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
import numpy as np
from sklearn import mixture
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')
# gaussian mixture model
# fit gaussian mixture model to the pixel intensities observation
X = img.reshape((-1, 1))
g = mixture.GMM(n_components=2)
g.fit(X)
print '# of Observations:', X.shape
print 'Gaussian Weights:', g.weights_
print 'Gaussian Means:', g.means_
# predict classes of pixel intensities from normal gmm
img_clustered1 = g.predict(X)
img_clustered1.shape = img.shape
img_clustered1 = img_clustered1.astype(np.float)
IO.write(img_clustered1, DIR + '/gmm.tif')
# predict classes of pixel intensities by thresholding the gmm probabilities
img_clustered2 = g.predict_proba(X)
img_clustered2 = np.array(map(lambda x: False if x[1] > 0.1 else True,
img_clustered2))
img_clustered2.shape = img.shape
IO.write(img_clustered2, DIR + '/gmm-empirical.tif')
#!/usr/bin/env python import numpy as np from sklearn.cluster import estimate_bandwidth, MeanShift from skimage.filters import threshold_otsu from geotiff.io import IO # initialize driver DIR = 'C:/Data/Tewkesbury-LiDAR' img = IO.read(DIR + '/stack-lidar.data/Sigma0_HH_slv1_25Jul2007.img') # mean shift clustering of the image # cluster pixel intensities using k-means X = img.reshape((-1, 1)) bandwidth = estimate_bandwidth(X, quantile=.2, n_samples=500) mean_shift = MeanShift(bandwidth, bin_seeding=True) mean_shift.fit(X) # extract means of each cluster & clustered intensities population clusters_means = mean_shift.cluster_centers_.squeeze() X_clustered = mean_shift.labels_ print '# of Observations:', X.shape print 'Means:', clusters_means print 'Classified:', X_clustered # get clustered image from clustered intensities img_clustered = np.choose(X_clustered, clusters_means) img_clustered.shape = img.shape IO.write(img_clustered.astype(np.uint8), DIR + '/mean-shift.tif') # otsu thresholding of the binary image obtained threshold = threshold_otsu(img_clustered)
#!/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()
#!/usr/bin/env python import numpy as np from sklearn import mixture 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') # gaussian mixture model # fit gaussian mixture model to the pixel intensities observation X = img.reshape((-1, 1)) g = mixture.GMM(n_components=2) g.fit(X) print '# of Observations:', X.shape print 'Gaussian Weights:', g.weights_ print 'Gaussian Means:', g.means_ # predict classes of pixel intensities from normal gmm img_clustered = g.predict(X) img_clustered.shape = img.shape img_clustered = img_clustered.astype(np.float) # save clustered image IO.write(img_clustered, DIR + '/gmm.tif') # image size img_gmm = np.empty_like(img_clustered) img_gmm[:] = img_clustered l = img_clustered.shape[1] h = img_clustered.shape[0]
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 import numpy as np from skimage.filters import threshold_otsu from sklearn import cluster 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') # k-means clustering of the image (population of pixels intensities) # cluster pixel intensities using k-means X = img.reshape((-1, 1)) k_means = cluster.KMeans(n_clusters=2) k_means.fit(X) # extract means of each cluster & clustered intensities population clusters_means = k_means.cluster_centers_.squeeze() X_clustered = k_means.labels_ print '# of Observations:', X.shape print 'Clusters Means:', clusters_means # get clustered image from clustered intensities img_clustered = np.choose(X_clustered, (0, 1)) img_clustered.shape = img.shape IO.write(img_clustered, DIR + '/kmeans.tif') # otsu thresholding of the binary image obtained threshold = threshold_otsu(img_clustered) img_thresholded = img_clustered > threshold IO.write(img_thresholded, DIR + '/kmeans-thresholding.tif')
#!/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
#!/usr/bin/env python import numpy as np from skimage.feature import local_binary_pattern from sklearn import cluster from geotiff.io import IO # read image DIR = 'C:/Data/Tewkesbury-LiDAR' img = IO.read(DIR + '/stack-lidar.data/Sigma0_HH_slv1_25Jul2007.img') # extract texture using local binary pattern img_texture = local_binary_pattern(img, P=16, R=16, method='ror') IO.write(img_texture, DIR + '/texture.tif') # cluster texture using k-means X = img_texture.reshape((-1, 1)) k_means = cluster.KMeans(n_clusters=2) k_means.fit(X) # clustered intensities population X_clustered = k_means.labels_ # get clustered image from clustered intensities img_clustered = np.choose(X_clustered, [255, 0]) img_clustered.shape = img.shape IO.write(np.invert(img_clustered), DIR + '/texture-clustered.tif')