def get_label(img,
radius=20,
selemSeg=npy.empty([1]),
seg=False,
max_size_th=1000,
min_size_th=0,
p0=0.7,
sigma=None,
iter=2,
signal=None):
'''
Return the labeled image calculated from a radius disk
:param img: input image
:param radius: radius of the object to label
:param seg: True if the segmented image is return
:param max_size_th: Threshold on the maximum size
:param p0: percentile for the segmentation
:return: Labeled image
'''
#radius of the object to segment
if npy.size(selemSeg) == 1:
selemSeg = disk(radius)
if sigma:
img = filters.gaussian_filter(img, sigma)
signal.emit({
"msg": 'Segmentation:enhance constrast',
"value": 0,
"max": 100
})
img = rank.enhance_contrast_percentile(img, disk(3), p0=0.5, p1=1)
signal.emit({"msg": 'Segmentation:threshold', "value": 16})
Seg = rank.threshold_percentile(img, selemSeg, p0=p0)
signal.emit({"msg": 'Segmentation:morphology', "value": 2 * 16})
Seg = morphology.binary_fill_holes(Seg)
Seg = morphology.binary_erosion(Seg, iterations=iter)
Seg = morphology.binary_dilation(Seg, iterations=iter)
Seg = morphology.binary_fill_holes(Seg)
signal.emit({"msg": 'Segmentation:label', "value": 3 * 16})
labels, nr_objects = mh.label(Seg)
#too big
signal.emit({"msg": 'Segmentation:delete big', "value": 4 * 16})
sizes = mh.labeled.labeled_size(labels)
too_big = npy.where(sizes > max_size_th)
labels = mh.labeled.remove_regions(labels, too_big)
#too small
signal.emit({"msg": 'Segmentation:delete small', "value": 5 * 16})
sizes = mh.labeled.labeled_size(labels)
too_small = npy.where(sizes < min_size_th)
labels = mh.labeled.remove_regions(labels, too_small)
signal.emit({"msg": 'Segmentation finish', "value": 100})
if seg:
return labels, Seg
return labels
def check_all():
selem = morphology.disk(1)
refs = np.load(
os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"], rank.autolevel(self.image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(self.image, selem))
assert_equal(refs["bottomhat"], rank.bottomhat(self.image, selem))
assert_equal(refs["equalize"], rank.equalize(self.image, selem))
assert_equal(refs["gradient"], rank.gradient(self.image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(self.image, selem))
assert_equal(refs["maximum"], rank.maximum(self.image, selem))
assert_equal(refs["mean"], rank.mean(self.image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(self.image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(self.image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(self.image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(self.image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(self.image, selem))
assert_equal(refs["median"], rank.median(self.image, selem))
assert_equal(refs["minimum"], rank.minimum(self.image, selem))
assert_equal(refs["modal"], rank.modal(self.image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(self.image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(self.image, selem))
assert_equal(refs["pop"], rank.pop(self.image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(self.image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(self.image, selem))
assert_equal(refs["sum"], rank.sum(self.image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(self.image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(self.image, selem))
assert_equal(refs["threshold"], rank.threshold(self.image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(self.image, selem))
assert_equal(refs["tophat"], rank.tophat(self.image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(self.image, selem))
assert_equal(refs["entropy"], rank.entropy(self.image, selem))
assert_equal(refs["otsu"], rank.otsu(self.image, selem))
assert_equal(refs["percentile"],
rank.percentile(self.image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(self.image, selem))
def check_all():
np.random.seed(0)
image = np.random.rand(25, 25)
selem = morphology.disk(1)
refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"], rank.autolevel(image, selem))
assert_equal(refs["autolevel_percentile"], rank.autolevel_percentile(image, selem))
assert_equal(refs["bottomhat"], rank.bottomhat(image, selem))
assert_equal(refs["equalize"], rank.equalize(image, selem))
assert_equal(refs["gradient"], rank.gradient(image, selem))
assert_equal(refs["gradient_percentile"], rank.gradient_percentile(image, selem))
assert_equal(refs["maximum"], rank.maximum(image, selem))
assert_equal(refs["mean"], rank.mean(image, selem))
assert_equal(refs["mean_percentile"], rank.mean_percentile(image, selem))
assert_equal(refs["mean_bilateral"], rank.mean_bilateral(image, selem))
assert_equal(refs["subtract_mean"], rank.subtract_mean(image, selem))
assert_equal(refs["subtract_mean_percentile"], rank.subtract_mean_percentile(image, selem))
assert_equal(refs["median"], rank.median(image, selem))
assert_equal(refs["minimum"], rank.minimum(image, selem))
assert_equal(refs["modal"], rank.modal(image, selem))
assert_equal(refs["enhance_contrast"], rank.enhance_contrast(image, selem))
assert_equal(refs["enhance_contrast_percentile"], rank.enhance_contrast_percentile(image, selem))
assert_equal(refs["pop"], rank.pop(image, selem))
assert_equal(refs["pop_percentile"], rank.pop_percentile(image, selem))
assert_equal(refs["pop_bilateral"], rank.pop_bilateral(image, selem))
assert_equal(refs["sum"], rank.sum(image, selem))
assert_equal(refs["sum_bilateral"], rank.sum_bilateral(image, selem))
assert_equal(refs["sum_percentile"], rank.sum_percentile(image, selem))
assert_equal(refs["threshold"], rank.threshold(image, selem))
assert_equal(refs["threshold_percentile"], rank.threshold_percentile(image, selem))
assert_equal(refs["tophat"], rank.tophat(image, selem))
assert_equal(refs["noise_filter"], rank.noise_filter(image, selem))
assert_equal(refs["entropy"], rank.entropy(image, selem))
assert_equal(refs["otsu"], rank.otsu(image, selem))
assert_equal(refs["percentile"], rank.percentile(image, selem))
assert_equal(refs["windowed_histogram"], rank.windowed_histogram(image, selem))
def processAlgorithm(self, parameters, context, feedback):
try:
import networkx as nx
from osgeo import gdal as osgdal
from skimage.io import imread
from skimage.color import rgb2gray
from skimage.filters import threshold_local, threshold_otsu
from skimage.util import invert
from skimage.morphology import disk
from skimage.filters.rank import modal, threshold_percentile, otsu
from skimage.util import img_as_ubyte
except Exception as e:
feedback.reportError(
QCoreApplication.translate('Error', '%s' % (e)))
feedback.reportError(QCoreApplication.translate('Error', ' '))
feedback.reportError(
QCoreApplication.translate(
'Error',
'Error loading modules - please install the necessary dependencies'
))
return {}
rlayer = self.parameterAsRasterLayer(parameters, self.Raster, context)
inv = parameters[self.inv]
block_size = parameters[self.blocks]
adaptMethod = parameters[self.adaptMethod]
mode = parameters[self.blur]
method = parameters[self.Method]
p = parameters[self.percent]
aMethod = {0: "gaussian", 1: "mean", 2: "median"}
outputRaster = self.parameterAsOutputLayer(parameters, self.outRaster,
context)
fname = ''.join(
random.choice(string.ascii_lowercase) for i in range(10))
outFname = os.path.join(tempfile.gettempdir(), '%s.tif' % (fname))
rect = rlayer.extent()
dp = rlayer.dataProvider()
raster = dp.dataSourceUri()
img = imread(raster)
if dp.bandCount() == 1:
grayscale = img
else:
try:
grayscale = rgb2gray(img)
except Exception as e:
feedback.reportError(
QCoreApplication.translate('Error', str(e)))
feedback.reportError(QCoreApplication.translate('Error', ' '))
feedback.reportError(
QCoreApplication.translate(
'Error',
'Failed to convert image from RGB to grayscale'))
return {}
if inv:
grayscale = invert(grayscale)
if block_size > 0 and block_size % 2 == 0:
block_size += 1
feedback.reportError(
QCoreApplication.translate(
'Info',
'Warning: Algorithm requires an odd value for the block size parameter - selecting a value of %s'
% (block_size)))
nrows, ncols = grayscale.shape
if method == 0:
if block_size > 0:
grayscale = img_as_ubyte(grayscale)
thresh = otsu(grayscale, disk(block_size))
binary = (grayscale < thresh).astype(float)
else:
thresh = threshold_otsu(grayscale)
binary = (grayscale < thresh).astype(float)
else:
if block_size == 0.0:
block_size = int((nrows * 0.01) * (ncols * 0.01))
if block_size % 2 == 0:
block_size += 1
feedback.pushInfo(
QCoreApplication.translate(
'Info', 'Automatically selecting a block size of %s' %
(block_size)))
if method == 1:
local_thresh = threshold_local(image=grayscale,
block_size=block_size,
method=aMethod[adaptMethod])
binary = (grayscale < local_thresh).astype(float)
else:
thresh = threshold_percentile(grayscale,
disk(block_size),
p0=p)
binary = (grayscale > thresh).astype(float)
if mode > 0:
binary = modal(binary, disk(mode))
binary = (binary > 1).astype(float)
xres = rlayer.rasterUnitsPerPixelX()
yres = rlayer.rasterUnitsPerPixelY()
driver = osgdal.GetDriverByName('GTiff')
dataset = driver.Create(
outputRaster,
ncols,
nrows,
1,
osgdal.GDT_Float32,
)
dataset.SetGeoTransform(
(rect.xMinimum(), xres, 0, rect.yMaximum(), 0, -yres))
wkt_prj = rlayer.crs().toWkt()
dataset.SetProjection(wkt_prj)
band = dataset.GetRasterBand(1)
band.SetNoDataValue(0)
band.WriteArray(binary)
dataset, band = None, None
return {self.outRaster: outputRaster}
