def labelcolormap(N=256):
def bitget(byteval, idx):
return ((byteval & (1 << idx)) != 0)
cmap = np.zeros((N, 3))
for i in range(0, N):
id = i
r, g, b = 0, 0, 0
for j in range(0, 8):
r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
id = (id >> 3)
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
cmap = cmap.astype(np.float32) / 255
return cmap
python类bitwise_or()的实例源码
def loadDepthMap(self,filename):
"""
Read a depth-map
:param filename: file name to load
:return: image data of depth image
"""
img = Image.open(filename)
# top 8 bits of depth are packed into green channel and lower 8 bits into blue
assert len(img.getbands()) == 3
r, g, b = img.split()
r = np.asarray(r, np.int32)
g = np.asarray(g, np.int32)
b = np.asarray(b, np.int32)
dpt = np.bitwise_or(np.left_shift(g, 8), b)
imgdata = np.asarray(dpt, np.float32)
return imgdata
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def label_colormap(N=256):
def bitget(byteval, idx):
return ((byteval & (1 << idx)) != 0)
cmap = np.zeros((N, 3))
for i in range(0, N):
id = i
r, g, b = 0, 0, 0
for j in range(0, 8):
r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
id = (id >> 3)
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
cmap = cmap.astype(np.float32) / 255
return cmap
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
test_ufunc.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
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def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def loadDepthMap(self, filename):
"""
Read a depth-map
:param filename: file name to load
:return: image data of depth image
"""
img = Image.open(filename)
# top 8 bits of depth are packed into green channel and lower 8 bits into blue
assert len(img.getbands()) == 3
r, g, b = img.split()
r = np.asarray(r, np.int32)
g = np.asarray(g, np.int32)
b = np.asarray(b, np.int32)
dpt = np.bitwise_or(np.left_shift(g, 8), b)
imgdata = np.asarray(dpt, np.float32)
return imgdata
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
utils.py 文件源码
项目：Semantic-Segmentation-using-Adversarial-Networks
作者: oyam
项目源码
文件源码
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def labelcolormap(N=256):
cmap = np.zeros((N, 3))
for i in range(0, N):
id = i
r, g, b = 0, 0, 0
for j in range(0, 8):
r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
id = (id >> 3)
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
cmap = cmap.astype(np.float32) / 255
return cmap
# -----------------------------------------------------------------------------
# Evaluation
# -----------------------------------------------------------------------------
def test_values(self):
for dt in self.bitwise_types:
zeros = np.array([0], dtype=dt)
ones = np.array([-1], dtype=dt)
msg = "dt = '%s'" % dt.char
assert_equal(np.bitwise_not(zeros), ones, err_msg=msg)
assert_equal(np.bitwise_not(ones), zeros, err_msg=msg)
assert_equal(np.bitwise_or(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_or(zeros, ones), ones, err_msg=msg)
assert_equal(np.bitwise_or(ones, zeros), ones, err_msg=msg)
assert_equal(np.bitwise_or(ones, ones), ones, err_msg=msg)
assert_equal(np.bitwise_xor(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_xor(zeros, ones), ones, err_msg=msg)
assert_equal(np.bitwise_xor(ones, zeros), ones, err_msg=msg)
assert_equal(np.bitwise_xor(ones, ones), zeros, err_msg=msg)
assert_equal(np.bitwise_and(zeros, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_and(zeros, ones), zeros, err_msg=msg)
assert_equal(np.bitwise_and(ones, zeros), zeros, err_msg=msg)
assert_equal(np.bitwise_and(ones, ones), ones, err_msg=msg)
def set_ufunc(self, scalar_op):
# This is probably a speed up of the implementation
if isinstance(scalar_op, theano.scalar.basic.Add):
self.ufunc = numpy.add
elif isinstance(scalar_op, theano.scalar.basic.Mul):
self.ufunc = numpy.multiply
elif isinstance(scalar_op, theano.scalar.basic.Maximum):
self.ufunc = numpy.maximum
elif isinstance(scalar_op, theano.scalar.basic.Minimum):
self.ufunc = numpy.minimum
elif isinstance(scalar_op, theano.scalar.basic.AND):
self.ufunc = numpy.bitwise_and
elif isinstance(scalar_op, theano.scalar.basic.OR):
self.ufunc = numpy.bitwise_or
elif isinstance(scalar_op, theano.scalar.basic.XOR):
self.ufunc = numpy.bitwise_xor
else:
self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
def __or__(self, other):
return bitwise_or(self, other)
def __ior__(self, other):
return bitwise_or(self, other, self)
def __ror__(self, other):
return bitwise_or(other, self)
def label_colormap(N=256):
cmap = np.zeros((N, 3))
for i in six.moves.range(0, N):
id = i
r, g, b = 0, 0, 0
for j in six.moves.range(0, 8):
r = np.bitwise_or(r, (bitget(id, 0) << 7 - j))
g = np.bitwise_or(g, (bitget(id, 1) << 7 - j))
b = np.bitwise_or(b, (bitget(id, 2) << 7 - j))
id = (id >> 3)
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
cmap = cmap.astype(np.float32) / 255
return cmap
def create_binary_wf_data(wf, sync_mkr=0, samp_mkr=0, vertical_resolution=12):
"""Given numpy arrays of waveform and marker data convert to binary format.
Assumes waveform data is np.float in range -1 to 1 and marker data can be cast to bool
Binary format is waveform in MSB and and markers in LSB
waveform sync_mkr samp_mkr
15 downto 4/2 1 0
"""
#cast the waveform to integers
if not((vertical_resolution == 12) or (vertical_resolution == 14)):
raise ValueError("vertical resolution must be 12 or 14 bits")
#convert waveform to integers
scale_factor = 2**(vertical_resolution-1)
bin_data = np.int16((scale_factor-1)*np.array(wf))
#clip if necessary
if np.max(bin_data) > scale_factor-1 or np.min(bin_data) < -scale_factor:
warnings.warn("Clipping waveform. Max value: {:d} Min value: {:d}. Scale factor: {:d}.".format(np.max(bin_data), np.min(bin_data),scale_factor))
bin_data = np.clip(bin_data, -scale_factor, scale_factor-1)
# bin_data = bin_data.byteswap()
#shift up to the MSB
bin_data = np.left_shift(bin_data, 4 if vertical_resolution == 12 else 2)
#add in the marker bits
bin_data = np.bitwise_or(bin_data, np.bitwise_or(np.left_shift(np.bitwise_and(sync_mkr, 0x1), 1), np.bitwise_and(samp_mkr, 0x1)))
return bin_data
def get_mask_overlap(mask1, mask2):
intersect = np.bitwise_and(mask1, mask2).sum()
union = np.bitwise_or(mask1, mask2).sum()
return 1.0 * intersect / union
def _find_DoG_extrema(self, DoG):
# TODO: sample ?
ext = []
for octave in DoG:
_, rows, cols = octave.shape
# assert octave.shape = (<layers>(=s+2), <rows>, <columns>)
##########################
# time ?
##########################
peeled = [octave[ind1, ind2, ind3] for ind1, ind2, ind3 in
product(*[[slice(1, -1), slice(2, None), slice(None, -2)]]*3)]
center_block = peeled[0] # octave[1:-1,1:-1,1:-1], the center part
neighbor_blocks = peeled[1:] # neighbors in 26 directions in 3-D DoG space with offset 1
is_extreme = \
np.bitwise_or(center_block > (np.max(neighbor_blocks, axis=0)),
center_block < (np.min(neighbor_blocks, axis=0)))
# assert is_extreme.shape = (s, rows-2, columns-2)
ext_coord = np.array(
list(product(*[range(1, i-1) for i in octave.shape]))).reshape([x-2 for x in octave.shape]+[-1])
assert ext_coord.shape[:-1] == is_extreme.shape
ext_coord = ext_coord[is_extreme].astype(np.float)
print("%d key point candidates found" % ext_coord.shape[0])
# assert ext_coord.shape = (<number of key points>, 3)
ext_coord /= [1, rows, cols] # convert row, col coord to relative
ext_coord[:, 0] = get_sigma_by_layer(ext_coord[:, 0])
ext.extend(list(ext_coord))
# filters
# for layer, row, col in ext_coord:
# nb = octave[layer-1:layer+2, row-1:row+2, col-1:col+2] # 3x3x3 neighbor
# x_hat, d_x_hat = self._fit_extremum(nb)
# if
# TODO: if x[i] > 0.5, do it recursivey
# print(x_hat, d_x_hat)
return ext
def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
def __ior__(self, other):
np.bitwise_or(self, other, out=self)
return self
def spread_bitsv(ival, level):
res = np.zeros_like(ival, dtype='int64')
for i in range(level):
ares = np.bitwise_and(ival, 1<<i) << (i*2)
np.bitwise_or(res, ares, res)
return res
def get_keyv(iarr, level):
i1, i2, i3 = (v.astype("int64") for v in iarr)
i1 = spread_bitsv(i1, level)
i2 = spread_bitsv(i2, level) << 1
i3 = spread_bitsv(i3, level) << 2
np.bitwise_or(i1, i2, i1)
np.bitwise_or(i1, i3, i1)
return i1
def pwdist_jaccard(self, seq1idx, seq2idx):
"""Compute the Jaccard-Needham dissimilarity
between two boolean 1-D arrays.
Returns:
distance value (double)
"""
u = self[seq1idx]
v = self[seq2idx]
dist = (np.double(np.bitwise_and((u != v),
np.bitwise_or(u != 0, v != 0)).sum()) /
np.double(np.bitwise_or(u != 0, v != 0).sum()))
return dist
def loadNYU(self, path):
img = Image.open(path)
if len(img.getbands()) != 3:
raise ValueError('NYU input should be with 3 channel')
r, g, b = img.split()
r = np.asarray(r,np.int32)
g = np.asarray(g,np.int32)
b = np.asarray(b,np.int32)
dpt = np.bitwise_or(np.left_shift(g,8),b)
self.dmData = np.asarray(dpt, np.float32)
return self.dmData
test_umath.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
def test_truth_table_bitwise(self):
arg1 = [False, False, True, True]
arg2 = [False, True, False, True]
out = [False, True, True, True]
assert_equal(np.bitwise_or(arg1, arg2), out)
out = [False, False, False, True]
assert_equal(np.bitwise_and(arg1, arg2), out)
out = [False, True, True, False]
assert_equal(np.bitwise_xor(arg1, arg2), out)
