def process(path_in):
"""Open/Process/Write the image in the given path.
Args:
path_in(str)
"""
arr_in = IO.read(path_in)
arr_out = arr_in
path_out = path_in
IO.write(arr_out, path_out)
logging.info('%s processed [Ok]' % path_in)
def process(path_in):
"""Open/Process/Write the image in the given path.
Args:
path_in(str)
"""
arr_in = IO.read(path_in)
arr_out = arr_in
path_out = path_in
IO.write(arr_out, path_out)
logging.info('%s processed [Ok]' % path_in)
#!/usr/bin/env python
import numpy as np
import skfuzzy as fuzz
from geotiff.io import IO
# load original image
DIR = 'C:/Data/Tewkesbury-LiDAR'
img = IO.read(DIR + '/stack-lidar.data/Sigma0_HH_slv1_25Jul2007.img')
# pixel intensities
I = img.reshape((1, -1))
# params
n_centers = 2
fuzziness_degree = 2
error = 0.005
maxiter = 1000
# fuzz c-means clustering
centers, u, u0, d, jm, n_iters, fpc = fuzz.cluster.cmeans(I,
c=n_centers,
m=fuzziness_degree,
error=error,
maxiter=maxiter,
init=None)
img_clustered = np.argmax(u, axis=0).astype(float)
# display clusters
img_clustered.shape = img.shape
IO.write(img_clustered, DIR + '/fuzzy-clustered.tif')
#!/usr/bin/env python
import matplotlib.pyplot as plt
from geotiff.io import IO
# load original image
input_file = '/'.join(('C:', 'Data', 'Tewkesbury-LiDAR', 'subset.data',
'Sigma0_HH_slv1_25Jul2007.img'))
img = IO.read(input_file)
# histogram of the image
plt.hist(img.flatten(), bins=10, range=[0, 10])
plt.show()
#!/usr/bin/env python
import numpy as np
from skimage.filters import threshold_otsu
from geotiff.io import IO
# load two images
DIR = 'C:/Data/Tewkesbury-LiDAR'
img_pre = IO.read(DIR + '/subset.data/Sigma0_HH_slv2_22Jul2008.img')
img_post = IO.read(DIR + '/subset.data/Sigma0_HH_slv1_25Jul2007.img')
# otsu thresholding of the two images
threshold_pre = threshold_otsu(img_pre)
img_thresholded_pre = img_pre < threshold_pre
print 'Threshold for image pre:', threshold_pre
IO.write(img_thresholded_pre, DIR + '/thresholded-pre.tif')
threshold_post = threshold_otsu(img_post)
img_thresholded_post = img_post < threshold_post
print 'Threshold for image post:', threshold_post
IO.write(img_thresholded_post, DIR + '/thresholded-post.tif')
# difference between the two thresholded images
img_difference = np.subtract(img_thresholded_post, img_thresholded_pre,
dtype=np.float)
IO.write(img_difference, DIR + '/difference.tif')
#!/usr/bin/env python
"""Split-based thresholding of a GeoTIFF image."""
from skimage.filters import threshold_otsu
from geotiff.io import IO
# constants
SPLIT_SIZE = 100
PATH = 'C:/Data'
IN_PATH = PATH + '/namibia-flipped.data/Sigma0_HH.img'
OUT_PATH = PATH + '/namibia-splits/serbia-split-(%s,%s).tiff'
# load original image
img = IO.read(IN_PATH)
img_xsize = img.shape[1]
img_ysize = img.shape[0]
# extract splits iteratively & their thresholds
local_thresholds = []
y_offset = 0
while y_offset < img_ysize:
x_offset = 0
while x_offset < img_xsize:
# estimate the otsu threshold for each split
split_xsize = (
SPLIT_SIZE if x_offset + SPLIT_SIZE < img_xsize
else img_xsize - x_offset
)
split_ysize = (
SPLIT_SIZE if y_offset + SPLIT_SIZE < img_ysize
else img_ysize - y_offset
#!/usr/bin/env python
import matplotlib.pyplot as plt
from geotiff.io import IO
# load original image
input_file = '/'.join(('C:', 'Data', 'Tewkesbury-LiDAR', 'subset.data',
'Sigma0_HH_slv1_25Jul2007.img'))
img = IO.read(input_file)
# histogram of the image
plt.hist(img.flatten(), bins=10, range=[0, 10])
plt.show()
if sqterm < 0:
print "MinError(I): not converging. (1)"
return t
# The updated threshold is the integer part of the solution of the
# quadratic equation
t_prev = t
temp = (w1 + np.sqrt(sqterm)) / w0
if isnan(temp):
print "MinError(I): not converging. (2)"
t = t_prev
else:
t = int(floor(temp))
return t
# open image, convert to dB, and shift to positive values
DIR = 'C:/Data/Tewkesbury-LiDAR'
img = IO.read(DIR + '/subset.data/Sigma0_HH_slv1_25Jul2007.img')
img_db = 10 * np.log10(img)
img_db_int = np.round(img_db).astype(int)
img_db_int_pos = img_db_int + abs(np.min(img_db_int))
# calculate threshold
n = np.max(img_db_int_pos) # 255
threshold = min_error_thresholding(img_db_int_pos.ravel(), n)
print 'threshold:', threshold
#!/usr/bin/env python
from skimage.filters import threshold_otsu
from geotiff.io import IO
# load original image
img = IO.read('C:/Data/namibia-flipped.data/Sigma0_HH.img')
# otsu thresholding of the original image
threshold = threshold_otsu(img)
img_thresholded = img > threshold
IO.write(img_thresholded, 'C:/Data/namibia-flipped-thresholded.tif')
print 'Threshold for original image:', threshold
