The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def mk_rotations(img):
#
# DESCRIPTION
# This function create 8 roatation image fro an input image 4 rotation from the raw image and 4 rotation form the transposed
#
# INPUTS
# img np.array
#
# OUTPUTS
# rotated_image_img, img90, img180, img270, imgT, imgT90, imgT180,imgT270
#
#
img90 = np.rot90(img)
img180 = np.rot90(img,k=2)
img270 = np.rot90(img,k=3)
imgT = np.zeros(img.shape)
if np.size(img.shape)>2:
for i in range(3):
imgT[:,:,i] =img[:,:,i].T
else:
imgT = img.T
imgT90 = np.rot90(imgT)
imgT180 = np.rot90(imgT, k=2)
imgT270 = np.rot90(imgT, k=3)
return img, img90, img180, img270, imgT, imgT90, imgT180,imgT270 Example 2
def mk_rotations(img):
##INPUT:
## img: a 3D RGB array
##OUTPUT
## 8 rotated and transposed versions of img
img90 = np.rot90(img)
img180 = np.rot90(img,k=2)
img270 = np.rot90(img,k=3)
imgT = np.zeros(img.shape)
if np.size(img.shape)>2:
for i in range(3):
imgT[:,:,i] =img[:,:,i].T
else:
imgT = img.T
imgT90 = np.rot90(imgT)
imgT180 = np.rot90(imgT, k=2)
imgT270 = np.rot90(imgT, k=3)
return img, img90, img180, img270, imgT, imgT90, imgT180,imgT270
## Formats an image to save format Example 3
def __imul__(self, factor):
"""define ``self *= factor``.
As a shortcut for::
self = self.__imul__(factor)
"""
try:
if factor == 1:
return self
except: pass
try:
if (np.size(factor) == np.size(self.scaling) and
all(factor == 1)):
return self
except: pass
if self.is_identity and np.size(self.scaling) == 1:
self.scaling = np.ones(np.size(factor))
self.is_identity = False
self.scaling *= factor
self.dim = np.size(self.scaling)
return self Example 4
def fixed_label_diversity(model, config,step=''):
sample_dir=make_sample_dir(model)
str_step=str(step) or guess_model_step(model)
N=64#per image
n_combo=5#n label combinations
#0,1 label combinations
fixed_labels=model.attr.sample(n_combo)[model.cc.node_names]
size=infer_grid_image_shape(N)
for j, fx_label in enumerate(fixed_labels.values):
fx_label=np.reshape(fx_label,[1,-1])
fx_label=np.tile(fx_label,[N,1])
do_dict={model.cc.labels: fx_label}
images, feed_dict= sample(model, do_dict=do_dict)
fx_file=os.path.join(sample_dir, str_step+'fxlab'+str(j)+'.pdf')
save_figure_images(model.model_type,images['G'],fx_file,size=size)
#which image is what label
fixed_labels=fixed_labels.reset_index(drop=True)
fixed_labels.to_csv(os.path.join(sample_dir,str_step+'fxlab'+'.csv')) Example 5
def unscentedTransform(X, Wm, Wc, f):
Y = None
Ymean = None
fdim = None
N = np.shape(X)[1]
for j in range(0,N):
fImage = f(X[:,j])
if Y is None:
fdim = np.size(fImage)
Y = np.zeros((fdim, np.shape(X)[1]))
Ymean = np.zeros(fdim)
Y[:,j] = fImage
Ymean += Wm[j] * Y[:,j]
Ycov = np.zeros((fdim, fdim))
for j in range(0, N):
meanAdjustedYj = Y[:,j] - Ymean
Ycov += np.outer(Wc[j] * meanAdjustedYj, meanAdjustedYj)
return Y, Ymean, Ycov Example 6
def nufft_T(N, J, K, alpha, beta):
'''
equation (29) and (26)Fessler's paper
create the overlapping matrix CSSC (diagonal dominent matrix)
of J points
and then find out the pseudo-inverse of CSSC '''
# import scipy.linalg
L = numpy.size(alpha) - 1
# print('L = ', L, 'J = ',J, 'a b', alpha,beta )
cssc = numpy.zeros((J, J))
[j1, j2] = numpy.mgrid[1:J + 1, 1:J + 1]
overlapping_mat = j2 - j1
for l1 in range(-L, L + 1):
for l2 in range(-L, L + 1):
alf1 = alpha[abs(l1)]
# if l1 < 0: alf1 = numpy.conj(alf1)
alf2 = alpha[abs(l2)]
# if l2 < 0: alf2 = numpy.conj(alf2)
tmp = overlapping_mat + beta * (l1 - l2)
tmp = dirichlet(1.0 * tmp / (1.0 * K / N))
cssc = cssc + alf1 * numpy.conj(alf2) * tmp
return mat_inv(cssc) Example 7
def nufft_r(om, N, J, K, alpha, beta):
'''
equation (30) of Fessler's paper
'''
M = numpy.size(om) # 1D size
gam = 2.0 * numpy.pi / (K * 1.0)
nufft_offset0 = nufft_offset(om, J, K) # om/gam - nufft_offset , [M,1]
dk = 1.0 * om / gam - nufft_offset0 # om/gam - nufft_offset , [M,1]
arg = outer_sum(-numpy.arange(1, J + 1) * 1.0, dk)
L = numpy.size(alpha) - 1
# print('alpha',alpha)
rr = numpy.zeros((J, M), dtype=numpy.float32)
rr = iterate_l1(L, alpha, arg, beta, K, N, rr)
return (rr, arg) Example 8
def test_multidimension():
for ndims in range(1, 6):
Nd= ()
Kd=()
Jd=()
om = numpy.random.randn(2,ndims)
for pp in range(0, ndims):
Nd += (128,)
Kd += (256,)
Jd += (4,)
# Nd =tuple([slice(0, 16) for ss in range(0, ndims)]) # image size
# print('setting image dimension Nd...', Nd)
# Kd = tuple([slice(0, 32) for ss in range(0, ndims)]) # k-space size
# print('setting spectrum dimension Kd...', Kd)
# Jd = tuple([slice(0, 6) for ss in range(0, ndims)]) # interpolation size
# print('setting interpolation size Jd...', Jd)
NufftObj = NUFFT()
NufftObj.plan(om, Nd, Kd, Jd)
print(ndims,'-dimensional NUFFT created!')
# y = NufftObj.forward(image) Example 9
def nufft_T(N, J, K, alpha, beta):
'''
The Equation (29) and (26) in Fessler and Sutton 2003.
Create the overlapping matrix CSSC (diagonal dominent matrix)
of J points and find out the pseudo-inverse of CSSC '''
# import scipy.linalg
L = numpy.size(alpha) - 1
# print('L = ', L, 'J = ',J, 'a b', alpha,beta )
cssc = numpy.zeros((J, J))
[j1, j2] = numpy.mgrid[1:J + 1, 1:J + 1]
overlapping_mat = j2 - j1
for l1 in range(-L, L + 1):
for l2 in range(-L, L + 1):
alf1 = alpha[abs(l1)]
# if l1 < 0: alf1 = numpy.conj(alf1)
alf2 = alpha[abs(l2)]
# if l2 < 0: alf2 = numpy.conj(alf2)
tmp = overlapping_mat + beta * (l1 - l2)
tmp = dirichlet(1.0 * tmp / (1.0 * K / N))
cssc = cssc + alf1 * alf2 * tmp
return mat_inv(cssc) Example 10
def computePolarVecs(self,karg=False):
N = len(self.times)
L = np.reshape(self.L,(3,N))
if karg is False:
A = self.computeRotMatrix()
elif np.size(karg) is 3:
A = self.computeRotMatrix(karg)
elif np.size(karg) is 9:
A = karg
q = np.zeros((6,N))
for pp in range(0,N):
Lpp = np.diag(L[:,pp])
p = np.dot(A,np.dot(Lpp,A.T))
q[:,pp] = np.r_[p[:,0],p[[1,2],1],p[2,2]]
return q Example 11
def computeMisfit(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
Phi = np.dot(v.T,v)
return Phi/N Example 12
def computeVecFcn(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
return v Example 13
def computeMisfit(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
Phi = np.dot(v.T,v)
return Phi/N Example 14
def computeVecFcn(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
return v Example 15
def get_dobs_dunc(self,dpre,FloorVal,Pct):
# Floor is a fraction of the largest amplitude anomaly for the earliest time channel
M = np.shape(dpre)[0]
# Floor = np.zeros(np.shape(dpre))
# Floor[0:M:3,:] = FloorVal*np.max(np.abs(dpre[0:M:3,:]))
# Floor[1:M:3,:] = FloorVal*np.max(np.abs(dpre[1:M:3,:]))
# Floor[2:M:3,:] = FloorVal*np.max(np.abs(dpre[2:M:3,:]))
Floor = FloorVal*np.max(np.abs(dpre))*np.ones(np.shape(dpre))
if len(Pct) is 1:
dunc = Floor + Pct*np.abs(dpre)
else:
dunc = Floor
for ii in range(0,3):
dunc[ii:M:3] = dunc[ii:M:3] + Pct[ii]*np.abs(dpre[ii:M:3])
dobs = dpre + dunc*np.random.normal(size=np.shape(dpre))
self.dunc = dunc
self.dobs = dobs
return dobs,dunc Example 16
def computeMisfit(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
Phi = np.dot(v.T,v)
return Phi/N Example 17
def computeVecFcn(self,r0):
assert self.q is not None, "Must have current estimate of polarizations"
assert self.dunc is not None, "Must have set uncertainties"
assert self.dobs is not None, "Must have observed data"
dunc = self.dunc
dobs = self.dobs
q = self.q
Hp = self.computeHp(r0=r0,update=False)
Brx = self.computeBrx(r0=r0,update=False)
P = self.computeP(Hp,Brx)
N = np.size(dobs)
dpre = np.dot(P,q)
v = mkvc((dpre-dobs)/dunc)
return v Example 18
def setUp(self):
self.f = np.ones(256, dtype=np.float32)
self.ef = np.ones(self.f.size, dtype=np.bool)
self.d = np.ones(128, dtype=np.float64)
self.ed = np.ones(self.d.size, dtype=np.bool)
# generate values for all permutation of 256bit simd vectors
s = 0
for i in range(32):
self.f[s:s+8] = [i & 2**x for x in range(8)]
self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)]
s += 8
s = 0
for i in range(16):
self.d[s:s+4] = [i & 2**x for x in range(4)]
self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)]
s += 4
self.nf = self.f.copy()
self.nd = self.d.copy()
self.nf[self.ef] = np.nan
self.nd[self.ed] = np.nan Example 19
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1)) Example 20
def __getitem__(self, indx):
"""
Get the index.
"""
m = self._mask
if isinstance(m[indx], ndarray):
# Can happen when indx is a multi-dimensional field:
# A = ma.masked_array(data=[([0,1],)], mask=[([True,
# False],)], dtype=[("A", ">i2", (2,))])
# x = A[0]; y = x["A"]; then y.mask["A"].size==2
# and we can not say masked/unmasked.
# The result is no longer mvoid!
# See also issue #6724.
return masked_array(
data=self._data[indx], mask=m[indx],
fill_value=self._fill_value[indx],
hard_mask=self._hardmask)
if m is not nomask and m[indx]:
return masked
return self._data[indx] Example 21
def plot_policy_learned(data_unpickle, color, fig_dir=None):
#recover the policy
poli = data_unpickle['policy']
#range to plot it
x = np.arange(-3,3,0.01)
means = np.zeros(np.size(x))
logstd = np.zeros(np.size(x))
for i,s in enumerate(x):
means[i] = poli.get_action(np.array((s,)))[1]['mean']
logstd[i] = poli.get_action(np.array((s,)))[1]['log_std']
# means[i] = poli.get_action(np.array([s,]))[1]['mean']
# logstd[i] = poli.get_action(np.array([s,]))[1]['log_std']
plt.plot(x, means, color=color, label = 'mean')
plt.plot(x, logstd, color=color * 0.7, label = 'logstd')
plt.legend(loc = 5)
plt.title('Final policy')
plt.xlabel('state')
plt.ylabel('Action')
if fig_dir:
plt.savefig(os.path.join(fig_dir,'policy_learned'))
else:
print("No directory for saving plots") Example 22
def distance_function(values, medians):
"""This function calculates the distance metric.
N.B. Only uses the non-NaN values.
dist = sum( (s - m)^2 )
s is the vector of sample values
m is the vector of probe medians
Args:
values (numpy array of floats)
medians (numpy array of floats)
Returns:
dist (float)
"""
non_nan_idx = ~np.isnan(values)
assert np.size(non_nan_idx) != 0, "All values in this sample are NaN!"
non_nan_values = values[non_nan_idx]
non_nan_medians = medians[non_nan_idx]
dist = sum(np.square(non_nan_values - non_nan_medians))
return dist
# tested # Example 23
def update(self, es, function_values, **kwargs):
"""the first and second value in ``function_values``
must reflect two mirrored solutions sampled
in direction / in opposite direction of
the previous mean shift, respectively.
"""
# TODO: on the linear function, the two mirrored samples lead
# to a sharp increase of condition of the covariance matrix.
# They should not be used to update the covariance matrix,
# if the step-size inreases quickly. This should be fine with
# negative updates though.
if not self.initialized:
self.initialize(es.N, es.opts)
if 1 < 3:
# use the ranking difference of the mirrors for adaptation
# damp = 5 should be fine
z = np.where(es.fit.idx == 1)[0][0] - np.where(es.fit.idx == 0)[0][0]
z /= es.popsize - 1 # z in [-1, 1]
self.s = (1 - self.sp.c) * self.s + self.sp.c * np.sign(z) * np.abs(z)**self.sp.z_exponent
if self.s > 0:
es.sigma *= exp(self.s / self.sp.dampup)
else:
es.sigma *= exp(self.s / self.sp.dampdown)
#es.more_to_write.append(10**z) Example 24
def _finalize_plotting(self):
pyplot.ion()
pyplot.draw() # update "screen"
pyplot.show() # show figure
# matplotlib.interactive(interactive_status)
pyplot.rcParams['font.size'] = self.original_fontsize Example 25
def F1(tested, truth):
tested = (tested-0.5)*2
truth = (truth-0.5)*2
truth[truth<=0] = -1.
truth[truth>0] = 1.
res = tested+truth
true_pos = np.size(np.where(res==2))/2.
pos = np.size(np.where(truth ==1))/2.
found_pos = np.size(np.where(tested ==1))/2.
precision = true_pos/found_pos
recall = true_pos/pos
F1 = 2.*precision*recall/(precision+recall)
return F1
# PRE-PROCESSING FUNCTIONS
# TRAINING SET FUNCTIONS Example 26
def calc_stoi_from_spec(clean_spec, degraded_spec, analysis_len=30):
freq_bins = np.size(clean_spec, 0)
frames = np.size(clean_spec, 1)
x = np.zeros((freq_bins, frames - analysis_len + 1, analysis_len), dtype=np.float32)
y = np.zeros((freq_bins, frames - analysis_len + 1, analysis_len), dtype=np.float32)
for j in range(0, freq_bins):
for m in range(analysis_len - 1, frames, 1):
x[j, m] = clean_spec[j, m - analysis_len + 1:m + 1]
y[j, m] = degraded_spec[j, m - analysis_len + 1:m + 1]
y[j, m] = np.minimum(np.linalg.norm(x[j,m,:])/np.linalg.norm(y[j,m,:])*y[j,m,:],
(1.+np.power(10., 15./20.))*x[j,m,:]) # y is normalized and clipped
x_mean = np.mean(x, axis=(0, 1))
y_mean = np.mean(y, axis=(0, 1))
score = 0.
for j in range(0, freq_bins):
for m in range(analysis_len - 1, frames, 1):
score += np.dot(x[j, m, :] - x_mean, y[j, m, :] - y_mean) / \
(np.linalg.norm(x[j, m, :] - x_mean) * np.linalg.norm(y[j, m, :] - y_mean))
score /= (freq_bins * analysis_len)
return score Example 27
def cochleagram_extractor(xx, sr, win_len, shift_len, channel_number, win_type):
fcoefs, f = make_erb_filters(sr, channel_number, 50)
fcoefs = np.flipud(fcoefs)
xf = erb_frilter_bank(xx, fcoefs)
if win_type == 'hanning':
window = np.hanning(channel_number)
elif win_type == 'hamming':
window = np.hamming(channel_number)
elif win_type == 'triangle':
window = (1 - (np.abs(channel_number - 1 - 2 * np.arange(1, channel_number + 1, 1)) / (channel_number + 1)))
else:
window = np.ones(channel_number)
window = window.reshape((channel_number, 1))
xe = np.power(xf, 2.0)
frames = 1 + ((np.size(xe, 1)-win_len) // shift_len)
cochleagram = np.zeros((channel_number, frames))
for i in range(frames):
one_frame = np.multiply(xe[:, i*shift_len:i*shift_len+win_len], np.repeat(window, win_len, 1))
cochleagram[:, i] = np.sqrt(np.mean(one_frame, 1))
cochleagram = np.where(cochleagram == 0.0, np.finfo(float).eps, cochleagram)
return cochleagram Example 28
def postaud(x, fmax, fbtype=None):
if fbtype is None:
fbtype = 'bark'
nbands = x.shape[0]
nframes = x.shape[1]
nfpts = nbands
if fbtype == 'bark':
bancfhz = bark2freq(np.linspace(0, freq2bark(fmax), nfpts))
fsq = bancfhz * bancfhz
ftmp = fsq + 1.6e5
eql = ((fsq/ftmp)**2) * ((fsq + 1.44e6)/(fsq + 9.61e6))
eql = eql.reshape(np.size(eql), 1)
z = np.repeat(eql, nframes, axis=1) * x
z = z ** (1./3.)
y = np.vstack((z[1, :], z[1:nbands-1, :], z[nbands-2, :]))
return y Example 29
def lpc2cep(a, nout=None):
nin = np.size(a, 0)
ncol = np.size(a, 1)
order = nin - 1
if nout is None:
nout = order + 1
c = np.zeros((nout, ncol))
c[0, :] = -1. * np.log(a[0, :])
renormal_coef = np.reshape(a[0,:], (1, ncol))
renormal_coef = np.repeat(renormal_coef, nin, axis=0)
a = a / renormal_coef
for n in range(1, nout):
sumn = np.zeros(ncol)
for m in range(1, n+1):
sumn = sumn + (n-m) * a[m, :] * c[n-m, :]
c[n, :] = -1. * (a[n, :] + 1. / n * sumn)
return c Example 30
def lpc2cep(a, nout=None):
nin = np.size(a, 0)
ncol = np.size(a, 1)
order = nin - 1
if nout is None:
nout = order + 1
c = np.zeros((nout, ncol))
c[0, :] = -1. * np.log(a[0, :])
renormal_coef = np.reshape(a[0,:], (1, ncol))
renormal_coef = np.repeat(renormal_coef, nin, axis=0)
a = a / renormal_coef
for n in range(1, nout):
sumn = np.zeros(ncol)
for m in range(1, n+1):
sumn = sumn + (n-m) * a[m, :] * c[n-m, :]
c[n, :] = -1. * (a[n, :] + 1. / n * sumn)
return c Example 31
def _run_TR_from_scan_onsets(self, n_T, scan_onsets=None):
if scan_onsets is None:
# assume that all data are acquired within the same scan.
n_run = 1
run_TRs = np.array([n_T], dtype=int)
else:
# Each value in the scan_onsets tells the index at which
# a new scan starts. For example, if n_T = 500, and
# scan_onsets = [0,100,200,400], this means that the time points
# of 0-99 are from the first scan, 100-199 are from the second,
# 200-399 are from the third and 400-499 are from the fourth
run_TRs = np.int32(np.diff(np.append(scan_onsets, n_T)))
run_TRs = np.delete(run_TRs, np.where(run_TRs == 0))
n_run = run_TRs.size
# delete run length of 0 in case of duplication in scan_onsets.
logger.info('I infer that the number of volumes'
' in each scan are: {}'.format(run_TRs))
return run_TRs, n_run Example 32
def _sum_loglike_marginalized(self, L_vec, s2XTAcorrX, YTAcorrY_diag,
sXTAcorrY, half_log_det_X0TAX0,
log_weights, log_fixed_terms,
l_idx, n_C, n_T, n_V, n_X0,
n_grid, rank=None):
sum_LL_total = 0
sum_grad_L = np.zeros(np.size(l_idx[0]))
for subj in range(len(YTAcorrY_diag)):
LL_total, grad_L = self._loglike_marginalized(
L_vec, s2XTAcorrX[subj], YTAcorrY_diag[subj],
sXTAcorrY[subj], half_log_det_X0TAX0[subj], log_weights,
log_fixed_terms[subj], l_idx, n_C, n_T[subj],
n_V[subj], n_X0[subj], n_grid, rank)
sum_LL_total += LL_total
sum_grad_L += grad_L
return sum_LL_total, sum_grad_L Example 33
def unRollImagesForConv(self,index):
'''?????index?????
?? ?width*?height, ?????*?channel? ????size??????
'''
i = index
old_images = self.last_layer.images
m,old_channel,old_height,old_width = old_images.shape
newData = []
#Process unroll the data
for h in range(0,old_height-self.squareSize+1,self.stride):
for w in range(0,old_width-self.squareSize+1,self.stride):
tmp = []
for c in range(old_channel):
tmp.append(old_images[i,c,h:h+self.squareSize,w:w+self.squareSize].reshape(1,self.squareSize**2))
#h,w?????,??????????,??old_channel * squaireSize?????
tmp = np.array(tmp).reshape(1,self.squareSize**2*old_channel)
#?????reshape,????????,????newData
newData.append(tmp)
#??????????????????,?????????
newData = np.array(newData).reshape(self.width*self.height,self.squareSize**2*old_channel)
return newData Example 34
def backward_compute(self):
m = np.size(self.images,0)
self.delta = self.delta.reshape(m,self.channel,self.height,self.width)
newDelta = np.zeros([m,self.last_layer.channel,self.last_layer.height,self.last_layer.width])
for i in range(m):
for j in range(self.channel):
for h in range(self.height):
for w in range(self.width):
tmpLoc = self.maxIndex[i,j,h*self.width+w]
relativeH = tmpLoc//self.squareSize
relativeW = tmpLoc - relativeH * self.squareSize
lastW = w*self.stride+relativeW
lastH = h*self.stride+relativeH
newDelta[i,j,lastH,lastW] += self.delta[i,j,h,w]
self.last_layer.delta = newDelta
pass Example 35
def bestMap(L1, L2):
if L1.__len__() != L2.__len__():
print('size(L1) must == size(L2)')
Label1 = np.unique(L1)
nClass1 = Label1.__len__()
Label2 = np.unique(L2)
nClass2 = Label2.__len__()
nClass = max(nClass1, nClass2)
G = np.zeros((nClass, nClass))
for i in range(nClass1):
for j in range(nClass2):
G[i][j] = np.nonzero((L1 == Label1[i]) * (L2 == Label2[j]))[0].__len__()
c = linear_assignment_.linear_assignment(-G.T)[:, 1]
newL2 = np.zeros(L2.__len__())
for i in range(nClass2):
for j in np.nonzero(L2 == Label2[i])[0]:
if len(Label1) > c[i]:
newL2[j] = Label1[c[i]]
return accuracy_score(L1, newL2) Example 36
def entropy(pk, *args, **kwargs):
"""Proxy for scipy.stats.entropy, with normalized Shannon entropy."""
if 'normalize' in kwargs:
normalize = kwargs['normalize']
del kwargs['normalize']
else:
normalize = False
e = scipy.stats.entropy(pk, *args, **kwargs)
if normalize:
num_classes = np.size(pk)
base = kwargs['base'] if 'base' in kwargs else None
maximum_entropy = np.log(num_classes)
if base:
maximum_entropy /= np.log(base)
e /= maximum_entropy
return e Example 37
def is_grid(self, grid, image):
"""
Checks the "gridness" by analyzing the results of a hough transform.
:param grid: binary image
:return: wheter the object in the image might be a grid or not
"""
# - Distance resolution = 1 pixel
# - Angle resolution = 1° degree for high line density
# - Threshold = 144 hough intersections
# 8px digit + 3*2px white + 2*1px border = 16px per cell
# => 144x144 grid
# 144 - minimum number of points on the same line
# (but due to imperfections in the binarized image it's highly
# improbable to detect a 144x144 grid)
lines = cv2.HoughLines(grid, 1, np.pi / 180, 144)
if lines is not None and np.size(lines) >= 20:
lines = lines.reshape((lines.size / 2), 2)
# theta in [0, pi] (theta > pi => rho < 0)
# normalise theta in [-pi, pi] and negatives rho
lines[lines[:, 0] < 0, 1] -= np.pi
lines[lines[:, 0] < 0, 0] *= -1
criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)
# split lines into 2 groups to check whether they're perpendicular
if cv2.__version__[0] == '2':
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, criteria, 5, cv2.KMEANS_RANDOM_CENTERS)
else:
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, None, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
if self.debug:
self.save_hough(lines, clmap)
# Overall variance from respective centers
var = density / np.size(clmap)
sin = abs(np.sin(centers[0] - centers[1]))
# It is probably a grid only if:
# - centroids difference is almost a 90° angle (+-15° limit)
# - variance is less than 5° (keeping in mind surface distortions)
return sin > 0.99 and var <= (5*np.pi / 180) ** 2
else:
return False Example 38
def save_hough(self, lines, clmap):
"""
:param lines: (rho, theta) pairs
:param clmap: clusters assigned to lines
:return: None
"""
height, width = self.image.shape
ratio = 600. * (self.step+1) / min(height, width)
temp = cv2.resize(self.image, None, fx=ratio, fy=ratio,
interpolation=cv2.INTER_CUBIC)
temp = cv2.cvtColor(temp, cv2.COLOR_GRAY2BGR)
colors = [(0, 127, 255), (255, 0, 127)]
for i in range(0, np.size(lines) / 2):
rho = lines[i, 0]
theta = lines[i, 1]
color = colors[clmap[i, 0]]
if theta < np.pi / 4 or theta > 3 * np.pi / 4:
pt1 = (rho / np.cos(theta), 0)
pt2 = (rho - height * np.sin(theta) / np.cos(theta), height)
else:
pt1 = (0, rho / np.sin(theta))
pt2 = (width, (rho - width * np.cos(theta)) / np.sin(theta))
pt1 = (int(pt1[0]), int(pt1[1]))
pt2 = (int(pt2[0]), int(pt2[1]))
cv2.line(temp, pt1, pt2, color, 5)
self.save2image(temp) Example 39
def morph(roi):
ratio = min(28. / np.size(roi, 0), 28. / np.size(roi, 1))
roi = cv2.resize(roi, None, fx=ratio, fy=ratio,
interpolation=cv2.INTER_NEAREST)
dx = 28 - np.size(roi, 1)
dy = 28 - np.size(roi, 0)
px = ((int(dx / 2.)), int(np.ceil(dx / 2.)))
py = ((int(dy / 2.)), int(np.ceil(dy / 2.)))
squared = np.pad(roi, (py, px), 'constant', constant_values=0)
return squared Example 40
def is_grid(self, grid, image):
"""
Checks the "gridness" by analyzing the results of a hough transform.
:param grid: binary image
:return: wheter the object in the image might be a grid or not
"""
# - Distance resolution = 1 pixel
# - Angle resolution = 1° degree for high line density
# - Threshold = 144 hough intersections
# 8px digit + 3*2px white + 2*1px border = 16px per cell
# => 144x144 grid
# 144 - minimum number of points on the same line
# (but due to imperfections in the binarized image it's highly
# improbable to detect a 144x144 grid)
lines = cv2.HoughLines(grid, 1, np.pi / 180, 144)
if lines is not None and np.size(lines) >= 20:
lines = lines.reshape((lines.size/2), 2)
# theta in [0, pi] (theta > pi => rho < 0)
# normalise theta in [-pi, pi] and negatives rho
lines[lines[:, 0] < 0, 1] -= np.pi
lines[lines[:, 0] < 0, 0] *= -1
criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)
# split lines into 2 groups to check whether they're perpendicular
if cv2.__version__[0] == '2':
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
else:
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, None, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
# Overall variance from respective centers
var = density / np.size(clmap)
sin = abs(np.sin(centers[0] - centers[1]))
# It is probably a grid only if:
# - centroids difference is almost a 90° angle (+-15° limit)
# - variance is less than 5° (keeping in mind surface distortions)
return sin > 0.99 and var <= (5*np.pi / 180) ** 2
else:
return False Example 41
def prewhiten(x):
mean = np.mean(x)
std = np.std(x)
std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1/std_adj)
return y Example 42
def get_label_batch(label_data, batch_size, batch_index):
nrof_examples = np.size(label_data, 0)
j = batch_index*batch_size % nrof_examples
if j+batch_size<=nrof_examples:
batch = label_data[j:j+batch_size]
else:
x1 = label_data[j:nrof_examples]
x2 = label_data[0:nrof_examples-j]
batch = np.vstack([x1,x2])
batch_int = batch.astype(np.int64)
return batch_int Example 43
def get_batch(image_data, batch_size, batch_index):
nrof_examples = np.size(image_data, 0)
j = batch_index*batch_size % nrof_examples
if j+batch_size<=nrof_examples:
batch = image_data[j:j+batch_size,:,:,:]
else:
x1 = image_data[j:nrof_examples,:,:,:]
x2 = image_data[0:nrof_examples-j,:,:,:]
batch = np.vstack([x1,x2])
batch_float = batch.astype(np.float32)
return batch_float Example 44
def calculate_accuracy(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
tp = np.sum(np.logical_and(predict_issame, actual_issame))
fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
acc = float(tp+tn)/dist.size
return tpr, fpr, acc Example 45
def test_field2d_init():
# create a field where the main material is 5
fld = fls.Field2D(100, 0.1, 100, 0.1, 100, 0.1, int(5))
# check if the "material parameter" 'real' for the complete field is 5
assert np.allclose(fld.material_vector('real'), 5)
assert np.size(fld.material_vector('real')) == 10000 Example 46
def heap_size(self):
"""Gets the heap size maintained in the class."""
return len(self._heap) Example 47
def accumulate(self, predictions, actuals, num_positives=None):
"""Accumulate the predictions and their ground truth labels.
After the function call, we may call peek_ap_at_n to actually calculate
the average precision.
Note predictions and actuals must have the same shape.
Args:
predictions: a list storing the prediction scores.
actuals: a list storing the ground truth labels. Any value
larger than 0 will be treated as positives, otherwise as negatives.
num_positives = If the 'predictions' and 'actuals' inputs aren't complete,
then it's possible some true positives were missed in them. In that case,
you can provide 'num_positives' in order to accurately track recall.
Raises:
ValueError: An error occurred when the format of the input is not the
numpy 1-D array or the shape of predictions and actuals does not match.
"""
if len(predictions) != len(actuals):
raise ValueError("the shape of predictions and actuals does not match.")
if not num_positives is None:
if not isinstance(num_positives, numbers.Number) or num_positives < 0:
raise ValueError("'num_positives' was provided but it wan't a nonzero number.")
if not num_positives is None:
self._total_positives += num_positives
else:
self._total_positives += numpy.size(numpy.where(actuals > 0))
topk = self._top_n
heap = self._heap
for i in range(numpy.size(predictions)):
if topk is None or len(heap) < topk:
heapq.heappush(heap, (predictions[i], actuals[i]))
else:
if predictions[i] > heap[0][0]: # heap[0] is the smallest
heapq.heappop(heap)
heapq.heappush(heap, (predictions[i], actuals[i])) Example 48
def heap_size(self):
"""Gets the heap size maintained in the class."""
return len(self._heap) Example 49
def accumulate(self, predictions, actuals, num_positives=None):
"""Accumulate the predictions and their ground truth labels.
After the function call, we may call peek_ap_at_n to actually calculate
the average precision.
Note predictions and actuals must have the same shape.
Args:
predictions: a list storing the prediction scores.
actuals: a list storing the ground truth labels. Any value
larger than 0 will be treated as positives, otherwise as negatives.
num_positives = If the 'predictions' and 'actuals' inputs aren't complete,
then it's possible some true positives were missed in them. In that case,
you can provide 'num_positives' in order to accurately track recall.
Raises:
ValueError: An error occurred when the format of the input is not the
numpy 1-D array or the shape of predictions and actuals does not match.
"""
if len(predictions) != len(actuals):
raise ValueError("the shape of predictions and actuals does not match.")
if not num_positives is None:
if not isinstance(num_positives, numbers.Number) or num_positives < 0:
raise ValueError("'num_positives' was provided but it wan't a nonzero number.")
if not num_positives is None:
self._total_positives += num_positives
else:
self._total_positives += numpy.size(numpy.where(actuals > 0))
topk = self._top_n
heap = self._heap
for i in range(numpy.size(predictions)):
if topk is None or len(heap) < topk:
heapq.heappush(heap, (predictions[i], actuals[i]))
else:
if predictions[i] > heap[0][0]: # heap[0] is the smallest
heapq.heappop(heap)
heapq.heappush(heap, (predictions[i], actuals[i])) Example 50
def heap_size(self):
"""Gets the heap size maintained in the class."""
return len(self._heap) 