def analyseData(dd):
#ss = dd.sum(0)
#dnp.plot.image(ss)
##ss is the flat field
print "Calculating median for the dataset ..."
med = dnp.median(dd, 0)
print "Median calculated"
if plotData: dnp.plot.image(med)
sleep(2)
#flatFieldCorrectedSum = ss/med
#dnp.plot.image(flatFieldCorrectedSum)
xVals = []
yVals = []
##
print "Calculating centroid ..."
centroids = []
for i in range(18):
#dnp.plot.image(dd[i, :, :] / med)
im = dd[i, :, :] / med
im = javaImage.medianFilter(im, [3, 3])
im = dnp.array(im)
threshold = 0.85
if plotData: dnp.plot.image(im)
if plotData: sleep(1)
if plotData: dnp.plot.image(im < threshold)
if plotData: sleep(1)
cent = dnp.centroid(im < threshold)
yVals.append(cent[0])
xVals.append(cent[1])
centroids.append(cent)
print "y:" + ` yVals `
print('')
print "x:" + ` xVals `
#help(dnp.fit.ellipsefit)
if plotData: dnp.plot.line(dnp.array(xVals), dnp.array(yVals))
ellipseFitValues = dnp.fit.ellipsefit(xVals, yVals)
print "major: " + ` ellipseFitValues[0] `
print "minor semi-axis: " + ` ellipseFitValues[1] `
print "major axis angle: " + ` ellipseFitValues[2] `
print "centre co-ord 1: " + ` ellipseFitValues[3] `
print "centre co-ord 2: " + ` ellipseFitValues[4] `
print "centroids:" + ` centroids `
xOffset = getXOffset(ellipseFitValues[1], ellipseFitValues[0])
zOffset = getZOffset(ellipseFitValues[2])
return xOffset, zOffset, centroids, ellipseFitValues
def threshold_global(image, threshold, down=True):
'''Applies a global threshold across the whole image. If 'down' is true, then pixels with values <= to 'threshold'
are set to 1 and the others set to 0. If 'down' is false, then pixels with values >= to 'threshold' are set to 1
and the others set to 0.'''
return _image.globalThreshold(image, threshold, down)
def threshold_global_mean(image, down=True):
'''Applies a global mean threshold across the whole image with the mean pixel intensity value as a threshold value'''
return _image.globalMeanThreshold(image, down)
def findshift(a, b, rect=None):
'''Find translation vector from b to a using rectangular ROI rect'''
return _image.findTranslation2D(a, b, rect._jroi())
def extract_blob(image, rule=8):
'''Given a binary image, connect together pixels to form blobs/clusters using the specified connectivity rule
(can be 4 or 8, set to 8 by default). The found blobs will be labeled in an output image and also described
as a set of contours.'''
return _image.extractBlob(image, rule)
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###
'''
Image processing package
'''
import org.eclipse.dawnsci.analysis.dataset.impl.Image as _image
import uk.ac.diamond.scisoft.analysis.dataset.function.MapToShiftedCartesian as _mapshift
from jycore import _wrap
#import ImageFilterServiceCreator and instanciate it
import org.dawnsci.boofcv.BoofCVImageFilterServiceCreator as _creator
_image.setImageFilterService(_creator.createFilterService())
@_wrap
def findshift(a, b, rect=None):
'''Find translation vector from b to a using rectangular ROI rect'''
return _image.findTranslation2D(a, b, rect._jroi())
@_wrap
def shiftimage(image, shift):
'''Translate an image by specified shift'''
sfn = _mapshift(shift[0], shift[1])
return sfn.value([image])[0]
def threshold_adaptive_gaussian(image, radius, down=True):
'''Thresholds the image using an adaptive threshold that is computed using a local square region centered on each
pixel. The threshold is equal to the gaussian weighted sum of the surrounding pixels plus the bias. If down is
true then b(x,y) = I(x,y) <= T(x,y) + bias ? 1 : 0. Otherwise b(x,y) = I(x,y) >= T(x,y) + bias ? 0 : 1'''
return _image.adaptiveGaussianThreshold(image, radius, down)
def threshold_adaptive_gaussian(image, radius, down=True):
'''Thresholds the image using an adaptive threshold that is computed using a local square region centered on each
pixel. The threshold is equal to the gaussian weighted sum of the surrounding pixels plus the bias. If down is
true then b(x,y) = I(x,y) <= T(x,y) + bias ? 1 : 0. Otherwise b(x,y) = I(x,y) >= T(x,y) + bias ? 0 : 1'''
return _image.adaptiveGaussianThreshold(image, radius, down)
def threshold_global_otsu(image, down=True):
'''Applies a global mean threshold across the whole image with the variance based threshold using Otsu's method'''
return _image.globalOtsuThreshold(image, down)
def threshold_global_entropy(image, down=True):
'''Applies a global mean threshold across the whole image with the threshold which maximizes the entropy between the
foreground and background regions.'''
return _image.globalEntropyThreshold(image, down)
def threshold_global(image, threshold, down=True):
'''Applies a global threshold across the whole image. If 'down' is true, then pixels with values <= to 'threshold'
are set to 1 and the others set to 0. If 'down' is false, then pixels with values >= to 'threshold' are set to 1
and the others set to 0.'''
return _image.globalThreshold(image, threshold, down)
def threshold_global_mean(image, down=True):
'''Applies a global mean threshold across the whole image with the mean pixel intensity value as a threshold value'''
return _image.globalMeanThreshold(image, down)
def findshift(a, b, rect=None):
'''Find translation vector from b to a using rectangular ROI rect'''
if rect is not None:
rect = rect._jroi()
return _image.findTranslation2D(a, b, rect)
def threshold_global_otsu(image, down=True):
'''Applies a global mean threshold across the whole image with the variance based threshold using Otsu's method'''
return _image.globalOtsuThreshold(image, down)
def threshold_adaptive_sauvola(image, radius, down=True):
'''Applies Sauvola thresholding to the input image. Intended for use with text image'''
return _image.adaptiveSauvolaThreshold(image, radius, down)
def threshold_global_entropy(image, down=True):
'''Applies a global mean threshold across the whole image with the threshold which maximizes the entropy between the
foreground and background regions.'''
return _image.globalEntropyThreshold(image, down)
def extract_blob(image, rule = 8):
'''Given a binary image, connect together pixels to form blobs/clusters using the specified connectivity rule
(can be 4 or 8, set to 8 by default). The found blobs will be labeled in an output image and also described
as a set of contours.'''
return _image.extractBlob(image, rule)
def threshold_adaptive_sauvola(image, radius, down=True):
'''Applies Sauvola thresholding to the input image. Intended for use with text image'''
return _image.adaptiveSauvolaThreshold(image, radius, down)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###
'''
Image processing package
'''
import org.eclipse.dawnsci.analysis.dataset.impl.Image as _image
import uk.ac.diamond.scisoft.analysis.dataset.function.MapToShiftedCartesian as _mapshift
from jycore import _wrap
#import ImageFilterServiceCreator and instanciate it
import org.dawnsci.boofcv.BoofCVImageFilterServiceCreator as _creator
_image.setImageFilterService(_creator.createFilterService())
@_wrap
def findshift(a, b, rect=None):
'''Find translation vector from b to a using rectangular ROI rect'''
return _image.findTranslation2D(a, b, rect._jroi())
@_wrap
def shiftimage(image, shift):
'''Translate an image by specified shift'''
sfn = _mapshift(shift[0], shift[1])
return sfn.value([image])[0]
import uk.ac.diamond.scisoft.analysis.dataset.function.BicubicInterpolator as _bicubic
