def get_bqa_water_mask(bqa):
bqa = bqa.astype(np.int16)
conf_is_cumulative = False
masker = LandsatMasker(bqa, collection=0)
one_img_mask = np.zeros_like(bqa).astype(np.int16)
num_images, height, width, num_times = bqa.shape
for i in xrange(num_images):
for j in xrange(num_times):
conf = 2 # water mask only accepts values of 0 or 2
mask = masker.get_water_mask(conf, cumulative=conf_is_cumulative)
one_img_mask[i, :, :, j] = mask[i, :, :, j]
return one_img_mask
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)
def get_cloud_mask(bqa):
'''
Takes in a bqa array and returns a mask of clouds
'''
landsat_confidences = {
LandsatConfidence.low: 1,
LandsatConfidence.medium: 2,
LandsatConfidence.high: 3
}
bqa = bqa.astype(np.int16)
conf_is_cumulative = False
masker = LandsatMasker(bqa, collection=0)
cloud_conf = np.zeros_like(bqa).astype(np.int16)
for conf in landsat_confidences:
mask = masker.get_cloud_mask(conf, cumulative=conf_is_cumulative)
cloud_conf += mask * landsat_confidences[
conf] # multiply each conf by a value
return cloud_conf
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)
from osgeo import gdal
from pymasker import LandsatMasker
from pymasker import LandsatConfidence
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()
shadow = args.shadow
bqa_band_path = "{0}/{1}/{1}_BQA.TIF".format(landsat_scenes_path, sceneID)
output_cloud_mask_path = "{0}/{1}/{1}_bqa_cloud_mask.TIF".format(
landsat_scenes_path, sceneID)
output_shadow_mask_path = "{0}/{1}/{1}_bqa_shadow_mask.TIF".format(
landsat_scenes_path, sceneID)
output_cloudshadow_mask_path = "{0}/{1}/{1}_bqa_cloudshadow_mask.TIF".format(
landsat_scenes_path, sceneID)
# load the QA band directly
#
# The "collection" parameter is required for landsat to specify the collection
# number. Acceptable number: 0 (pre-collection), 1 (collection-1)
masker = LandsatMasker(bqa_band_path, collection=1)
# algorithm has high confidence that this condition exists
# (67-100 percent confidence)
conf = LandsatConfidence.medium
# Get mask indicating cloud pixels with high confidence
cloud_mask = masker.get_cloud_mask(conf, cumulative=True)
# save the result
masker.save_tif(cloud_mask, output_cloud_mask_path)
if shadow:
# Get mask indicating cloud pixels with high confidence
cloud_shadow_mask = masker.get_cloud_shadow_mask(conf, cumulative=True)
masker.save_tif(cloud_shadow_mask, output_shadow_mask_path)
# Merge the Cloud and Cloud-Shadow mask
