def ModalValue(image):
"""look for the modal value of an image"""
# print image.dtype
if image.dtype == "uint8":
depthmax = 255
print "8bits"
if image.dtype == "uint16":
depthmax = 65535
print "16bits"
histo = mahotas.fullhistogram(image)
countmax = histo.max()
print "countmax:", countmax
print "image max", image.max()
mig = image.min() # image min graylevel
mag = image.max() # image max gray level
mode = 0
countmax = 0 # occurence of a given grayscale
print "mig=", mig, " mag=", mag
for i in range(mig, mag - 1, 1):
test = histo[i] > countmax
# print "test:",test,"histo(",i,")=", histo[i],"max",countmax
if test:
countmax = histo[i]
mode = i
# print "mode",mode
return mode
def ModalValue(image):
'''look for the modal value of an image'''
#print image.dtype
if image.dtype=="uint8":
depthmax=255
print "8bits"
if image.dtype=="uint16":
depthmax=65535
print "16bits"
histo=mahotas.fullhistogram(image)
countmax=histo.max()
print "countmax:",countmax
print "image max",image.max()
mig=image.min()#image min graylevel
mag=image.max()#image max gray level
mode=0
countmax=0#occurence of a given grayscale
print "mig=",mig," mag=",mag
for i in range(mig,mag-1,1):
test=histo[i]>countmax
#print "test:",test,"histo(",i,")=", histo[i],"max",countmax
if test:
countmax=histo[i]
mode=i
#print "mode",mode
return mode
def perimeter(bwimage, n=4, mode="constant"):
"""
p = perimeter(bwimage, n=4, mode="constant")
Calculate total perimeter of all objects in binary image.
Parameters
----------
bwimage : array
binary image
n : int, optional
passed to ``bwperim`` as is
mode : str, optional
passed to ``bwperim`` as is
Returns
-------
p : float
total perimeter of all objects in binary image
See Also
--------
bwperim : function
Finds the perimeter region
References
----------
.. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of
a Perimeter Estimator. The Queen's University of Belfast.
http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
"""
global _perimeter_values
perim = bwperim(bwimage, n, mode)
perim = perim.astype(np.uint8)
histogram = mh.fullhistogram(
mh.convolve(perim, _perimeter_magic))
if _perimeter_values is None:
_perimeter_values = np.zeros(34, float)
_perimeter_values[[5, 7, 15, 17, 25, 27]] = 1
_perimeter_values[[21, 33]] = np.sqrt(2)
_perimeter_values[[13, 23]] = (1 + np.sqrt(2)) / 2
size = min(34, len(histogram))
return np.dot(histogram[:size], _perimeter_values[:size])
def perimeter(bwimage, n=4, mode="constant"):
"""
p = perimeter(bwimage, n=4, mode="constant")
Calculate total perimeter of all objects in binary image.
Parameters
----------
bwimage : array
binary image
n : int, optional
passed to ``bwperim`` as is
mode : str, optional
passed to ``bwperim`` as is
Returns
-------
p : float
total perimeter of all objects in binary image
See Also
--------
bwperim : function
Finds the perimeter region
References
----------
.. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of
a Perimeter Estimator. The Queen's University of Belfast.
http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
"""
global _perimeter_values
perim = bwperim(bwimage, n, mode)
perim = perim.astype(np.uint8)
histogram = mh.fullhistogram(
mh.convolve(perim, _perimeter_magic))
if _perimeter_values is None:
_perimeter_values = np.zeros(34, float)
_perimeter_values[[5, 7, 15, 17, 25, 27]] = 1
_perimeter_values[[21, 33]] = np.sqrt(2)
_perimeter_values[[13, 23]] = (1 + np.sqrt(2)) / 2
size = min(34, len(histogram))
return np.dot(histogram[:size], _perimeter_values[:size])
def get_histogram(array):
'''
'''
return mh.fullhistogram(array)
def get_histogram(array):
'''
'''
return mh.fullhistogram(array.astype(np.uint64))
