Python scipy.ndimage.correlate() Examples
The following are 30
code examples of scipy.ndimage.correlate().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
scipy.ndimage
, or try the search function
.
.
Example #1
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate03(self):
array = numpy.array([1])
weights = numpy.array([1, 1])
expected = [2]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #2
| Source File: full_ref.py From sewar with MIT License | 6 votes |
|
def _scc_single(GT,P,win,ws): def _scc_filter(inp, axis, output, mode, cval): return correlate(inp, win , output, mode, cval, 0) GT_hp = generic_laplace(GT.astype(np.float64), _scc_filter) P_hp = generic_laplace(P.astype(np.float64), _scc_filter) win = fspecial(Filter.UNIFORM,ws) sigmaGT_sq,sigmaP_sq,sigmaGT_P = _get_sigmas(GT_hp,P_hp,win) sigmaGT_sq[sigmaGT_sq<0] = 0 sigmaP_sq[sigmaP_sq<0] = 0 den = np.sqrt(sigmaGT_sq) * np.sqrt(sigmaP_sq) idx = (den==0) den = _replace_value(den,0,1) scc = sigmaGT_P / den scc[idx] = 0 return scc
Example #3
| Source File: saliency_detection.py From aitom with GNU General Public License v3.0 | 6 votes |
|
def gabor_feature_single_job(a, filters, fm_i, label, cluster_center_number, save_flag):
# convolution
start_time = time.time()
# b=SN.correlate(a,filters[i]) # too slow
b = signal.correlate(a, filters[fm_i], mode='same')
end_time = time.time()
print('feature %d done (%f s)' % (fm_i, end_time - start_time))
# show Gabor filter output
if save_flag:
img = (b[:, :, int(a.shape[2] / 2)]).copy()
plt.imsave('./result/gabor_output(%d).png' % fm_i, img, cmap='gray') # save fig
# generate feature vector
start_time = time.time()
result = generate_feature_vector(b=b, label=label, cluster_center_number=cluster_center_number)
end_time = time.time()
print('feature vector %d done (%f s)' % (fm_i, end_time - start_time))
return fm_i, result
Example #4
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_generic_filter01(self):
filter_ = numpy.array([[1.0, 2.0], [3.0, 4.0]])
footprint = numpy.array([[1, 0], [0, 1]])
cf = numpy.array([1., 4.])
def _filter_func(buffer, weights, total=1.0):
weights = cf / total
return (buffer * weights).sum()
for type_ in self.types:
a = numpy.arange(12, dtype=type_)
a.shape = (3, 4)
r1 = ndimage.correlate(a, filter_ * footprint)
if type_ in self.float_types:
r1 /= 5
else:
r1 //= 5
r2 = ndimage.generic_filter(
a, _filter_func, footprint=footprint, extra_arguments=(cf,),
extra_keywords={'total': cf.sum()})
assert_array_almost_equal(r1, r2)
Example #5
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate19(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
output = ndimage.correlate(array, kernel,
output=numpy.float32,
mode='nearest', origin=[-1, 0])
assert_array_almost_equal([[5, 6, 8], [8, 9, 11]], output)
assert_equal(output.dtype.type, numpy.float32)
output = ndimage.convolve(array, kernel,
output=numpy.float32,
mode='nearest', origin=[-1, 0])
assert_array_almost_equal([[3, 5, 6], [6, 8, 9]], output)
assert_equal(output.dtype.type, numpy.float32)
Example #6
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate14(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
for type2 in self.types:
output = numpy.zeros(array.shape, type2)
ndimage.correlate(array, kernel,
output=output)
assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
assert_equal(output.dtype.type, type2)
ndimage.convolve(array, kernel, output=output)
assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
assert_equal(output.dtype.type, type2)
Example #7
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_correlate01(self):
array = numpy.array([1, 2])
weights = numpy.array([2])
expected = [2, 4]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #8
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_generic_filter01(self):
filter_ = numpy.array([[1.0, 2.0], [3.0, 4.0]])
footprint = numpy.array([[1, 0], [0, 1]])
cf = numpy.array([1., 4.])
def _filter_func(buffer, weights, total=1.0):
weights = cf / total
return (buffer * weights).sum()
for type in self.types:
a = numpy.arange(12, dtype=type)
a.shape = (3,4)
r1 = ndimage.correlate(a, filter_ * footprint)
if type in self.float_types:
r1 /= 5
else:
r1 //= 5
r2 = ndimage.generic_filter(a, _filter_func,
footprint=footprint, extra_arguments=(cf,),
extra_keywords={'total': cf.sum()})
assert_array_almost_equal(r1, r2)
Example #9
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate19(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
output = ndimage.correlate(array, kernel,
output=numpy.float32,
mode='nearest', origin=[-1, 0])
assert_array_almost_equal([[5, 6, 8], [8, 9, 11]], output)
assert_equal(output.dtype.type, numpy.float32)
output = ndimage.convolve(array, kernel,
output=numpy.float32,
mode='nearest', origin=[-1, 0])
assert_array_almost_equal([[3, 5, 6], [6, 8, 9]], output)
assert_equal(output.dtype.type, numpy.float32)
Example #10
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate14(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
for type2 in self.types:
output = numpy.zeros(array.shape, type2)
ndimage.correlate(array, kernel,
output=output)
assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
assert_equal(output.dtype.type, type2)
ndimage.convolve(array, kernel, output=output)
assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
assert_equal(output.dtype.type, type2)
Example #11
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate13(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
for type2 in self.types:
output = ndimage.correlate(array, kernel,
output=type2)
assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
assert_equal(output.dtype.type, type2)
output = ndimage.convolve(array, kernel,
output=type2)
assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
assert_equal(output.dtype.type, type2)
Example #12
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate01(self):
array = numpy.array([1, 2])
weights = numpy.array([2])
expected = [2, 4]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #13
| Source File: test_ndimage.py From Computable with MIT License | 6 votes |
|
def test_correlate03(self):
array = numpy.array([1])
weights = numpy.array([1, 1])
expected = [2]
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, expected)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, expected)
Example #14
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate10(self):
array = [[]]
kernel = numpy.array([[1, 1]])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
Example #15
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate09(self):
array = []
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(array, output)
Example #16
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate11(self):
array = numpy.array([[1, 2, 3],
[4, 5, 6]])
kernel = numpy.array([[1, 1],
[1, 1]])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal([[4, 6, 10], [10, 12, 16]], output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal([[12, 16, 18], [18, 22, 24]], output)
Example #17
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate13(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
for type2 in self.types:
output = ndimage.correlate(array, kernel, output=type2)
assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
assert_equal(output.dtype.type, type2)
output = ndimage.convolve(array, kernel,
output=type2)
assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
assert_equal(output.dtype.type, type2)
Example #18
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate05(self):
array = numpy.array([1, 2, 3])
tcor = [2, 3, 5]
tcov = [3, 5, 6]
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(tcov, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(tcov, output)
Example #19
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate15(self):
kernel = numpy.array([[1, 0],
[0, 1]])
for type1 in self.types:
array = numpy.array([[1, 2, 3],
[4, 5, 6]], type1)
output = ndimage.correlate(array, kernel,
output=numpy.float32)
assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output)
assert_equal(output.dtype.type, numpy.float32)
output = ndimage.convolve(array, kernel,
output=numpy.float32)
assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output)
assert_equal(output.dtype.type, numpy.float32)
Example #20
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate16(self):
kernel = numpy.array([[0.5, 0],
[0, 0.5]])
for type1 in self.types:
array = numpy.array([[1, 2, 3], [4, 5, 6]], type1)
output = ndimage.correlate(array, kernel, output=numpy.float32)
assert_array_almost_equal([[1, 1.5, 2.5], [2.5, 3, 4]], output)
assert_equal(output.dtype.type, numpy.float32)
output = ndimage.convolve(array, kernel, output=numpy.float32)
assert_array_almost_equal([[3, 4, 4.5], [4.5, 5.5, 6]], output)
assert_equal(output.dtype.type, numpy.float32)
Example #21
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate17(self):
array = numpy.array([1, 2, 3])
tcor = [3, 5, 6]
tcov = [2, 3, 5]
kernel = numpy.array([1, 1])
output = ndimage.correlate(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
output = ndimage.correlate1d(array, kernel, origin=-1)
assert_array_almost_equal(tcor, output)
output = ndimage.convolve1d(array, kernel, origin=-1)
assert_array_almost_equal(tcov, output)
Example #22
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate04(self):
array = numpy.array([1, 2])
tcor = [2, 3]
tcov = [3, 4]
weights = numpy.array([1, 1])
output = ndimage.correlate(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve(array, weights)
assert_array_almost_equal(output, tcov)
output = ndimage.correlate1d(array, weights)
assert_array_almost_equal(output, tcor)
output = ndimage.convolve1d(array, weights)
assert_array_almost_equal(output, tcov)
Example #23
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_extend05(self):
array = numpy.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
weights = numpy.array([[1, 0], [0, 0]])
expected_values = [[[1, 1, 2], [1, 1, 2], [4, 4, 5]],
[[9, 7, 8], [3, 1, 2], [6, 4, 5]],
[[1, 1, 2], [1, 1, 2], [4, 4, 5]],
[[5, 4, 5], [2, 1, 2], [5, 4, 5]],
[[0, 0, 0], [0, 1, 2], [0, 4, 5]]]
for mode, expected_value in zip(self.modes, expected_values):
output = ndimage.correlate(array, weights,
mode=mode, cval=0)
assert_array_equal(output, expected_value)
Example #24
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_extend06(self):
array = numpy.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
weights = numpy.array([[0, 0, 0], [0, 0, 0], [0, 0, 1]])
expected_values = [[[5, 6, 6], [8, 9, 9], [8, 9, 9]],
[[5, 6, 4], [8, 9, 7], [2, 3, 1]],
[[5, 6, 6], [8, 9, 9], [8, 9, 9]],
[[5, 6, 5], [8, 9, 8], [5, 6, 5]],
[[5, 6, 0], [8, 9, 0], [0, 0, 0]]]
for mode, expected_value in zip(self.modes, expected_values):
output = ndimage.correlate(array, weights,
mode=mode, cval=0)
assert_array_equal(output, expected_value)
Example #25
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_extend07(self):
array = numpy.array([1, 2, 3])
weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1])
expected_values = [[3, 3, 3],
[2, 3, 1],
[2, 1, 1],
[1, 2, 3],
[0, 0, 0]]
for mode, expected_value in zip(self.modes, expected_values):
output = ndimage.correlate(array, weights, mode=mode, cval=0)
assert_array_equal(output, expected_value)
Example #26
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_extend09(self):
array = numpy.array([1, 2, 3])
weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1])
expected_values = [[3, 3, 3],
[2, 3, 1],
[2, 1, 1],
[1, 2, 3],
[0, 0, 0]]
for mode, expected_value in zip(self.modes, expected_values):
output = ndimage.correlate(array, weights,
mode=mode, cval=0)
assert_array_equal(output, expected_value)
Example #27
| Source File: test_ndimage.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_extend10(self):
array = numpy.array([[1], [2], [3]])
weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]])
expected_values = [[[3], [3], [3]],
[[2], [3], [1]],
[[2], [1], [1]],
[[1], [2], [3]],
[[0], [0], [0]]]
for mode, expected_value in zip(self.modes, expected_values):
output = ndimage.correlate(array, weights,
mode=mode, cval=0)
assert_array_equal(output, expected_value)
Example #28
| Source File: test_filters.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_correlate(self):
d = np.random.randn(500, 500)
k = np.random.randn(10, 10)
os = np.empty([4] + list(d.shape))
ot = np.empty_like(os)
self.check_func_serial(4, sndi.correlate, (d, k), os)
self.check_func_thread(4, sndi.correlate, (d, k), ot)
assert_array_equal(os, ot)
Example #29
| Source File: test_lin_ops.py From ProxImaL with MIT License | 5 votes |
|
def test_combo(self):
"""Test subsampling followed by convolution.
"""
# Forward.
var = Variable((2, 3))
kernel = np.array([[1, 2, 3]]) # 2x3
fn = vstack([conv(kernel, subsample(var, (2, 1)))])
fn = CompGraph(fn)
x = np.arange(6) * 1.0
x = np.reshape(x, (2, 3))
out = np.zeros(fn.output_size)
fn.forward(x.flatten(), out)
y = np.zeros((1, 3))
xsub = x[::2, ::1]
y = ndimage.convolve(xsub, kernel, mode='wrap')
self.assertItemsAlmostEqual(np.reshape(out, y.shape), y)
# Adjoint.
x = np.arange(3) * 1.0
x = np.reshape(x, (1, 3))
out = np.zeros(var.size)
fn.adjoint(x.flatten(), out)
y = ndimage.correlate(x, kernel, mode='wrap')
y2 = np.zeros((2, 3))
y2[::2, :] = y
self.assertItemsAlmostEqual(np.reshape(out, y2.shape), y2)
out = np.zeros(var.size)
fn.adjoint(x.flatten(), out)
self.assertItemsAlmostEqual(np.reshape(out, y2.shape), y2)
Example #30
| Source File: test_ndimage.py From Computable with MIT License | 5 votes |
|
def test_correlate02(self):
array = numpy.array([1, 2, 3])
kernel = numpy.array([1])
output = ndimage.correlate(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.correlate1d(array, kernel)
assert_array_almost_equal(array, output)
output = ndimage.convolve1d(array, kernel)
assert_array_almost_equal(array, output)
