def setup_peak_search_algorithm(self, algorithm, mask=None):
# init the peak search algorithm
if algorithm == 'Massif':
self.peak_search_algorithm = Massif(self.img_data.img_data)
elif algorithm == 'Blob':
if mask is not None:
self.peak_search_algorithm = BlobDetection(
self.img_data.img_data * mask)
else:
self.peak_search_algorithm = BlobDetection(
self.img_data.img_data)
self.peak_search_algorithm.process()
else:
return
def test_local_max(self):
bd = BlobDetection(self.img)
bd._one_octave(shrink=False, refine=False, n_5=False)
self.assert_(numpy.alltrue(_blob.local_max(bd.dogs, bd.cur_mask, False) == \
local_max(bd.dogs, bd.cur_mask, False)), "max test, 3x3x3")
self.assert_(numpy.alltrue(_blob.local_max(bd.dogs, bd.cur_mask, True) == \
local_max(bd.dogs, bd.cur_mask, True)), "max test, 3x5x5")
def setup_peak_search_algorithm(self, algorithm, mask=None):
"""
Initializes the peak search algorithm on the current image
:param algorithm:
peak search algorithm used. Possible algorithms are 'Massif' and 'Blob'
:param mask:
if a mask is used during the process this is provided here as a 2d array for the image.
"""
if algorithm == 'Massif':
self.peak_search_algorithm = Massif(self.img_model.raw_img_data)
elif algorithm == 'Blob':
if mask is not None:
self.peak_search_algorithm = BlobDetection(self.img_model.raw_img_data * mask)
else:
self.peak_search_algorithm = BlobDetection(self.img_model.raw_img_data)
self.peak_search_algorithm.process()
else:
return
__author__ = 'Clemens Prescher'
from pyFAI.blob_detection import BlobDetection
from Data.ImgData import ImgData
import numpy as np
import pylab
img_data = ImgData()
# img_data.load('/Users/Doomgoroth/Programming/Large Projects/Dioptas/Testing/pyFAITest/17_LaB6_dc300-00000.tif')
img_data.load(
'/Users/Doomgoroth/Programming/Large Projects/Dioptas/Testing/pyFAITest/LaB6_WOS_30keV_005.tif'
)
bd = BlobDetection(np.log1p(img_data.get_img_data()))
bd.process()
x = []
y = []
int = []
sigma = []
print bd.keypoints.__len__()
for j in range(bd.keypoints.__len__()):
k = bd.keypoints[j]
int.append(k[2])
sigma.append(k[3])
if sigma[-1] > 0.25:
x.append(k[0])
y.append(k[1])
pylab.hist(int)
reshape=False)
return im
if len(UtilsTest.options.args) > 0:
data = fabio.open(UtilsTest.options.args[0]).data
if len(UtilsTest.options.args) > 1:
msk = fabio.open(UtilsTest.options.args[1]).data
else:
msk = None
else:
data = image_test_rings()
msk = None
bd = BlobDetection(
data, mask=msk
) # , cur_sigma=0.25, init_sigma=numpy.sqrt(2)/2, dest_sigma=numpy.sqrt(2), scale_per_octave=2)
pylab.ion()
f = pylab.figure(1)
ax = f.add_subplot(111)
ax.imshow(numpy.log1p(data), interpolation='nearest')
for i in range(5):
print('Octave #%i' % i)
bd._one_octave(shrink=True, refine=True, n_5=False)
print("Octave #%i Total kp: %i" % (i, bd.keypoints.size))
# bd._one_octave(False, True ,False)
print('Final size of keypoints : %i' % bd.keypoints.size)
def search_peaks_on_ring(self,
peak_index,
delta_tth=0.1,
algorithm='Massif',
min_mean_factor=1,
upper_limit=55000):
if not self.is_calibrated:
return
#transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropiate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
tth_calibrant = np.float(tth_calibrant_list[peak_index])
print tth_calibrant
# get the calculated two theta values for the whole image
if self.geometry._ttha is None:
tth_array = self.geometry.twoThetaArray(
self.img_data.img_data.shape)
else:
tth_array = self.geometry._ttha
# create mask based on two_theta position
mask = abs(tth_array - tth_calibrant) <= delta_tth
# init the peak search algorithm
if algorithm == 'Massif':
peak_search_algorithm = Massif(self.img_data.img_data)
elif algorithm == 'Blob':
peak_search_algorithm = BlobDetection(self.img_data.img_data *
mask)
peak_search_algorithm.process()
else:
return
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_data.img_data.ravel()[np.where(
mask.ravel())],
dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_data.img_data > threshold, mask)
mask2[np.where(self.img_data.img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
res = peak_search_algorithm.peaks_from_area(mask2,
Imin=mean - std,
keep=keep)
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(peak_index)
