def shadow_mask_array(image_difference_array, threshold=-5500, filter_size=13, morphing_size=0): # TODO: Is it 0 in morphing_size?? is not 10??
'''
This method returns a mask for the given array, it stacks all the bands into
one and filters values that match the threshold. The new array will be filled
with 0 when the values are above the threshold and 1 when the values are bellow
the threshold. We assume that shadows will have darker values when compared to
the reference array.
'''
shadows = filter_median((numpy.sum(image_difference_array[3:, :, :], axis=0) < threshold).astype(numpy.int), filter_size)
if morphing_size:
shadows = morph_dilation(shadows, morphing_size)
return shadows
def cloud_mask_array(image_difference_array, threshold=30000, filter_size=13, morphing_size=0): # TODO: Is it 0 in morphing_size?? is not 10??
'''
This method returns a mask for the given array, it stacks all the bands into
one and filters values that match the threshold. The new array will be filled
with 0 when the values are below the threshold and 1 when the values are above
the threshold. We assume that clouds will have brighter values when compared
to the reference array.
'''
clouds = filter_median((numpy.sum(image_difference_array, axis=0) > threshold).astype(numpy.int), filter_size)
if morphing_size:
clouds = morph_dilation(clouds, morphing_size)
return clouds
def cloud_shadow_mask_array(image_array, image_difference_array, solar_zenith, solar_azimuth, resolution):
'''
This method creates a mask for clouds and shadows using a reference array.
'''
clouds = cloud_mask_array(image_difference_array)
shadows = shadow_mask_array(image_difference_array)
inbetween = calculate_cloud_shadow(clouds, shadows, solar_zenith, solar_azimuth, resolution)
image_mask_array = outside_mask_array(image_array, outside_value=255)
pixel_sizes = [250, 150, 50]
number_of_sizes = len(pixel_sizes)
for pixel_size in pixel_sizes:
numpy.putmask(image_mask_array, morph_dilation(clouds, pixel_size) == 1, FMASK_CLOUD * 10 + number_of_sizes)
number_of_sizes = number_of_sizes - 1
numpy.putmask(image_mask_array, inbetween == 1, FMASK_CLOUD_SHADOW)
numpy.putmask(image_mask_array, clouds == 1, FMASK_CLOUD)
return image_mask_array
def shadow_mask_array(
image_difference_array,
threshold=-5500,
filter_size=13,
morphing_size=0): # TODO: Is it 0 in morphing_size?? is not 10??
'''
This method returns a mask for the given array, it stacks all the bands into
one and filters values that match the threshold. The new array will be filled
with 0 when the values are above the threshold and 1 when the values are bellow
the threshold. We assume that shadows will have darker values when compared to
the reference array.
'''
shadows = filter_median((numpy.sum(image_difference_array[3:, :, :],
axis=0) < threshold).astype(numpy.int),
filter_size)
if morphing_size:
shadows = morph_dilation(shadows, morphing_size)
return shadows
def cloud_mask_array(
image_difference_array,
threshold=30000,
filter_size=13,
morphing_size=0): # TODO: Is it 0 in morphing_size?? is not 10??
'''
This method returns a mask for the given array, it stacks all the bands into
one and filters values that match the threshold. The new array will be filled
with 0 when the values are below the threshold and 1 when the values are above
the threshold. We assume that clouds will have brighter values when compared
to the reference array.
'''
clouds = filter_median(
(numpy.sum(image_difference_array, axis=0) > threshold).astype(
numpy.int), filter_size)
if morphing_size:
clouds = morph_dilation(clouds, morphing_size)
return clouds
def cloud_shadow_mask_array(image_array, image_difference_array, solar_zenith,
solar_azimuth, resolution):
'''
This method creates a mask for clouds and shadows using a reference array.
'''
clouds = cloud_mask_array(image_difference_array)
shadows = shadow_mask_array(image_difference_array)
inbetween = calculate_cloud_shadow(clouds, shadows, solar_zenith,
solar_azimuth, resolution)
image_mask_array = outside_mask_array(image_array, outside_value=255)
pixel_sizes = [250, 150, 50]
number_of_sizes = len(pixel_sizes)
for pixel_size in pixel_sizes:
numpy.putmask(image_mask_array,
morph_dilation(clouds, pixel_size) == 1,
FMASK_CLOUD * 10 + number_of_sizes)
number_of_sizes = number_of_sizes - 1
numpy.putmask(image_mask_array, inbetween == 1, FMASK_CLOUD_SHADOW)
numpy.putmask(image_mask_array, clouds == 1, FMASK_CLOUD)
return image_mask_array
