = plt.get_cmap('Reds'), alpha=0.3)
# plt.title('Cloud-2')
# plt.show()
# =============================================================================
img_m = np.reshape(mask, (mask.shape[0], mask.shape[1], 1))
return (orig, img_m)
from skimage import io
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import optimizers
from keras.utils import to_categorical
import os
#%%
path = os.path.dirname(__file__)
tdimn = (256, 256)
masker = LandsatMasker('LC08_L1TP_210017_20170711_20170711_01_RT_BQA.TIF',
collection=1)
conf = LandsatConfidence.high
mask1 = masker.get_cirrus_mask(conf)
mask2 = masker.get_cloud_mask(conf)
mask = np.bitwise_or(mask1, mask2)
mask = misc.imresize(mask, tdimn)
mask = misc.imrotate(mask, 18, interp='nearest')
#%%
img1 = gdal.Open("LC08_L1TP_210017_20170711_20170711_01_RT_B4.TIF")
orig1 = img1.ReadAsArray()
orig1 = misc.imresize(orig1, tdimn)
orig1 = orig1[:, :, np.newaxis]
img2 = gdal.Open("LC08_L1TP_210017_20170711_20170711_01_RT_B3.TIF")
orig2 = img2.ReadAsArray()
orig2 = misc.imresize(orig2, tdimn)
orig2 = orig2[:, :, np.newaxis]
