def Inversion(Qsca,Qabs,wavelength,diameter,nMin=1,nMax=3,kMin=0.001,kMax=1,scatteringPrecision=0.010,absorptionPrecision=0.010,spaceSize=120,interp=2):
nRange = np.linspace(nMin,nMax,spaceSize)
kRange = np.logspace(np.log10(kMin),np.log10(kMax),spaceSize)
scaSpace = np.zeros((spaceSize,spaceSize))
absSpace = np.zeros((spaceSize,spaceSize))
for ni,n in enumerate(nRange):
for ki,k in enumerate(kRange):
_derp = fastMieQ(n+(1j*k),wavelength,diameter)
scaSpace[ni][ki] = _derp[0]
absSpace[ni][ki] = _derp[1]
if interp is not None:
nRange = zoom(nRange,interp)
kRange = zoom(kRange,interp)
scaSpace = zoom(scaSpace,interp)
absSpace = zoom(absSpace,interp)
scaSolutions = np.where(np.logical_and(Qsca*(1-scatteringPrecision)<scaSpace, scaSpace<Qsca*(1+scatteringPrecision)))
absSolutions = np.where(np.logical_and(Qabs*(1-absorptionPrecision)<absSpace, absSpace<Qabs*(1+absorptionPrecision)))
validScattering = nRange[scaSolutions[0]]+1j*kRange[scaSolutions[1]]
validAbsorption = nRange[absSolutions[0]]+1j*kRange[absSolutions[1]]
solution = np.intersect1d(validScattering,validAbsorption)
# errors = [error()]
return solution
def imageUp(img, order=1):
"""Upsample input image by a factor of 2.
Parameters
----------
img : ndarray
Image array. It can be a 2D or 3D array. If it is a 3D array,
the smoothing is applied independently to each channel.
order : integer, optional
Interpolation order. Defaults to 1
Returns :
imgUp : ndarray
Upsampled image of size (2*H, 2*W, D) where (H, W, D) is the
width, height and depth of the input image
"""
if img.ndim == 2:
imgZoomed = np.zeros([2*img.shape[0], 2*img.shape[1]], dtype=img.dtype)
nd.zoom(img, 2.0, output=imgZoomed, order=order, mode='reflect')
return imgZoomed
else:
zoomList = list()
for d in range(img.shape[2]):
imgZoomed = np.zeros([2*img.shape[0], 2*img.shape[1]], dtype=img.dtype)
nd.zoom(img[...,d], 2.0, output=imgZoomed, order=order, mode='reflect')
zoomList.append(imgZoomed)
# recombine channels and return
return np.concatenate([p[...,np.newaxis] for p in zoomList], axis=2)
def Inversion_SD(Bsca,Babs,wavelength,dp,ndp,nMin=1,nMax=3,kMin=0,kMax=1,scatteringPrecision=0.001,absorptionPrecision=0.001,spaceSize=40,interp=2):
dp = coerceDType(dp)
ndp = coerceDType(ndp)
nRange = np.linspace(nMin,nMax,spaceSize)
kRange = np.linspace(kMin,kMax,spaceSize)
scaSpace = np.zeros((spaceSize,spaceSize))
absSpace = np.zeros((spaceSize,spaceSize))
for ni,n in enumerate(nRange):
for ki,k in enumerate(kRange):
_derp = fastMie_SD(n+(1j*k),wavelength,dp,ndp)
scaSpace[ni][ki] = _derp[0]
absSpace[ni][ki] = _derp[1]
if interp is not None:
nRange = zoom(nRange,interp)
kRange = zoom(kRange,interp)
scaSpace = zoom(scaSpace,interp)
absSpace = zoom(absSpace,interp)
scaSolutions = np.where(np.logical_and(Bsca*(1-scatteringPrecision)<scaSpace, scaSpace<Bsca*(1+scatteringPrecision)))
absSolutions = np.where(np.logical_and(Babs*(1-absorptionPrecision)<absSpace, absSpace<Babs*(1+absorptionPrecision)))
validScattering = nRange[scaSolutions[0]]+1j*kRange[scaSolutions[1]]
validAbsorption = nRange[absSolutions[0]]+1j*kRange[absSolutions[1]]
return np.intersect1d(validScattering,validAbsorption)
def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_4c/output', clip=True, **step_params):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in range(octave_n - 1):
octaves.append(
nd.zoom(octaves[-1], (1, 1.0 / octave_scale, 1.0 / octave_scale), order=1))
src = net.blobs['data']
# allocate image for network-produced details
detail = np.zeros_like(octaves[-1])
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0 * h / h1, 1.0 * w / w1), order=1)
src.reshape(1, 3, h, w) # resize the network's input image size
src.data[0] = octave_base + detail
print("octave %d %s" % (octave, end))
for i in range(iter_n):
make_step(net, end=end, clip=clip, **step_params)
sys.stdout.write("%d " % i)
sys.stdout.flush()
print("")
# extract details produced on the current octave
detail = src.data[0] - octave_base
# returning the resulting image
return deprocess(net, src.data[0])
def deepdream(
net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, **step_params
):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in xrange(octave_n - 1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0 / octave_scale, 1.0 / octave_scale), order=1))
src = net.blobs["data"]
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0 * h / h1, 1.0 * w / w1), order=1)
src.reshape(1, 3, h, w) # resize the network's input image size
src.data[0] = octave_base + detail
for i in xrange(iter_n):
make_step(net, end=end, clip=clip, **step_params)
# visualization
vis = deprocess(net, src.data[0])
if not clip: # adjust image contrast if clipping is disabled
vis = vis * (255.0 / np.percentile(vis, 99.98))
showarray(vis)
print octave, i, end, vis.shape
clear_output(wait=True)
# extract details produced on the current octave
detail = src.data[0] - octave_base
# returning the resulting image
return deprocess(net, src.data[0])
def compareData(x1, y1, x2, y2, **kwargs):
"""
"""
# First compare that there x-axis are same. else report warning.
x1 = np.array(x1)
x2 = np.array(x2)
y1 = np.array(y1)
y2 = np.array(y2)
print("[INFO] Plotting")
p1, = pylab.plot(x1, y1)
p2, = pylab.plot(x2, y2)
pylab.legend([p1, p2], ["MOOSE", "NEURON"])
outfile = kwargs.get('outfile', None)
if not outfile:
pylab.show()
else:
mu.info("Saving figure to %s" % outfile)
pylab.savefig(outfile)
if len(y1) > len(y2): y1 = ndimage.zoom(y1, len(y1)/len(y2))
else: y2 = ndimage.zoom(y2, len(y2)/len(y1))
diff = y1 - y2
linDiff = diff.sum()
rms = np.zeros(len(diff))
for i, d in enumerate(diff):
rms[i] = d**2.0
rms = rms.sum() ** 0.5
print(" |- RMS diff is: {}".format(rms))
def deepdream(base_img, iter_n=5, octave_n=4, octave_scale=1.4, **step_params):
# prepare base images for all octaves
octaves = [preprocess(base_img)]
for i in xrange(octave_n - 1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0 / octave_scale, 1.0 / octave_scale), order=1))
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0 * h / h1, 1.0 * w / w1), order=1)
x = np.array((1, 3, h, w)) # resize the network's input image size
x = octave_base + detail
for i in xrange(iter_n):
print h, w
make_step(x.reshape(1, 3, h, w))
# visualization
vis = deprocess(x)
# showarray(vis)
# print octave, i, end, vis.shape
# extract details produced on the current octave
detail = x - octave_base
# returning the resulting image
return deprocess(x)
def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4,
end='inception_5b/pool_proj', jitter = 32,step_size=1.5):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in xrange(octave_n-1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
src = net.blobs['data']
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
src.reshape(1,3,h,w) # resize the network's input image size
src.data[0] = octave_base+detail
for i in xrange(iter_n):
make_step(net, end=end,step_size=step_size,jitter=jitter)
# extract details produced on the current octave
detail = src.data[0]-octave_base
# returning the resulting image
return deprocess(net, src.data[0])
def upsample_pyramid(self, pyramid):
target_shape = self.residual_hipass.shape
result = []
for level in pyramid:
new_level = []
for band in level:
band_shape = band.shape
if len(target_shape) > len(band_shape):
band_shape = (band_shape[0], band_shape[1], 1)
zf = array(target_shape) / array(band_shape)
band.shape = band_shape
tmp = ones(target_shape)
if any(zf != 1):
ndi.zoom(band, zf, tmp, order=1)
upsamped = tmp
else:
upsamped = band
new_level.append(upsamped)
result.append(new_level)
return result
def deepdream(self, base_img, iter_n=10, octave_n=4, octave_scale=1.4,
end='inception_4c/output'):
# prepare base images for all octaves
octaves = [preprocess(self.net, base_img)]
for i in xrange(octave_n-1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
source = self.net.blobs['data'] # original image
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:] # octave size
if octave > 0:
# upscale details from previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0*h/h1, 1.0*w/w1), order=1)
source.reshape(1, 3, h, w) # resize the network's input image size
source.data[0] = octave_base + detail
for i in xrange(iter_n):
self.make_step(end=end)
# extract details produced on the current octave
detail = source.data[0] - octave_base
return deprocess(self.net, source.data[0]) # return final image
def deepdream(net, base_img, end, iter_n=10, octave_n=4, octave_scale=1.4, clip=True, **step_params):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in xrange(octave_n-1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
src = net.blobs['data']
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
src.reshape(1,3,h,w) # resize the network's input image size
src.data[0] = octave_base+detail
for i in xrange(iter_n):
make_step(net, end, clip=clip, **step_params)
# display step
#vis = deprocess(net, src.data[0])
#if not clip: # adjust image contrast if clipping is disabled
# vis = vis*(255.0/np.percentile(vis, 99.98))
#ename = '-'.join(end.split('/'))
#saveimage(vis, '{}-{}-{}'.format(octave, i))
#print octave, i, end, vis.shape
# extract details produced on the current octave
detail = src.data[0]-octave_base
# returning the resulting image
return deprocess(net, src.data[0])
def dream(model,
base_img,
octave_n=6,
octave_scale=1.4,
control=None,
distance=objective_L2):
octaves = [base_img]
for i in range(octave_n - 1):
octaves.append(
nd.zoom(
octaves[-1], (1, 1, 1.0 / octave_scale, 1.0 / octave_scale),
order=1))
detail = np.zeros_like(octaves[-1])
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
h1, w1 = detail.shape[-2:]
detail = nd.zoom(
detail, (1, 1, 1.0 * h / h1, 1.0 * w / w1), order=1)
input_oct = octave_base + detail
print(input_oct.shape)
out = make_step(input_oct, model, control, distance=distance)
detail = out - octave_base
def __init__(self, polmap, I0, ne, flip_ne=False):
self.fn=polmap.fn[:8]
I0=plt.imread(I0)
self.I0s=np.sum(I0,2)
I1=np.loadtxt(ne, delimiter=',')
I1=I1-np.nan_to_num(I1).min()
self.I1=np.nan_to_num(I1)
self.pm=polmap
#scale and flip to data
B0=self.pm.B0
scale=B0.shape[0]/self.I0s.shape[0]
I0z=zoom(self.I0s, scale)
crop=(I0z.shape[1]-B0.shape[1])//2
if B0.shape[1]%2==0:
I0zc=I0z[:,crop:-crop]
elif B0.shape[1]%2==1:
I0zc=I0z[:,crop:-crop-1]
self.I0zcn=np.flipud(I0zc/I0zc.max())
I1z=zoom(self.I1, scale)
if B0.shape[1]%2==0:
I1zc=I1z[:,crop:-crop]
elif B0.shape[1]%2==1:
I1zc=I1z[:,crop:-crop-1]
self.I1zc=np.flipud(I1zc)
if flip_ne is True:
self.I1zc=np.flipud(self.I1zc)
self.cmap='seismic'
def deepdream_stepped(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_3b/5x5_reduce', start_sigma=2.5, end_sigma=.1, start_jitter=48., end_jitter=4., start_step_size=3.0, end_step_size=1.5, clip=True, **step_params):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in xrange(octave_n-1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
src = net.blobs['data']
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0: # upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
src.reshape(1,3,h,w) # resize the network's input image size
src.data[0] = octave_base+detail
for i in xrange(iter_n):
sigma = start_sigma + ((end_sigma - start_sigma) * i) / iter_n
jitter = start_jitter + ((end_jitter - start_jitter) * i) / iter_n
step_size = start_step_size + ((end_step_size - start_step_size) * i) / iter_n
make_step(net, end=end, clip=clip, jitter=jitter, step_size=step_size, **step_params)
#src.data[0] = blur(src.data[0], sigma)
# extract details produced on the current octave
detail = src.data[0]-octave_base
#returning the resulting image
return deprocess(net, src.data[0])
def show_downsize(): for im in gen_images(n=-1, crop=True): t_im = im['T1c'] gt = im['gt'] t_im = np.asarray(t_im, dtype='float32') gt = np.asarray(gt, dtype='float32') d_im = zoom(t_im, 0.5, order=3) d_gt = zoom(gt, 0.5, order=0) print 'New shape: ', d_im.shape slices1 = np.arange(0, d_im.shape[0], d_im.shape[0]/20) slices2 = np.arange(0, t_im.shape[0], t_im.shape[0]/20) for s1, s2 in zip(slices1, slices2): d_im_slice = d_im[s1] d_gt_slice = d_gt[s1] im_slice = t_im[s2] gt_slice = gt[s2] title0= 'Original' title1= 'Downsized' vis_ims(im0=im_slice, gt0=gt_slice, im1=d_im_slice, gt1=d_gt_slice, title0=title0, title1=title1)
def overlay_velocities(self, ax):
"""Given an axes instance, overlay a quiver plot
of Uf_ and Wf_.
Uses interpolation (scipy.ndimage.zoom) to reduce
number of quivers to readable number.
Will only work sensibly if the thing plotted in ax
has same shape as Uf_
"""
zoom_factor = (0.5, 0.05)
# TODO: proper x, z
Z, X = np.indices(self.uf_.shape)
# TODO: are the velocities going at the middle of their grid?
# NB. these are not averages. ndi.zoom makes a spline and
# then interpolates a value from this
# TODO: gaussian filter first?
# both are valid approaches
Xr = ndi.zoom(X, zoom_factor)
Zr = ndi.zoom(Z, zoom_factor)
Uf_r = ndi.zoom(self.uf_, zoom_factor)
Wf_r = ndi.zoom(self.wf_, zoom_factor)
ax.quiver(Xr, Zr, Uf_r, Wf_r, scale=100)
def deepdream(net, base_imarray, iter_n=50, octave_n=4, octave_scale=1.4, end='inception_4c/output', clip=True, **step_params): octaves = [preprocess(net, base_imarray)] for i in xrange(octave_n-1): octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1)) src = net.blobs['data'] detail = np.zeros_like(octaves[-1]) for octave, octave_base in enumerate(octaves[::-1]): h, w = octave_base.shape[-2:] if octave > 0: h1, w1 = detail.shape[-2:] detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1) src.reshape(1,3,h,w) src.data[0] = octave_base+detail for i in xrange(iter_n): make_step(net, end=end, clip=clip, **step_params) vis = deprocess(net, src.data[0]) if not clip: vis = vis*(255.0/np.percentile(vis, 99.98)) showarray(vis) print octave, i, end, vis.shape clear_output(wait=True) detail = src.data[0]-octave_base return deprocess(net, src.data[0])
def plot_all_params(filen='obj_props', out_filen='ppv_grid', log_Z=False):
"""
Read in the pickled tree parameter dictionary and plot the containing
parameters.
Parameters
----------
filen : str
File name of pickled reduced property dictionary.
out_filen : str
Basename of plots, the key of the object dictionary is appended to the
filename.
log_Z : bool
Create plots with logarithmic Z axis
"""
cmap = cm.RdYlBu_r
obj_dict = pickle.load(open(filen + '.pickle', 'rb'))
X = obj_dict['velo']
Y = obj_dict['angle']
X = ndimage.zoom(X, 3)
Y = ndimage.zoom(Y, 3)
W = ndimage.zoom(obj_dict['conflict_frac'], 3)
obj_dict['reward'] = np.log10(obj_dict['new_kdar_assoc']) / obj_dict['conflict_frac']
params = [(k, v) for k, v in obj_dict.iteritems()
if k not in ['velo', 'angle']]
clevels = [0.06, 0.12, 0.20, 0.30, 0.5]
for key, Z in params:
print ':: ', key
fig, ax = plt.subplots(figsize=(4, 4.5))
cax = fig.add_axes([0.15, 0.88, 0.8, 0.03])
plt.subplots_adjust(top=0.85, left=0.15, right=0.95, bottom=0.125)
if log_Z:
Z = np.log10(Z)
key += '_(log)'
Z = ndimage.zoom(Z, 3)
pc = ax.pcolor(X, Y, Z, cmap=cmap, vmin=Z.min(), vmax=Z.max())
cb = plt.colorbar(pc, ax=ax, cax=cax, orientation='horizontal',
ticklocation='top')
ax.plot([4], [0.065], 'ko', ms=10, markerfacecolor='none', markeredgewidth=2)
# Contours for conflict frac
cn = ax.contour(X, Y, W, levels=clevels,
colors='k', linewidth=2)
plt.setp(cn.collections,
path_effects=[PathEffects.withStroke(linewidth=2,
foreground='w')])
cl = ax.clabel(cn, fmt='%1.2f', inline=1, fontsize=10,
use_clabeltext=True)
plt.setp(cl, path_effects=[PathEffects.withStroke(linewidth=2,
foreground='w')])
# Labels
ax.set_xlabel(r'$v \ \ [{\rm km \ s^{-1}}]$')
ax.set_ylabel(r'$\theta \ \ [^{\circ}]$')
# Limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
# Save
plt.savefig(out_filen + '_' + key + '.pdf')
plt.savefig(out_filen + '_' + key + '.png', dpi=300)
plt.close()
def scaleImage(path_img, dilated_img, depth, color_depth, scale=1):
final_vessel = ndimage.zoom(dilated_img, scale, order=0)
final_path = skeletonize_Image(255*ndimage.zoom(path_img, scale, order=0))/255# use nearest neighbour
final_depth = final_path*ndimage.zoom(depth, scale, order=0)
final_color_depth = ndimage.zoom(color_depth, scale, order=0)
return final_path,final_vessel,final_depth, final_color_depth
def __call__(self, locs, wfImage):
"""Align a set of localizations to a widefield image.
Parameters
----------
locs : Pandas DataFrame
The DataFrame containing the localizations. x- and y-column
labels are specified in self.coordCols.
wfImage : array of int or array of float
The widefield image to align the localizations to.
Returns
-------
offsets : tuple of float
The estimated offset between the localizations and widefield
image. The first element is the offset in x and the second
in y. These should be subtracted from the input localizations
to align them to the widefield image.
"""
upsampleFactor = self.upsampleFactor
# Bin the localizations into a 2D histogram;
# x corresponds to rows for histogram2d
binsX = np.arange(0, upsampleFactor * wfImage.shape[0] + 1, 1) \
* self.pixelSize / upsampleFactor
binsY = np.arange(0, upsampleFactor * wfImage.shape[1] + 1, 1) \
* self.pixelSize / upsampleFactor
H, _, _ = np.histogram2d(locs[self.coordCols[0]],
locs[self.coordCols[1]],
bins = [binsX, binsY])
# Upsample and flip the image to align it to the histogram;
# then compute the cross correlation
crossCorr = fftconvolve(H,
zoom(np.transpose(wfImage)[::-1, ::-1],
upsampleFactor, order = 0),
mode = 'same')
# Find the maximum of the cross correlation
centerLoc = np.unravel_index(np.argmax(crossCorr), crossCorr.shape)
# Find the center of the widefield image
imgCorr = fftconvolve(zoom(np.transpose(wfImage),
upsampleFactor, order = 0),
zoom(np.transpose(wfImage)[::-1, ::-1],
upsampleFactor, order = 0),
mode = 'same')
centerWF = np.unravel_index(np.argmax(imgCorr), imgCorr.shape)
# Find the shift between the images.
# dx -> rows, dy -> cols because the image was transposed during
# fftconvolve operation.
dy = (centerLoc[1] - centerWF[1]) / upsampleFactor * self.pixelSize
dx = (centerLoc[0] - centerWF[0]) / upsampleFactor * self.pixelSize
offsets = (dx, dy)
return offsets
def _make_tuples(self, key):
from scipy import ndimage
from .utils import registration
print('Registering', key)
# Get stack
stack_rel = (stack.CorrectedStack() & key & {'session': key['stack_session']})
stack_ = stack_rel.get_stack(key['stack_channel'])
# Get average field
field_key = {'animal_id': key['animal_id'], 'session': key['scan_session'],
'scan_idx': key['scan_idx'], 'field': key['field'],
'channel': key['scan_channel']} #no pipe_version
frames = (meso.Quality().SummaryFrames() & field_key).fetch1('summary')
field = frames[:, :, int(frames.shape[-1] / 2)]
# Drop some edges (only y and x) to avoid artifacts
skip_dims = [max(1, int(round(s * 0.025))) for s in stack_.shape]
stack_ = stack_[:, skip_dims[1] : -skip_dims[1], skip_dims[2]: -skip_dims[2]]
skip_dims = [max(1, int(round(s * 0.025))) for s in field.shape]
field = field[skip_dims[0] : -skip_dims[0], skip_dims[1]: -skip_dims[1]]
# Rescale to match lowest resolution (isotropic pixels/voxels)
field_res = (meso.ScanInfo.Field() & field_key).microns_per_pixel
dims = stack_rel.fetch1('um_depth', 'px_depth', 'um_height', 'px_height',
'um_width', 'px_width')
stack_res = np.array([dims[0] / dims[1], dims[2] / dims[3], dims[4] / dims[5]])
common_res = max(*field_res, *stack_res) # minimum available resolution
stack_ = ndimage.zoom(stack_, stack_res / common_res, order=1)
field = ndimage.zoom(field, field_res / common_res, order=1)
# Get estimated depth of the field (from experimenters)
stack_x, stack_y, stack_z = stack_rel.fetch1('x', 'y', 'z') # z of the first slice (zero is at surface depth)
field_z = (meso.ScanInfo.Field() & field_key).fetch1('z') # measured in microns (zero is at surface depth)
if field_z < stack_z or field_z > stack_z + dims[0]:
msg_template = 'Warning: Estimated depth ({}) outside stack range ({}-{}).'
print(msg_template.format(field_z, stack_z , stack_z + dims[0]))
estimated_px_z = (field_z - stack_z + 0.5) / common_res # in pixels
# Register
z_range = 40 / common_res # search 40 microns up and down
# Run rigid registration with no rotations
result = registration.register_rigid(stack_, field, estimated_px_z, z_range)
score, (x, y, z), (yaw, pitch, roll) = result
# Map back to stack coordinates
final_x = stack_x + x * (common_res / stack_res[2]) # in stack pixels
final_y = stack_y + y * (common_res / stack_res[1]) # in stack pixels
final_z = stack_z + (z + stack_.shape[0] / 2) * common_res # in microns*
#* Best match in slice 0 will not result in z = 0 but 0.5 * z_step.
# Insert
self.insert1({**key, 'common_res': common_res, 'reg_x': final_x, 'reg_y': final_y,
'reg_z': final_z, 'score': score})
self.notify(key)
def zoomSmooth(inArr, smoothing, inAffine):
zoomReg = zoom(inArr.data, smoothing, order=0)
zoomed = zoom(inArr.data, smoothing, order=1)
zoomMask = zoom(inArr.mask, smoothing, order=0)
zoomed[np.where(zoomed > inArr.max())] = inArr.max()
zoomed[np.where(zoomed < inArr.min())] = inArr.min()
inArr = np.ma.array(zoomed, mask=zoomMask)
oaff = tools.resampleAffine(inAffine, smoothing)
del zoomed, zoomMask
return inArr, oaff
def _detectHaarFeatures(image, options={}):
if options is None:
options = _haarDefaultOptions(image)
levels = options.get('levels')
maxpoints = options.get('maxpoints')
threshold = options.get('threshold')
locality = options.get('locality')
haarData = haar2d(image, levels)
avgRows = haarData.shape[0] / 2 ** levels
avgCols = haarData.shape[1] / 2 ** levels
SalientPoints = {}
siloH = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
siloD = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
siloV = np.zeros([haarData.shape[0]/2, haarData.shape[1]/2, levels])
# Build the saliency silos
for i in range(levels):
level = i + 1
halfRows = haarData.shape[0] / 2 ** level
halfCols = haarData.shape[1] / 2 ** level
siloH[:,:,i] = nd.zoom(haarData[:halfRows, halfCols:halfCols*2], 2**(level-1))
siloD[:,:,i] = nd.zoom(haarData[halfRows:halfRows*2, halfCols:halfCols*2], 2**(level-1))
siloV[:,:,i] = nd.zoom(haarData[halfRows:halfRows*2, :halfCols], 2**(level-1))
# Calculate saliency heat-map
saliencyMap = np.max(np.array([
np.sum(np.abs(siloH), axis=2),
np.sum(np.abs(siloD), axis=2),
np.sum(np.abs(siloV), axis=2)
]), axis=0)
# Determine global maximum and saliency threshold
maximum = np.max(saliencyMap)
sthreshold = threshold * maximum
# Extract features by finding local maxima
rows = haarData.shape[0] / 2
cols = haarData.shape[1] / 2
features = {}
id = 0
for row in range(locality,rows-locality):
for col in range(locality,cols-locality):
saliency = saliencyMap[row,col]
if saliency > sthreshold:
if saliency >= np.max(saliencyMap[row-locality:row+locality, col-locality:col+locality]):
features[id] = (row*2,col*2)
id += 1
result = {}
result['points'] = features
return result
def scale_dif(image,full_size):
#compare images
x,y = image.shape[0:2]
i,j = full_size.shape[0:2]
print("scale dif {} {} {} {}".format(x,y,i,j))
if i == x and j == y:
return image
original = nd.zoom(full_size, (1.0*x/i,1.0*y/j,1))
dif_zoom = nd.zoom(original - image, (1.0*i/x,1.0*j/y,1))
return np.clip(full_size - dif_zoom,0,255)
def myzoom(img,factor,order):
auxf=numpy.array(factor)
order=1 #force order to be 1 otherwise I do not know if it is still correct
from scipy.ndimage import zoom
aux=img.copy()
while (auxf[0]<0.5 and auxf[1]<0.5):
aux=zoom(aux,(0.5,0.5,1),order=order)
auxf[0]=auxf[0]*2
auxf[1]=auxf[1]*2
aux=zoom(aux,auxf,order=order)
return aux
def scale_and_crop(self):
B0=self.pm.B0
scale=B0.shape[0]/self.I0s.shape[0]
I0z=zoom(self.I0s, scale)
crop=(I0z.shape[1]-B0.shape[1])/2
I0zc=I0z[:,crop:-crop]
self.I0zcn=np.fliplr(I0zc/I0zc.max())
I1z=zoom(self.I1, scale)
I1zc=I1z[:,crop:-crop]
self.I1zc=np.flipud(I1z[:,crop:-crop])
def test_CubicSpline_estimate():
"""
Asserts that scaling a warp field is a reasonable thing to do.
"""
scale = 2.0
# Form a high resolution image.
high = register.RegisterData(misc.lena().astype(np.double))
# Form a low resolution image.
low = high.downsample(scale)
# Make a deformed low resolution image.
p = model.CubicSpline(low.coords).identity
p += np.random.rand(p.shape[0]) * 100 - 50
warp = model.CubicSpline(low.coords).transform(p)
dlow = sampler.Nearest(low.coords).f(
low.data,
low.coords.tensor - warp
).reshape(low.data.shape)
# Scale the low resolution warp field to the same size as the high resolution
# image.
hwarp = np.array( [nd.zoom(warp[0],scale), nd.zoom(warp[1],scale)] ) * scale
# Estimate the high resolution spline parameters that best fit the
# enlarged warp field.
invB = np.linalg.pinv(model.CubicSpline(high.coords).basis)
pHat = np.hstack(
(np.dot(invB, hwarp[1].flatten()),
np.dot(invB, hwarp[0].flatten()))
)
warpHat = model.CubicSpline(high.coords).warp(pHat)
# Make a deformed high resolution image.
dhigh = sampler.Nearest(high.coords).f(high.data, warpHat).reshape(high.data.shape)
# down-sample the deformed high-resolution image and assert that the
# pixel values are "close".
dhigh_low = nd.zoom(dhigh, 1.0/scale)
# Assert that the down-sampler highresolution image is "roughly" similar to
# the low resolution image.
assert (np.abs((dhigh_low[:] - dlow[:])).sum() / dlow.size < 10.0), \
"Normalized absolute error is greater than 10 pixels."
def deepdream(
net, base_img, iter_n=10, octave_n=4, octave_scale=1.4,
end='inception_4c/output', clip=True, **step_params
):
'''
Implement an ascent through different scales. We call these scales
"octaves".
'''
# Prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in range(octave_n - 1):
octaves.append(
# ndimage? numpy?
ndimage.zoom(
octaves[-1], (1, 1.0 / octave_scale, 1.0 / octave_scale),
order=1
)
)
src = net.blobs['data']
# Allocate image for network-produced details
detail = numpy.zeros_like(octaves[-1])
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# Upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = ndimage.zoom(
detail, (1, 1.0 * h / h1, 1.0 * w / w1), order=1
)
# Resize the network's input image size
src.reshape(1, 3, h, w)
src.data[0] = octave_base + detail
for i in range(iter_n):
make_step(net, end=end, clip=clip, **step_params)
# Visualization
vis = postprocess(net, src.data[0])
if not clip:
# Adjust image contrast if clipping is disabled
vis = vis * (255.0 / numpy.percentile(vis, 99.98))
showarray(vis)
print(octave, i, end, vis.shape)
clear_output(wait=True)
# Extract details produced on the current octave
detail = src.data[0]-octave_base
# Returning the resulting image
return postprocess(net, src.data[0])
def measure_shift(da, db, use_md=True):
"""
use_md (bool): if False, will not use metadata and assume the 2 images are
of the same area
return (float, float): shift of the second image compared to the first one,
in pixels of the first image.
"""
da_res = da.shape[1], da.shape[0] # X/Y are inverted
db_res = db.shape[1], db.shape[0]
if any(sa < sb for sa, sb in zip(da_res, db_res)):
logging.warning("Comparing a large image %s to a small image %s, you should do the opposite", db_res, da_res)
# if db FoV is smaller than da, crop da
if use_md:
dafov = [pxs * s for pxs, s in zip(da.metadata[model.MD_PIXEL_SIZE], da_res)]
dbfov = [pxs * s for pxs, s in zip(db.metadata[model.MD_PIXEL_SIZE], db_res)]
fov_ratio = [fa / fb for fa, fb in zip(dafov, dbfov)]
else:
fov_ratio = (1, 1)
if any(r < 1 for r in fov_ratio):
logging.warning("Cannot compare an image with a large FoV %g to a small FoV %g",
dbfov, dafov)
shift_px = measure_shift(db, da)
return [-s for s in shift_px]
crop_res = [int(s / r) for s, r in zip(da_res, fov_ratio)]
logging.debug("Cropping da to %s", crop_res)
da_ctr = [s // 2 for s in da_res]
da_lt = [int(c - r // 2) for c, r in zip(da_ctr, crop_res)]
da_crop = da[da_lt[1]: da_lt[1] + crop_res[1],
da_lt[0]: da_lt[0] + crop_res[0]]
scale = [sa / sb for sa, sb in zip(da_crop.shape, db.shape)]
if scale[0] != scale[1]:
raise ValueError("Comparing images with different zooms levels %s on each axis is not supported" % (scale,))
# Resample the smaller image to fit the resolution of the larger image
if scale[0] < 1:
# The "big" image has actually less pixels than the small FoV image
# => zoom the big image, and compensate later for the shift
logging.info("Rescaling large FoV image by scale %f", 1 / scale[0])
da_crop = zoom(da_crop, 1 / scale[0])
db_big = db
shift_ratio = scale[0]
else:
logging.info("Rescaling small FoV image by scale %f", scale[0])
db_big = zoom(db, scale[0])
shift_ratio = 1
# Apply phase correlation
shift_px = MeasureShift(da_crop, db_big, 10)
return shift_px[0] * shift_ratio, shift_px[1] * shift_ratio
def analyseSpotsDeconvolution(files):
"""
Analyse spot measurements using deconvolutions.
Note: does not really work... perhaps an issue with sizes.
:param files: a list of input files
:type files: list
:return: None
"""
d = {}
data = []
for filename in files:
tmp = readData(filename, crop=False)
f = filename.replace('.fits', '')
d[f] = tmp
data.append(tmp)
data = np.asarray(data)
#sanity check plots
#stackData(data)
#deconvolve with top hat
dec1 = {}
y, x = data[0].shape
top = np.zeros((y, x))
top[y/2, x/2] = 1.
fileIO.writeFITS(top, 'tophat.fits', int=False)
for filename, im in zip(files, data):
deconv = weinerFilter(im, top, normalize=False)
f = filename.replace('.fits', 'deconv1.fits')
fileIO.writeFITS(deconv, f, int=False)
dec1[f] = deconv
print "Tophat deconvolution done"
#deconvolve with a Besssel
dec2 = {}
bes = generateBessel(radius=0.13)
bes = ndimage.zoom(bes, 1./2.5, order=0)
bes /= np.max(bes)
fileIO.writeFITS(bes, 'bessel.fits', int=False)
for key, value in dec1.iteritems():
value = ndimage.zoom(value, 4., order=0)
value -= np.median(value)
deconv = weinerFilter(value, bes, reqularization=2.0, normalize=False)
f = key.replace('deconv1.fits', 'deconv2.fits')
fileIO.writeFITS(deconv, f, int=False)
dec2[f] = deconv
print 'Bessel deconvolution done'
def merge_images_landmarks_maps_gt(images,
maps,
maps_gt,
landmarks=None,
image_size=256,
num_landmarks=68,
num_samples=9,
scale=255,
circle_size=2,
fast=False):
"""create image for log - containing input face images, predicted heatmaps and GT heatmaps (if exists)"""
images = images[:num_samples]
if maps.shape[1] is not image_size:
images = zoom(images, (1, 0.25, 0.25, 1))
image_size /= 4
image_size = int(image_size)
if maps_gt is not None:
if maps_gt.shape[1] is not image_size:
maps_gt = zoom(maps_gt, (1, 0.25, 0.25, 1))
cmap = plt.get_cmap('jet')
row = int(np.sqrt(num_samples))
if maps_gt is None:
merged = np.zeros([row * image_size, row * image_size * 2, 3])
else:
merged = np.zeros([row * image_size, row * image_size * 3, 3])
for idx, img in enumerate(images):
i = idx // row
j = idx % row
if landmarks is None:
img_landmarks = heat_maps_to_landmarks(maps[idx, :, :, :],
image_size=image_size,
num_landmarks=num_landmarks)
else:
img_landmarks = landmarks[idx]
if fast:
map_image = np.amax(maps[idx, :, :, :], 2)
map_image = (map_image - map_image.min()) / (map_image.max() -
map_image.min())
else:
map_image = heat_maps_to_image(maps[idx, :, :, :],
img_landmarks,
image_size=image_size,
num_landmarks=num_landmarks)
rgba_map_image = cmap(map_image)
map_image = np.delete(rgba_map_image, 3, 2) * 255
img = create_img_with_landmarks(img,
img_landmarks,
image_size,
num_landmarks,
scale=scale,
circle_size=circle_size)
if maps_gt is not None:
if fast:
map_gt_image = np.amax(maps_gt[idx, :, :, :], 2)
map_gt_image = (map_gt_image - map_gt_image.min()) / (
map_gt_image.max() - map_gt_image.min())
else:
map_gt_image = heat_maps_to_image(maps_gt[idx, :, :, :],
image_size=image_size,
num_landmarks=num_landmarks)
rgba_map_gt_image = cmap(map_gt_image)
map_gt_image = np.delete(rgba_map_gt_image, 3, 2) * 255
merged[i * image_size:(i + 1) * image_size,
(j * 3) * image_size:(j * 3 + 1) * image_size, :] = img
merged[i * image_size:(i + 1) * image_size, (j * 3 + 1) *
image_size:(j * 3 + 2) * image_size, :] = map_image
merged[i * image_size:(i + 1) * image_size, (j * 3 + 2) *
image_size:(j * 3 + 3) * image_size, :] = map_gt_image
else:
merged[i * image_size:(i + 1) * image_size,
(j * 2) * image_size:(j * 2 + 1) * image_size, :] = img
merged[i * image_size:(i + 1) * image_size, (j * 2 + 1) *
image_size:(j * 2 + 2) * image_size, :] = map_image
return merged
import sys
from cv2 import imwrite
from numpy import mean, binary_repr, ones
from numpy.random import randint
from scipy.ndimage import zoom
for i in range(0, 16):
img = ones((6, 6)) * 255
img[1, 1] = 0
img[4, 1] = 0
img[1, 4] = 0
if i % 2 == 1:
img[2, 2] = 0
if (i >> 1) % 2 == 1:
img[2, 3] = 0
if (i >> 2) % 2 == 1:
img[3, 2] = 0
if (i >> 3) % 2 == 1:
img[3, 3] = 0
print(img)
marker = zoom(img, zoom=50, order=0)
imwrite('marker_images/marker_{}.png'.format(i), marker)
def resize(self, arr, size_):
from scipy.ndimage import zoom
s, w, h = arr.shape[0], arr.shape[1], arr.shape[2]
return zoom(arr,
(self.size[2] / s, self.size[0] / w, self.size[1] / h))
def process_image(img):
img = zoom(img, np.random.uniform(MIN_ZOOM, MAX_ZOOM), order=0)
img = rotate(img, np.random.uniform(-MAX_ROTATE, MAX_ROTATE), order=0)
img = gaussian_filter(img, np.random.uniform(-MIN_BLUR, MAN_BLUR))
img = random_crop(img, (299, 299))
return img
def nirps_pp(files):
ref_hdr = fits.getheader('ref_hdr.fits')
if type(files) == str:
files = glob.glob(files)
for file in files:
outname = '_pp.'.join(file.split('.'))
if '_pp.' in file:
print(file + ' is a _pp file')
continue
if os.path.isfile(outname):
print('File : ' + outname + ' exists')
continue
else:
print('We pre-process ' + file)
hdr = fits.getheader(file)
im = fits.getdata(file)
mask = np.array(fits.getdata('mask.fits'), dtype=bool)
im2 = np.array(im)
im2[mask] = np.nan
# we find the low level frequencies
# we bin in regions of 32x32 pixels. This CANNOT be
# smaller than the order footprint on the array
# as it would lead to a set of NaNs in the downsized
# image and chaos afterward
binsize = 32 # pixels
# median-bin and expand back to original size
lowf = zoom(medbin(im2, binsize, binsize), 4096 // binsize)
# subtract low-frequency from masked image
im2 -= lowf
# find the amplifier x-talk map
xtalk = med32(im2)
im2 -= xtalk
# subtract both low-frequency and x-talk from input image
im -= (lowf + xtalk)
tmp = np.nanmedian(im2, axis=0)
im -= np.tile(tmp, 4096).reshape(4096, 4096)
# rotates the image so that it matches the order geometry of SPIRou and HARPS
# redder orders at the bottom and redder wavelength within each order on the left
# NIRPS = 5
# SPIROU = 3
im = rot8(im, 5)
#DPRTYPE
"""
MJDMID = 58875.10336167315 / Mid Observation time [mjd]
BERVOBSM= 'header ' / BERV method used to calc observation time
DPRTYPE = 'FP_FP ' / The type of file (from pre-process)
PVERSION= '0.6.029 ' / DRS Pre-Processing version
DRSVDATE= '2020-01-27' / DRS Release date
DRSPDATE= '2020-01-30 22:16:00.344' / DRS Processed date
DRSPID = 'PID-00015804225603440424-JKBM' / The process ID that outputted this f
INF1000 = '2466774a.fits' / Input file used to create output infile=0
QCC001N = 'snr_hotpix' / All quality control passed
QCC001V = 876.2474157597072 / All quality control passed
QCC001L = 'snr_hotpix < 1.00000e+01' / All quality control passed
QCC001P = 1 / All quality control passed
QCC002N = 'max(rms_list)' / All quality control passed
QCC002V = 0.002373232122258537 / All quality control passed
QCC002L = 'max(rms_list) > 1.5000e-01' / All quality control passed
QCC002P = 1 / All quality control passed
QCC_ALL = T
DETOFFDX= 0 / Pixel offset in x from readout lag
DETOFFDY= 0 / Pixel offset in y from readout lag
"""
if 'MJDEND' not in hdr:
hdr['MJDEND'] = 0.00
hdr['EXPTIME'] = 5.57 * len(hdr['INTT*'])
hdr['MJDMID'] = hdr['MJDEND'] - hdr['EXPTIME'] / 2.0 / 86400.0
hdr['INF1000'] = file
DPRTYPES = [
'DARK_DARK', 'DARK_FP', 'FLAT_FLAT', 'DARK_FLAT', 'FLAT_DARK',
'HC_FP', 'FP_HC', 'FP_FP', 'OBJ_DARK', 'OBJ_FP', 'HC_DARK',
'DARK_HC', 'HC_HC'
]
if 'STAR_DARK' in file:
hdr['DPRTYPE'] = 'OBJ_DARK'
if 'STAR_FP' in file:
hdr['DPRTYPE'] = 'OBJ_FP'
for DPRTYPE in DPRTYPES:
if DPRTYPE in file:
if DPRTYPE == 'DARK_DARK':
hdr['DPRTYPE'] = 'DARK_DARK_TEL'
elif DPRTYPE == 'HC_HC':
hdr['DPRTYPE'] = 'HCONE_HCONE'
elif DPRTYPE == 'FP_HC':
hdr['DPRTYPE'] = 'FP_HCONE'
elif DPRTYPE == 'HC_FP':
hdr['DPRTYPE'] = 'HCONE_FP'
elif DPRTYPE == 'DARK_HC':
hdr['DPRTYPE'] = 'DARK_HCONE'
elif DPRTYPE == 'HC_DARK':
hdr['DPRTYPE'] = 'HCONE_DARK'
elif DPRTYPE == 'FP_DARK':
hdr['DPRTYPE'] = 'FP_DARK'
elif DPRTYPE == 'DARK_FP':
hdr['DPRTYPE'] = 'DARK_FP'
else:
hdr['DPRTYPE '] = DPRTYPE
if 'DPRTYPE' not in hdr:
print('error, with DPRTYPE for ', file)
return
if 'OBJECT' not in hdr:
hdr['OBJECT'] = 'none'
if 'RDNOISE' not in hdr:
hdr['RDNOISE'] = 10.0, 'rdnoise *not* provided, added by _pp'
if 'GAIN' not in hdr:
hdr['GAIN'] = 1.000, 'gain *not* provided, added by _pp'
if 'SATURATE' not in hdr:
hdr['SATURATE'] = 60000, 'saturate *not* provided, added by _pp'
if 'PVERSION' not in hdr:
hdr['PVERSION'] = 'NIRPS_SIMU_PP'
if 'OBSTYPE' not in hdr:
if hdr['DPRTYPE'][0:4] == 'FLAT':
hdr['OBSTYPE'] = 'FLAT'
if hdr['DPRTYPE'][0:4] == 'DARK':
hdr['OBSTYPE'] = 'DARK'
if hdr['DPRTYPE'][0:2] == 'FP':
hdr['OBSTYPE'] = 'ALIGN'
if hdr['DPRTYPE'][0:2] == 'HC':
hdr['OBSTYPE'] = 'COMPARISON'
if hdr['DPRTYPE'][0:3] == 'OBJ':
hdr['OBSTYPE'] = 'OBJECT'
if hdr['DPRTYPE'][0:3] == 'OBJ':
hdr['TRG_TYPE'] = 'TARGET'
else:
hdr['TRG_TYPE'] = ''
necessary_kwrd = [
'OBSTYPE', 'TRG_TYPE', 'OBJECT', 'OBJRA', 'OBJDEC', 'OBJECT',
'OBJEQUIN', 'OBJRAPM', 'OBJDECPM', 'AIRMASS', 'RELHUMID',
'OBJTEMP', 'GAIA_ID', 'OBJPLX', 'OBSRV', 'GAIN', 'RDNOISE',
'FRMTIME', 'EXPTIME', 'PI_NAME', 'CMPLTEXP', 'NEXP', 'MJDATE',
'MJDEND', 'SBCREF_P', 'SBCCAS_P', 'SBCALI_P', 'SBCDEN_P',
'DATE-OBS', 'UTC-OBS', 'SATURATE', 'TEMPERAT', 'SB_POL_T'
]
missing = False
for key in necessary_kwrd:
if key not in hdr:
print('missing keyword : {0}'.format(key))
missing = True
if key in ref_hdr:
hdr[key] = ref_hdr[key]
b = fits.getdata(file, ext=2)
errslope = fits.getdata(file, ext=3)
n = fits.getdata(file, ext=4)
b = rot8(b, 5)
errslope = rot8(errslope, 5)
n = rot8(n, 5)
hdu1 = fits.PrimaryHDU()
hdu1.header = hdr
hdu1.header['NEXTEND'] = 4
hdu2 = fits.ImageHDU(im)
hdu2.header['UNITS'] = ('ADU/S', 'Slope of fit, flux vs time')
hdu2.header['EXTNAME'] = ('slope', 'Slope of fit, flux vs time')
hdu3 = fits.ImageHDU(b)
hdu3.header['UNITS'] = ('ADU', 'Intercept of the pixel/time fit.')
hdu3.header['EXTNAME'] = ('intercept',
'Intercept of the pixel/time fit.')
hdu4 = fits.ImageHDU(errslope)
hdu4.header['UNITS'] = ('ADU/S', 'Formal error on slope fit')
hdu4.header['EXTNAME'] = ('errslope', 'Formal error on slope fit')
hdu5 = fits.ImageHDU(n)
hdu5.header['UNITS'] = ('Nimages', 'N readouts below saturation')
hdu5.header['EXTNAME'] = ('count', 'N readouts below saturation')
new_hdul = fits.HDUList([hdu1, hdu2, hdu3, hdu4, hdu5])
# just to avoid an error message with writeto
if os.path.isfile(outname):
print('file : ' + outname + ' exists, we are overwriting it')
os.system('rm ' + outname + '')
new_hdul.writeto(outname, overwrite=True)
return []
def _imgs_stim():
X = ndi.zoom(imgs[istim, ...], (1, img_scale, img_scale), order=1)
img_dim = X.shape[1:]
X = np.reshape(X, [len(istim), -1])
return img_dim, zscore(X, axis=0) / np.sqrt(len(istim)).astype(
np.float32)
(0, 0), (0, 0)), 'constant')
else:
x_start = round(x / 2) - IMAGE_WIDTH_HALF
x_end = round(x / 2) + IMAGE_WIDTH_HALF
img_arr = img_arr[x_start:x_end, :, :]
if y < IMAGE_LENGTH_HALF * 2:
img_arr = np.pad(img_arr,
((0, 0), (IMAGE_LENGTH_HALF - math.ceil(y / 2), IMAGE_LENGTH_HALF - math.floor(y / 2)),
(0, 0)), 'constant')
else:
y_start = round(y / 2) - IMAGE_LENGTH_HALF
y_end = round(y / 2) + IMAGE_LENGTH_HALF
img_arr = img_arr[:, y_start:y_end, :]
if z < IMAGE_HEIGHT_HALF * 2:
img_arr = np.pad(img_arr, ((0, 0), (0, 0),
(IMAGE_HEIGHT_HALF - math.ceil(z / 2), IMAGE_HEIGHT_HALF - math.floor(z / 2))),
'constant')
else:
z_start = round(z / 2) - IMAGE_HEIGHT_HALF
z_end = round(z / 2) + IMAGE_HEIGHT_HALF
img_arr = img_arr[:, :, z_start:z_end]
print("shape = " + str(img_arr.shape))
img_arr = ndi.zoom(img_arr, 0.25) # resize the image from 256*256*256 to 64*64*64
print("shape = " + str(img_arr.shape))
nib_img = nib.Nifti1Image(img_arr, img.affine)
nib.save(nib_img, resized_path + file)
# nib.save(nib_img, "E:/Y4/DT/data/Resized64_MNI152_T1_1mm_brain.nii.gz")
def inference(deploy_set,
output_dir,
model_path,
FineNet_path=None,
set_name=None,
file_ext='.bmp',
isHavingFineNet=False):
if set_name is None:
set_name = deploy_set.split('/')[-2]
mkdir(output_dir + '/' + set_name + '/')
mkdir(output_dir + '/' + set_name + '/mnt_results/')
mkdir(output_dir + '/' + set_name + '/seg_results/')
mkdir(output_dir + '/' + set_name + '/OF_results/')
logging.info("Predicting %s:" % (set_name))
_, img_name = get_files_in_folder(deploy_set + 'img_files/', file_ext)
print deploy_set
# ====== Load FineNet to verify
if isHavingFineNet == True:
model_FineNet = FineNetmodel(num_classes=2,
pretrained_path=FineNet_path,
input_shape=(224, 224, 3))
model_FineNet.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0),
metrics=['accuracy'])
time_c = []
main_net_model = CoarseNetmodel((None, None, 1), model_path, mode='deploy')
for i in xrange(0, len(img_name)):
print i
image = misc.imread(deploy_set + 'img_files/' + img_name[i] + file_ext,
mode='L') # / 255.0
img_size = image.shape
img_size = np.array(img_size, dtype=np.int32) // 8 * 8
# read the mask from files
try:
mask = misc.imread(
deploy_set + 'seg_files/' + img_name[i] + '.jpg',
mode='L') / 255.0
except:
mask = np.ones((img_size[0], img_size[1]))
image = image[:img_size[0], :img_size[1]]
mask = mask[:img_size[0], :img_size[1]]
original_image = image.copy()
texture_img = FastEnhanceTexture(image, sigma=2.5, show=False)
dir_map, fre_map = get_maps_STFT(texture_img,
patch_size=64,
block_size=16,
preprocess=True)
image = image * mask
logging.info("%s %d / %d: %s" %
(set_name, i + 1, len(img_name), img_name[i]))
time_start = time()
image = np.reshape(image, [1, image.shape[0], image.shape[1], 1])
enh_img, enh_img_imag, enhance_img, ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out \
= main_net_model.predict(image)
time_afterconv = time()
# If use mask from model
round_seg = np.round(np.squeeze(seg_out))
seg_out = 1 - round_seg
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_CLOSE, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
seg_out = cv2.dilate(seg_out, kernel)
# If use mask from outside
# seg_out = cv2.resize(mask, dsize=(seg_out.shape[1], seg_out.shape[0]))
max_num_minu = 20
min_num_minu = 6
early_minutiae_thres = 0.5
# New adaptive threshold
mnt = label2mnt(np.squeeze(mnt_s_out) * np.round(np.squeeze(seg_out)),
mnt_w_out,
mnt_h_out,
mnt_o_out,
thresh=0)
# Previous exp: 0.2
mnt_nms_1 = py_cpu_nms(mnt, 0.5)
mnt_nms_2 = nms(mnt)
mnt_nms_1.view('f8,f8,f8,f8').sort(order=['f3'], axis=0)
mnt_nms_1 = mnt_nms_1[::-1]
mnt_nms_1_copy = mnt_nms_1.copy()
mnt_nms_2_copy = mnt_nms_2.copy()
# Adaptive threshold goes here
# Make sure the maximum number of minutiae is max_num_minu
# Sort minutiae by score
while early_minutiae_thres > 0:
mnt_nms_1 = mnt_nms_1_copy[
mnt_nms_1_copy[:, 3] > early_minutiae_thres, :]
mnt_nms_2 = mnt_nms_2_copy[
mnt_nms_2_copy[:, 3] > early_minutiae_thres, :]
if mnt_nms_1.shape[0] > max_num_minu or mnt_nms_2.shape[
0] > max_num_minu:
mnt_nms_1 = mnt_nms_1[:max_num_minu, :]
mnt_nms_2 = mnt_nms_2[:max_num_minu, :]
if mnt_nms_1.shape[0] > min_num_minu and mnt_nms_2.shape[
0] > min_num_minu:
break
early_minutiae_thres = early_minutiae_thres - 0.05
mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2)
final_minutiae_score_threashold = early_minutiae_thres - 0.05
print early_minutiae_thres, final_minutiae_score_threashold
mnt_refined = []
if isHavingFineNet == True:
# ======= Verify using FineNet ============
patch_minu_radio = 22
if FineNet_path != None:
for idx_minu in range(mnt_nms.shape[0]):
try:
# Extract patch from image
x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio
y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio
patch_minu = original_image[x_begin:x_begin +
2 * patch_minu_radio,
y_begin:y_begin +
2 * patch_minu_radio]
patch_minu = cv2.resize(
patch_minu,
dsize=(224, 224),
interpolation=cv2.INTER_NEAREST)
ret = np.empty(
(patch_minu.shape[0], patch_minu.shape[1], 3),
dtype=np.uint8)
ret[:, :, 0] = patch_minu
ret[:, :, 1] = patch_minu
ret[:, :, 2] = patch_minu
patch_minu = ret
patch_minu = np.expand_dims(patch_minu, axis=0)
# # Can use class as hard decision
# # 0: minu 1: non-minu
# [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1)
#
# if class_Minutiae == 0:
# mnt_refined.append(mnt_nms[idx_minu,:])
# Use soft decision: merge FineNet score with CoarseNet score
[isMinutiaeProb] = model_FineNet.predict(patch_minu)
isMinutiaeProb = isMinutiaeProb[0]
#print isMinutiaeProb
tmp_mnt = mnt_nms[idx_minu, :].copy()
tmp_mnt[3] = (4 * tmp_mnt[3] + isMinutiaeProb) / 5
mnt_refined.append(tmp_mnt)
except:
mnt_refined.append(mnt_nms[idx_minu, :])
else:
mnt_refined = mnt_nms
mnt_nms_backup = mnt_nms.copy()
mnt_nms = np.array(mnt_refined)
if mnt_nms.shape[0] > 0:
mnt_nms = mnt_nms[mnt_nms[:,
3] > final_minutiae_score_threashold, :]
final_mask = ndimage.zoom(np.round(np.squeeze(seg_out)), [8, 8],
order=0)
# Show the orientation
show_orientation_field(original_image,
dir_map + np.pi,
mask=final_mask,
fname="%s/%s/OF_results/%s_OF.jpg" %
(output_dir, set_name, img_name[i]))
fuse_minu_orientation(dir_map, mnt_nms, mode=3)
time_afterpost = time()
mnt_writer(
mnt_nms, img_name[i], img_size,
"%s/%s/mnt_results/%s.mnt" % (output_dir, set_name, img_name[i]))
draw_minutiae(original_image,
mnt_nms,
"%s/%s/%s_minu.jpg" %
(output_dir, set_name, img_name[i]),
saveimage=True)
misc.imsave(
"%s/%s/seg_results/%s_seg.jpg" %
(output_dir, set_name, img_name[i]), final_mask)
time_afterdraw = time()
time_c.append([
time_afterconv - time_start, time_afterpost - time_afterconv,
time_afterdraw - time_afterpost
])
logging.info("load+conv: %.3fs, seg-postpro+nms: %.3f, draw: %.3f" %
(time_c[-1][0], time_c[-1][1], time_c[-1][2]))
# time_c = np.mean(np.array(time_c), axis=0)
# logging.info(
# "Average: load+conv: %.3fs, oir-select+seg-post+nms: %.3f, draw: %.3f" % (time_c[0], time_c[1], time_c[2]))
return
def Workflow_atp2a2(
struct_img: np.ndarray,
rescale_ratio: float = -1,
output_type: str = "default",
output_path: Union[str, Path] = None,
fn: Union[str, Path] = None,
output_func=None,
):
"""
classic segmentation workflow wrapper for structure ATP2A2
Parameter:
-----------
struct_img: np.ndarray
the 3D image to be segmented
rescale_ratio: float
an optional parameter to allow rescale the image before running the
segmentation functions, default is no rescaling
output_type: str
select how to handle output. Currently, four types are supported:
1. default: the result will be saved at output_path whose filename is
original name without extention + "_struct_segmentaiton.tiff"
2. array: the segmentation result will be simply returned as a numpy array
3. array_with_contour: segmentation result will be returned together with
the contour of the segmentation
4. customize: pass in an extra output_func to do a special save. All the
intermediate results, names of these results, the output_path, and the
original filename (without extension) will be passed in to output_func.
"""
##########################################################################
# PARAMETERS:
# note that these parameters are supposed to be fixed for the structure
# and work well accross different datasets
intensity_norm_param = [2.5, 9.0]
vesselness_sigma = [1]
vesselness_cutoff = 0.25
minArea = 15
##########################################################################
out_img_list = []
out_name_list = []
###################
# PRE_PROCESSING
###################
# intenisty normalization (min/max)
struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
# rescale if needed
if rescale_ratio > 0:
struct_img = zoom(struct_img, (1, rescale_ratio, rescale_ratio), order=2)
struct_img = (struct_img - struct_img.min() + 1e-8) / (
struct_img.max() - struct_img.min() + 1e-8
)
# smoothing with boundary preserving smoothing
structure_img_smooth = edge_preserving_smoothing_3d(struct_img)
out_img_list.append(structure_img_smooth.copy())
out_name_list.append("im_smooth")
###################
# core algorithm
###################
# 2d vesselness slice by slice
response = vesselnessSliceBySlice(
structure_img_smooth, sigmas=vesselness_sigma, tau=1, whiteonblack=True
)
bw = response > vesselness_cutoff
###################
# POST-PROCESSING
###################
bw = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
for zz in range(bw.shape[0]):
bw[zz, :, :] = remove_small_objects(
bw[zz, :, :], min_size=3, connectivity=1, in_place=False
)
seg = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
# output
seg = seg > 0
seg = seg.astype(np.uint8)
seg[seg > 0] = 255
out_img_list.append(seg.copy())
out_name_list.append("bw_final")
if output_type == "default":
# the default final output, simply save it to the output path
save_segmentation(seg, False, Path(output_path), fn)
elif output_type == "customize":
# the hook for passing in a customized output function
# use "out_img_list" and "out_name_list" in your hook to
# customize your output functions
output_func(out_img_list, out_name_list, Path(output_path), fn)
elif output_type == "array":
return seg
elif output_type == "array_with_contour":
return (seg, generate_segmentation_contour(seg))
else:
raise NotImplementedError("invalid output type: {output_type}")
def resize(scale, old_mats):
new_mats = []
for mat in old_mats:
new_mats.append(zoom(mat, scale, order=0))
return np.array(new_mats)
def _generate_bss(self, x_batch, y_batch, c):
"""
Generate adversarial examples for a batch of inputs with a specific batch of constants.
:param x_batch: A batch of original examples.
:type x_batch: `np.ndarray`
:param y_batch: A batch of targets (0-1 hot).
:type y_batch: `np.ndarray`
:param c: A batch of constants.
:type c: `np.ndarray`
:return: A tuple of best elastic distances, best labels, best attacks
:rtype: `tuple`
"""
def compare(object1, object2):
return object1 == object2 if self.targeted else object1 != object2
x_orig = x_batch.astype(NUMPY_DTYPE)
fine_tuning = np.full(x_batch.shape[0], False, dtype=bool)
prev_loss = 1e6 * np.ones(x_batch.shape[0])
prev_l2dist = np.zeros(x_batch.shape[0])
# Resize and initialize Adam
if self.use_resize:
x_orig = self._resize_image(x_orig, self._init_size,
self._init_size, True)
assert (x_orig != 0).any()
x_adv = x_orig.copy()
else:
x_orig = x_batch
self._reset_adam(np.prod(self.classifier.input_shape))
self._current_noise.fill(0)
# Initialize best distortions, best changed labels and best attacks
best_dist = np.inf * np.ones(x_adv.shape[0])
best_label = -np.inf * np.ones(x_adv.shape[0])
best_attack = [x_adv[i] for i in range(x_adv.shape[0])]
for iter_ in range(self.max_iter):
logger.debug('Iteration step %i out of %i', iter_, self.max_iter)
# Upscaling for very large number of iterations
if self.use_resize:
if iter_ == 2000:
x_adv = self._resize_image(x_adv, 64, 64)
x_orig = zoom(x_orig, [
1, x_adv.shape[1] / x_orig.shape[1], x_adv.shape[2] /
x_orig.shape[2], x_adv.shape[3] / x_orig.shape[3]
])
elif iter_ == 10000:
x_adv = self._resize_image(x_adv, 128, 128)
x_orig = zoom(x_orig, [
1, x_adv.shape[1] / x_orig.shape[1], x_adv.shape[2] /
x_orig.shape[2], x_adv.shape[3] / x_orig.shape[3]
])
# Compute adversarial examples and loss
x_adv = self._optimizer(x_adv, y_batch, c)
preds, l2dist, loss = self._loss(x_orig, x_adv, y_batch, c)
# Reset Adam if a valid example has been found to avoid overshoot
mask_fine_tune = (~fine_tuning) & (loss == l2dist) & (prev_loss !=
prev_l2dist)
fine_tuning[mask_fine_tune] = True
self._reset_adam(self.adam_mean.size,
np.repeat(mask_fine_tune, x_adv[0].size))
prev_l2dist = l2dist
# Abort early if no improvement is obtained
if self.abort_early and iter_ % self._early_stop_iters == 0:
if (loss > .9999 * prev_loss).all():
break
prev_loss = loss
# Adjust the best result
labels_batch = np.argmax(y_batch, axis=1)
for i, (dist,
pred) in enumerate(zip(l2dist, np.argmax(preds, axis=1))):
if dist < best_dist[i] and compare(pred, labels_batch[i]):
best_dist[i] = dist
best_attack[i] = x_adv[i]
best_label[i] = pred
# Resize images to original size before returning
best_attack = np.array(best_attack)
if self.use_resize:
if self.classifier.channel_index == 3:
best_attack = zoom(best_attack, [
1,
int(x_batch.shape[1]) / best_attack.shape[1],
int(x_batch.shape[2]) / best_attack.shape[2], 1
])
elif self.classifier.channel_index == 1:
best_attack = zoom(best_attack, [
1, 1,
int(x_batch.shape[2]) / best_attack.shape[2],
int(x_batch.shape[2]) / best_attack.shape[3]
])
return best_dist, best_label, best_attack
def globalPredSK(metaFN):
meta = landsat_metadata(metaFN)
sceneID = meta.LANDSAT_SCENE_ID
base = os.getcwd()
regr_1 = DecisionTreeRegressor(max_depth=15)
rng = np.random.RandomState(1)
regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=15),
n_estimators=5,
random_state=rng)
fn = os.path.join(base, 'th_samples.data')
df = pd.read_csv(fn)
X = np.array(df.iloc[:, 3:-4])
w = np.array(df.iloc[:, -1])
w = np.reshape(w, [w.shape[0], 1])
X = np.concatenate((X, w), axis=1)
y = np.array(df.iloc[:, -2])
regr_1.fit(X, y)
regr_2.fit(X, y)
blue = os.path.join(landsat_temp, "%s_sr_band2.tif" % sceneID)
green = os.path.join(landsat_temp, "%s_sr_band3.tif" % sceneID)
red = os.path.join(landsat_temp, "%s_sr_band4.tif" % sceneID)
nir = os.path.join(landsat_temp, "%s_sr_band5.tif" % sceneID)
swir1 = os.path.join(landsat_temp, "%s_sr_band6.tif" % sceneID)
swir2 = os.path.join(landsat_temp, "%s_sr_band7.tif" % sceneID)
# open files and assepble them into 2-d numpy array
Gblue = gdal.Open(blue)
blueData = Gblue.ReadAsArray()
blueVec = np.reshape(blueData, [blueData.shape[0] * blueData.shape[1]])
Ggreen = gdal.Open(green)
greenData = Ggreen.ReadAsArray()
greenVec = np.reshape(greenData, [greenData.shape[0] * greenData.shape[1]])
Gnir = gdal.Open(nir)
nirData = Gnir.ReadAsArray()
nirVec = np.reshape(nirData, [nirData.shape[0] * nirData.shape[1]])
Gred = gdal.Open(red)
redData = Gred.ReadAsArray()
redVec = np.reshape(redData, [redData.shape[0] * redData.shape[1]])
Gswir1 = gdal.Open(swir1)
swir1Data = Gswir1.ReadAsArray()
swir1Vec = np.reshape(swir1Data, [swir1Data.shape[0] * swir1Data.shape[1]])
Gswir2 = gdal.Open(swir2)
swir2Data = Gswir2.ReadAsArray()
swir2Vec = np.reshape(swir2Data, [swir2Data.shape[0] * swir2Data.shape[1]])
ylocs = (np.tile(range(0, blueData.shape[0]),
(blueData.shape[1], 1)).T) / 3
xlocs = (np.tile(range(0, blueData.shape[1]), (blueData.shape[0], 1))) / 3
pixID = ylocs * 10000 + xlocs
pixIDvec = np.reshape(pixID, [swir2Data.shape[0] * swir2Data.shape[1]])
newDF = pd.DataFrame({
'pixID': pixIDvec,
'green': greenVec,
'red': redVec,
'nir': nirVec,
'swir1': swir1Vec,
'swir2': swir2Vec
})
#newDF.replace(to_replace=-9999,value=np.)
dnMean = newDF.groupby('pixID').mean()
cv = newDF.groupby('pixID').std() / dnMean
meanCV = np.array(cv.mean(axis=1))
meanCV[np.isinf(meanCV)] = 10.
meanCV[np.where(meanCV == 0)] = 10.
weight = 0.1 / meanCV
weight[np.isinf(weight)] = 20.
weight[np.where(meanCV < 0.01)] = 10.
weight[weight == 20.] = 0.
weight[np.where(weight < 0.)] = 0.
rows = np.array(dnMean.index / 10000)
cols = np.array(dnMean.index - ((dnMean.index / 10000) * 10000))
w_array = np.nan * np.empty(
(greenData.shape[0] / 3, greenData.shape[1] / 3))
w_array[list(rows), list(cols)] = list(weight)
w_array2 = zoom(w_array, 3.)
weight = np.reshape(w_array2, [greenData.shape[0] * greenData.shape[1]])
newDF['weight'] = weight
xNew = np.stack((greenVec, redVec, nirVec, swir1Vec, swir2Vec, weight),
axis=-1)
outData = regr_1.predict(xNew)
outData = regr_2.predict(xNew)
return np.reshape(outData, [blueData.shape[0], blueData.shape[1]])
# Geometrical transformation - zoom
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from scipy import ndimage
# open the input image as numpy array, set datatype
img = np.array(Image.open("car-rgb.png"), dtype=np.uint8)
# apply zoom function by zoom factor=
# (row/y ratio, column/x Ratio, channel ratio)
# order=0: nearest interpolation
img_zm = ndimage.zoom(img, zoom=(2, 2, 1), order=0)
# save image
imgz = Image.fromarray(img_zm)
imgz.save('car-op-rgb-zoom.png')
# set plot size in inch
plt.figure(figsize=(10, 5), dpi=140)
# plot gray image
plt.subplot(121)
plt.axis('on')
plt.title('Original', fontsize=10)
plt.imshow(img)
# plot gray edge image
plt.subplot(122)
plt.axis('on')
plt.title('Zoom', fontsize=10)
plt.imshow(img_zm)
def reshape_mask(mask, tbox, origsize):
res = np.ones(origsize) * 0
resize = [tbox[2] - tbox[0], tbox[3] - tbox[1]]
imgres = ndimage.zoom(mask, resize / np.asarray(mask.shape), order=0)
res[tbox[0]:tbox[2], tbox[1]:tbox[3]] = imgres
return res
def deploy_with_GT(deploy_set,
output_dir,
model_path,
FineNet_path=None,
set_name=None):
if set_name is None:
set_name = deploy_set.split('/')[-2]
# Read image and GT
img_name, folder_name, img_size = get_maximum_img_size_and_names(
deploy_set)
mkdir(output_dir + '/' + set_name + '/')
mkdir(output_dir + '/' + set_name + '/mnt_results/')
mkdir(output_dir + '/' + set_name + '/seg_results/')
mkdir(output_dir + '/' + set_name + '/OF_results/')
logging.info("Predicting %s:" % (set_name))
isHavingFineNet = False
main_net_model = CoarseNetmodel((None, None, 1), model_path, mode='deploy')
if isHavingFineNet == True:
# ====== Load FineNet to verify
model_FineNet = FineNetmodel(num_classes=2,
pretrained_path=FineNet_path,
input_shape=(224, 224, 3))
model_FineNet.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0),
metrics=['accuracy'])
time_c = []
ave_prf_nms = []
for i, test in enumerate(
load_data((img_name, folder_name, img_size),
tra_ori_model,
rand=False,
aug=0.0,
batch_size=1)):
print i, img_name[i]
logging.info("%s %d / %d: %s" %
(set_name, i + 1, len(img_name), img_name[i]))
time_start = time()
image = misc.imread(deploy_set + 'img_files/' + img_name[i] + '.bmp',
mode='L') # / 255.0
mask = misc.imread(deploy_set + 'seg_files/' + img_name[i] + '.bmp',
mode='L') / 255.0
img_size = image.shape
img_size = np.array(img_size, dtype=np.int32) // 8 * 8
image = image[:img_size[0], :img_size[1]]
mask = mask[:img_size[0], :img_size[1]]
original_image = image.copy()
# Generate OF
texture_img = FastEnhanceTexture(image, sigma=2.5, show=False)
dir_map, fre_map = get_maps_STFT(texture_img,
patch_size=64,
block_size=16,
preprocess=True)
image = np.reshape(image, [1, image.shape[0], image.shape[1], 1])
enh_img, enh_img_imag, enhance_img, ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out \
= main_net_model.predict(image)
time_afterconv = time()
# Use post processing to smooth image
round_seg = np.round(np.squeeze(seg_out))
seg_out = 1 - round_seg
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_CLOSE, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
seg_out = cv2.dilate(seg_out, kernel)
# If use mask from outside
# seg_out = cv2.resize(mask, dsize=(seg_out.shape[1], seg_out.shape[0]))
mnt_gt = label2mnt(test[7], test[4], test[5], test[6])
final_minutiae_score_threashold = 0.45
early_minutiae_thres = final_minutiae_score_threashold + 0.05
# In cases of small amount of minutiae given, try adaptive threshold
while final_minutiae_score_threashold >= 0:
mnt = label2mnt(np.squeeze(mnt_s_out) *
np.round(np.squeeze(seg_out)),
mnt_w_out,
mnt_h_out,
mnt_o_out,
thresh=early_minutiae_thres)
# Previous exp: 0.2
mnt_nms_1 = py_cpu_nms(mnt, 0.5)
mnt_nms_2 = nms(mnt)
# Make sure good result is given
if mnt_nms_1.shape[0] > 4 and mnt_nms_2.shape[0] > 4:
break
else:
final_minutiae_score_threashold = final_minutiae_score_threashold - 0.05
early_minutiae_thres = early_minutiae_thres - 0.05
mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2)
mnt_nms = mnt_nms[mnt_nms[:, 3] > early_minutiae_thres, :]
mnt_refined = []
if isHavingFineNet == True:
# ======= Verify using FineNet ============
patch_minu_radio = 22
if FineNet_path != None:
for idx_minu in range(mnt_nms.shape[0]):
try:
# Extract patch from image
x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio
y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio
patch_minu = original_image[x_begin:x_begin +
2 * patch_minu_radio,
y_begin:y_begin +
2 * patch_minu_radio]
patch_minu = cv2.resize(
patch_minu,
dsize=(224, 224),
interpolation=cv2.INTER_NEAREST)
ret = np.empty(
(patch_minu.shape[0], patch_minu.shape[1], 3),
dtype=np.uint8)
ret[:, :, 0] = patch_minu
ret[:, :, 1] = patch_minu
ret[:, :, 2] = patch_minu
patch_minu = ret
patch_minu = np.expand_dims(patch_minu, axis=0)
# # Can use class as hard decision
# # 0: minu 1: non-minu
# [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1)
#
# if class_Minutiae == 0:
# mnt_refined.append(mnt_nms[idx_minu,:])
# Use soft decision: merge FineNet score with CoarseNet score
[isMinutiaeProb] = model_FineNet.predict(patch_minu)
isMinutiaeProb = isMinutiaeProb[0]
# print isMinutiaeProb
tmp_mnt = mnt_nms[idx_minu, :].copy()
tmp_mnt[3] = (4 * tmp_mnt[3] + isMinutiaeProb) / 5
mnt_refined.append(tmp_mnt)
except:
mnt_refined.append(mnt_nms[idx_minu, :])
else:
mnt_refined = mnt_nms
mnt_nms = np.array(mnt_refined)
if mnt_nms.shape[0] > 0:
mnt_nms = mnt_nms[mnt_nms[:,
3] > final_minutiae_score_threashold, :]
final_mask = ndimage.zoom(np.round(np.squeeze(seg_out)), [8, 8],
order=0)
# Show the orientation
show_orientation_field(original_image,
dir_map + np.pi,
mask=final_mask,
fname="%s/%s/OF_results/%s_OF.jpg" %
(output_dir, set_name, img_name[i]))
fuse_minu_orientation(dir_map, mnt_nms, mode=3)
time_afterpost = time()
mnt_writer(
mnt_nms, img_name[i], img_size,
"%s/%s/mnt_results/%s.mnt" % (output_dir, set_name, img_name[i]))
draw_minutiae_overlay_with_score(image,
mnt_nms,
mnt_gt[:, :3],
"%s/%s/%s_minu.jpg" %
(output_dir, set_name, img_name[i]),
saveimage=True)
# misc.imsave("%s/%s/%s_score.jpg"%(output_dir, set_name, img_name[i]), np.squeeze(mnt_s_out_upscale))
misc.imsave(
"%s/%s/seg_results/%s_seg.jpg" %
(output_dir, set_name, img_name[i]), final_mask)
time_afterdraw = time()
time_c.append([
time_afterconv - time_start, time_afterpost - time_afterconv,
time_afterdraw - time_afterpost
])
logging.info("load+conv: %.3fs, seg-postpro+nms: %.3f, draw: %.3f" %
(time_c[-1][0], time_c[-1][1], time_c[-1][2]))
# Metrics calculating
p, r, f, l, o = metric_P_R_F(mnt_gt, mnt_nms)
ave_prf_nms.append([p, r, f, l, o])
print p, r, f
time_c = np.mean(np.array(time_c), axis=0)
ave_prf_nms = np.mean(np.array(ave_prf_nms), 0)
print "Precision: %f\tRecall: %f\tF1-measure: %f" % (
ave_prf_nms[0], ave_prf_nms[1], ave_prf_nms[2])
logging.info(
"Average: load+conv: %.3fs, oir-select+seg-post+nms: %.3f, draw: %.3f"
% (time_c[0], time_c[1], time_c[2]))
return
def resize(self, frames):
if self.state_type != 'features':
return [ndimage.zoom(f, self.zoom, order=2) for f in frames]
else:
return frames
# Specify the path to the file
filename = os.path.join('data', 'O2-ANU1024.txt.bz2')
# Name the output files
base_dir, name = os.path.split(filename)
name = name.split('.')[0]
output_image = name + '_inverse_Abel_transform_HansenLaw.png'
output_text = name + '_speeds_HansenLaw.dat'
output_plot = name + '_comparison_HansenLaw.png'
# Load an image file as a numpy array
print('Loading ' + filename)
im = np.loadtxt(filename)
print("scaling image to size 501 reduce the time of the basis set calculation")
im = zoom(im, 0.4892578125)
(rows, cols) = np.shape(im)
if cols % 2 == 0:
print("Even pixel image cols={:d}, adjusting image centre\n",
" center_image()".format(cols))
im = abel.tools.center.center_image(im, center="slice", odd_size=True)
# alternative
#im = shift(im,(0.5,0.5))
#im = im[:-1, 1::] # drop left col, bottom row
(rows, cols) = np.shape(im)
c2 = cols // 2 # half-image width
r2 = rows // 2 # half-image height
print('image size {:d}x{:d}'.format(rows, cols))
# Hansen & Law inverse Abel transform
def load_patients(i, j, base_path="", rescale=None):
'''
Function which loads patients from BraTS data
:param i: First patient to be loaded
:param j: From patient i to j load all patients
:param base_path: Specifies where data is
:return: A tuple with data in the first place and labels in the second place.
Data has shape (n,240,240,1) where n is the number of slices from patient i to j who contains tumors
and the labels has has shape (n, 240, 240, 2) which is a pixelwise binary softmax.
'''
assert j >= i, 'j>i has to be true, you have given an invalid range of patients.'
path = base_path + "MICCAI_BraTS_2019_Data_Training/*/*/*"
wild_t1ce = path + "_t1ce.nii.gz"
wild_gt = path + "_seg.nii.gz"
t1ce_paths = glob.glob(wild_t1ce)
gt_paths = glob.glob(wild_gt)
num_patients = j - i
ind = []
#fixme: the list and the set patients should be made into a dictionary
patients = set({})
num_non_empty_slices = 0
labels_of_interest = set([1, 4])
for k in range(i, j):
path_gt = gt_paths[k]
img_gt = nib.load(path_gt)
img_gt = img_gt.get_fdata()
curr_patient = []
for l in range(img_gt.shape[-1]):
labels_in_slice = set(np.unique(img_gt[:, :, l]))
if labels_of_interest.issubset(labels_in_slice):
curr_patient.append(l)
num_non_empty_slices += 1
patients.add(k)
if len(curr_patient) > 0:
ind.append(curr_patient)
image_data = np.zeros((1, 240, 240, num_non_empty_slices))
labels = np.zeros((num_non_empty_slices, 240, 240))
OHE_labels = np.zeros((num_non_empty_slices, 240, 240, 2))
next_ind = 0
for k, y in enumerate(patients):
print('Patient: ' + str(y))
curr_ind = ind[k]
path_t1ce = t1ce_paths[y]
path_gt = gt_paths[y]
img_t1ce = nib.load(path_t1ce)
img_gt = nib.load(path_gt)
img_t1ce = img_t1ce.get_fdata()
img_gt = img_gt.get_fdata()
# This code is necessary when we will use the data from Asgeir
if rescale:
img_gt = zoom(img_gt, rescale, order=0)
img_t1ce = zoom(img_t1ce, rescale, order=0)
temp = 0
for l, x in enumerate(curr_ind):
image_data[0, :, :, next_ind + l] = img_t1ce[:, :, x]
labels[next_ind + l, :, :] = img_gt[:, :, x]
temp += 1
next_ind += temp
for l in range(num_non_empty_slices):
image_data[0, :, :, l] = normalize(image_data[0, :, :, l])
OHE_labels[l, :, :, :] = OHE(labels[l, :, :])
# The last axis will become the first axis
image_data = np.moveaxis(image_data, -1, 0)
image_data = np.moveaxis(image_data, 1, 3)
return (image_data, OHE_labels, patients)
@author: Rifky
"""
# In[]
import scipy.io as io
voxels = io.loadmat(
"data/3DShapeNets/volumetric_data/chair/30/test/chair_000000000_1.mat"
)['instance']
#%%
import numpy as np
voxels = np.pad(voxels, (1, 1), 'constant', constant_values=(0, 0))
#%%
import scipy.ndimage as nd
voxels = nd.zoom(voxels, (2, 2, 2), mode='constant', order=0)
#%%
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
ax.voxels(voxels, edgecolor="red")
plt.show()
plt.savefig('data')
def forward(self,
color_heightmap,
depth_heightmap,
is_volatile=False,
specific_rotation=-1):
# Apply 2x scale to input heightmaps
color_heightmap_2x = ndimage.zoom(color_heightmap,
zoom=[2, 2, 1],
order=0)
depth_heightmap_2x = ndimage.zoom(depth_heightmap,
zoom=[2, 2],
order=0)
assert (color_heightmap_2x.shape[0:2] == depth_heightmap_2x.shape[0:2])
# Add extra padding (to handle rotations inside network)
diag_length = float(color_heightmap_2x.shape[0]) * np.sqrt(2)
diag_length = np.ceil(diag_length / 32) * 32
padding_width = int((diag_length - color_heightmap_2x.shape[0]) / 2)
color_heightmap_2x_r = np.pad(color_heightmap_2x[:, :, 0],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_r.shape = (color_heightmap_2x_r.shape[0],
color_heightmap_2x_r.shape[1], 1)
color_heightmap_2x_g = np.pad(color_heightmap_2x[:, :, 1],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_g.shape = (color_heightmap_2x_g.shape[0],
color_heightmap_2x_g.shape[1], 1)
color_heightmap_2x_b = np.pad(color_heightmap_2x[:, :, 2],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_b.shape = (color_heightmap_2x_b.shape[0],
color_heightmap_2x_b.shape[1], 1)
color_heightmap_2x = np.concatenate(
(color_heightmap_2x_r, color_heightmap_2x_g, color_heightmap_2x_b),
axis=2)
depth_heightmap_2x = np.pad(depth_heightmap_2x,
padding_width,
'constant',
constant_values=0)
# Pre-process color image (scale and normalize)
image_mean = [0.485, 0.456, 0.406]
image_std = [0.229, 0.224, 0.225]
input_color_image = color_heightmap_2x.astype(float) / 255
for c in range(3):
input_color_image[:, :, c] = (input_color_image[:, :, c] -
image_mean[c]) / image_std[c]
# Pre-process depth image (normalize)
image_mean = [0.01, 0.01, 0.01]
image_std = [0.03, 0.03, 0.03]
depth_heightmap_2x.shape = (depth_heightmap_2x.shape[0],
depth_heightmap_2x.shape[1], 1)
input_depth_image = np.concatenate(
(depth_heightmap_2x, depth_heightmap_2x, depth_heightmap_2x),
axis=2)
for c in range(3):
input_depth_image[:, :, c] = (input_depth_image[:, :, c] -
image_mean[c]) / image_std[c]
# Construct minibatch of size 1 (b,c,h,w)
input_color_image.shape = (input_color_image.shape[0],
input_color_image.shape[1],
input_color_image.shape[2], 1)
input_depth_image.shape = (input_depth_image.shape[0],
input_depth_image.shape[1],
input_depth_image.shape[2], 1)
input_color_data = torch.from_numpy(
input_color_image.astype(np.float32)).permute(3, 2, 0, 1)
input_depth_data = torch.from_numpy(
input_depth_image.astype(np.float32)).permute(3, 2, 0, 1)
# Pass input data through model
output_prob, state_feat = self.model.forward(input_color_data,
input_depth_data,
is_volatile,
specific_rotation)
if self.method == 'reactive':
# Return affordances (and remove extra padding)
for rotate_idx in range(len(output_prob)):
if rotate_idx == 0:
push_predictions = F.softmax(
output_prob[rotate_idx][0],
dim=1).cpu().data.numpy()[:, 0, (padding_width / 2):(
color_heightmap_2x.shape[0] / 2 -
padding_width / 2), (padding_width / 2):(
color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]
grasp_predictions = F.softmax(
output_prob[rotate_idx][1],
dim=1).cpu().data.numpy()[:, 0, (padding_width / 2):(
color_heightmap_2x.shape[0] / 2 -
padding_width / 2), (padding_width / 2):(
color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]
else:
push_predictions = np.concatenate(
(push_predictions,
F.softmax(output_prob[rotate_idx][0],
dim=1).cpu().data.numpy()
[:, 0, (padding_width /
2):(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
(padding_width /
2):(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]),
axis=0)
grasp_predictions = np.concatenate(
(grasp_predictions,
F.softmax(output_prob[rotate_idx][1],
dim=1).cpu().data.numpy()
[:, 0, (padding_width /
2):(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
(padding_width /
2):(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]),
axis=0)
elif self.method == 'reinforcement':
# Return Q values (and remove extra padding)
for rotate_idx in range(len(output_prob)):
if rotate_idx == 0:
push_predictions = output_prob[rotate_idx][0].cpu(
).data.numpy()[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]
grasp_predictions = output_prob[rotate_idx][1].cpu(
).data.numpy()[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]
else:
push_predictions = np.concatenate(
(push_predictions,
output_prob[rotate_idx][0].cpu().data.numpy()
[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]),
axis=0)
grasp_predictions = np.concatenate(
(grasp_predictions,
output_prob[rotate_idx][1].cpu().data.numpy()
[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]),
axis=0)
return push_predictions, grasp_predictions, state_feat
def apply(input_sino, model_path, save_path, max_apply, offset, verbose=False):
if verbose:
print(' -loading network')
# This loads the keras network and the first checkpoint file
model = tf.keras.models.load_model(os.path.join(
model_path, 'keras_model.h5'),
custom_objects={
'recofunc':
architecture.recofunc,
'shrinkageact':
architecture.shrinkageact,
'shrinkageact_dense':
architecture.shrinkageact_dense,
'shrinkageact64':
architecture.shrinkageact64,
'shrinkageact_slicing':
architecture.slicing,
'shrinkageact_padding':
architecture.padding,
'tf':
tf,
'cfg':
cfg
},
compile=False)
checkpoint = tf.train.Checkpoint(model=model)
latest_model = tf.train.latest_checkpoint(model_path)
restore_status = [checkpoint.restore(latest_model)]
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.7
config.allow_soft_placement = True # automatically choose a supported device when the specified one doesn't support an op
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config, graph=g) as sess:
sess.run(init_op)
for element in restore_status:
element.run_restore_ops()
if verbose:
print(' -succesfully loaded from ' + model_path)
model.summary()
print(' -running predictions')
# Predict the reconstructions and save as .tif
for i, sino in enumerate(input_sino[offset:]):
if i == max_apply:
break
try:
prediction = sess.run(
model(tf.expand_dims(tf.expand_dims(sino * 1000, 0), -1),
training=False))
prediction = np.array(prediction) * 0.012
prediction = ndimage.zoom(prediction,
[1, 972 / 486, 972 / 486, 1])
imageio.imwrite(save_path + '_' + str(i + 1 + offset) + '.tif',
np.squeeze(prediction))
except IndexError:
pass
sess.close()
if verbose:
print(' -predictions finished')
def nearest(self, data_img, dtype=np.float32):
'''return a 2d np.array polar image'''
if self.y_centerin_fac:
data_img = zoom(data_img, 1. / self.y_centerin_fac, order=1)
data = data_img.ravel()
return data[self.indices_1d]
def deepdraw(net,
base_img,
octaves,
random_crop=True,
visualize=False,
foci=None,
clip=True,
**step_params):
print "Target imageclasses"
print foci
# prepare base image
image = preprocess(net, base_img) # (3,224,224)
# get input dimensions from net
w = net.blobs['data'].width
h = net.blobs['data'].height
print "starting drawing"
src = net.blobs['data']
print "Reshaping input image size %d, %d" % (h, w)
src.reshape(1, 3, h, w) # resize the network's input image size
for e, o in enumerate(octaves):
if 'scale' in o:
# resize by o['scale'] if it exists
image = nd.zoom(image, (1, o['scale'], o['scale']))
_, imw, imh = image.shape
print "Image shape octave %d, %d" % (imw, imh)
# select layer
layer = o['layer']
for i in xrange(o['iter_n']):
if imw > w:
if random_crop:
# randomly select a crop
#ox = random.randint(0,imw-224)
#oy = random.randint(0,imh-224)
mid_x = (imw - w) / 2.
width_x = imw - w
ox = np.random.normal(mid_x, width_x * 0.3, 1)
ox = int(np.clip(ox, 0, imw - w))
mid_y = (imh - h) / 2.
width_y = imh - h
oy = np.random.normal(mid_y, width_y * 0.3, 1)
oy = int(np.clip(oy, 0, imh - h))
# insert the crop into src.data[0]
print "Cropping: %d, %d" % (ox, oy)
src.data[0] = image[:, ox:ox + w, oy:oy + h]
else:
ox = (imw - w) / 2.
oy = (imh - h) / 2.
src.data[0] = image[:, ox:ox + w, oy:oy + h]
else:
ox = 0
oy = 0
src.data[0] = image.copy()
sigma = o['start_sigma'] + (
(o['end_sigma'] - o['start_sigma']) * i) / o['iter_n']
step_size = o['start_step_size'] + (
(o['end_step_size'] - o['start_step_size']) * i) / o['iter_n']
make_step(net,
end=layer,
clip=clip,
foci=foci,
sigma=sigma,
step_size=step_size)
if visualize:
vis = deprocess(net, src.data[0])
if not clip: # adjust image contrast if clipping is disabled
vis = vis * (255.0 / np.percentile(vis, 99.98))
if i % 1 == 0:
writearray(vis, "./octave%d_f%d.jpg" % (e, i))
if i % 10 == 0:
print 'finished step %d in octave %d' % (i, e)
# insert modified image back into original image (if necessary)
image[:, ox:ox + w, oy:oy + h] = src.data[0]
print "octave %d image:" % e
writearray(deprocess(net, image), "./octave_" + str(e) + ".jpg")
# returning the resulting image
return deprocess(net, image)
def get_action(self):
color_img, depth_img = self.get_camera_data()
print('color_img.shape: {}'.format(color_img.shape))
color_img = get_prepared_img(color_img, 'rgb')
depth_img = get_prepared_img(depth_img, 'depth')
color_heightmap, depth_heightmap = get_heightmap(
color_img, depth_img, robot.cam_intrinsics, robot.cam_pose,
robot.workspace_limits, robot.heightmap_resolution)
valid_depth_heightmap = depth_heightmap.copy()
valid_depth_heightmap[np.isnan(valid_depth_heightmap)] = 0
depth_heightmap = valid_depth_heightmap
# Apply 2x scale to input heightmaps
color_heightmap_2x = ndimage.zoom(color_heightmap,
zoom=[2, 2, 1],
order=0)
depth_heightmap_2x = ndimage.zoom(depth_heightmap,
zoom=[2, 2],
order=0)
assert (
color_heightmap_2x.shape[0:2] == depth_heightmap_2x.shape[0:2])
# Add extra padding (to handle rotations inside network)
diag_length = float(color_heightmap_2x.shape[0]) * np.sqrt(2)
diag_length = np.ceil(diag_length / 32) * 32
padding_width = int(
(diag_length - color_heightmap_2x.shape[0]) / 2)
color_heightmap_2x_r = np.pad(color_heightmap_2x[:, :, 0],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_r.shape = (color_heightmap_2x_r.shape[0],
color_heightmap_2x_r.shape[1], 1)
color_heightmap_2x_g = np.pad(color_heightmap_2x[:, :, 1],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_g.shape = (color_heightmap_2x_g.shape[0],
color_heightmap_2x_g.shape[1], 1)
color_heightmap_2x_b = np.pad(color_heightmap_2x[:, :, 2],
padding_width,
'constant',
constant_values=0)
color_heightmap_2x_b.shape = (color_heightmap_2x_b.shape[0],
color_heightmap_2x_b.shape[1], 1)
color_heightmap_2x = np.concatenate(
(color_heightmap_2x_r, color_heightmap_2x_g,
color_heightmap_2x_b),
axis=2)
depth_heightmap_2x = np.pad(depth_heightmap_2x,
padding_width,
'constant',
constant_values=0)
# Pre-process color image (scale and normalize)
image_mean = [0.485, 0.456, 0.406]
image_std = [0.229, 0.224, 0.225]
input_color_image = color_heightmap_2x.astype(float) / 255
for c in range(3):
input_color_image[:, :, c] = (input_color_image[:, :, c] -
image_mean[c]) / image_std[c]
# Pre-process depth image (normalize)
image_mean = [0.01, 0.01, 0.01]
image_std = [0.03, 0.03, 0.03]
depth_heightmap_2x.shape = (depth_heightmap_2x.shape[0],
depth_heightmap_2x.shape[1], 1)
input_depth_image = np.concatenate(
(depth_heightmap_2x, depth_heightmap_2x, depth_heightmap_2x),
axis=2)
for c in range(3):
input_depth_image[:, :, c] = (input_depth_image[:, :, c] -
image_mean[c]) / image_std[c]
# Construct minibatch of size 1 (b,c,h,w)
input_color_image.shape = (input_color_image.shape[0],
input_color_image.shape[1],
input_color_image.shape[2], 1)
input_depth_image.shape = (input_depth_image.shape[0],
input_depth_image.shape[1],
input_depth_image.shape[2], 1)
input_color_data = torch.from_numpy(
input_color_image.astype(np.float32)).permute(3, 2, 0, 1)
input_depth_data = torch.from_numpy(
input_depth_image.astype(np.float32)).permute(3, 2, 0, 1)
# Pass input data through model
output_prob, state_feat = self.model.forward(
input_color_data,
input_depth_data) # is_volatile, specific_rotation)
# Return Q values (and remove extra padding)
for rotate_idx in range(len(output_prob)):
if rotate_idx == 0:
grasp_predictions = output_prob[rotate_idx][0].cpu(
).data.numpy()[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]
else:
grasp_predictions = np.concatenate(
(grasp_predictions,
output_prob[rotate_idx][0].cpu().data.numpy()
[:, 0,
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2),
int(padding_width /
2):int(color_heightmap_2x.shape[0] / 2 -
padding_width / 2)]),
axis=0)
print('grasp_predictions.shape: {}'.format(
grasp_predictions.shape))
# Get pixel location and rotation with highest affordance prediction from heuristic algorithms (rotation, y, x)
best_pix_ind = np.unravel_index(np.argmax(grasp_predictions),
grasp_predictions.shape)
predicted_value = np.max(grasp_predictions)
# Compute 3D position of pixel
print('Action: %s at (%d, %d, %d)' %
('Grasp', best_pix_ind[0], best_pix_ind[1], best_pix_ind[2]))
best_rotation_angle = np.deg2rad(
best_pix_ind[0] * (360.0 / robot.model.num_rotations))
best_pix_x = best_pix_ind[2]
best_pix_y = best_pix_ind[1]
primitive_position = [
best_pix_x * self.heightmap_resolution +
self.workspace_limits[0][0],
best_pix_y * self.heightmap_resolution +
self.workspace_limits[1][0],
valid_depth_heightmap[best_pix_y][best_pix_x] +
self.workspace_limits[2][0]
]
return primitive_position # grasp_predictions, state_feat
def ApplyAugmentation(d3_img,
type_of_augmentation=None,
dict_parameter=None,
seed=1):
random.seed(seed)
d3_img = d3_img.reshape((16, 144, 144))
if dict_parameter is None:
dict_parameter = {
'rotation_xy': [-20, 20],
'rotation_zx': [-20, 20],
'rotation_zy': [-20, 20],
'zooming': [1.05, 1.15],
'down_scale': [0.85, 0.99]
}
if type_of_augmentation is None:
seq = [
'None',
'rotation_xy',
'rotation_zx',
'rotation_zy',
'zooming',
'h_flip',
#'elastic'
#'v_flip',
#'z_flip',
#'rotate_90_k1',
#'None'
#'down_scale',
#'h_flip',
#'v_flip',
#'z_flip',
#'rotate_90_k1',
#'rotate_90_k2',
#'rotate_90_k3'
]
type_of_augmentation = random.choice(seq)
if type_of_augmentation == 'rotation_xy':
angle = random.randint(dict_parameter[type_of_augmentation][0],
dict_parameter[type_of_augmentation][1])
new_3d_img = scipy.ndimage.rotate(d3_img,
angle,
axes=(1, 2),
reshape=False)
elif type_of_augmentation == 'rotation_zx':
angle = random.randint(dict_parameter[type_of_augmentation][0],
dict_parameter[type_of_augmentation][1])
new_3d_img = scipy.ndimage.rotate(d3_img,
angle,
axes=(0, 2),
reshape=False)
elif type_of_augmentation == 'rotation_zy':
angle = random.randint(dict_parameter[type_of_augmentation][0],
dict_parameter[type_of_augmentation][1])
new_3d_img = scipy.ndimage.rotate(d3_img,
angle,
axes=(0, 1),
reshape=False)
elif type_of_augmentation == 'zooming':
value_factor = random.uniform(dict_parameter[type_of_augmentation][0],
dict_parameter[type_of_augmentation][1])
new_img_zoom = ndimage.zoom(d3_img, (1, value_factor, value_factor))
x_a = new_img_zoom.shape[2] // 2 - 72
y_a = new_img_zoom.shape[1] // 2 - 72
new_3d_img = new_img_zoom[:, y_a:y_a + 144, x_a:x_a + 144]
elif type_of_augmentation == 'down_scale':
value_factor = random.uniform(dict_parameter[type_of_augmentation][0],
dict_parameter[type_of_augmentation][1])
new_img_zoom = ndimage.zoom(d3_img, (1, value_factor, value_factor))
new_img_zoom_tmp = np.zeros_like(d3_img)
x_a = new_img_zoom.shape[2] // 2
y_a = new_img_zoom.shape[1] // 2
x_a_b = new_img_zoom_tmp.shape[2] // 2 - x_a
y_a_b = new_img_zoom_tmp.shape[1] // 2 - y_a
new_img_zoom_tmp[:, y_a_b:y_a_b + new_img_zoom.shape[1],
x_a_b:x_a_b + new_img_zoom.shape[1]] = new_img_zoom
new_3d_img = new_img_zoom_tmp.copy()
elif type_of_augmentation == 'h_flip':
new_3d_img = np.flip(d3_img, axis=1)
elif type_of_augmentation == 'v_flip':
new_3d_img = np.flip(d3_img, axis=2)
elif type_of_augmentation == 'z_flip':
new_3d_img = np.flip(d3_img, axis=0)
elif type_of_augmentation == 'rotate_90_k1':
new_3d_img = np.rot90(d3_img, axes=(1, 2))
elif type_of_augmentation == 'rotate_90_k2':
new_3d_img = np.rot90(d3_img, k=2, axes=(1, 2))
elif type_of_augmentation == 'rotate_90_k3':
new_3d_img = np.rot90(d3_img, k=3, axes=(1, 2))
'''
elif type_of_augmentation=='elastic':
transformation = augment.create_identity_transformation(d3_img.shape)
# jitter in 3D
transformation += augment.create_elastic_transformation(
d3_img.shape,
control_point_spacing=100,
jitter_sigma=0.2)
# apply transformation
new_3d_img = augment.apply_transformation(d3_img, transformation)
'''
else:
new_3d_img = d3_img
bool_val = random.choice(['T', 'F'])
if bool_val == 'T':
new_3d_img = elastic_3d_transform(new_3d_img, seed=seed)
return new_3d_img.reshape((16, 144, 144, 1))
def Workflow_npm1(
struct_img: np.ndarray,
rescale_ratio: float = -1,
output_type: str = "default",
output_path: Union[str, Path] = None,
fn: Union[str, Path] = None,
output_func=None,
):
"""
classic segmentation workflow wrapper for structure NPM1
Parameter:
-----------
struct_img: np.ndarray
the 3D image to be segmented
rescale_ratio: float
an optional parameter to allow rescale the image before running the
segmentation functions, default is no rescaling
output_type: str
select how to handle output. Currently, four types are supported:
1. default: the result will be saved at output_path whose filename is
original name without extention + "_struct_segmentaiton.tiff"
2. array: the segmentation result will be simply returned as a numpy array
3. array_with_contour: segmentation result will be returned together with
the contour of the segmentation
4. customize: pass in an extra output_func to do a special save. All the
intermediate results, names of these results, the output_path, and the
original filename (without extension) will be passed in to output_func.
"""
##########################################################################
# PARAMETERS:
# note that these parameters are supposed to be fixed for the structure
# and work well accross different datasets
intensity_norm_param = [0.5, 15]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
dot_2d_sigma = 2
dot_2d_sigma_extra = 1
dot_2d_cutoff = 0.025
minArea = 5
low_level_min_size = 700
##########################################################################
out_img_list = []
out_name_list = []
###################
# PRE_PROCESSING
###################
# intenisty normalization (min/max)
struct_img = intensity_normalization(struct_img,
scaling_param=intensity_norm_param)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
# rescale if needed
if rescale_ratio > 0:
struct_img = zoom(struct_img, (1, rescale_ratio, rescale_ratio),
order=2)
struct_img = (struct_img - struct_img.min() +
1e-8) / (struct_img.max() - struct_img.min() + 1e-8)
gaussian_smoothing_truncate_range = (
gaussian_smoothing_truncate_range * rescale_ratio)
# smoothing with gaussian filter
structure_img_smooth = image_smoothing_gaussian_3d(
struct_img,
sigma=gaussian_smoothing_sigma,
truncate_range=gaussian_smoothing_truncate_range,
)
out_img_list.append(structure_img_smooth.copy())
out_name_list.append("im_smooth")
###################
# core algorithm
###################
# step 1: low level thresholding
# global_otsu = threshold_otsu(structure_img_smooth)
global_tri = threshold_triangle(structure_img_smooth)
global_median = np.percentile(structure_img_smooth, 50)
th_low_level = (global_tri + global_median) / 2
bw_low_level = structure_img_smooth > th_low_level
bw_low_level = remove_small_objects(bw_low_level,
min_size=low_level_min_size,
connectivity=1,
in_place=True)
bw_low_level = dilation(bw_low_level, selem=ball(2))
# step 2: high level thresholding
local_cutoff = 0.333 * threshold_otsu(structure_img_smooth)
bw_high_level = np.zeros_like(bw_low_level)
lab_low, num_obj = label(bw_low_level, return_num=True, connectivity=1)
for idx in range(num_obj):
single_obj = lab_low == (idx + 1)
local_otsu = threshold_otsu(structure_img_smooth[single_obj])
if local_otsu > local_cutoff:
bw_high_level[np.logical_and(
structure_img_smooth > 0.98 * local_otsu, single_obj)] = 1
out_img_list.append(bw_high_level.copy())
out_name_list.append("bw_coarse")
response_bright = dot_slice_by_slice(structure_img_smooth,
log_sigma=dot_2d_sigma)
response_dark = dot_slice_by_slice(1 - structure_img_smooth,
log_sigma=dot_2d_sigma)
response_dark_extra = dot_slice_by_slice(1 - structure_img_smooth,
log_sigma=dot_2d_sigma_extra)
# inner_mask = bw_high_level.copy()
# for zz in range(inner_mask.shape[0]):
# inner_mask[zz,:,:] = binary_fill_holes(inner_mask[zz,:,:])
holes = np.logical_or(response_dark > dot_2d_cutoff,
response_dark_extra > dot_2d_cutoff)
# holes[~inner_mask] = 0
bw_extra = response_bright > dot_2d_cutoff
# bw_extra[~bw_high_level]=0
bw_extra[~bw_low_level] = 0
bw_final = np.logical_or(bw_extra, bw_high_level)
bw_final[holes] = 0
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw_final,
min_size=minArea,
connectivity=1,
in_place=True)
# output
seg = seg > 0
seg = seg.astype(np.uint8)
seg[seg > 0] = 255
out_img_list.append(seg.copy())
out_name_list.append("bw_fine")
if output_type == "default":
# the default final output, simply save it to the output path
save_segmentation(seg, False, Path(output_path), fn)
elif output_type == "customize":
# the hook for passing in a customized output function
# use "out_img_list" and "out_name_list" in your hook to
# customize your output functions
output_func(out_img_list, out_name_list, Path(output_path), fn)
elif output_type == "array":
return seg
elif output_type == "array_with_contour":
return (seg, generate_segmentation_contour(seg))
else:
raise NotImplementedError("invalid output type: {output_type}")
def acolite_map(inputfile=None, output=None, parameters=None,
dpi=300, ext='png', mapped=True, max_dim = 1000, limit=None,
auto_range=False, range_percentiles=(5,95), dataset_rescale=False,
map_title=True,
map_colorbar=False, map_colorbar_orientation='vertical',#'horizontal',
rgb_rhot = False, rgb_rhos = False,
red_wl = 660, green_wl = 560, blue_wl = 480, rgb_min = [0.0]*3, rgb_max = [0.15]*3, rgb_pan_sharpen = False, map_parameters_pan=True,
map_fillcolor='White',
map_scalepos = 'LR', map_scalebar = True, map_scalecolor='Black', map_scalecolor_rgb='White', map_scalelen=None, map_projection='tmerc',
map_colorbar_edge=True, map_points=None, return_image=False, map_raster=False):
import os, copy
import datetime, time, dateutil.parser
from acolite.shared import datascl,nc_data,nc_datasets,nc_gatts,qmap,closest_idx
from acolite.acolite import pscale
import acolite as ac
from numpy import nanpercentile, log10, isnan, dstack
from scipy.ndimage import zoom
import matplotlib
if not os.path.exists(inputfile):
print('File {} not found.'.format(inputfile))
return(False)
## run through maps
maps = {'rhot':rgb_rhot,'rhos':rgb_rhos, 'parameters':parameters != None}
if all([maps[m] == False for m in maps]): return()
## get parameter scaling
psc = pscale()
## read netcdf info
l2w_datasets = nc_datasets(inputfile)
print(l2w_datasets)
gatts = nc_gatts(inputfile)
if 'MISSION_INDEX' in gatts:
sat, sen = gatts['MISSION'], gatts['MISSION_INDEX']
stime = dateutil.parser.parse(gatts['IMAGING_DATE']+' '+gatts['IMAGING_TIME'])
obase = '{}_{}_{}'.format(sat, sen, stime.strftime('%Y_%m_%d_%H_%M_%S'))
else:
sp = gatts['sensor'].split('_') if 'sensor' in gatts else gatts['SATELLITE_SENSOR'].split('_')
sat, sen = sp[0], sp[1]
stime = dateutil.parser.parse(gatts['isodate'] if 'isodate' in gatts else gatts['ISODATE'])
obase = gatts['output_name'] if 'output_name' in gatts else gatts['obase']
## find pan sharpening dataset
if rgb_pan_sharpen:
if sat not in ['L7','L8']: rgb_pan_sharpen = False
tmp = os.path.splitext(inputfile)
l1_pan_ncdf = '{}L1R_pan{}'.format(tmp[0][0:-3],tmp[1])
if os.path.exists(l1_pan_ncdf):
pan_data = nc_data(l1_pan_ncdf, 'rhot_pan')
else:
print('L1 pan NetCDF file not found')
rgb_pan_sharpen=False
if output is not None:
odir = output
else:
odir = gatts['output_dir']
if not os.path.exists(odir): os.makedirs(odir)
scf= 1.
rescale = 1.0
#if dataset_rescale or mapped:
lon = nc_data(inputfile, 'lon')
if mapped:
lat = nc_data(inputfile, 'lat')
if rgb_pan_sharpen:
lon_pan = zoom(lon, zoom=2, order=1)
lat_pan = zoom(lat, zoom=2, order=1)
## set up mapping info
if True:
from numpy import linspace, tile, ceil, isnan, nan
mask_val = -9999.9999
from scipy.ndimage.interpolation import map_coordinates
## rescale to save memory
dims = lon.shape
dsc = (dims[0]/max_dim, dims[1]/max_dim)
scf/=max(dsc)
if rgb_pan_sharpen: scf = 1.0
if (scf < 1.) and dataset_rescale:
sc_dims = (int(ceil(dims[0] * scf)), int(ceil(dims[1] * scf)))
xdim = linspace(0,dims[1],sc_dims[1]).reshape(1,sc_dims[1])
ydim = linspace(0,dims[0],sc_dims[0]).reshape(sc_dims[0],1)
xdim = tile(xdim, (sc_dims[0],1))
ydim = tile(ydim, (1,sc_dims[1]))
resc = [ydim,xdim]
xdim, ydim = None, None
lon = map_coordinates(lon, resc, mode='nearest')
lat = map_coordinates(lat, resc, mode='nearest')
else:
rescale = scf
## run through parameters
for mi in maps:
if not maps[mi]: continue
if mi == 'parameters':
if rgb_pan_sharpen:
if map_parameters_pan & mapped:
lon = lon_pan * 1.0
lon_pan = None
lat = lat_pan * 1.0
lat_pan = None
pan_data, lon_pan, lat_pan = None, None, None
print('Mapping {}'.format(mi))
if type(parameters) is not list: parameters=[parameters]
for pid, par in enumerate(parameters):
pard = None
## check if this parameter exists
if par not in l2w_datasets:
print('Parameter {} not in file {}.'.format(par, inputfile))
continue
print('Mapping {}'.format(par))
## read data
data = nc_data(inputfile, par)
if (rgb_pan_sharpen) & (map_parameters_pan):
data = zoom(data, zoom=2, order=1)
## rescale data
if (scf != 1.0) and dataset_rescale:
data[isnan(data)] = mask_val
data = map_coordinates(data, resc, cval=mask_val)
data[data <= int(mask_val)] = nan
data[data <= 0] = nan
data_range = nanpercentile(data, range_percentiles)
## get parameter mapping configuration
if par in psc:
pard = copy.deepcopy(psc[par])
else:
tmp = par.split('_')
par_generic = '_'.join((tmp[0:-1]+['*']))
if par_generic in psc:
pard = copy.deepcopy(psc[par_generic])
try: ## add wavelength to generic name
wave = int(tmp[len(tmp)-1])
pard['name'] = '{} ({} nm)'.format(pard['name'], wave)
except:
pass
else: pard= {'color table':'default', 'min':data_range[0], 'max':data_range[1],
'log': False, 'name':par, 'unit':'', 'parameter':par}
if pard['color table'] == 'default': pard['color table']='viridis'
ctfile = "{}/{}/{}.txt".format(ac.config['pp_data_dir'], 'Shared/ColourTables', pard['color table'])
if os.path.exists(ctfile):
from matplotlib.colors import ListedColormap
from numpy import loadtxt
pard['color table'] = ListedColormap(loadtxt(ctfile)/255.)
if 'title' not in pard: pard['title']='{} [{}]'.format(pard['name'],pard['unit'])
if auto_range:
pard['min']=data_range[0]
pard['max']=data_range[1]
if isnan(pard['min']): pard['min']=data_range[0]
if isnan(pard['max']): pard['max']=data_range[1]
## outputfile
outputfile = '{}/{}_{}.png'.format(odir,obase,par)
if map_title:
title = '{} {}/{} {}'.format(pard['name'], sat, sen, stime.strftime('%Y-%m-%d (%H:%M UTC)'))
else:
title = None
## use qmap option
if mapped:
range = (pard['min'], pard['max'])
if 'limit' in gatts:
limit = gatts['limit']
if ('xx' not in locals()):
xx, yy, m = qmap(data, lon, lat, outputfile=outputfile, title=title, rescale=rescale,
colorbar=map_colorbar_orientation, colorbar_edge=map_colorbar_edge, cmap=pard['color table'],
label=pard['title'], range=range, log = pard['log'], map_fillcolor=map_fillcolor,
limit=limit, dpi=dpi, points=map_points, projection=map_projection,
scalebar=map_scalebar, scalepos=map_scalepos,
scalecolor=map_scalecolor, scalelen=map_scalelen)
else:
xx, yy, m = qmap(data, lon, lat, outputfile=outputfile, title=title, rescale=rescale,
colorbar=map_colorbar_orientation, colorbar_edge=map_colorbar_edge, cmap=pard['color table'],
label=pard['title'], range=range, log = pard['log'], map_fillcolor=map_fillcolor,
limit=limit, dpi=dpi, points=map_points, projection=map_projection,
scalebar=map_scalebar, scalepos=map_scalepos,
scalecolor=map_scalecolor, scalelen=map_scalelen, xx=xx, yy=yy, m=m)
else:
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
cmap = cm.get_cmap(pard['color table'])
cmap.set_bad(map_fillcolor)
cmap.set_under(map_fillcolor)
if not map_raster:
## set up plot
fig = matplotlib.figure.Figure()
canvas = matplotlib.backends.backend_agg.FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
print(pard['min'], pard['max'])
if pard['log']:
from matplotlib.colors import LogNorm
cax = ax.imshow(data, vmin=pard['min'], vmax=pard['max'], cmap=cmap,
norm=LogNorm(vmin=pard['min'], vmax=pard['max']))
else:
cax = ax.imshow(data, vmin=pard['min'], vmax=pard['max'], cmap=cmap)
if map_colorbar:
if map_colorbar_orientation == 'vertical':
cbar = fig.colorbar(cax, orientation='vertical')
cbar.ax.set_ylabel(pard['title'])
else:
cbar = fig.colorbar(cax, orientation='horizontal')
cbar.ax.set_xlabel(pard['title'])
if map_title: ax.set_title(title)
ax.axis('off')
canvas.print_figure(outputfile, dpi=dpi, bbox_inches='tight')
else:
from PIL import Image
## rescale for mapping
if pard['log']:
from numpy import log10
datasc = datascl(log10(data), dmin=log10(pard['min']), dmax=log10(pard['max']))
else:
datasc = datascl(data, dmin=pard['min'], dmax=pard['max'])
d = cmap(datasc)
for wi in (0,1,2):
## convert back to 8 bit channels (not ideal)
d_ = datascl(d[:,:,wi], dmin=0, dmax=1)
if wi == 0: im = d_
else: im = dstack((im,d_))
img = Image.fromarray(im)
## output image
img.save(outputfile)
print('Wrote {}'.format(outputfile))
else:
print('Mapping RGB {}'.format(mi))
## RGBs
waves = [float(ds.split('_')[1]) for ds in l2w_datasets if ds[0:4] == mi]
if len(waves) == 0:
print('No appropriate datasets found for RGB {} in {}'.format(mi, inputfile))
continue
## read datasets
for wi, wl in enumerate([red_wl, green_wl, blue_wl]):
idx, wave = closest_idx(waves, wl)
cpar = '{}_{}'.format(mi, int(wave))
## read data
data = nc_data(inputfile, cpar)
if rgb_pan_sharpen:
data = zoom(data, zoom=2, order=1)
if wi == 0: vis_i = data * 1.0
else: vis_i += data
if wi == 2:
vis_i /= 3
pan_i = vis_i/pan_data
vis_i = None
## rescale data
if (scf != 1.0) and dataset_rescale:
data[isnan(data)] = mask_val
data = map_coordinates(data, resc, cval=mask_val)
data[data <= int(mask_val)] = nan
data[data <= 0] = nan
## stack image
if wi == 0:
image = data
else:
image = dstack((image,data))
## rescale data between 0 and 1
for wi in (2,1,0):
if rgb_pan_sharpen: image[:,:,wi] /= pan_i
image[:,:,wi] = datascl(image[:,:,wi], dmin=rgb_min[wi], dmax=rgb_max[wi])/255.
par = r'$\rho_{}$'.format(mi[3]) + ' RGB'
if map_title:
title = '{} {}/{} {}'.format(par, sat, sen, stime.strftime('%Y-%m-%d (%H:%M UTC)'))
else:
title = None
## outputfile
if rgb_pan_sharpen:
outputfile = '{}/{}_rgb_{}_pan.png'.format(odir,obase,mi)
else:
outputfile = '{}/{}_rgb_{}.png'.format(odir,obase,mi)
# use qmap option
if mapped:
if 'limit' in gatts:
limit = gatts['limit']
if rgb_pan_sharpen:
ret = qmap(image, lon_pan, lat_pan, outputfile=outputfile, title=title, rescale=rescale,
colorbar=map_colorbar_orientation, colorbar_edge=map_colorbar_edge,
limit=limit, dpi=dpi, points=map_points, projection=map_projection,
scalebar=map_scalebar, scalepos=map_scalepos,
scalecolor=map_scalecolor_rgb, scalelen=map_scalelen)
ret = None
else:
if ('xx' not in locals()):
xx, yy, m = qmap(image, lon, lat, outputfile=outputfile, title=title, rescale=rescale,
colorbar=map_colorbar_orientation, colorbar_edge=map_colorbar_edge,
limit=limit, dpi=dpi, points=map_points, projection=map_projection,
scalebar=map_scalebar, scalepos=map_scalepos,
scalecolor=map_scalecolor_rgb, scalelen=map_scalelen)
else:
xx, yy, m = qmap(image, lon, lat, outputfile=outputfile, title=title, rescale=rescale,
colorbar=map_colorbar_orientation, colorbar_edge=map_colorbar_edge,
limit=limit, dpi=dpi, points=map_points, projection=map_projection,
scalebar=map_scalebar, scalepos=map_scalepos,
scalecolor=map_scalecolor_rgb, scalelen=map_scalelen, xx=xx, yy=yy, m=m)
else:
if not map_raster:
## set up plot
fig = matplotlib.figure.Figure()
canvas = matplotlib.backends.backend_agg.FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.imshow(image)
image = None
if map_title: ax.set_title(title)
ax.axis('off')
canvas.print_figure(outputfile, dpi=dpi, bbox_inches='tight')
else:
from PIL import Image
for wi in (0,1,2):
# convert again to 8 bit channels (not ideal)
data = datascl(image[:,:,wi], dmin=0, dmax=1)
if wi == 0:
im = data
else:
im = dstack((im,data))
img = Image.fromarray(im)
img.save(outputfile)
print('Wrote {}'.format(outputfile))
def Workflow_st6gal1(
struct_img: np.ndarray,
rescale_ratio: float = -1,
output_type: str = "default",
output_path: Union[str, Path] = None,
fn: Union[str, Path] = None,
output_func=None,
):
"""
classic segmentation workflow wrapper for structure ST6GAL1
Parameter:
-----------
struct_img: np.ndarray
the 3D image to be segmented
rescale_ratio: float
an optional parameter to allow rescale the image before running the
segmentation functions, default is no rescaling
output_type: str
select how to handle output. Currently, four types are supported:
1. default: the result will be saved at output_path whose filename is
original name without extention + "_struct_segmentaiton.tiff"
2. array: the segmentation result will be simply returned as a numpy array
3. array_with_contour: segmentation result will be returned together with
the contour of the segmentation
4. customize: pass in an extra output_func to do a special save. All the
intermediate results, names of these results, the output_path, and the
original filename (without extension) will be passed in to output_func.
"""
##########################################################################
# PARAMETERS:
# note that these parameters are supposed to be fixed for the structure
# and work well accross different datasets
intensity_norm_param = [9, 19]
gaussian_smoothing_sigma = 1
gaussian_smoothing_truncate_range = 3.0
cell_wise_min_area = 1200
dot_3d_sigma = 1.6
dot_3d_cutoff = 0.02
minArea = 10
thin_dist = 1
thin_dist_preserve = 1.6
##########################################################################
out_img_list = []
out_name_list = []
###################
# PRE_PROCESSING
###################
# intenisty normalization (min/max)
struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
out_img_list.append(struct_img.copy())
out_name_list.append("im_norm")
# rescale if needed
if rescale_ratio > 0:
struct_img = zoom(struct_img, (1, rescale_ratio, rescale_ratio), order=2)
struct_img = (struct_img - struct_img.min() + 1e-8) / (
struct_img.max() - struct_img.min() + 1e-8
)
gaussian_smoothing_truncate_range = (
gaussian_smoothing_truncate_range * rescale_ratio
)
# smoothing with gaussian filter
structure_img_smooth = image_smoothing_gaussian_3d(
struct_img,
sigma=gaussian_smoothing_sigma,
truncate_range=gaussian_smoothing_truncate_range,
)
out_img_list.append(structure_img_smooth.copy())
out_name_list.append("im_smooth")
###################
# core algorithm
###################
# cell-wise local adaptive thresholding
th_low_level = threshold_triangle(structure_img_smooth)
bw_low_level = structure_img_smooth > th_low_level
bw_low_level = remove_small_objects(
bw_low_level, min_size=cell_wise_min_area, connectivity=1, in_place=True
)
bw_low_level = dilation(bw_low_level, selem=ball(2))
bw_high_level = np.zeros_like(bw_low_level)
lab_low, num_obj = label(bw_low_level, return_num=True, connectivity=1)
for idx in range(num_obj):
single_obj = lab_low == (idx + 1)
local_otsu = threshold_otsu(structure_img_smooth[single_obj > 0])
bw_high_level[
np.logical_and(structure_img_smooth > local_otsu * 0.98, single_obj)
] = 1
# LOG 3d to capture spots
response = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
bw_extra = response > dot_3d_cutoff
# thinning
bw_high_level = topology_preserving_thinning(
bw_high_level, thin_dist_preserve, thin_dist
)
# combine the two parts
bw = np.logical_or(bw_high_level, bw_extra)
###################
# POST-PROCESSING
###################
seg = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
# output
seg = seg > 0
seg = seg.astype(np.uint8)
seg[seg > 0] = 255
out_img_list.append(seg.copy())
out_name_list.append("bw_final")
if output_type == "default":
# the default final output, simply save it to the output path
save_segmentation(seg, False, Path(output_path), fn)
elif output_type == "customize":
# the hook for passing in a customized output function
# use "out_img_list" and "out_name_list" in your hook to
# customize your output functions
output_func(out_img_list, out_name_list, Path(output_path), fn)
elif output_type == "array":
return seg
elif output_type == "array_with_contour":
return (seg, generate_segmentation_contour(seg))
else:
raise NotImplementedError("invalid output type: {output_type}")
def _update_thumbnail(self):
"""Update thumbnail with current image data and colormap."""
if self.dims.ndisplay == 3 and self.dims.ndim > 2:
image = np.max(self._data_thumbnail, axis=0)
else:
image = self._data_thumbnail
# float16 not supported by ndi.zoom
dtype = np.dtype(image.dtype)
if dtype in [np.dtype(np.float16)]:
image = image.astype(np.float32)
raw_zoom_factor = np.divide(self._thumbnail_shape[:2],
image.shape[:2]).min()
new_shape = np.clip(
raw_zoom_factor * np.array(image.shape[:2]),
1, # smallest side should be 1 pixel wide
self._thumbnail_shape[:2],
)
zoom_factor = tuple(new_shape / image.shape[:2])
if self.rgb:
# warning filter can be removed with scipy 1.4
with warnings.catch_warnings():
warnings.simplefilter("ignore")
downsampled = ndi.zoom(image,
zoom_factor + (1, ),
prefilter=False,
order=0)
if image.shape[2] == 4: # image is RGBA
colormapped = np.copy(downsampled)
colormapped[..., 3] = downsampled[..., 3] * self.opacity
if downsampled.dtype == np.uint8:
colormapped = colormapped.astype(np.uint8)
else: # image is RGB
if downsampled.dtype == np.uint8:
alpha = np.full(
downsampled.shape[:2] + (1, ),
int(255 * self.opacity),
dtype=np.uint8,
)
else:
alpha = np.full(downsampled.shape[:2] + (1, ),
self.opacity)
colormapped = np.concatenate([downsampled, alpha], axis=2)
else:
# warning filter can be removed with scipy 1.4
with warnings.catch_warnings():
warnings.simplefilter("ignore")
downsampled = ndi.zoom(image,
zoom_factor,
prefilter=False,
order=0)
low, high = self.contrast_limits
downsampled = np.clip(downsampled, low, high)
color_range = high - low
if color_range != 0:
downsampled = (downsampled - low) / color_range
downsampled = downsampled**self.gamma
color_array = self.colormap[1][downsampled.ravel()]
colormapped = color_array.rgba.reshape(downsampled.shape + (4, ))
colormapped[..., 3] *= self.opacity
self.thumbnail = colormapped
rsphere=(6378273., 6356889.))
#Change the projection
lon = (x[0], x[1], x[3], x[2], x[0])
lat = (y[0], y[1], y[3], y[2], y[0])
x, y = m(lon, lat)
#New "stere" coorner coordinates
X = array([[x[0], x[1]], [x[3], x[2]]], dtype=float)
Y = array([[y[0], y[1]], [y[3], y[2]]], dtype=float)
rows = 15996
cols = 2528
#coordinates interpolation
X1 = ndi.zoom(X, (rows / 2, cols / 2), order=1)
Y1 = ndi.zoom(Y, (rows / 2, cols / 2), order=1)
#Draw coastlines, state and country boundaries, edge of map.
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.drawmapboundary(fill_color='dodgerblue')
m.drawrivers()
m.fillcontinents(color='snow', lake_color='aqua', zorder=0)
m.drawparallels(np.arange(70.9, 71.6, 0.1), labels=[1, 0, 0, 1], fontsize=10)
m.drawmeridians(np.arange(-157.4, -156.0, 0.3),
labels=[1, 0, 0, 1],
labelstyle='+/-',
fontsize=10)
m.plot(x, y, linewidth=3, color='r')