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add histogram_increment and decrement to core16

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This commit is contained in:
Olivier Debeir
2012-10-16 12:36:05 +02:00
parent 8f70a1d01c
commit b6b202ae7b
3 changed files with 81 additions and 30 deletions
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skimage/rank/_core16.pyx
+24 -27
View File
@@ -22,6 +22,14 @@ from libc.stdlib cimport malloc, free
cdef inline int int_max(int a, int b): return a if a >= b else b
cdef inline int int_min(int a, int b): return a if a <= b else b
cdef inline void histogram_increment(Py_ssize_t* histo,float *pop,np.uint16_t value):
histo[value] += 1
pop[0] += 1.
cdef inline void histogram_decrement(Py_ssize_t* histo,float *pop,np.uint16_t value):
histo[value] -= 1
pop[0] -= 1.
cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask):
""" returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and
inside the given mask
@@ -79,6 +87,9 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
else:
mask = np.ascontiguousarray(mask)
if image is out:
raise NotImplementedError("Cannot perform rank operation in place.")
if out is None:
out = np.zeros((rows, cols), dtype=np.uint16)
else:
@@ -165,9 +176,7 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
cc = c - centre_c
if selem[r, c]:
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] += 1
pop += 1.
histogram_increment(histo,&pop,image_data[rr * cols + cc])
r = 0
c = 0
@@ -185,16 +194,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
rr = r + se_e_r[s]
cc = c + se_e_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] += 1
pop += 1.
histogram_increment(histo,&pop,image_data[rr * cols + cc])
for s in range(num_se_w):
rr = r + se_w_r[s]
cc = c + se_w_c[s] - 1
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] -= 1
pop -= 1.
histogram_decrement(histo,&pop,image_data[rr * cols + cc])
# kernel -------------------------------------------
out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ],
@@ -210,16 +216,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
rr = r + se_s_r[s]
cc = c + se_s_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] += 1
pop += 1.
histogram_increment(histo,&pop,image_data[rr * cols + cc])
for s in range(num_se_n):
rr = r + se_n_r[s] - 1
cc = c + se_n_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] -= 1
pop -= 1.
histogram_decrement(histo,&pop,image_data[rr * cols + cc])
# kernel -------------------------------------------
out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c],
@@ -232,16 +235,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
rr = r + se_w_r[s]
cc = c + se_w_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] += 1
pop += 1.
histogram_increment(histo,&pop,image_data[rr * cols + cc])
for s in range(num_se_e):
rr = r + se_e_r[s]
cc = c + se_e_c[s] + 1
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] -= 1
pop -= 1.
histogram_decrement(histo,&pop,image_data[rr * cols + cc])
# kernel -------------------------------------------
out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ],
@@ -257,16 +257,13 @@ cdef inline _core16(np.uint16_t kernel(Py_ssize_t*, float, np.uint16_t,Py_ssize_
rr = r + se_s_r[s]
cc = c + se_s_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] += 1
pop += 1.
histogram_increment(histo,&pop,image_data[rr * cols + cc])
for s in range(num_se_n):
rr = r + se_n_r[s] - 1
cc = c + se_n_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
value = image_data[rr * cols + cc]
histo[value] -= 1
pop -= 1.
histogram_decrement(histo,&pop,image_data[rr * cols + cc])
# kernel -------------------------------------------
out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c ],
skimage/rank/_core8.pyx
+4 -1
View File
@@ -76,6 +76,9 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa
else:
mask = np.ascontiguousarray(mask)
if image is out:
raise NotImplementedError("Cannot perform rank operation in place.")
if out is None:
out = np.zeros((rows, cols), dtype=np.uint8)
else:
@@ -183,7 +186,7 @@ cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t, float, floa
cc = c + se_e_c[s]
if is_in_mask(rows,cols,rr,cc,mask_data):
histogram_increment(histo,&pop,image_data[rr * cols + cc])
for s in range(num_se_w):
rr = r + se_w_r[s]
cc = c + se_w_c[s] - 1
skimage/rank/tests/test_suite.py
+53 -2
View File
@@ -16,6 +16,7 @@ class TestSequenceFunctions(unittest.TestCase):
def test_random_sizes(self):
# make sure the size is not a problem
niter = 10
elem = np.asarray([[1,1,1],[1,1,1],[1,1,1]],dtype='uint8')
for m,n in np.random.random_integers(1,100,size=(10,2)):
@@ -39,8 +40,9 @@ class TestSequenceFunctions(unittest.TestCase):
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=+1,shift_y=+1,p0=.1,p1=.9)
self.assertTrue(a16.shape == r.shape)
def test_compare_with_cmorph(self):
def test_compare_with_cmorph_dilate(self):
#compare the result of maximum filter with dilate
a = (np.random.random((500,500))*256).astype('uint8')
for r in range(1,20,1):
@@ -50,7 +52,21 @@ class TestSequenceFunctions(unittest.TestCase):
cm = cmorph.dilate(image=a,selem = elem)
self.assertTrue((rc==cm).all())
def test_compare_with_cmorph_erode(self):
#compare the result of maximum filter with erode
a = (np.random.random((500,500))*256).astype('uint8')
for r in range(1,20,1):
elem = np.ones((r,r),dtype='uint8')
# elem = (np.random.random((r,r))>.5).astype('uint8')
rc = _crank8.minimum(image=a,selem = elem)
cm = cmorph.erode(image=a,selem = elem)
self.assertTrue((rc==cm).all())
def test_bitdepth(self):
# test the different bit depth for rank16
elem = np.ones((3,3),dtype='uint8')
a16 = np.ones((100,100),dtype='uint16')*255
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=8)
@@ -64,6 +80,8 @@ class TestSequenceFunctions(unittest.TestCase):
r = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=12)
def test_population(self):
# check the number of valid pixels in the neighborhood
a = np.zeros((5,5),dtype='uint8')
elem = np.ones((3,3),dtype='uint8')
p = _crank8.pop(image=a,selem = elem)
@@ -75,6 +93,8 @@ class TestSequenceFunctions(unittest.TestCase):
np.testing.assert_array_equal(r,p)
def test_structuring_element(self):
# check the output for a custom structuring element
a = np.zeros((6,6),dtype='uint8')
a[2,2] = 255
elem = np.asarray([[1,1,0],[1,1,1],[0,0,1]],dtype='uint8')
@@ -91,11 +111,37 @@ class TestSequenceFunctions(unittest.TestCase):
@unittest.expectedFailure
def test_fail_on_bitdepth(self):
# should fail because data bitdepth is too high for the function
a16 = np.ones((100,100),dtype='uint16')*255
elem = np.ones((3,3),dtype='uint8')
f = _crank16_percentiles.mean(image=a16,selem = elem,shift_x=0,shift_y=0,p0=.1,p1=.9,bitdepth=4)
def test_output(self):
#check rank function with external OUT output array
selem = disk(20)
a = (np.random.random((500,500))*256).astype('uint8')
out = np.zeros_like(a)
f1 = rank.mean(a,selem,out=out)
f2 = rank.mean(a,selem)
np.testing.assert_array_equal(f1,f2)
np.testing.assert_array_equal(out,f2)
@unittest.expectedFailure
def test_inplace_output(self):
#rank filters are not supposed to filter inplace
selem = disk(20)
a = (np.random.random((500,500))*256).astype('uint8')
out = a
f = rank.mean(a,selem,out=out)
np.testing.assert_array_equal(f,out)
def test_compare_autolevels(self):
# compare autolevel and percentile autolevel with p0=0.0 and p1=1.0
# should returns the same arrays
image = data.camera()
selem = disk(20)
@@ -105,6 +151,9 @@ class TestSequenceFunctions(unittest.TestCase):
assert (loc_autolevel==loc_perc_autolevel).all()
def test_compare_autolevels_16bit(self):
# compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and p1=1.0
# should returns the same arrays
image = data.camera().astype(np.uint16)*4
selem = disk(20)
@@ -114,7 +163,9 @@ class TestSequenceFunctions(unittest.TestCase):
assert (loc_autolevel==loc_perc_autolevel).all()
def test_compare_8bit_vs_16bit(self):
# filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic) should be identical
# filters applied on 8bit image ore 16bit image (having only real 8bit of dynamic)
# should be identical
i8 = data.camera()
i16 = i8.astype(np.uint16)
assert (i8==i16).all()