def process_image(self, scanparams, pointparams, edf):
delta, omega, alfa, beta, chi, phi, mon, transm = pointparams
wavelength, UB = scanparams
image = edf.GetData(0)
header = edf.GetHeader(0)
weights = numpy.ones_like(image)
if not self.config.centralpixel:
self.config.centralpixel = (int(header['y_beam']), int(header['x_beam']))
if not self.config.sdd:
self.config.sdd = float(header['det_sample_dist'])
if self.config.background:
data = image / mon
else:
data = image / mon / transm
if mon == 0:
raise errors.BackendError('Monitor is zero, this results in empty output. Scannumber = {0}, pointnumber = {1}. Did you forget to open the shutter?'.format(self.dbg_scanno, self.dbg_pointno))
util.status('{4}| beta: {0:.3f}, delta: {1:.3f}, omega: {2:.3f}, alfa: {3:.3f}'.format(beta, delta, omega, alfa, time.ctime(time.time())))
# pixels to angles
pixelsize = numpy.array(self.config.pixelsize)
sdd = self.config.sdd
app = numpy.arctan(pixelsize / sdd) * 180 / numpy.pi
centralpixel = self.config.centralpixel # (column, row) = (delta, gamma)
beta_range= -app[1] * (numpy.arange(data.shape[1]) - centralpixel[1]) + beta
delta_range= app[0] * (numpy.arange(data.shape[0]) - centralpixel[0]) + delta
# masking
if self.config.maskmatrix is not None:
if self.config.maskmatrix.shape != data.shape:
raise errors.BackendError('The mask matrix does not have the same shape as the images')
weights *= self.config.maskmatrix
delta_range = delta_range[self.config.ymask]
beta_range = beta_range[self.config.xmask]
weights = self.apply_mask(weights, self.config.xmask, self.config.ymask)
intensity = self.apply_mask(data, self.config.xmask, self.config.ymask)
intensity = numpy.rot90(intensity)
intensity = numpy.fliplr(intensity)
intensity = numpy.flipud(intensity)
weights = numpy.rot90(weights)
weights = numpy.fliplr(weights)
weights = numpy.flipud(weights)
#polarisation correction
delta_grid, beta_grid = numpy.meshgrid(delta_range, beta_range)
Pver = 1 - numpy.sin(delta_grid * numpy.pi / 180.)**2 * numpy.cos(beta_grid * numpy.pi / 180.)**2
#intensity /= Pver
return intensity, weights, (wavelength, UB, beta_range, delta_range, omega, alfa, chi, phi)
def state_index_number(dims, index):
"""
Return a quantum number representation given a state index, for a system
of composite structure defined by dims.
Example:
>>> state_index_number([2, 2, 2], 6)
[1, 1, 0]
Parameters
----------
dims : list or array
The quantum state dimensions array, as it would appear in a Qobj.
index : integer
The index of the state in standard enumeration ordering.
Returns
-------
state : list
The state number array corresponding to index `index` in standard
enumeration ordering.
"""
state = np.empty_like(dims)
D = np.concatenate([np.flipud(np.cumprod(np.flipud(dims[1:]))), [1]])
for n in range(len(dims)):
state[n] = index / D[n]
index -= state[n] * D[n]
return list(state)
def data_augmentation(image, mode):
if mode == 0:
# original
return image
elif mode == 1:
# flip up and down
return np.flipud(image)
elif mode == 2:
# rotate counterwise 90 degree
return np.rot90(image)
elif mode == 3:
# rotate 90 degree and flip up and down
image = np.rot90(image)
return np.flipud(image)
elif mode == 4:
# rotate 180 degree
return np.rot90(image, k=2)
elif mode == 5:
# rotate 180 degree and flip
image = np.rot90(image, k=2)
return np.flipud(image)
elif mode == 6:
# rotate 270 degree
return np.rot90(image, k=3)
elif mode == 7:
# rotate 270 degree and flip
image = np.rot90(image, k=3)
return np.flipud(image)
def produce_heatmap(model, every = True, save = False):
col_label = range(28)
row_label = range(28)
if every:
for i in range(10):
plt.pcolor(np.flipud(model[i]))
plt.xticks(col_label)
plt.yticks(row_label)
plt.axis('off')
plt.title("HeatMap for %d" % (i))
cb = plt.colorbar()
cb.set_label("Frequency")
if save:
plt.savefig('imgs/%d.png' % (i), bbox_inches='tight')
else:
plt.show()
plt.close()
else:
plt.pcolor(np.flipud(model))
plt.xticks(col_label)
plt.yticks(row_label)
plt.axis('off')
cb = plt.colorbar()
cb.set_label("Frequency")
if save:
plt.savefig('imgs/temp.png', bbox_inches='tight')
else:
plt.show()
plt.close()
def rotate_data(bg, overlay, slices_list, axis_name, shape):
# Rotate the data as required
# Return the rotated data, and an updated slice list if necessary
if axis_name == 'axial':
# Align so that right is right
overlay = np.rot90(overlay)
overlay = np.fliplr(overlay)
bg = np.rot90(bg)
bg = np.fliplr(bg)
elif axis_name == 'coronal':
overlay = np.rot90(overlay)
bg = np.rot90(bg)
overlay = np.flipud(np.swapaxes(overlay, 0, 2))
bg = np.flipud(np.swapaxes(bg, 0, 2))
slices_list[1] = [ shape - n - 3 for n in slices_list[1] ]
elif axis_name == 'sagittal':
overlay = np.flipud(np.swapaxes(overlay, 0, 2))
bg = np.flipud(np.swapaxes(bg, 0, 2))
else:
print '\n************************'
print 'ERROR: data could not be rotated\n'
parser.print_help()
sys.exit()
return bg, overlay, slices_list
def calculate_violin(self):
# Get stats
stats = self._stats
# Get kernel density estimate
nbins = stats.best_number_of_bins(8, 128)
centers, values = stats.kde(nbins)
# Normalize values
values = values * (0.5 * self._width / values.max())
# Create array with locations
n = values.size
points = np.zeros((n*2+1,3), np.float32)
points[:n,0] = values
points[:n,1] = centers
points[n:2*n,0] = -np.flipud(values)
points[n:2*n,1] = np.flipud(centers)
points[2*n,0] = values[0]
points[2*n,1] = centers[0]
#
self._points = points
# Update limits
self._limits = vv.Range(centers[0], centers[-1])
def get_output(self, idx):
img_id=idx/self.pertnum
pert_id=idx%self.pertnum
rot_id=pert_id%self.param['rotate']
off_id=pert_id/self.param['rotate']
[h, w]=self.output[img_id].shape
[dy, dx]=self.get_offset(h, w, off_id)
dy+=self.param['mrgsize']
dx+=self.param['mrgsize']
res=self.output[img_id][dy:dy+self.param['outsize'], dx:dx+self.param['outsize']]
#res=np.rot90(res) #rotate 90
if rot_id==1:
res=np.fliplr(res)
elif rot_id==2:
res=np.flipud(res).T
elif rot_id==3:
res=res.T
elif rot_id==4:
res=np.fliplr(res).T
elif rot_id==5:
res=np.flipud(res)
elif rot_id==6:
res=np.rot90(res,2)
elif rot_id==7:
res=np.rot90(res,2).T
return res
def put_image_quadrants (Q,odd_size=True):
"""
Reassemble image from 4 quadrants Q = (Q0, Q1, Q2, Q3)
The reverse process to get_image_quadrants()
Qi defined in abel.hansenlaw.iabel_hansenlaw
Parameters:
- Q: tuple of numpy array quadrants
- even_size: boolean, whether final image is even or odd pixel size
odd size requires trimming 1 row from Q1, Q0, and
1 column from Q1, Q2
Returns:
- rows x cols numpy array - the reassembled image
"""
if not odd_size:
Top = np.concatenate((np.fliplr(Q[1]), Q[0]), axis=1)
Bottom = np.flipud(np.concatenate((np.fliplr(Q[2]), Q[3]), axis=1))
else:
# odd size image remove extra row/column added in get_image_quadrant()
Top = np.concatenate((np.fliplr(Q[1][:-1,:-1]), Q[0][:-1,:]), axis=1)
Bottom = np.flipud(np.concatenate((np.fliplr(Q[2][:,:-1]), Q[3]), axis=1))
IM = np.concatenate((Top,Bottom), axis=0)
return IM
def generateWedge(a, b, n, type, plotShape=False):
# Define x and y coordinate
xFrontTop_ = np.linspace(0, a / 2.0, n); xFrontTop = xFrontTop_
xBackTop_ = np.linspace(a / 2.0, a, n); xBackTop = xBackTop_[1:]
xBackBottom_ = np.flipud(np.linspace(a / 2.0, a, n)); xBackBottom = xBackBottom_[1:]
xFrontBottom_ = np.flipud(np.linspace(0, a / 2.0, n)); xFrontBottom = xFrontBottom_[1:-1]
yFrontTop_ = b / a * xFrontTop_; yFrontTop = yFrontTop_
yBackTop_ = -b / a * (xBackTop_ - a / 2.0) + b / 2; yBackTop = yBackTop_[1:]
yBackBottom_ = b / a * (xBackBottom_ - a / 2.0) - b / 2; yBackBottom = yBackBottom_[1:]
yFrontBottom_ = -b / a * xFrontBottom_; yFrontBottom = yFrontBottom_[1:-1]
if type == 'twoSided':
# x = np.concatenate((xFrontTop, xBackTop, xBackBottom, xFrontBottom))
# y = np.concatenate((yFrontTop, yBackTop, yBackBottom, yFrontBottom))
xTop = np.concatenate((xFrontTop, xBackTop))
xBottom = np.concatenate((xBackBottom, xFrontBottom))
yTop = np.concatenate((yFrontTop, yBackTop))
yBottom = np.concatenate((yBackBottom, yFrontBottom))
elif type == 'oneSided':
x = np.concatenate((xFrontTop, xBackTop))
y = np.concatenate((yFrontTop, yBackTop))
if plotShape:
plt.figure()
plt.plot(x, y, 'k')
plt.xlim([-5, 14])
plt.ylim([-5, 5])
plt.show()
return (x, y)
def wrapper(*args):
x = args[0]
w = args[1]
if x.ndim == 3:
w = np.flipud(w)
w = np.transpose(w, (1, 2, 0))
if args[3] == 'channels_last':
x = np.transpose(x, (0, 2, 1))
elif x.ndim == 4:
w = np.fliplr(np.flipud(w))
w = np.transpose(w, (2, 3, 0, 1))
if args[3] == 'channels_last':
x = np.transpose(x, (0, 3, 1, 2))
else:
w = np.flip(np.fliplr(np.flipud(w)), axis=2)
w = np.transpose(w, (3, 4, 0, 1, 2))
if args[3] == 'channels_last':
x = np.transpose(x, (0, 4, 1, 2, 3))
y = func(x, w, args[2], args[3])
if args[3] == 'channels_last':
if y.ndim == 3:
y = np.transpose(y, (0, 2, 1))
elif y.ndim == 4:
y = np.transpose(y, (0, 2, 3, 1))
else:
y = np.transpose(y, (0, 2, 3, 4, 1))
return y
def get_image_quadrants(IM, reorient=False):
"""
Given an image (m,n) return its 4 quadrants Q0, Q1, Q2, Q3
as defined in abel.hansenlaw.iabel_hansenlaw
Parameters:
- IM: 1D or 2D array
- reorient: reorient image as required by abel.hansenlaw.iabel_hansenlaw
"""
IM = np.atleast_2d(IM)
n, m = IM.shape
n_c = n//2 + n%2
m_c = m//2 + m%2
# define 4 quadrants of the image
# see definition in abel.hansenlaw.iabel_hansenlaw
Q1 = IM[:n_c, :m_c]
Q2 = IM[-n_c:, :m_c]
Q0 = IM[:n_c, -m_c:]
Q3 = IM[-n_c:, -m_c:]
if reorient:
Q1 = np.fliplr(Q1)
Q3 = np.flipud(Q3)
Q2 = np.fliplr(np.flipud(Q2))
return Q0, Q1, Q2, Q3
def apply_Gumbel_original(gumbelFile, trgFolder, prefix, return_periods, cellArea, logger):
# read gumbel file
nc_src = nc4.Dataset(gumbelFile, 'r')
# read axes and revert the y-axis
x = nc_src.variables['lon'][:]
y = np.flipud(nc_src.variables['lat'][:])
# read different variables
loc = nc_src.variables['flvol_location'][0,:,:]#; loc = gumbel_loc.data; loc[loc==gumbel_loc._FillValue] = np.nan
scale = nc_src.variables['flvol_scale'][0,:,:]#; scale = gumbel_scale.data;scale[scale==gumbel_scale._FillValue] = np.nan
p_zero = nc_src.variables['flvol_zero_prob'][0,:,:]#;p_zero = zero_prob.data; p_zero[p_zero==zero_prob._FillValue] = np.nan
# loop over all return periods
for return_period in return_periods:
logger.info('Preparing return period %05.f' % return_period)
flvol = inv_gumbel(p_zero, loc, scale, return_period)
# any area with cell > 0, fill in a zero. This may occur because:
# a) dynRout produces missing values (occuring in some pixels in the Sahara)
# b) the forcing data is not exactly overlapping the cell area mask (e.g. EU-WATCH land cells are slightly different from PCR-GLOBWB mask)
# c) the probability of zero flooding is 100%. This causes a division by zero in the inv_gumbel function
test = logical_and(flvol.mask, cellArea > 0)
flvol[test] = 0.
test = logical_and(np.isnan(flvol), cellArea > 0)
flvol[test] = 0.
# if any values become negative due to the statistical extrapolation, fix them to zero (may occur if the sample size for fitting was small and a small return period is requested)
flvol = np.maximum(flvol, 0.)
# write to a PCRaster file
flvol_data = flvol.data
# finally mask the real not-a-number cells
flvol_data[flvol.mask] = -9999.
fileName = os.path.join(trgFolder, '%s_RP_%05.f.map') % (prefix, return_period)
writeMap(fileName, 'PCRaster', x, y, np.flipud(flvol_data), -9999.)
nc_src.close()
def setUp(self):
"""
This generates a minimum data-set to be used for the regression.
"""
# Test A: Generates a data set assuming b=1 and N(m=4.0)=10.0 events
self.dmag = 0.1
mext = np.arange(4.0, 7.01, 0.1)
self.mval = mext[0:-1] + self.dmag / 2.0
self.bval = 1.0
self.numobs = np.flipud(
np.diff(np.flipud(10.0 ** (-self.bval * mext + 8.0))))
# Test B: Generate a completely artificial catalogue using the
# Gutenberg-Richter distribution defined above
numobs = np.around(self.numobs)
size = int(np.sum(self.numobs))
magnitude = np.zeros(size)
lidx = 0
for mag, nobs in zip(self.mval, numobs):
uidx = int(lidx + nobs)
magnitude[lidx:uidx] = mag + 0.01
lidx = uidx
year = np.ones(size) * 1999
self.catalogue = Catalogue.make_from_dict(
{'magnitude': magnitude, 'year': year})
# Create the seismicity occurrence calculator
self.aki_ml = AkiMaxLikelihood()
def polysmooth(x,y,z,NI,NJ):
# size of the incoming array
Nx, Ny = np.shape(z)
x1d = x[:,0]
y1d = y[0,:]
# Get the C coefficients
#NI = 7
CIj = np.zeros((NI,Ny))
for j in range (Ny):
CIj[:,j] = np.flipud(np.polyfit(x1d,z[:,j],NI-1))
# Get the D coefficients
#NJ = 7
DIJ = np.zeros((NI,NJ))
for I in range (NI):
DIJ[I,:] = np.flipud(np.polyfit(y1d,CIj[I,:],NJ-1))
# Reconstruct the entire surface
zsmooth = np.zeros((Nx,Ny))
for I in range(NI):
for J in range(NJ):
zsmooth += DIJ[I,J]*x**I*y**J
return zsmooth
def update_lambda(self, sstats, word_list, opt_o):
self.m_status_up_to_date = False
# rhot will be between 0 and 1, and says how much to weight
# the information we got from this mini-chunk.
rhot = self.m_scale * pow(self.m_tau + self.m_updatect, -self.m_kappa)
if rhot < rhot_bound:
rhot = rhot_bound
self.m_rhot = rhot
# Update appropriate columns of lambda based on documents.
self.m_lambda[:, word_list] = self.m_lambda[:, word_list] * (1 - rhot) + \
rhot * self.m_D * sstats.m_var_beta_ss / sstats.m_chunksize
self.m_lambda_sum = (1 - rhot) * self.m_lambda_sum + \
rhot * self.m_D * np.sum(sstats.m_var_beta_ss, axis=1) / sstats.m_chunksize
self.m_updatect += 1
self.m_timestamp[word_list] = self.m_updatect
self.m_r.append(self.m_r[-1] + np.log(1 - rhot))
self.m_varphi_ss = (1.0 - rhot) * self.m_varphi_ss + rhot * \
sstats.m_var_sticks_ss * self.m_D / sstats.m_chunksize
if opt_o:
self.optimal_ordering()
## update top level sticks
self.m_var_sticks[0] = self.m_varphi_ss[:self.m_T - 1] + 1.0
var_phi_sum = np.flipud(self.m_varphi_ss[1:])
self.m_var_sticks[1] = np.flipud(np.cumsum(var_phi_sum)) + self.m_gamma
def generateBoundingBox(imap, reg, scale, t):
# use heatmap to generate bounding boxes
stride = 2
cellsize = 12
imap = np.transpose(imap)
dx1 = np.transpose(reg[:, :, 0])
dy1 = np.transpose(reg[:, :, 1])
dx2 = np.transpose(reg[:, :, 2])
dy2 = np.transpose(reg[:, :, 3])
y, x = np.where(imap >= t)
if y.shape[0] == 1:
dx1 = np.flipud(dx1)
dy1 = np.flipud(dy1)
dx2 = np.flipud(dx2)
dy2 = np.flipud(dy2)
score = imap[(y, x)]
reg = np.transpose(np.vstack([dx1[(y, x)], dy1[(y, x)], dx2[(y, x)], dy2[(y, x)]]))
if reg.size == 0:
reg = np.empty((0, 3))
bb = np.transpose(np.vstack([y, x]))
q1 = np.fix((stride * bb + 1) / scale)
q2 = np.fix((stride * bb + cellsize - 1 + 1) / scale)
boundingbox = np.hstack([q1, q2, np.expand_dims(score, 1), reg])
return boundingbox, reg
def _northup( self, latitude='lat' ):
''' this works only for global grids to be downscaled flips it northup '''
if self.ds[ latitude ][0].data < 0: # meaning that south is north globally
self.ds[ latitude ] = np.flipud( self.ds[ latitude ] )
# flip each slice of the array and make a new one
flipped = np.array( [ np.flipud( arr ) for arr in self.ds[ self.variable ].data ] )
self.ds[ self.variable ] = (('time', 'lat', 'lon' ), flipped )
def rkstab(A, b, xmin, xmax, ymax, n):
""" Evaluate the stability region of a Runge-Kutta method
given its coefficients A and b
"""
d = b.size
m = int(0.5 * n)
x = np.linspace(xmin, xmax, n)
y = np.linspace(0, ymax, m)
X, Y = np.meshgrid(x, y)
z = X + 1j * Y
I = np.identity(d)
e = np.ones(d)
r = np.zeros((m, n), dtype=complex)
for ix in xrange(n):
for iy in xrange(m):
s = solve(I - z[iy, ix] * A, e)
r[iy, ix] = 1 + z[iy, ix] * np.dot(b, s)
R = np.abs(r)
X = np.vstack((np.flipud(X[1:, :]), X))
Y = np.vstack((-np.flipud(Y[1:, :]), Y))
R = np.vstack((np.flipud(R[1:, :]), R))
return X, Y, R
def _readDem(self):
"""
Read coordinates defining DEM and create vectors of x, y, and z values.
"""
# Load each coordinate as a numpy array.
x, y, z = numpy.loadtxt(self.inputDem, dtype=numpy.float64, unpack=True)
self.numZIn = len(z)
if (y[0] == y[1]):
# Ordered by rows.
self.numXIn = max(numpy.argmax(x) + 1, numpy.argmin(x) + 1)
self.xIn = x[0:self.numXIn]
self.numYIn = self.numZIn/self.numXIn
self.yIn = y[0:self.numZIn:self.numXIn]
self.zIn = numpy.reshape(z, (self.numYIn, self.numXIn))
else:
# Ordered by columns.
self.numYIn = max(numpy.argmax(y) + 1, numpy.argmin(y) + 1)
self.yIn = y[0:self.numYIn]
self.numXIn = self.numZIn/self.numYIn
self.xIn = x[0:self.numZIn:self.numYIn]
self.ZIn = numpy.transpose(numpy.reshape(z, (self.numXIn, self.numYIn)))
if (self.xIn[0] > self.xIn[1]):
self.xIn = numpy.flipud(self.xIn)
self.zIn = numpy.fliplr(self.zIn)
if (self.yIn[0] > self.yIn[1]):
self.yIn = numpy.flipud(self.yIn)
self.zIn = numpy.flipud(self.zIn)
return
def MatForChannel(signals, picHeight, picWidth):
# signals is a vector! not a mat
newSignals = signals * ((picHeight - 1) / 2)
width = len(newSignals)
stride = int(np.ceil(float(width) / picWidth))
# pad
temp = np.zeros(picWidth * stride)
temp[0:width] = newSignals
# 1 - reshape according to stride
maxVec = temp.reshape((picWidth, stride))
# 2 - get max vector of each stride
maxVec = maxVec.max(axis=1).astype(int)
# 3 - get max mat
maxMat = np.zeros((picWidth, picHeight / 2))
maxMat[np.arange(picWidth), maxVec] = 1
# flip it when it's little...
# TODO: maybe we can cheat this part (but it's really fast)
maxMat = maxMat.T
maxMat = np.flipud(maxMat)
# 4 - create fill matrix
# TODO: can do a static mat of this
fillMat = np.ones((picHeight / 2, picHeight / 2))
fillMat = np.tril(fillMat, 0)
# result
endMat = np.dot(fillMat, maxMat)
endMat = np.vstack((endMat, np.flipud(endMat)))
return endMat
def mirror_edges(X, nPixels):
assert(nPixels>0)
[s, height, width] = X.shape
Xm = np.zeros([s, height+2*nPixels, width+2*nPixels], dtype=X.dtype)
Xm[:, nPixels:height+nPixels, nPixels:width+nPixels] = X
for i in range(s):
# top left corner
Xm[i, 0:nPixels, 0:nPixels] = np.fliplr(np.flipud(X[i, 0:nPixels, 0:nPixels]))
# top right corner
Xm[i, 0:nPixels, width+nPixels:width+2*nPixels] = np.fliplr(np.flipud(X[i, 0:nPixels, width-nPixels:width]))
# bottom left corner
Xm[i, height+nPixels:height+2*nPixels, 0:nPixels] = np.fliplr(np.flipud(X[i, height-nPixels:height, 0:nPixels]))
# bottom right corner
Xm[i, height+nPixels:height+2*nPixels, width+nPixels:width+2*nPixels] = np.fliplr(np.flipud(X[i, height-nPixels:height, width-nPixels:width]))
# top
Xm[i, 0:nPixels, nPixels:width+nPixels] = np.flipud(X[i, 0:nPixels, 0:width])
# bottom
Xm[i, height+nPixels:height+2*nPixels, nPixels:width+nPixels] = np.flipud(X[i, height-nPixels:height, 0:width])
# left
Xm[i, nPixels:height+nPixels, 0:nPixels] = np.fliplr(X[i, 0:height, 0:nPixels])
# right
Xm[i, nPixels:height+nPixels, width+nPixels:width+2*nPixels] = np.fliplr(X[i, 0:height, width-nPixels:width])
return Xm
def zwriter(self, imageslice, fname):
if "OpenEXR" in self.backends:
imageslice = numpy.flipud(imageslice)
exr.save_depth(imageslice, fname)
elif "VTK" in self.backends:
height = imageslice.shape[1]
width = imageslice.shape[0]
file = open(fname, mode='w')
file.write("Image type: L 32F image\r\n")
file.write("Name: A cinema depth image\r\n")
file.write("Image size (x*y): "+str(height) + "*" + str(width) + "\r\n")
file.write("File size (no of images): 1\r\n")
file.write(chr(26))
imageslice.tofile(file)
file.close()
elif "PIL" in self.backends:
imageslice = numpy.flipud(imageslice)
pimg = PIL.Image.fromarray(imageslice)
#TODO:
# don't let ImImagePlugin.py insert the Name: filename in line two
# why? because ImImagePlugin.py reader has a 100 character limit
pimg.save(fname)
else:
print "Warning: need OpenEXR or PIL or VTK to write to " + fname
def plot_kmeans(dist_m, shape_port, dat_port, dat_star, dat_kclass, ft, humfile, sonpath, base, p):
Zdist = dist_m[shape_port[-1]*p:shape_port[-1]*(p+1)]
extent = shape_port[1]
levels = [0.5,0.75,1.25,1.5,1.75,2,3]
fig = plt.figure()
plt.subplot(2,1,1)
ax = plt.gca()
plt.imshow(np.vstack((np.flipud(dat_port), dat_star)), cmap='gray',extent=[min(Zdist), max(Zdist), -extent*(1/ft), extent*(1/ft)],origin='upper')
CS = plt.contourf(np.flipud(dat_kclass), levels, alpha=0.4, extent=[min(Zdist), max(Zdist), -extent*(1/ft), extent*(1/ft)],origin='upper', cmap='YlOrRd', vmin=0.5, vmax=3)
plt.ylabel('Horizontal distance (m)')
plt.xlabel('Distance along track (m)')
plt.axis('tight')
try:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(CS, cax=cax)
except:
plt.colorbar()
custom_save(sonpath,base+'class_kmeans'+str(p))
del fig
def to_unit(self, unit):
"""Return wavelength and transmission in new wavelength units.
If the requested units are the same as the current units, self is
returned.
Parameters
----------
unit : `~astropy.units.Unit` or str
Target wavelength unit.
Returns
-------
wave : `~numpy.ndarray`
trans : `~numpy.ndarray`
"""
if unit is u.AA:
return self.wave, self.trans
d = u.AA.to(unit, self.wave, u.spectral())
t = self.trans
if d[0] > d[-1]:
d = np.flipud(d)
t = np.flipud(t)
return d, t
def schedule_jobs(job_matrix, mode='ratio'):
job_weight = job_matrix[:, 0]
job_length = job_matrix[:, 1]
if mode == 'difference':
job_metric = numpy.subtract(job_weight, job_length)
job_matrix = numpy.vstack((job_weight, job_length, job_metric)).T
job_matrix = job_matrix[job_matrix[:, 2].argsort()]
job_matrix = numpy.flipud(job_matrix)
metric_value_freq = Counter(job_matrix[:, 2])
# Break ties between jobs using weight (i.e. higher weight goes first)
i = 0
for key in sorted(metric_value_freq.keys(), reverse=True):
subarray = job_matrix[i:i + metric_value_freq[key]]
sorted_subarray = subarray[subarray[:, 0].argsort()]
sorted_subarray = numpy.flipud(sorted_subarray)
job_matrix[i:i + metric_value_freq[key]] = sorted_subarray
i += metric_value_freq[key]
elif mode == 'ratio':
job_metric = numpy.divide(job_weight, job_length)
job_matrix = numpy.vstack((job_weight, job_length, job_metric)).T
job_matrix = job_matrix[job_matrix[:, 2].argsort()]
job_matrix = numpy.flipud(job_matrix)
return job_matrix
def _interpolate_for_irf(w_orig, w_interp, mat_in):
'''
Interpolate matrices for the IRF calculations
'''
mat_interp = np.zeros([mat_in.shape[0], mat_in.shape[1], w_interp.size])
flip = False
if w_orig[0] > w_orig[1]:
w_tmp = np.flipud(w_orig)
flip = True
else:
w_tmp = w_orig
for i in xrange(mat_in.shape[0]):
for j in xrange(mat_in.shape[1]):
if flip is True:
rdTmp = np.flipud(mat_in[i, j, :])
else:
rdTmp = mat_in[i, j, :]
f = interpolate.interp1d(x=w_tmp, y=rdTmp)
mat_interp[i, j, :] = f(w_interp)
return mat_interp
def testCircular(self):
g = Graph()
op = OpLazyCC(graph=g)
op.ChunkShape.setValue((3, 3, 1))
vol = np.asarray(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
],
dtype=np.uint8,
)
vol1 = vigra.taggedView(vol, axistags="yx")
vol2 = vigra.taggedView(vol, axistags="xy")
vol3 = vigra.taggedView(np.flipud(vol), axistags="yx")
vol4 = vigra.taggedView(np.flipud(vol), axistags="xy")
for v in (vol1, vol2, vol3, vol4):
op.Input.setValue(v)
for x in [0, 3, 6]:
for y in [0, 3, 6]:
if x == 3 and y == 3:
continue
op.Input.setDirty(slice(None))
out = op.Output[x : x + 3, y : y + 3].wait()
print(out.squeeze())
assert out.max() == 1
def data_reorient_udrot(d, pl, ud=None, rot90=None):
"""
"""
print >>sys.stderr, "Reorienting...",
if (not ud is None) and (not rot90 is None):
if ud > 0:
d = np.flipud(d)
if rot90 > 0:
d = np.rot90(d, rot90)
print >>sys.stderr, "done."
return d
ud = pl.get_val('REORIENT_CCD_FLIPUD')
if ud == None:
ud = pl.get_val('CORRECTION_FLIPUD')
ud = int(ud)
rot90 = pl.get_val('REORIENT_CCD_ROT90')
if rot90 == None:
rot90 = pl.get_val('CORRECTION_ROTATION')
rot90 = int(rot90)
if ud > 0:
d = np.flipud(d)
if rot90 > 0:
d = np.rot90(d, rot90)
print >>sys.stderr, "done."
return d
def var_border(v, di=1, dj=1):
"""
Border of 2d numpy array
di,dj is the interval between points along columns and lines
Corner points are kept even with di and dj not 1
"""
j, i = v.shape
if (di, dj) == (1, 1):
xb = np.arange(2 * i + 2 * j, dtype=v.dtype)
yb = np.arange(2 * i + 2 * j, dtype=v.dtype)
xb[0:j] = v[:, 0]
xb[j : j + i] = v[-1, :]
xb[j + i : j + i + j] = np.flipud(v[:, -1])
xb[j + i + j :] = np.flipud(v[0, :])
else:
# ensure corner points are kept!!
tmp1 = v[::dj, 0]
tmp2 = v[-1, ::di]
tmp3 = np.flipud(v[:, -1])[::dj]
tmp4 = np.flipud(v[0, :])[::di]
xb = np.concatenate((tmp1, tmp2, tmp3, tmp4))
return xb
def filtfilt(b,a,x):
"""
What does this function do? Alberto??
"""
#For now only accepting 1d arrays
ntaps=max(len(a),len(b))
edge=ntaps*3
if x.ndim != 1:
raise ValueError, "Filiflit is only accepting 1 dimension arrays."
#x must be bigger than edge
if x.size < edge:
raise ValueError, "Input vector needs to be bigger than 3 * max(len(a),len(b)."
if len(a) < ntaps:
a=np.r_[a,zeros(len(b)-len(a))]
if len(b) < ntaps:
b=np.r_[b,zeros(len(a)-len(b))]
zi=lfilter_zi(b,a)
#Grow the signal to have edges for stabilizing
#the filter with inverted replicas of the signal
s=np.r_[2*x[0]-x[edge:1:-1],x,2*x[-1]-x[-1:-edge:-1]]
#in the case of one go we only need one of the extrems
# both are needed for filtfilt
(y,zf)=lfilter(b,a,s,-1,zi*s[0])
(y,zf)=lfilter(b,a,np.flipud(y),-1,zi*y[-1])
return np.flipud(y[edge-1:-edge+1])
# In[12]: # exposure_time = 0.1 * u.s # camera_model_str = 'Canon 70D' # author_str = 'Julius Berkowski' print(str(time)) time = time #datetime(2017, 8, 21, 17, 27, 13) # don't forget to convert your time to UTC! #gps = [44.37197, -116.87393] * u.deg # latitude, longitude of Mann Creek, Idaho # ## Read in the image data # In[14]: # read in the image and flip it so that it's correct im_rgb = np.flipud(matplotlib.image.imread(f)) #raw = rawpy.imread(f) #im_rgb = np.flipud(raw.postprocess()) # remove color info im = np.average(im_rgb, axis=2) # In[15]: plt.imshow(im, origin='lower') # # Get info from the image # We need the following information from the image # * the location of the center of the Sun/Moon and
maitempif8=ma.filled(maitempif8, fill_value=0.)
maizetrof8=maitropf8+maitropif8
maizetemf8=maitempf8+maitempif8
maizetorf8=maitropf8+maitempf8
maizetoif8=maitropif8+maitempif8
maizetof8 = maitropf8+maitempf8+maitropif8+maitempif8
clmtropf=N.zeros((10,360,720))
clmtempf=N.zeros((10,360,720))
clmtropfi=N.zeros((10,360,720))
clmtempfi=N.zeros((10,360,720))
for j in range(0,10):
clm=NetCDFFile('/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/soytrop_rcp45_constco2_rf_nofert_0.5x0.5.nc','r')
aa= N.flipud(clm.variables['yield'][84+j,:,:])
clmtropf[j,:,:] = aa
clm1=NetCDFFile('/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/soytemp_rcp45_constco2_rf_nofert_0.5x0.5.nc','r')
bb = N.flipud(clm1.variables['yield'][84+j,:,:])
clmtempf[j,:,:] = bb
clm2=NetCDFFile('/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/soytrop_rcp45_co2_rf_nofert_0.5x0.5.nc','r')
cc = N.flipud(clm2.variables['yield'][84+j,:,:])
clmtropfi[j,:,:] = cc
clm3=NetCDFFile('/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/soytemp_rcp45_co2_rf_nofert_0.5x0.5.nc','r')
dd = N.flipud(clm3.variables['yield'][84+j,:,:])
clmtempfi[j,:,:] = dd
def do(now, realtime=False):
""" Generate for this timestep! """
szx = 7000
szy = 3500
# Create the image data
imgdata = np.zeros((szy, szx), 'u1')
metadata = {
'start_valid': now.strftime("%Y-%m-%dT%H:%M:%SZ"),
'end_valid': now.strftime("%Y-%m-%dT%H:%M:%SZ"),
'product': 'lcref',
'units': '0.5 dBZ'
}
fn = now.strftime("SeamlessHSR_00.00_%Y%m%d-%H%M00.grib2.gz")
uri = now.strftime(("http://mtarchive.geol.iastate.edu/%Y/%m/%d/"
"mrms/ncep/SeamlessHSR/" + fn))
gribfn = "%s/%s" % (TMP, fn)
res = requests.get(uri, timeout=30)
if res.status_code != 200:
print("mrms_lcref_comp.py MISSING %s" % (gribfn, ))
return
o = open(gribfn, 'wb')
o.write(res.content)
o.close()
fp = gzip.GzipFile(gribfn, 'rb')
(_, tmpfn) = tempfile.mkstemp()
tmpfp = open(tmpfn, 'wb')
tmpfp.write(fp.read())
tmpfp.close()
grbs = pygrib.open(tmpfn)
grb = grbs[1]
os.unlink(tmpfn)
os.unlink(gribfn)
val = grb['values']
# -999 is no coverage, go to 0
# -99 is missing , go to 255
val = np.where(val >= -32, (val + 32) * 2.0, val)
# val = np.where(val < -990., 0., val)
# val = np.where(val < -90., 255., val)
# This is an upstream BUG
val = np.where(val < 0., 0., val)
imgdata[:, :] = np.flipud(val.astype('int'))
(tmpfp, tmpfn) = tempfile.mkstemp()
# Create Image
png = Image.fromarray(np.flipud(imgdata))
png.putpalette(make_colorramp())
png.save('%s.png' % (tmpfn, ))
mrms.write_worldfile('%s.wld' % (tmpfn, ))
# Inject WLD file
prefix = 'lcref'
pqstr = ("/home/ldm/bin/pqinsert -i -p 'plot ac %s "
"gis/images/4326/mrms/%s.wld GIS/mrms/%s_%s.wld wld' %s.wld") % (
now.strftime("%Y%m%d%H%M"), prefix, prefix,
now.strftime("%Y%m%d%H%M"), tmpfn)
subprocess.call(pqstr, shell=True)
# Now we inject into LDM
pqstr = ("/home/ldm/bin/pqinsert -i -p 'plot ac %s "
"gis/images/4326/mrms/%s.png GIS/mrms/%s_%s.png png' %s.png") % (
now.strftime("%Y%m%d%H%M"), prefix, prefix,
now.strftime("%Y%m%d%H%M"), tmpfn)
subprocess.call(pqstr, shell=True)
# Create 900913 image
cmd = ("gdalwarp -s_srs EPSG:4326 -t_srs EPSG:3857 -q -of GTiff "
"-tr 1000.0 1000.0 %s.png %s.tif") % (tmpfn, tmpfn)
subprocess.call(cmd, shell=True)
# Insert into LDM
pqstr = ("/home/ldm/bin/pqinsert -i -p 'plot c %s "
"gis/images/900913/mrms/%s.tif GIS/mrms/%s_%s.tif tif' %s.tif"
) % (now.strftime("%Y%m%d%H%M"), prefix, prefix,
now.strftime("%Y%m%d%H%M"), tmpfn)
subprocess.call(pqstr, shell=True)
j = open("%s.json" % (tmpfn, ), 'w')
j.write(json.dumps(dict(meta=metadata)))
j.close()
# Insert into LDM
pqstr = ("/home/ldm/bin/pqinsert -p 'plot c %s "
"gis/images/4326/mrms/%s.json GIS/mrms/%s_%s.json json' %s.json"
) % (now.strftime("%Y%m%d%H%M"), prefix, prefix,
now.strftime("%Y%m%d%H%M"), tmpfn)
subprocess.call(pqstr, shell=True)
for suffix in ['tif', 'json', 'png', 'wld']:
os.unlink('%s.%s' % (tmpfn, suffix))
os.close(tmpfp)
os.unlink(tmpfn)
def steric(Tb, Sb, dT, dS, deta, eta):
ogrid = ModelGrid(fname='../wrkdir/grid_spec.nc')
p = np.ones(np.shape(Sb))
h = np.ones(np.shape(Sb))
for index in range(0, np.shape(p)[0]):
p[index, :, :] = ogrid.zt[index]
if (index == 0):
h[index, :, :] = ogrid.zb[index] #-eta
else:
h[index, :, :] = abs(ogrid.zb[index] - ogrid.zb[index - 1])
rho_b = eos.density(Sb, Tb, p)
rho_a = eos.density(Sb + dS, Tb + dT, p)
rho_a_halo = eos.density(Sb + dS, Tb, p)
rho_a_thermo = eos.density(Sb, Tb + dT, p)
dsteric = -np.nansum(h * (rho_a - rho_b) / rho_b, axis=0)
dhalosteric = -np.nansum(h * (rho_a_halo - rho_b) / rho_b, axis=0)
dthermosteric = -np.nansum(h * (rho_a_thermo - rho_b) / rho_b, axis=0)
return dsteric, dhalosteric, dthermosteric
'''
print np.shape(dsteric), np.shape(Sb)
dsteric[dsteric==0.0]=np.nan
deta[deta==0.0]=np.nan
print 'deta-dsteric=',np.nansum(np.abs(deta-dsteric))
fig = plt.figure(num=1, figsize=(19,12), facecolor='w')
#grid = AxesGrid(fig, 111, nrows_ncols = (2, 3), axes_pad = 0.5,
# cbar_location="bottom",
# cbar_mode="each",
# cbar_size="7%",
# cbar_pad="2%")
vmax=0.1
vmin=-vmax
#plt.sca(grid[0])
plt.subplot(231)
ch=plt2d(ogrid.x, ogrid.y, dsteric, minval=vmin, maxval=vmax, colmap=cm.jet)
#grid.cbar_axes[0].colorbar(ch)
plt.title('Steric height infered increment [m]')
#plt.sca(grid[1])
plt.subplot(232)
incr_s=np.flipud(dhalosteric)
incr_s[incr_s==0.0]=np.nan
ch=plt2d(ogrid.x, ogrid.y, dhalosteric, minval=vmin, maxval=vmax, colmap=cm.jet)
#grid.cbar_axes[1].colorbar(ch)
plt.title('Halo Steric height infered increment [m]')
#plt.sca(grid[2])
plt.subplot(233)
incr_t=np.flipud(dthermosteric)
incr_t[incr_t==0.0]=np.nan
ch=plt2d(ogrid.x, ogrid.y, dthermosteric, minval=vmin, maxval=vmax, colmap=cm.jet)
#grid.cbar_axes[2].colorbar(ch)
plt.title('Thermo Steric height infered increment [m]')
#plt.sca(grid[3])
plt.subplot(234)
ch=plt2d(ogrid.x, ogrid.y, deta, minval=vmin, maxval=vmax, colmap=cm.jet)
#grid.cbar_axes[3].colorbar(ch)
plt.title('SSH increment [m]')
vmin=-0.1
vmax=0.1
#plt.sca(grid[4])
plt.subplot(235)
err=deta-dsteric
#err[np.abs(err)<0.05]=np.nan
#err=np.abs(deta/dsteric)
ch=plt2d(ogrid.x, ogrid.y, err, minval=vmin, maxval=vmax, colmap=cm.bwr)
#grid.cbar_axes[4].colorbar(ch)
plt.title('SSH - Steric [m]')
#plt.sca(grid[5])
plt.subplot(236)
err=deta-dthermosteric
#err=np.abs(deta/dthermosteric)
#err[np.abs(err)<0.05]=np.nan
ch=plt2d(ogrid.x, ogrid.y, err, minval=vmin, maxval=vmax, colmap=cm.bwr)
#grid.cbar_axes[5].colorbar(ch)
plt.title('SSH - Thermosteric [m]')
'''
plt.subplot(235)
incr_t = np.flipud(dthermosteric)
incr_t[incr_t == 0.0] = np.nan
plt.imshow(incr_t, vmin=vmin, vmax=vmax)
#plt.imshow(np.flipud(dthermosteric),vmin=vmin,vmax=vmax)
plt.colorbar()
plt.subplot(231)
plt.imshow(np.flipud(deta), vmin=vmin, vmax=vmax)
plt.title('SSH increment [m]')
plt.colorbar()
plt.subplot(233)
err = np.flipud(deta - dsteric)
err[np.abs(err) < 0.02] = np.nan
plt.imshow(err, vmin=vmin, vmax=vmax)
#plt.imshow(np.flipud(deta-dsteric),vmin=vmin,vmax=vmax)
plt.title('SSH - Steric height [m]')
plt.colorbar()
'''
COLORS = ["#ffffff", "#000000"]
RASTERIZE_COLOR_FIELD = "__color__"
class fcycle(object):
def __init__(self, iterable):
"""Call functions from the iterable each time it is called."""
self.funcs = cycle(iterable)
def __call__(self, *args, **kwargs):
f = next(self.funcs)
return f(*args, **kwargs)
# SI and IS operators for 2D and 3D.
_P2 = [np.eye(3), np.array([[0, 1, 0]] * 3), np.flipud(np.eye(3)), np.rot90([[0, 1, 0]] * 3)]
_P3 = [np.zeros((3, 3, 3)) for i in range(9)]
_P3[0][:, :, 1] = 1
_P3[1][:, 1, :] = 1
_P3[2][1, :, :] = 1
_P3[3][:, [0, 1, 2], [0, 1, 2]] = 1
_P3[4][:, [0, 1, 2], [2, 1, 0]] = 1
_P3[5][[0, 1, 2], :, [0, 1, 2]] = 1
_P3[6][[0, 1, 2], :, [2, 1, 0]] = 1
_P3[7][[0, 1, 2], [0, 1, 2], :] = 1
_P3[8][[0, 1, 2], [2, 1, 0], :] = 1
_aux = np.zeros(0)
def process(frame):
# steps
# 1. select region of intrest
# 2. undistort the image
# 3. compute the prepective transform and warp image
# 4. find the thresholded image
# 5. create histogram and then find line pixels by anaylsing peaks and sliding window technique
roi = utils.region_of_intrest(frame)
undistorted_img = cv2.undistort(roi,cam_mat, dist_coff, None, cam_mat)
img_size = (undistorted_img.shape[1], undistorted_img.shape[0])
trans_mat = utils.get_prespective(undistorted_img)
inv_trans_mat = utils.get_prespective(undistorted_img, unwrapped = True)
warped_img = cv2.warpPerspective(undistorted_img, trans_mat,img_size,cv2.INTER_LINEAR )
thresh_img = utils.image_threshold(warped_img)
# histogram to find peaks for staring points of lane detection
histogram = np.sum(thresh_img[int(thresh_img.shape[0] / 2):, :], axis=0)
out_img = np.dstack((thresh_img, thresh_img, thresh_img)) * 255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0] / 2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
window_height = np.int(thresh_img.shape[0] / nwindows)
nonzero = thresh_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
leftx_current = leftx_base
rightx_current = rightx_base
margin = 100
minpix = 50
left_lane_inds = []
right_lane_inds = []
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = thresh_img.shape[0] - (window + 1) * window_height
win_y_high = thresh_img.shape[0] - window * window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img, (win_xleft_low, win_y_low), (win_xleft_high, win_y_high), (0, 255, 0), 2)
cv2.rectangle(out_img, (win_xright_low, win_y_low), (win_xright_high, win_y_high), (0, 255, 0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (
nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (
nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, thresh_img.shape[0] - 1, thresh_img.shape[0])
left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]
# out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
# out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# plt.imshow(out_img)
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.xlim(0, 1280)
# plt.ylim(720, 0)
# plt.show()
# calulation the lane curvature and distance from center
radius_of_curvature = lane_curvature(left_fit, right_fit)
center_calc = dist_from_center(frame, left_fitx, right_fitx)
warp_zero = np.zeros_like(thresh_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# this is a hamfisted solution to replace curves with extreme values with the previous frame's curve
newwarp = cv2.warpPerspective(color_warp, inv_trans_mat, (frame.shape[1], frame.shape[0]))
result = cv2.addWeighted(frame, 1, newwarp, 0.3, 0)
cv2.putText(result, 'Radius of Curvature: %.2fm' % radius_of_curvature, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2)
if center_calc < 0:
text = 'left'
else:
text = 'right'
cv2.putText(result, 'Distance From Center: %.2fm %s' % (np.absolute(center_calc), text), (20, 80),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
return result
def create_output_images(Rover):
# Create a scaled map for plotting and clean up obs/nav pixels a bit
if np.max(Rover.worldmap[:, :, 2]) > 0:
nav_pix = Rover.worldmap[:, :, 2] > 0
navigable = Rover.worldmap[:, :, 2] * (
255 / np.mean(Rover.worldmap[nav_pix, 2]))
else:
navigable = Rover.worldmap[:, :, 2]
if np.max(Rover.worldmap[:, :, 0]) > 0:
obs_pix = Rover.worldmap[:, :, 0] > 0
obstacle = Rover.worldmap[:, :, 0] * (
255 / np.mean(Rover.worldmap[obs_pix, 0]))
else:
obstacle = Rover.worldmap[:, :, 0]
likely_nav = navigable >= obstacle
obstacle[likely_nav] = 0
plotmap = np.zeros_like(Rover.worldmap)
plotmap[:, :, 0] = obstacle
plotmap[:, :, 2] = navigable
plotmap = plotmap.clip(0, 255)
# Overlay obstacle and navigable terrain map with ground truth map
map_add = cv2.addWeighted(plotmap, 1, Rover.ground_truth, 0.5, 0)
# Check whether any rock detections are present in worldmap
rock_world_pos = Rover.worldmap[:, :, 1].nonzero()
# If there are, we'll step through the known sample positions
# to confirm whether detections are real
samples_located = 0
if rock_world_pos[0].any():
rock_size = 2
for idx in range(len(Rover.samples_pos[0])):
test_rock_x = Rover.samples_pos[0][idx]
test_rock_y = Rover.samples_pos[1][idx]
rock_sample_dists = np.sqrt((test_rock_x - rock_world_pos[1])**2 + \
(test_rock_y - rock_world_pos[0])**2)
# If rocks were detected within 3 meters of known sample positions
# consider it a success and plot the location of the known
# sample on the map
if np.min(rock_sample_dists) < 3:
samples_located += 1
map_add[test_rock_y - rock_size:test_rock_y + rock_size,
test_rock_x - rock_size:test_rock_x +
rock_size, :] = 255
# Calculate some statistics on the map results
# First get the total number of pixels in the navigable terrain map
tot_nav_pix = np.float(len((plotmap[:, :, 2].nonzero()[0])))
# Next figure out how many of those correspond to ground truth pixels
good_nav_pix = np.float(
len(((plotmap[:, :, 2] > 0) &
(Rover.ground_truth[:, :, 1] > 0)).nonzero()[0]))
# Next find how many do not correspond to ground truth pixels
bad_nav_pix = np.float(
len(((plotmap[:, :, 2] > 0) &
(Rover.ground_truth[:, :, 1] == 0)).nonzero()[0]))
# Grab the total number of map pixels
tot_map_pix = np.float(len((Rover.ground_truth[:, :, 1].nonzero()[0])))
# Calculate the percentage of ground truth map that has been successfully found
perc_mapped = round(100 * good_nav_pix / tot_map_pix, 1)
# Calculate the number of good map pixel detections divided by total pixels
# found to be navigable terrain
if tot_nav_pix > 0:
fidelity = round(100 * good_nav_pix / (tot_nav_pix), 1)
else:
fidelity = 0
# Flip the map for plotting so that the y-axis points upward in the display
map_add = np.flipud(map_add).astype(np.float32)
# Add some text about map and rock sample detection results
cv2.putText(map_add, "Time: " + str(np.round(Rover.total_time, 1)) + ' s',
(0, 10), cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(map_add, "Mapped: " + str(perc_mapped) + '%', (0, 25),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(map_add, "Fidelity: " + str(fidelity) + '%', (0, 40),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(map_add, "Rocks", (0, 55), cv2.FONT_HERSHEY_COMPLEX, 0.4,
(255, 255, 255), 1)
cv2.putText(map_add, " Located: " + str(samples_located), (0, 70),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(map_add, " Collected: " + str(Rover.samples_collected),
(0, 85), cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
# Convert map and vision image to base64 strings for sending to server
pil_img = Image.fromarray(map_add.astype(np.uint8))
buff = BytesIO()
pil_img.save(buff, format="JPEG")
encoded_string1 = base64.b64encode(buff.getvalue()).decode("utf-8")
pil_img = Image.fromarray(Rover.vision_image.astype(np.uint8))
buff = BytesIO()
pil_img.save(buff, format="JPEG")
encoded_string2 = base64.b64encode(buff.getvalue()).decode("utf-8")
return encoded_string1, encoded_string2
def spec(X, y=None, mode='rank', **kwargs):
"""
This function implements the SPEC feature selection
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input data
kwargs: {dictionary}
style: {int}
style == -1, the first feature ranking function, use all eigenvalues
style == 0, the second feature ranking function, use all except the 1st eigenvalue
style >= 2, the third feature ranking function, use the first k except 1st eigenvalue
W: {sparse matrix}, shape (n_samples, n_samples}
input affinity matrix
Output
------
w_fea: {numpy array}, shape (n_features,)
SPEC feature score for each feature
Reference
---------
Zhao, Zheng and Liu, Huan. "Spectral Feature Selection for Supervised and Unsupervised Learning." ICML 2007.
"""
def feature_ranking(score, **kwargs):
if 'style' not in kwargs:
kwargs['style'] = 0
style = kwargs['style']
# if style = -1 or 0, ranking features in descending order, the higher the score, the more important the feature is
if style == -1 or style == 0:
idx = np.argsort(score, 0)
return idx[::-1]
# if style != -1 and 0, ranking features in ascending order, the lower the score, the more important the feature is
elif style != -1 and style != 0:
idx = np.argsort(score, 0)
return idx
if 'style' not in kwargs:
kwargs['style'] = 0
if 'is_classification' not in kwargs:
# if y is available then we do supervised SPEC algo.
kwargs['is_classification'] = True
if 'W' not in kwargs:
print("W not in kwargs")
print(kwargs)
if y is None:
kwargs['W'] = rbf_kernel(X, gamma=1)
elif kwargs['is_classification']:
kwargs['W'] = similiarity_classification(X, y)
else:
kwargs['W'] = similarity_regression(X, y, kwargs.get('n_neighbors', None))
style = kwargs['style']
W = kwargs['W']
if type(W) is numpy.ndarray:
W = csc_matrix(W)
n_samples, n_features = X.shape
# build the degree matrix
X_sum = np.array(W.sum(axis=1))
D = np.zeros((n_samples, n_samples))
for i in range(n_samples):
D[i, i] = X_sum[i]
# build the laplacian matrix
L = D - W
d1 = np.power(np.array(W.sum(axis=1)), -0.5)
d1[np.isinf(d1)] = 0
d2 = np.power(np.array(W.sum(axis=1)), 0.5)
v = np.dot(np.diag(d2[:, 0]), np.ones(n_samples))
v = v/LA.norm(v)
# build the normalized laplacian matrix
L_hat = (np.matlib.repmat(d1, 1, n_samples)) * np.array(L) * np.matlib.repmat(np.transpose(d1), n_samples, 1)
# calculate and construct spectral information
s, U = np.linalg.eigh(L_hat)
s = np.flipud(s)
U = np.fliplr(U)
# begin to select features
w_fea = np.ones(n_features)*1000
for i in range(n_features):
f = X[:, i]
F_hat = np.dot(np.diag(d2[:, 0]), f)
l = LA.norm(F_hat)
if l < 100*np.spacing(1):
w_fea[i] = 1000
continue
else:
F_hat = F_hat/l
a = np.array(np.dot(np.transpose(F_hat), U))
a = np.multiply(a, a)
a = np.transpose(a)
# use f'Lf formulation
if style == -1:
w_fea[i] = np.sum(a * s)
# using all eigenvalues except the 1st
elif style == 0:
a1 = a[0:n_samples-1]
w_fea[i] = np.sum(a1 * s[0:n_samples-1])/(1-np.power(np.dot(np.transpose(F_hat), v), 2))
# use first k except the 1st
else:
a1 = a[n_samples-style:n_samples-1]
w_fea[i] = np.sum(a1 * (2-s[n_samples-style: n_samples-1]))
if style != -1 and style != 0:
w_fea[w_fea == 1000] = -1000
if mode == 'raw':
return w_fea
return feature_ranking(w_fea)
def evaluate_data(self):
self.depthcam_midpixel2 = [
self.new_K_kin[1, 2] - HORIZ_CUT,
(960 - self.new_K_kin[0, 2]) - pre_VERT_CUT
]
#function_input = np.array(function_input)*np.array([10, 10, 10, 10, 10, 10, 0.1, 0.1, 0.1, 0.1, 1])
#function_input += np.array([2.2, 32, -1, 1.2, 32, -5, 1.0, 1.0, 0.96, 0.95, 0.8])
function_input = np.array(self.calibration_optim_values) * np.array(
[10, 10, 10, 0.1, 0.1, 0.1, 0.1])
function_input += np.array([1.2, 32, -5, 1.0, 1.0, 0.96, 0.95])
kinect_rotate_angle = function_input[3 - 3]
kinect_shift_up = int(function_input[4 - 3])
kinect_shift_right = int(function_input[5 - 3])
camera_alpha_vert = function_input[6 - 3]
camera_alpha_horiz = function_input[7 - 3]
blah = True
file_dir = "/media/henry/multimodal_data_2/CVPR2020_study/" + PARTICIPANT + "/data_checked_" + PARTICIPANT + "-" + POSE_TYPE
file_dirs = [file_dir]
init_time = time.time()
RESAVE_DICT = {}
RESAVE_DICT['images'] = []
RESAVE_DICT['RGB'] = []
RESAVE_DICT['depth'] = []
RESAVE_DICT['pc'] = []
RESAVE_DICT['pmat_corners'] = []
RESAVE_DICT['pose_type'] = []
for file_dir in file_dirs:
V3D.load_next_file(file_dir)
start_num = 0
#print self.color_all.shape
#for im_num in range(29, 100):
for im_num in range(start_num, self.color_all.shape[0]):
#For P188: skip 5. 13 good cross legs
if PARTICIPANT == "S114" and POSE_TYPE == "2" and im_num in [
26, 29
]:
continue #these don't have point clouds
if PARTICIPANT == "S165" and POSE_TYPE == "2" and im_num in [
1, 3, 15
]:
continue #these don't have point clouds
if PARTICIPANT == "S188" and POSE_TYPE == "2" and im_num in [
5, 17, 21
]:
continue #these don't have point clouds
print "NEXT IM!", im_num, " ", time.time(
) - init_time, self.pose_type_2_list[im_num]
if POSE_TYPE == "2":
RESAVE_DICT['pose_type'].append(
self.pose_type_2_list[im_num])
elif POSE_TYPE == "1":
if im_num == 0:
if PARTICIPANT == "S145":
RESAVE_DICT['pose_type'].append('p_sel_sup')
elif PARTICIPANT == "S188":
RESAVE_DICT['pose_type'].append('p_sel_ll')
else:
RESAVE_DICT['pose_type'].append('p_sel_any')
if im_num == 1:
if PARTICIPANT == "S140" or PARTICIPANT == "S145":
RESAVE_DICT['pose_type'].append('p_sel_ll')
elif PARTICIPANT == "S188":
RESAVE_DICT['pose_type'].append('p_sel_rl')
else:
RESAVE_DICT['pose_type'].append('p_sel_sup')
if im_num == 2:
if PARTICIPANT == "S140" or PARTICIPANT == "S145":
RESAVE_DICT['pose_type'].append('p_sel_rl')
elif PARTICIPANT == "S188":
RESAVE_DICT['pose_type'].append('p_sel_prn')
else:
RESAVE_DICT['pose_type'].append('p_sel_ll')
if im_num == 3:
if PARTICIPANT == "S140" or PARTICIPANT == "S145":
RESAVE_DICT['pose_type'].append('p_sel_prn')
elif PARTICIPANT == "S188":
RESAVE_DICT['pose_type'].append('p_sel_any')
else:
RESAVE_DICT['pose_type'].append('p_sel_rl')
if im_num == 4:
if PARTICIPANT == "S140" or PARTICIPANT == "S188":
RESAVE_DICT['pose_type'].append('p_sel_sup')
else:
RESAVE_DICT['pose_type'].append('p_sel_prn')
print RESAVE_DICT['pose_type'][-1]
self.overall_image_scale_amount = 0.85
half_w_half_l = [0.4, 0.4, 1.1, 1.1]
all_image_list = []
self.label_single_image = []
self.label_index = 0
self.color = self.color_all[im_num]
self.depth_r = self.depth_r_all[im_num]
self.pressure = self.pressure_all[im_num]
self.bed_state = self.bedstate_all[im_num]
if self.point_cloud_autofil_all[im_num].shape[0] == 0:
self.point_cloud_autofil_all[im_num] = np.array(
[[0.0, 0.0, 0.0]])
self.point_cloud_autofil = self.point_cloud_autofil_all[
im_num] + self.markers_all[im_num][
2] #[0.0, 0.0, 0.0]#0.1]
#print self.markers_all[im_num]
#print self.point_cloud_autofil.shape, 'PC AUTOFIL ORIG'
self.bed_state[
0] = self.bed_state[0] * 0.0 #*head_angle_multiplier
self.bed_state *= 0
#self.bed_state += 60.
#print self.bed_state, np.shape(self.pressure)
if im_num == start_num and blah == True:
markers_c = []
markers_c.append(self.markers_all[im_num][0])
markers_c.append(self.markers_all[im_num][1])
markers_c.append(self.markers_all[im_num][2])
markers_c.append(self.markers_all[im_num][3])
#for idx in range(4):
#if markers_c[idx] is not None:
#markers_c[idx] = np.array(markers_c[idx])*213./228.
blah = False
#print markers_c, 'Markers C'
# Get the marker points in 2D on the color image
u_c, v_c = ArTagLib().color_2D_markers(markers_c,
self.new_K_kin)
# Get the marker points dropped to the height of the pressure mat
u_c_drop, v_c_drop, markers_c_drop = ArTagLib(
).color_2D_markers_drop(markers_c, self.new_K_kin)
#print markers_c_drop, self.new_K_kin, self.pressure_im_size_required, self.bed_state, half_w_half_l
# Get the geometry for sizing the pressure mat
pmat_ArTagLib = ArTagLib()
self.pressure_im_size_required, u_c_pmat, v_c_pmat, u_p_bend, v_p_bend, half_w_half_l = \
pmat_ArTagLib.p_mat_geom(markers_c_drop, self.new_K_kin, self.pressure_im_size_required, self.bed_state, half_w_half_l)
tf_corners = np.zeros((8, 2))
tf_corners[0:8, :] = np.copy(self.tf_corners)
#COLOR
color_reshaped, color_size = VizLib().color_image(
self.color, self.kcam, self.new_K_kin, u_c, v_c, u_c_drop,
v_c_drop, u_c_pmat, v_c_pmat, camera_alpha_vert,
camera_alpha_horiz)
color_reshaped = imutils.rotate(color_reshaped,
kinect_rotate_angle)
color_reshaped = color_reshaped[pre_VERT_CUT +
kinect_shift_up:-pre_VERT_CUT +
kinect_shift_up, HORIZ_CUT +
kinect_shift_right:540 -
HORIZ_CUT +
kinect_shift_right, :]
#all_image_list.append(color_reshaped)
tf_corners[0:4, :], _ = self.transform_selected_points(
color_reshaped, camera_alpha_vert, camera_alpha_horiz,
kinect_rotate_angle, kinect_shift_right, kinect_shift_up,
[1.0, 0], [1.0, 0], np.copy(self.tf_corners[0:4][:]))
#should blur face here
#color_reshaped = self.blur_face(color_reshaped)
RESAVE_DICT['RGB'].append(color_reshaped)
RESAVE_DICT['pmat_corners'].append(tf_corners[0:4, :])
#SAVE CALIBRATED COLOR HERE, color_reshaped
#SAVE CALIBRATED TF CORNERS HERE, tf_corners[0:4, :]
all_image_list.append(color_reshaped)
#DEPTH
h = VizLib().get_new_K_kin_homography(camera_alpha_vert,
camera_alpha_horiz,
self.new_K_kin)
depth_r_orig = cv2.warpPerspective(
self.depth_r, h,
(self.depth_r.shape[1], self.depth_r.shape[0]))
depth_r_orig = imutils.rotate(depth_r_orig,
kinect_rotate_angle)
depth_r_orig = depth_r_orig[HORIZ_CUT +
kinect_shift_right:540 -
HORIZ_CUT + kinect_shift_right,
pre_VERT_CUT -
kinect_shift_up:-pre_VERT_CUT -
kinect_shift_up]
#SAVE CALIBRATED DEPTH HERE, depth_r_orig
RESAVE_DICT['depth'].append(depth_r_orig)
depth_r_reshaped, depth_r_size, depth_r_orig = self.depth_image(
depth_r_orig)
all_image_list.append(depth_r_reshaped)
#PRESSURE
self.pressure = np.clip(self.pressure * 1, 0, 100)
pressure_reshaped, pressure_size = self.pressure_image(
self.pressure, color_size, tf_corners)
pressure_reshaped = pressure_reshaped[
pre_VERT_CUT:-pre_VERT_CUT, HORIZ_CUT:540 - HORIZ_CUT, :]
all_image_list.append(pressure_reshaped)
self.all_images = np.zeros(
(960 - np.abs(pre_VERT_CUT) * 2, 1, 3)).astype(np.uint8)
for image in all_image_list:
#print image.shape
self.all_images = np.concatenate((self.all_images, image),
axis=1)
self.all_images = self.all_images[VERT_CUT:960 -
VERT_CUT, :, :]
is_not_mult_4 = True
while is_not_mult_4 == True:
is_not_mult_4 = cv2.resize(
self.all_images, (0, 0),
fx=self.overall_image_scale_amount,
fy=self.overall_image_scale_amount).shape[1] % 4
self.overall_image_scale_amount += 0.001
self.all_images = cv2.resize(
self.all_images, (0, 0),
fx=self.overall_image_scale_amount,
fy=self.overall_image_scale_amount)
self.cursor_shift = self.all_images.shape[1] / 4
self.all_images_clone = self.all_images.copy()
cv2.imshow('all_images', self.all_images)
k = cv2.waitKey(1)
#cv2.waitKey(0)
#now do 3D rendering
pmat = np.fliplr(
np.flipud(
np.clip(self.pressure.reshape(MAT_SIZE) * float(1),
a_min=0,
a_max=100)))
#SAVE PRESSURE HERE, self.pressure
RESAVE_DICT['images'].append(pmat)
pc_autofil_red = self.trim_pc_sides(
camera_alpha_vert, camera_alpha_horiz, h,
kinect_rotate_angle) #this is the point cloud
#SAVE POINT CLOUD HERE, pc_autofil_red
RESAVE_DICT['pc'].append(pc_autofil_red)
camera_point = [
1.09898028, 0.46441343, -CAM_BED_DIST
] #[dist from foot of bed, dist from left side of mat, dist normal]
#
self.pyRender.render_3D_data(camera_point,
pmat=pmat,
pc=pc_autofil_red)
self.point_cloud_array = None
if POSE_TYPE == "2":
save_name = '/prescribed'
elif POSE_TYPE == "1":
save_name = '/p_select'
pkl.dump(RESAVE_DICT,
open(participant_directory + save_name + '.p', 'wb'))
print "SAVED."
def search_around_poly(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 100
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Plot the polynomial lines onto the image
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
## End visualization steps ##
return result
def detect_similar_lines(img, line_fits=None, return_img=False):
if line_fits is None:
return detect_lines(img, return_img)
left_fit = line_fits[0]
right_fit = line_fits[1]
nonzero = img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = (
(nonzerox >
(left_fit[0] *
(nonzeroy**2) + left_fit[1] * nonzeroy + left_fit[2] - margin)) &
(nonzerox <
(left_fit[0] *
(nonzeroy**2) + left_fit[1] * nonzeroy + left_fit[2] + margin)))
right_lane_inds = (
(nonzerox >
(right_fit[0] *
(nonzeroy**2) + right_fit[1] * nonzeroy + right_fit[2] - margin)) &
(nonzerox <
(right_fit[0] *
(nonzeroy**2) + right_fit[1] * nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# If any of the lines could not be found,
# perform a more exhaustive search
if (leftx.size == 0 or rightx.size == 0):
return detect_lines(img, return_img)
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img.shape[0] - 1, img.shape[0])
left_fitx = left_fit[0] * ploty**2 + left_fit[1] * ploty + left_fit[2]
right_fitx = right_fit[0] * ploty**2 + right_fit[1] * ploty + right_fit[2]
if return_img:
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((img, img, img)) * 255
window_img = np.zeros_like(out_img)
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array(
[np.transpose(np.vstack([left_fitx - margin, ploty]))])
left_line_window2 = np.array(
[np.flipud(np.transpose(np.vstack([left_fitx + margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array(
[np.transpose(np.vstack([right_fitx - margin, ploty]))])
right_line_window2 = np.array(
[np.flipud(np.transpose(np.vstack([right_fitx + margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
out_img = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds],
nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds],
nonzerox[right_lane_inds]] = [0, 0, 255]
for index in range(img.shape[0]):
cv2.circle(out_img, (int(left_fitx[index]), int(ploty[index])), 3,
(255, 255, 0))
cv2.circle(out_img, (int(right_fitx[index]), int(ploty[index])), 3,
(255, 255, 0))
return (left_fit, right_fit), (left_fitx,
ploty), (right_fitx,
ploty), out_img.astype(int)
return (left_fit, right_fit), (left_fitx, ploty), (right_fitx, ploty)
def get(self):
self.application.close_figures()
try:
filename1 = self.get_argument("fitsfile1")
filename2 = self.get_argument("fitsfile2")
log.info(f"Analyzing {filename1} and {filename2}...")
except Exception as e:
log.warning(
f"No or not enough CWFS files specified. ({e.__class__})")
connect = self.get_argument("connect", default=True)
spher = self.get_argument("spher", default=False)
thresh = self.get_argument("thresh", default=150.0)
thresh = float(thresh) * u.nm
focoff = self.get_argument("focoff", default=1000.0)
focoff = float(focoff)
tel = self.application.wfs.telescope
if spher == "true":
spher_mask = ['Z11', 'Z22']
log.info(f"Ignoring the spherical terms {spher_mask}...")
else:
spher_mask = []
if os.path.isfile(filename1) and os.path.isfile(
filename2) and not self.application.busy:
self.application.busy = True
if connect == "true":
self.application.wfs.connect()
else:
self.application.wfs.disconnect()
# get rotator and focus values from the headers, if available
rots = []
focusvals = []
images = [filename1, filename2]
arrays = []
for image in images:
h = fits.open(image)
hdr = h[-1].header
if 'ROT' in hdr:
rots.append(hdr['ROT'])
rot = hdr['ROT']
else:
rot = 0.0
if 'FOCUS' in hdr:
focusvals.append(hdr['FOCUS'])
data = h[-1].data
orig_shape = data.shape
pup_mask = tel.pupil_mask(rotation=rot, size=120)
x, y, fig = center_pupil(data,
pup_mask,
threshold=0.8,
plot=False)
cenx = int(np.round(x))
ceny = int(np.round(y))
data = data[ceny - 96:ceny + 96, cenx - 96:cenx + 96]
if len(h) > 1 or orig_shape == (
516,
532): # check if we're raw binospec SOG images
log.info(
f"Found raw Binospec SOG image. Flipping {image} up/down."
)
data = np.flipud(data)
arrays.append(data)
if len(rots) > 0:
rot = np.array(rots).mean() * u.deg
log.info(f"Using rotator angle of {rot.round(2)}.")
else:
log.warning(
"WARNING: No rotator information in headers. Assuming rotator angle of 0.0 degrees."
)
rot = 0.0 * u.deg
if len(focusvals) == 2:
focusvals = np.array(focusvals)
I1 = Image(arrays[np.argmin(focusvals)], [0, 0],
Image.INTRA)
I2 = Image(arrays[np.argmax(focusvals)], [0, 0],
Image.EXTRA)
intra_file = pathlib.Path(
images[np.argmin(focusvals)]).name
extra_file = pathlib.Path(
images[np.argmax(focusvals)]).name
focoff = focusvals.max() - focusvals.mean()
log.info(f"Using an M2 focus offset of +/- {focoff} um.")
log.info(
f"Intra-focal image: {images[np.argmin(focusvals)]}")
log.info(
f"Extra-focal image: {images[np.argmax(focusvals)]}")
else:
I1 = Image(readFile(images[0]), [0, 0], Image.INTRA)
I2 = Image(readFile(images[1]), [0, 0], Image.EXTRA)
intra_file = pathlib.Path(images[0]).name
extra_file = pathlib.Path(images[1]).name
log.warning(
f"WARNING: No focus information in image headers. Assuming M2 focus offset of +/- {focoff} um."
)
log.warning(
f"WARNING: Assuming intra-focal image is {image[0]}")
log.warning(
f"WARNING: Assuming extra-focal image is {image[1]}")
# use second image filename as the root for output files
output = filename2
# The pupil rotation in the single-object guider on binospec was determined to be 0 deg.
rotation = 0 * u.deg - rot
log.info(f"Total pupil rotation: {rotation.round(2)}")
# load instrument and algorithm parameters
inst = Instrument('mmto', I1.sizeinPix)
# this is a MMTO hack. 0.0 doesn't work, but this will yield an annular zernike solution that is very close
# to circular. the MMTO wfs code currently doesn't support annular zernikes for calculating corrections.
inst.obscuration = 0.01
# convert M2 focus offset in microns to meters of focus shift at the instrument focal plane
inst.offset = focoff * 1.0e-6 * 18.8
# set up fitting algorithm
algo = Algorithm("exp", inst, 0)
log.info(
"Fitting wavefront using 'exp' algorithm and the 'onAxis' model."
)
# run it
algo.runIt(inst, I1, I2, 'onAxis')
log.info("Wavefront fit complete.")
# output parameters
outParam(output + ".param", algo, inst, I1, I2, 'onAxis')
# set up ZernikeVector, denormalize it, and then derotate it
zvec = ZernikeVector()
zvec.from_array(algo.zer4UpNm, modestart=4, normalized=True)
zvec.denormalize()
zvec.rotate(angle=-rotation)
log.debug("\n" + repr(zvec))
# output Zernikes 4 and up
outZer4Up(algo.zer4UpNm, 'nm', output + ".raw.lsst.zernikes")
zvec.save(filename=output + ".rot.zernikes")
m1gain = self.application.wfs.m1_gain
# this is the total if we try to correct everything as fit
totforces, totm1focus, zv_totmasked = tel.calculate_primary_corrections(
zvec, gain=m1gain)
figures = {}
ax = {}
# show intra-focal image
figures['intra'], ax['intra'] = plt.subplots()
figures['intra'].set_label("Intra-focal Image")
im1 = ax['intra'].imshow(I1.image,
cmap='Greys',
origin='lower',
interpolation='None')
ax['intra'].set_title(intra_file)
figures['intra'].colorbar(im1)
# show extra-focal image
figures['extra'], ax['extra'] = plt.subplots()
figures['extra'].set_label("Extra-focal Image")
im2 = ax['extra'].imshow(I2.image,
cmap='Greys',
origin='lower',
interpolation='None')
ax['extra'].set_title(extra_file)
figures['extra'].colorbar(im2)
# show wavefront map
figures['wavefront'], ax['wavefront'] = plt.subplots()
wfim = ax['wavefront'].imshow(algo.Wconverge * 1.0e9,
origin="lower",
cmap='RdBu')
cbar_wf = figures['wavefront'].colorbar(wfim)
cbar_wf.set_label(zvec.units.name, rotation=0)
# show RMS bar chart
zernike_rms = zvec.rms
rms_asec = zernike_rms.value / self.application.wfs.tiltfactor * u.arcsec
log.info(f"Total wavefront RMS: {zernike_rms.round(2)}")
figures['barchart'] = zvec.bar_chart(
title=
f"Total Wavefront RMS: {zernike_rms.round(1)} ({rms_asec.round(2)})"
)
# show total forces and PSF
figures['totalforces'] = tel.plot_forces(totforces, totm1focus)
figures['totalforces'].set_label("Total M1 Actuator Forces")
psf, figures['psf'] = tel.psf(zv=zvec.copy())
self.application.wavefront_fit = zvec
self.application.pending_focus = self.application.wfs.calculate_focus(
zvec)
self.application.pending_cc_x, self.application.pending_cc_y = self.application.wfs.calculate_cc(
zvec)
self.application.pending_forces, self.application.pending_m1focus, zv_masked = \
self.application.wfs.calculate_primary(zvec, threshold=thresh, mask=spher_mask)
self.application.pending_forcefile = output + ".forces"
zvec_masked_file = output + ".masked.zernike"
zv_masked.save(filename=zvec_masked_file)
limit = np.round(
np.abs(self.application.pending_forces['force']).max())
figures['forces'] = tel.plot_forces(
self.application.pending_forces,
self.application.pending_m1focus,
limit=limit)
figures['forces'].set_label("Requested M1 Actuator Forces")
figures['fringebarchart'] = zvec.fringe_bar_chart(
title="Focus: {0:0.1f} CC_X: {1:0.1f} CC_Y: {2:0.1f}".
format(
self.application.pending_focus,
self.application.pending_cc_x,
self.application.pending_cc_y,
))
self.application.has_pending_m1 = True
self.application.has_pending_focus = True
self.application.has_pending_coma = True
self.application.refresh_figures(figures=figures)
else:
log.error(f"No such files: {filename1} {filename2}")
self.application.wavefront_fit.denormalize()
self.write(json.dumps(repr(self.application.wavefront_fit)))
self.application.busy = False
self.finish()
def figure_4_2_val_iter():
# value iteration for final policy and values in one sweep
# determinstic policies
value = np.zeros((L1_CAR_MAX + 1, L2_CAR_MAX + 1))
policy = np.zeros(value.shape, dtype=np.int)
iterations = 0
_, axes = plt.subplots(
2, 1, figsize=(40, 20)) # a fig of policy and a fig of optimal values
plt.subplots_adjust(wspace=0.1, hspace=0.2)
axes = axes.flatten()
while True:
# value iteration, similar to policy improvement
max_diff = 0
for L1_cars in range(L1_CAR_MAX + 1):
for L2_cars in range(L2_CAR_MAX + 1):
old_value = value[L1_cars, L2_cars]
action_values = []
for action in ACTION_LIST:
if 0 <= action <= L1_cars or (-1 * L2_cars) <= action <= 0:
# action_values.append(Expected_Return([L1_cars, L2_cars], action, value))
action_values.append(
Expected_Return_with_Mem([L1_cars, L2_cars],
action, value))
else:
# not available actions
action_values.append(-np.inf)
new_value = np.max(action_values)
diff = abs(old_value - new_value)
value[L1_cars, L2_cars] = new_value
if max_diff < diff:
max_diff = diff
iterations += 1
print('Iter {}: max diff: {:.6f}'.format(iterations, max_diff))
if max_diff < 1e-4:
break
# draw optimal values
fig = sns.heatmap(np.flipud(value), cmap="YlGnBu", ax=axes[-1])
fig.set_ylabel('# cars at first location', fontsize=30)
fig.set_yticks(list(reversed(range(MAX_CARS + 1))))
fig.set_xlabel('# cars at second location', fontsize=30)
fig.set_title('optimal value', fontsize=30)
# final optimal policy
for L1_cars in range(L1_CAR_MAX + 1):
for L2_cars in range(L2_CAR_MAX + 1):
action_values = []
for action in ACTION_LIST:
if 0 <= action <= L1_cars or (-1 * L2_cars) <= action <= 0:
# action_values.append(Expected_Return([L1_cars, L2_cars], action, value))
action_values.append(
Expected_Return_with_Mem([L1_cars, L2_cars], action,
value))
else:
# not available actions
action_values.append(-np.inf)
new_action = ACTION_LIST[np.argmax(action_values)]
policy[L1_cars, L2_cars] = new_action
# draw optimal policy
fig = sns.heatmap(np.flipud(policy), cmap="YlGnBu", ax=axes[0])
fig.set_ylabel('# cars at first location', fontsize=30)
fig.set_yticks(list(reversed(range(MAX_CARS + 1))))
fig.set_xlabel('# cars at second location', fontsize=30)
fig.set_title('optimal policy'.format(0), fontsize=30)
plt.savefig('./images/mine/exe_4_7_val_iter.png')
plt.close()
def main():
parser = argparse.ArgumentParser(description='This script post-process the F_out file that SSAGES print out, and create a FES from the estimation of the thermodynamic force')
parser.add_argument('-i','--input',default='F_out',help='name of the file containing the force')
parser.add_argument('-o','--output',default='G',help='file containing the free energy surface')
parser.add_argument('-p','--periodicity',nargs='*',default=[False,False,False],help='periodicity of the CVs (True is periodic, False is not)')
parser.add_argument('-n','--npoints',nargs='*',type=int,default=[200,200,200],help='number of points for the interpolation')
parser.add_argument('-s','--scale',type=float,default=1,help='scale for the interpolation')
parser.add_argument('-c','--contours',type=int,default=10,help='number of contours on final plot')
args=parser.parse_args()
inputfile=args.input
outputname=args.output
periodic=args.periodicity
interpolate=args.npoints
scale=args.scale
ncontours=args.contours
if interpolate == [0]:
interpolate = 0
periodic = [False if x == 'False' else x for x in periodic]
if not (os.path.isfile(inputfile)):
print('Input file is missing. Aborting! Please use -h to ask for help!')
exit()
print('Input file is :\n', inputfile, '\n and output file is : \n',outputname)
print('CV1 periodicity is set to \n',periodic[0],' \n')
if(len(periodic)>1):
print('CV2 periodicity is set to (if it exists) \n',periodic[1],' \n')
if(len(periodic)>2):
print('CV3 periodicity is set to (if it exists) \n',periodic[2],' \n')
print('Free energy will be interpolated with \n',interpolate,'\n points \n')
print('Free energy will be plotted with \n',ncontours,'\n contours')
f = open(outputname+'_integrated.txt','w')
vfield = []
with open(inputfile,"r") as infile:
for line in reversed(infile.readlines()):
if "The columns" in line:
headerinfo=re.findall(r'\d+',line)
break
elif not (line.strip()==""):
vfield.append(list(map(float,line.split())))
vfield = np.flipud(np.asarray(vfield))
shapeinfo = vfield.shape
headerfound = 0
try:
headerinfo = [int(i) for i in headerinfo]
headerfound = 1
except NameError:
print("Header not found. Proceeding with analysis, but the file read in is likely not created by ABF directly. Proceed with caution.")
if shapeinfo[1] == 2:
print("Two columns detected in input, which translates to a 1-dimensional grid of "+str(shapeinfo[0])+ " points.")
if interpolate == 0:
A=np.zeros((vfield.shape[0],vfield.shape[0]))
dx = vfield[2,0]-vfield[1,0]
X = vfield[:,0]
b = vfield[:,1]
else:
A=np.zeros((interpolate[0],interpolate[0]))
dx = (vfield[-1,0]-vfield[0,0])/interpolate[0]
X = np.linspace(vfield[0,0],vfield[-1,0],interpolate[0])
b = np.interp(X,vfield[:,0],vfield[:,1])
for i in range (1,A.shape[0]-1):
A[i][i-1]=-1
A[i][i]=1
if(periodic[0]):
A[0][-1] = -2
A[0][0] = 2
A[-1][-1] = 1
A[-1][-2] = -1
else:
A[0][0] = -1
A[0][1] = 1
A[-1][-1] = 1
A[-1][-2] = -1
A[1][1] = 1
A[1][0] = 0
b[1] = 0
A=A/dx
Asurf,c,d,e = npl.lstsq(A,b)
Asurf = -Asurf*scale
dx = vfield[2,0]-vfield[1,0]
vfieldy = vfield[:,1]
for k in range(0,A.shape[0]):
f.write("{0:4f} {1:4f} \n".format(X[k],Asurf[k]))
minimum = min(Asurf[:])
plt.plot(X,Asurf-minimum)
# plt.show()
plt.savefig(outputname+'_integrated.png')
elif shapeinfo[1] == 4:
if(len(periodic)<2):
print('Please enter periodicity for each dimension.')
exit()
unique,counts=np.unique(vfield[:,0],return_counts=True)
nx = len(unique)
unique,counts=np.unique(vfield[:,1],return_counts=True)
ny = len(unique)
boundx = [vfield[0,0],vfield[-1,0]]
boundy = [vfield[0,1],vfield[-1,1]]
plot1 = plt.figure()
plt.quiver(vfield[:,0],vfield[:,1],vfield[:,2],vfield[:,3],width = (0.0002*(vfield[nx*ny-1,0]-vfield[0,0])), headwidth=3)
plt.axis([boundx[0],boundx[1],boundy[0],boundy[1]])
plt.title('Gradient Field of the Free Energy')
plot1.savefig(outputname+'_GradientField.png')
if interpolate != 0:
grid_x, grid_y = np.mgrid[boundx[0]:boundx[1]:interpolate[0]*1j,boundy[0]:boundy[1]:interpolate[1]*1j]
dx = (boundx[1]-boundx[0])/interpolate[0]
dy = (boundy[1]-boundy[0])/interpolate[1]
nx = interpolate[0]
ny = interpolate[1]
fx=interp.griddata(vfield[:,0:2], vfield[:,2],(grid_x,grid_y), method='cubic')
fy=interp.griddata(vfield[:,0:2], vfield[:,3],(grid_x,grid_y), method='cubic')
fx=fx.T
fy=fy.T
else:
fy = vfield[:,3]
fx = vfield[:,2]
dx = vfield[1,0] - vfield[0,0]
dy = vfield[nx,1] - vfield[0,1]
grid_x = vfield[:,0].reshape((nx,ny),order='F')
grid_y = vfield[:,1].reshape((nx,ny),order='F')
fhat = intgrad2(fx,fy,nx,ny,dx,dy,1,periodic[0],periodic[1])
fhat = fhat*scale
zmin = min(fhat)
zmax = max(fhat)
fhat = fhat.reshape((nx,ny),order='F')
X = grid_x
Y = grid_y
for k in range(0,ny):
for n in range(0,nx):
f.write("{0:4f} {1:4f} {2:4f} \n".format(X[n,k],Y[n,k],-fhat[n,k]))
plot3 = plt.figure()
plt.pcolormesh(X,Y,-fhat)
plt.colorbar()
plt.axis([min(vfield[:,0]),max(vfield[:,0]),min(vfield[:,1]),max(vfield[:,1])])
plot3.savefig(outputname+'_EnergySurface.png')
plot4=plt.figure()
CS=plt.contour(X,Y,-fhat,antialiased=True,levels=np.linspace(np.amin(-fhat),np.amax(-fhat),30))
plot4.savefig(outputname+'_contourmap.png')
plot5 = plt.figure()
black_contours=np.linspace(0,np.amax(-fhat-np.amin(-fhat)),ncontours+1)
gray_contours=black_contours+np.amax(-fhat-np.amin(-fhat))/(2*(ncontours+1))
plt.pcolormesh(X,Y,-fhat-np.amin(-fhat))
plt.colorbar(label='kJ/mol')
plt.axis([min(vfield[:,0]),max(vfield[:,0]),min(vfield[:,1]),max(vfield[:,1])])
plt.contour(X,Y,(-fhat-np.amin(-fhat)),black_contours,colors='black',linewidths=0.75)
plt.contour(X,Y,(-fhat-np.amin(-fhat)),gray_contours,colors='grey',linewidths=0.75)
plt.tight_layout()
plot5.savefig(outputname+'_merged.png')
elif shapeinfo[1] == 6:
if(len(periodic)<3):
print('Please enter periodicity for each dimension.')
exit()
unique,counts=np.unique(vfield[:,0],return_counts=True)
nx = len(unique)
unique,counts=np.unique(vfield[:,1],return_counts=True)
ny = len(unique)
unique,counts=np.unique(vfield[:,2],return_counts=True)
nz = len(unique)
boundx = [vfield[0,0],vfield[-1,0]]
boundy = [vfield[0,1],vfield[-1,1]]
boundz = [vfield[0,2],vfield[-1,2]]
if interpolate != 0:
grid_x, grid_y, grid_z = np.mgrid[0:nx:interpolate[0]*1j,0:ny:interpolate[1]*1j,0:nz:interpolate[2]*1j]
X, Y, Z = np.mgrid[boundx[0]:boundx[1]:interpolate[0]*1j,boundy[0]:boundy[1]:interpolate[1]*1j,boundz[0]:boundz[1]:interpolate[2]*1j]
dx = X[1,0,0]-X[0,0,0]
dy = Y[0,1,0]-Y[0,0,0]
dz = Z[0,0,1]-Z[0,0,0]
x = np.linspace(boundx[0],boundx[1],nx)
y = np.linspace(boundy[0],boundy[1],ny)
z = np.linspace(boundz[0],boundz[1],nz)
datax = vfield[:,3].reshape((nx,ny,nz))
datay = vfield[:,4].reshape((nx,ny,nz))
dataz = vfield[:,5].reshape((nx,ny,nz))
nx = interpolate[0]
ny = interpolate[1]
nz = interpolate[2]
fx= interp2.map_coordinates(datax,np.vstack((np.ravel(grid_x),np.ravel(grid_y),np.ravel(grid_z))),order=3,mode='nearest')
fy= interp2.map_coordinates(datay,np.vstack((np.ravel(grid_x),np.ravel(grid_y),np.ravel(grid_z))),order=3,mode='nearest')
fz= interp2.map_coordinates(dataz,np.vstack((np.ravel(grid_x),np.ravel(grid_y),np.ravel(grid_z))),order=3,mode='nearest')
else:
fx = vfield[:,3]
fy = vfield[:,4]
fz = vfield[:,5]
dx = vfield[1,0] - vfield[0,0]
dy = vfield[nx,1] - vfield[0,1]
dz = vfield[ny*nx,2] - vfield[0,2]
grid_x = vfield[:,0].reshape((nx,ny,nz),order='F')
grid_y = vfield[:,1].reshape((nx,ny,nz),order='F')
grid_z = vfield[:,2].reshape((nx,ny,nz),order='F')
fhat = intgrad3double(fx,fy,fz,nx,ny,nz,dx,dy,dz,1,periodic[0],periodic[1],periodic[2])
fhat = fhat*scale
zmin = min(fhat)
zmax = max(fhat)
fhat = fhat.reshape((nx,ny,nz),order='F')
X = grid_x
Y = grid_y
Z = grid_z
for l in range(0,nz):
for k in range(0,ny):
for j in range(0,nx):
f.write("{0:4f} {1:4f} {2:4f} {3:4f} \n".format(X[j,k,l],Y[j,k,l],Z[j,k,l],-fhat[j,k,l]))
else:
print("Input file does not contain the corrent number of dimensions. This code only supports 1D, 2D and 3D currently.")
f.close()
exit()
f.close()
def plot_total_importance(axs, mfd, weights):
axs = np.flipud(axs)
c = plt.cm.tab20(range(weights.shape[1]))
for ax, m, w in zip(axs.flatten(), mfd, weights):
ax.scatter(m, w, c=c)
def read(self, dfs3file, item_numbers=None, layers=None, coordinates=None):
""" Function: Read data from a dfs3 file
Usage:
[data,time,name] = read( filename, item_numbers, layers=None, coordinates=None)
item_numbers
list of indices (base 0) to read from. If None then all the items.
layers
list of layer indices (base 0) to read
coordinates
list of list (x,y,layer) integers ( 0,0 at Bottom Left of Grid !! )
example coordinates = [[2,5,1], [11,41,2]]
Returns
1) the data contained in a dfs3 file in a list of numpy matrices
2) time index
3) name of the items
NOTE
Returns Dataset with data (t, z, y, x)
1) If coordinates is selected, then only return data at those coordinates
2) coordinates specified overules layers.
3) layer counts from the bottom
"""
# Open the dfs file for reading
dfs = DfsFileFactory.DfsGenericOpen(dfs3file)
# Determine the size of the grid
axis = dfs.ItemInfo[0].SpatialAxis
zNum = axis.ZCount
yNum = axis.YCount
xNum = axis.XCount
nt = dfs.FileInfo.TimeAxis.NumberOfTimeSteps
deleteValue = dfs.FileInfo.DeleteValueFloat
if item_numbers is None:
n_items = safe_length(dfs.ItemInfo)
item_numbers = list(range(n_items))
n_items = len(item_numbers)
data_list = []
if coordinates is None:
# if nt is 0, then the dfs is 'static' and must be handled differently
if nt != 0:
for item in range(n_items):
if layers is None:
# Initialize an empty data block
data = np.ndarray(shape=(nt, zNum, yNum, xNum),
dtype=float)
data_list.append(data)
else:
data = np.ndarray(shape=(nt, len(layers), yNum, xNum),
dtype=float)
data_list.append(data)
else:
raise ValueError(
"Static dfs3 files (with no time steps) are not supported."
)
else:
ncoordinates = len(coordinates)
for item in range(n_items):
# Initialize an empty data block
data = np.ndarray(shape=(nt, ncoordinates), dtype=float)
data_list.append(data)
t = []
startTime = dfs.FileInfo.TimeAxis.StartDateTime
if coordinates is None:
for it in range(nt):
for item in range(n_items):
itemdata = dfs.ReadItemTimeStep(item_numbers[item] + 1, it)
src = itemdata.Data
d = to_numpy(src)
# DO a direct copy instead of eleement by elment
d = d.reshape(zNum, yNum,
xNum) # .swapaxes(0, 2).swapaxes(0, 1)
d = np.flipud(d)
d[d == deleteValue] = np.nan
if layers is None:
data_list[item][it, :, :, :] = d
else:
for l in range(len(layers)):
data_list[item][it, l, :, :] = d[layers[l], :, :]
t.append(
startTime.AddSeconds(
itemdata.Time).ToString("yyyy-MM-dd HH:mm:ss"))
else:
indices = [
self.__calculate_index(xNum, yNum, zNum, x, y, z)
for x, y, z in coordinates
]
for it in range(nt):
for item in range(n_items):
itemdata = dfs.ReadItemTimeStep(item_numbers[item] + 1, it)
d = np.array([itemdata.Data[i] for i in indices])
d[d == deleteValue] = np.nan
data_list[item][it, :] = d
t.append(
startTime.AddSeconds(
itemdata.Time).ToString("yyyy-MM-dd HH:mm:ss"))
# time = pd.DatetimeIndex(t)
time = [datetime.strptime(x, "%Y-%m-%d %H:%M:%S") for x in t]
names = []
for item in range(n_items):
name = dfs.ItemInfo[item_numbers[item]].Name
names.append(name)
dfs.Close()
return Dataset(data_list, time, names)
def get_rawimage(self, raw_file, det):
"""
Read raw images and generate a few other bits and pieces
that are key for image processing.
Parameters
----------
raw_file : :obj:`str`
File to read
det : :obj:`int`
1-indexed detector to read
Returns
-------
detector_par : :class:`pypeit.images.detector_container.DetectorContainer`
Detector metadata parameters.
raw_img : `numpy.ndarray`_
Raw image for this detector.
hdu : `astropy.io.fits.HDUList`_
Opened fits file
exptime : :obj:`float`
Exposure time read from the file header
rawdatasec_img : `numpy.ndarray`_
Data (Science) section of the detector as provided by setting the
(1-indexed) number of the amplifier used to read each detector
pixel. Pixels unassociated with any amplifier are set to 0.
oscansec_img : `numpy.ndarray`_
Overscan section of the detector as provided by setting the
(1-indexed) number of the amplifier used to read each detector
pixel. Pixels unassociated with any amplifier are set to 0.
"""
# Check for file; allow for extra .gz, etc. suffix
fil = glob.glob(raw_file + '*')
if len(fil) != 1:
msgs.error("Found {:d} files matching {:s}".format(len(fil)))
# Read
msgs.info("Reading MMIRS file: {:s}".format(fil[0]))
hdu = io.fits_open(fil[0])
head1 = fits.getheader(fil[0], 1)
detector_par = self.get_detector_par(det if det is not None else 1,
hdu=hdu)
# get the x and y binning factors...
binning = head1['CCDSUM']
xbin, ybin = [int(ibin) for ibin in binning.split(' ')]
# First read over the header info to determine the size of the output array...
datasec = head1['DATASEC']
x1, x2, y1, y2 = np.array(parse.load_sections(
datasec, fmt_iraf=False)).flatten()
# ToDo: I am currently using the standard double correlated frame, that is a difference between
# the first and final read-outs. In the future need to explore up-the-ramp fitting.
if len(hdu) > 2:
data = mmirs_read_amp(
hdu[1].data.astype('float64')) - mmirs_read_amp(
hdu[2].data.astype('float64'))
else:
data = mmirs_read_amp(hdu[1].data.astype('float64'))
array = data[x1 - 1:x2, y1 - 1:y2]
## ToDo: This is a hack. Need to solve this issue. I cut at 998 due to the HK zero order contaminating
## the blue part of the zJ+HK spectrum. For other setup, you do not need to cut the detector.
if (head1['FILTER'] == 'zJ') and (head1['DISPERSE'] == 'HK'):
array = array[:int(998 / ybin), :]
rawdatasec_img = np.ones_like(array, dtype='int')
# NOTE: If there is no overscan, must be set to 0s
oscansec_img = np.zeros_like(array, dtype='int')
# Need the exposure time
exptime = hdu[self.meta['exptime']['ext']].header[self.meta['exptime']
['card']]
# Return, transposing array back to orient the overscan properly
return detector_par, np.flipud(array), hdu, exptime, np.flipud(rawdatasec_img),\
np.flipud(np.flipud(oscansec_img))
def constant_q(self, workspace, peak):
"""
Compute reflectivity using constant-Q binning
"""
# Extract the x-pixel vs TOF data
signal = workspace.extractY()
signal_err = workspace.extractE()
signal=np.flipud(signal)
signal_err=np.flipud(signal_err)
theta = self.calculate_scattering_angle(workspace)
two_theta_degrees = 2.0*theta*180.0/math.pi
AddSampleLog(Workspace=workspace, LogName='two_theta', LogText=str(two_theta_degrees),
LogType='Number', LogUnit='degree')
# Get an array with the center wavelength value for each bin
wl_values = workspace.readX(0)
AddSampleLog(Workspace=workspace, LogName='lambda_min', LogText=str(wl_values[0]),
LogType='Number', LogUnit='Angstrom')
AddSampleLog(Workspace=workspace, LogName='lambda_max', LogText=str(wl_values[-1]),
LogType='Number', LogUnit='Angstrom')
x_pixel_map = np.mgrid[peak[0]:peak[1]+1, 0:len(wl_values)]
x_pixel_map = x_pixel_map[0,:,:]
pixel_width = float(workspace.getInstrument().getNumberParameter("pixel-width")[0]) / 1000.0
det_distance = workspace.getRun()['SampleDetDis'].getStatistics().mean / 1000.0
round_up = self.getProperty("RoundUpPixel").value
ref_pix = self.getProperty("SpecularPixel").value
if round_up:
x_distance=(x_pixel_map-np.round(ref_pix)) * pixel_width
else:
# We offset by 0.5 pixels because the reference pixel is given as
# a fractional position relative to the start of a pixel, whereas the pixel map
# assumes the center of each pixel.
x_distance=(x_pixel_map-ref_pix-0.5) * pixel_width
theta_f = np.arcsin(x_distance/det_distance)/2.0
# Calculate Qx, Qz for each pixel
LL,TT = np.meshgrid(wl_values, theta_f[:,0])
qz=4*math.pi/LL * np.sin(theta + TT) * np.cos(TT)
qz = qz.T
AddSampleLog(Workspace=workspace, LogName='q_min', LogText=str(np.min(qz)),
LogType='Number', LogUnit='1/Angstrom')
AddSampleLog(Workspace=workspace, LogName='q_max', LogText=str(np.max(qz)),
LogType='Number', LogUnit='1/Angstrom')
# Transform q values to q indices
q_min_input = self.getProperty("QMin").value
q_step = self.getProperty("QStep").value
# We use np.digitize to bin. The output of digitize is a bin
# number for each entry, starting at 1. The first bin (0) is
# for underflow entries, and the last bin is for overflow entries.
if q_step > 0:
n_q_values = int((np.max(qz) - q_min_input) / q_step)
axis_z = np.linspace(q_min_input, np.max(qz), n_q_values)
else:
n_q_values = int((np.log10(np.max(qz))-np.log10(q_min_input)) / np.log10(1.0-q_step))
axis_z = np.array([q_min_input * (1.0-q_step)**i for i in range(n_q_values)])
refl = np.zeros(len(axis_z)-1)
refl_err = np.zeros(len(axis_z)-1)
signal_n = np.zeros(len(axis_z)-1)
err_count = 0
for tof in range(qz.shape[0]):
z_inds = np.digitize(qz[tof], axis_z)
# Move the indices so the valid bin numbers start at zero,
# since this is how we are going to address the output array
z_inds -= 1
for ix in range(signal.shape[0]):
if z_inds[ix]<len(axis_z)-1 and z_inds[ix]>=0 and not np.isnan(signal[ix][tof]):
refl[z_inds[ix]] += signal[ix][tof]
refl_err[z_inds[ix]] += signal_err[ix][tof] * signal_err[ix][tof]
signal_n[z_inds[ix]] += 1.0
elif signal[ix][tof]>0:
err_count += 1
logger.notice("Ignored pixels: %s" % err_count)
signal_n = np.where(signal_n > 0, signal_n, 1)
refl = float(signal.shape[0]) * refl / signal_n
refl_err = float(signal.shape[0]) * np.sqrt(refl_err) / signal_n
# Trim Qz bins with fewer than a certain number of wavelength bins
# contributing to them,
trim_factor = self.getProperty("ConstQTrim").value
for i in range(len(refl)):
if signal_n[i] < np.max(signal_n) * trim_factor:
refl[i] = 0.0
refl_err[i] = 0.0
q_rebin = CreateWorkspace(DataX=axis_z, DataY=refl, DataE=refl_err, ParentWorkspace=workspace)
# At this point we still have a histogram, and we need to convert to point data
q_rebin = ConvertToPointData(InputWorkspace=q_rebin)
return q_rebin
def run_with_adaptive_time_step_solver(self, dt=1.0):
"""Run step with CHILD-like solver that adjusts time steps to prevent
slope flattening.
Examples
--------
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> import numpy as np
>>> rg = RasterModelGrid((3, 4))
>>> z = rg.add_zeros('topographic__elevation', at='node')
>>> z[:] = 0.1 * rg.x_of_node
>>> H = rg.add_zeros('soil__depth', at='node')
>>> H += 0.1
>>> br = rg.add_zeros('bedrock__elevation', at='node')
>>> br[:] = z - H
>>> fa = FlowAccumulator(rg, flow_director='FlowDirectorSteepest')
>>> fa.run_one_step()
>>> sp = Space(rg, K_sed=1.0, K_br=0.1,
... F_f=0.5, phi=0.0, H_star=1., v_s=1.0,
... m_sp=0.5, n_sp = 1.0, sp_crit_sed=0,
... sp_crit_br=0, solver='adaptive')
>>> sp.run_one_step(dt=10.0)
>>> np.round(sp.Es[5:7], 4)
array([ 0.0029, 0.0074])
>>> np.round(sp.Er[5:7], 4)
array([ 0.0032, 0.0085])
>>> np.round(H[5:7], 3)
array([ 0.088, 0.078])
"""
# Initialize remaining_time, which records how much of the global time
# step we have yet to use up.
remaining_time = dt
z = self._grid.at_node["topographic__elevation"]
br = self._grid.at_node["bedrock__elevation"]
H = self._grid.at_node["soil__depth"]
r = self._flow_receivers
time_to_flat = np.zeros(len(z))
time_to_zero_alluv = np.zeros(len(z))
dzdt = np.zeros(len(z))
cores = self._grid.core_nodes
first_iteration = True
if not self._erode_flooded_nodes:
flood_status = self._grid.at_node["flood_status_code"]
flooded_nodes = np.nonzero(flood_status == _FLOODED)[0]
else:
flooded_nodes = []
flooded = np.full(self._grid.number_of_nodes, False, dtype=bool)
flooded[flooded_nodes] = True
# Outer WHILE loop: keep going until time is used up
while remaining_time > 0.0:
# Update all the flow-link slopes.
#
# For the first iteration, we assume this has already been done
# outside the component (e.g., by flow router), but we need to do
# it ourselves on subsequent iterations.
if not first_iteration:
# update the link slopes
self._update_flow_link_slopes()
# update where nodes are flooded. This shouuldn't happen because
# of the dynamic timestepper, but just in case, we update here.
new_flooded_nodes = np.where(self._slope < 0)[0]
flooded_nodes = np.asarray(
np.unique(
np.concatenate((flooded_nodes, new_flooded_nodes))),
dtype=np.int64,
)
else:
first_iteration = False
# Calculate rates of entrainment
self._calc_hydrology()
self._calc_erosion_rates()
# CORRECTION HERE?
self._Es[flooded_nodes] = 0.0
self._Er[flooded_nodes] = 0.0
# Zero out sediment influx for new iteration
self._qs_in[:] = 0
calculate_qs_in(
np.flipud(self._stack),
self._flow_receivers,
self._cell_area_at_node,
self._q,
self._qs,
self._qs_in,
self._Es,
self._Er,
self._v_s,
self._F_f,
)
self._depo_rate[self._q > 0] = self._qs[self._q > 0] * (
self._v_s / self._q[self._q > 0])
# TODO handle flooded nodes in the above fn
# Now look at upstream-downstream node pairs, and recording the
# time it would take for each pair to flatten. Take the minimum.
dzdt[cores] = self._depo_rate[cores] - (self._Es[cores] +
self._Er[cores])
rocdif = dzdt - dzdt[r]
zdif = z - z[r]
time_to_flat[:] = remaining_time
converging = np.where(rocdif < 0.0)[0]
time_to_flat[converging] = -(TIME_STEP_FACTOR * zdif[converging] /
rocdif[converging])
time_to_flat[np.where(zdif <= 0.0)[0]] = remaining_time
# From this, find the maximum stable time step with regard to slope
# evolution.
dt_max1 = np.amin(time_to_flat)
# Next we consider time to exhaust regolith
time_to_zero_alluv[:] = remaining_time
dHdt = self._porosity_factor * (self._depo_rate - self._Es)
decreasing_H = np.where(dHdt < 0.0)[0]
time_to_zero_alluv[decreasing_H] = -(
TIME_STEP_FACTOR * H[decreasing_H] / dHdt[decreasing_H])
# Now find the smallest time that would lead to near-empty alluv
dt_max2 = np.amin(time_to_zero_alluv)
# Take the smaller of the limits
dt_max = max(self._dt_min, min(dt_max1, dt_max2))
# Now a vector operation: apply dzdt and dhdt to all nodes
br[cores] -= self._Er[cores] * dt_max
H[cores] += dHdt[cores] * dt_max
z[cores] = br[cores] + H[cores]
# Update remaining time and continue
remaining_time -= dt_max
def run(self):
if not self.tello.connect():
print("Tello not connected")
return
if not self.tello.set_speed(self.speed):
print("Not set speed to lowest possible")
return
self.tello.streamoff()
self.tello.streamon()
cap = self.tello.get_frame_read()
should_stop = False
# print('loop started')
while not should_stop:
frame = cap.frame # 摄像头读取一帧
for event in pygame.event.get():
if event.type == USEREVENT + 1:
self.update()
elif event.type == QUIT:
should_stop = True
elif event.type == KEYDOWN:
if event.key == K_ESCAPE:
should_stop = True
else:
self.keydown(event.key)
elif event.type == KEYUP:
self.keyup(event.key)
self.screen.fill([0, 0, 0])
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
green_mask = cv2.inRange(
hsv, greenLower, greenUpper
) # 将低于lower_red和高于upper_red的部分分别变成0，lower_red～upper_red之间的值变成255
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
green_mask = cv2.morphologyEx(green_mask, cv2.MORPH_OPEN,
kernel) # 开操作
green_mask = cv2.morphologyEx(green_mask, cv2.MORPH_CLOSE,
kernel) # 闭操作
cnts, hie = cv2.findContours(green_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) > 3:
cnts.sort(key=cv2.contourArea, reverse=True)
x1, y1, w1, h1 = cv2.boundingRect(cnts[0])
x2, y2, w2, h2 = cv2.boundingRect(cnts[1])
x3, y3, w3, h3 = cv2.boundingRect(cnts[2])
x4, y4, w4, h4 = cv2.boundingRect(cnts[3])
center1 = (int(x1 + w1 / 2), int(y1 + h1 / 2))
center2 = (int(x2 + w2 / 2), int(y2 + h2 / 2))
center3 = (int(x3 + w3 / 2), int(y3 + h3 / 2))
center4 = (int(x4 + w4 / 2), int(y4 + h4 / 2))
center = (
int((center1[0] + center2[0] + center3[0] + center4[0]) /
4),
int((center1[1] + center2[1] + center3[1] + center4[1]) /
4))
w = max(abs(center1[0] - center2[0]),
abs(center1[0] - center3[0]),
abs(center1[0] - center4[0]))
h = max(abs(center1[1] - center2[1]),
abs(center1[1] - center3[1]),
abs(center1[1] - center4[1]))
# delta = min(abs(center2[1]-center1[1]), abs(center3[1]-center1[1]), abs(center4[1]-center1[1]))
temp_a = center2[1] - center1[1]
temp_b = center3[1] - center1[1]
temp_c = center4[1] - center1[1]
delta = min(abs(temp_a), abs(temp_b), abs(temp_c))
flag = bool(temp_a * temp_b * temp_c > 0) # 左偏
percent = (w * h / 691200.) * 100.0
cnt = torch.Tensor(center)
actions = self.actor(cnt)
actions = actions.cpu().data.numpy()
# actions = speedControlLinear(center)
done = bool(
get_dist(center[0], center[1]) < 60 and
(percent > 40)) and (delta < 10)
# done = bool(get_dist(center[0], center[1]) < 60 and (percent > 40))
if not done:
self.yaw_velocity = -int(actions[0]) # 强化学习需要加"-"
self.up_down_velocity = int(actions[1])
if percent < 40:
self.for_back_velocity = 15
if delta > 10:
# self.left_right_velocity = 10
# self.yaw_velocity = -10
if flag:
self.left_right_velocity = 10
self.yaw_velocity = -10
else:
self.left_right_velocity = -10
self.yaw_velocity = 10
cv2.circle(frame, center, 5, (255, 0, 0), -1)
else:
self.yaw_velocity = 0
self.up_down_velocity = 0
self.for_back_velocity = 0
else:
self.for_back_velocity = -15
# self.yaw_velocity = -self.yaw_velocity
# self.up_down_velocity = -self.up_down_velocity
frame = cv2.circle(frame, (480, 360), 5, (0, 0, 255), -1)
cv2.imshow("xx", green_mask)
k = cv2.waitKey(1)
if k == 27:
break
frame = np.rot90(frame)
frame = np.flipud(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = pygame.surfarray.make_surface(frame)
self.screen.blit(frame, (0, 0))
pygame.display.update()
# time.sleep(0.1)
# Call it always before finishing. I deallocate resources.
self.tello.end()
def flip_vert(self):
"flip image along vertical axis (up/down)"
self.data = np.flipud(self.data)
if self.ydata is not None:
self.ydata = self.ydata[::-1]
self.flip_ud = not self.flip_ud
def main(_):
tf_flags = tf.app.flags.FLAGS
# gpu config.
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
if tf_flags.phase == "train":
with tf.Session(config=config) as sess:
# sess = tf.Session(config=config) # when use queue to load data, not use with to define sess
train_model = MemNet_rgb.MemNet(sess, tf_flags)
train_model.train(tf_flags.training_steps, tf_flags.summary_steps,
tf_flags.checkpoint_steps, tf_flags.save_steps)
else:
with tf.Session(config=config) as sess:
test_model = MemNet_rgb.MemNet(sess, tf_flags)
test_model.load(tf_flags.checkpoint)
# test Set12
# get psnr and ssim outside
sigma =10/255.
save_path = "./test_output/memnet_10_rgb_no_augs_urban100/"
aug_mean = False
if not os.path.exists(save_path):
os.makedirs(save_path)
for image_file in glob.glob(tf_flags.testing_set):
print("testing {}...".format(image_file))
# testing_set is path/*.jpg.
# c_image = np.reshape(cv2.resize(cv2.imread(image_file, 0), (tf_flags.img_size, tf_flags.img_size)),
# (1, tf_flags.img_size, tf_flags.img_size, 1)) / 255.
# c_image = np.reshape(cv2.resize(cv2.imread(image_file, 0), (256, 256)), (1, 256, 256, 1)) / 255.
if aug_mean:
c_image = cv2.imread(image_file)/255.
c_image = c_image.transpose(2, 0, 1)
noise = np.random.normal(0.0, 1.0, size=c_image.shape)*sigma
noise2 = np.rot90(noise, 1, (1, 2))
c_image_aug = create_augmentations(c_image)
test_out = []
for aug_, c_image_aug_ in enumerate(c_image_aug):
if aug_%2==0:
c_image_aug_ = np.clip(c_image_aug_ + noise, 0, 1).transpose(1, 2, 0)
c_image_aug_ = c_image_aug_[np.newaxis, :, :, :]
elif aug_%2==1:
c_image_aug_ = np.clip(c_image_aug_ + noise2, 0, 1).transpose(1, 2, 0)
c_image_aug_ = c_image_aug_[np.newaxis, :, :, :]
recovery_image_ = test_model.test(c_image_aug_)
test_out.append(recovery_image_[0])
test_out[0] = test_out[0]
for aug in range(1, 8):
if aug < 4:
test_out[aug] = np.rot90(test_out[aug], 4 - aug)
else:
test_out[aug] = np.flipud(np.rot90(test_out[aug], 8 - aug))
recovery_image = np.mean(test_out, 0)
cv2.imwrite(os.path.join(save_path, image_file.split("/")[3]), np.uint8(recovery_image.clip(0., 1.) * 255.))
else:
c_image = cv2.imread(image_file)/255.
noise = np.random.normal(0.0, 1.0, size=c_image.shape) * sigma
c_image = np.clip(c_image+noise,0, 1)
c_image = c_image[np.newaxis,:,:,:]
# In Caffe, Tensorflow, might must divide 255.?
recovery_image = test_model.test(c_image)
# save image
cv2.imwrite(os.path.join(save_path, image_file.split("/")[3]),
np.uint8(recovery_image[0, :].clip(0., 1.) * 255.))
# recovery_image[0, :], 3D array.
print("Testing done.")
yieldfa = N.zeros((10, 360, 720))
yieldf2a = N.zeros((10, 360, 720))
yieldf = N.zeros((10, 360, 720))
yieldf2 = N.zeros((10, 360, 720))
years2 = range(2090, 2100)
clmtropfin = N.zeros((10, 360, 720))
clmtempfin = N.zeros((10, 360, 720))
for j in range(0, 10):
clm2n = NetCDFFile(
'/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/maizetrop_rcp45_co2_rf_fert_0.5x0.5.nc',
'r')
cc = N.flipud(clm2n.variables['yield'][84 + j, :, :])
clmtropfin[j, :, :] = cc
clm3n = NetCDFFile(
'/scratch2/scratchdirs/tslin2/plot/globalcrop/data/clm/clm45rcp45/maizetemp_rcp45_co2_rf_fert_0.5x0.5.nc',
'r')
dd = N.flipud(clm3n.variables['yield'][84 + j, :, :])
clmtempfin[j, :, :] = dd
yield_new2a = N.average(clmtropfin, axis=0)
yield_new2 = N.average(clmtempfin, axis=0)
yield_new2 = ma.masked_where(yield_new2 <= 0., yield_new2)
yield_new2a = ma.masked_where(yield_new2a <= 0., yield_new2a)
yield_new2 = ma.masked_where(maitoatemp <= 0, yield_new2)
yield_new2 = ma.filled(yield_new2, fill_value=0.)
def run_one_step_basic(self, dt=1.0):
"""Calculate change in rock and alluvium thickness for a time period
'dt'.
Parameters
----------
dt : float
Model timestep [T]
"""
# Choose a method for calculating erosion:
self._calc_hydrology()
self._calc_erosion_rates()
if not self._erode_flooded_nodes:
flood_status = self._grid.at_node["flood_status_code"]
flooded_nodes = np.nonzero(flood_status == _FLOODED)[0]
else:
flooded_nodes = []
flooded = np.full(self._grid.number_of_nodes, False, dtype=bool)
flooded[flooded_nodes] = True
self._qs_in[:] = 0
# iterate top to bottom through the stack, calculate qs
# cythonized version of calculating qs_in
calculate_qs_in(
np.flipud(self._stack),
self._flow_receivers,
self._cell_area_at_node,
self._q,
self._qs,
self._qs_in,
self._Es,
self._Er,
self._v_s,
self._F_f,
)
self._depo_rate[self._q > 0] = self._qs[self._q > 0] * (
self._v_s / self._q[self._q > 0])
# now, the analytical solution to soil thickness in time:
# need to distinguish D=kqS from all other cases to save from blowup!
# distinguish cases:
blowup = self._depo_rate == self._K_sed * self._Q_to_the_m * self._slope
# first, potential blowup case:
# positive slopes, not flooded
pos_not_flood = (self._q > 0) & (blowup) & (self._slope >
0) & (~flooded)
self._soil__depth[pos_not_flood] = self._H_star * np.log(
((self._sed_erosion_term[pos_not_flood] /
(1 - self._phi)) / self._H_star) * dt +
np.exp(self._soil__depth[pos_not_flood] / self._H_star))
# positive slopes, flooded
pos_flood = (self._q > 0) & (blowup) & (self._slope > 0) & (flooded)
self._soil__depth[pos_flood] = (self._depo_rate[pos_flood] /
(1 - self._phi)) * dt
# non-positive slopes, not flooded
non_pos_not_flood = (self._q > 0) & (blowup) & (self._slope <=
0) & (~flooded)
self._soil__depth[non_pos_not_flood] += (
self._depo_rate[non_pos_not_flood] / (1 - self._phi) * dt)
# more general case:
pos_not_flood = (self._q > 0) & (~blowup) & (self._slope >
0) & (~flooded)
self._soil__depth[pos_not_flood] = self._H_star * np.log(
(1 / ((self._depo_rate[pos_not_flood] / (1 - self._phi)) /
(self._sed_erosion_term[pos_not_flood] /
(1 - self._phi)) - 1)) *
(np.exp((self._depo_rate[pos_not_flood] / (1 - self._phi) -
(self._sed_erosion_term[pos_not_flood] /
(1 - self._phi))) * (dt / self._H_star)) *
(((self._depo_rate[pos_not_flood] / (1 - self._phi) /
(self._sed_erosion_term[pos_not_flood] /
(1 - self._phi))) - 1) *
np.exp(self._soil__depth[pos_not_flood] / self._H_star) + 1) -
1))
# places where slope <= 0 but not flooded:
neg_slope_not_flooded = ((self._q > 0) & (~blowup) & (self._slope <= 0)
& (~flooded))
self._soil__depth[neg_slope_not_flooded] += (
self._depo_rate[neg_slope_not_flooded] / (1 - self._phi) * dt)
# flooded nodes:
flooded_nodes = (self._q > 0) & (~blowup) & (flooded)
self._soil__depth[flooded_nodes] += (self._depo_rate[flooded_nodes] /
(1 - self._phi) * dt)
# where discharge exists
discharge_exists = self._q > 0
self._bedrock__elevation[discharge_exists] += dt * (
-self._br_erosion_term[discharge_exists] *
(np.exp(-self._soil__depth[discharge_exists] / self._H_star)))
# finally, determine topography by summing bedrock and soil
cores = self._grid.core_nodes
self._topographic__elevation[cores] = (
self._bedrock__elevation[cores] + self._soil__depth[cores])
def flow_read(src_file):
"""Read optical flow stored in a .flo, .pfm, or .png file
Args:
src_file: Path to flow file
Returns:
flow: optical flow in [h, w, 2] format
Refs:
- Interpret bytes as packed binary data
Per https://docs.python.org/3/library/struct.html#format-characters:
format: f -> C Type: float, Python type: float, Standard size: 4
format: d -> C Type: double, Python type: float, Standard size: 8
Based on:
- To read optical flow data from 16-bit PNG file:
https://github.com/ClementPinard/FlowNetPytorch/blob/master/datasets/KITTI.py
Written by Clément Pinard, Copyright (c) 2017 Clément Pinard
MIT License
- To read optical flow data from PFM file:
https://github.com/liruoteng/OpticalFlowToolkit/blob/master/lib/pfm.py
Written by Ruoteng Li, Copyright (c) 2017 Ruoteng Li
License Unknown
- To read optical flow data from FLO file:
https://github.com/daigo0927/PWC-Net_tf/blob/master/flow_utils.py
Written by Daigo Hirooka, Copyright (c) 2018 Daigo Hirooka
MIT License
"""
# Read in the entire file, if it exists
assert (os.path.exists(src_file))
if src_file.lower().endswith('.flo'):
with open(src_file, 'rb') as f:
# Parse .flo file header
tag = float(np.fromfile(f, np.float32, count=1)[0])
assert (tag == TAG_FLOAT)
w = np.fromfile(f, np.int32, count=1)[0]
h = np.fromfile(f, np.int32, count=1)[0]
# Read in flow data and reshape it
flow = np.fromfile(f, np.float32, count=h * w * 2)
flow.resize((h, w, 2))
elif src_file.lower().endswith('.png'):
# Read in .png file
flow_raw = cv2.imread(src_file, -1)
# Convert from [H,W,1] 16bit to [H,W,2] float formet
flow = flow_raw[:, :, 2:0:-1].astype(np.float32)
flow = flow - 32768
flow = flow / 64
# Clip flow values
flow[np.abs(flow) < 1e-10] = 1e-10
# Remove invalid flow values
invalid = (flow_raw[:, :, 0] == 0)
flow[invalid, :] = 0
elif src_file.lower().endswith('.pfm'):
with open(src_file, 'rb') as f:
# Parse .pfm file header
tag = f.readline().rstrip().decode("utf-8")
assert (tag == 'PF')
dims = f.readline().rstrip().decode("utf-8")
w, h = map(int, dims.split(' '))
scale = float(f.readline().rstrip().decode("utf-8"))
# Read in flow data and reshape it
flow = np.fromfile(f, '<f') if scale < 0 else np.fromfile(f, '>f')
flow = np.reshape(flow, (h, w, 3))[:, :, 0:2]
flow = np.flipud(flow)
elif src_file.lower().endswith('.npy'):
H, W = (376, 1241)
flow_raw = np.load(src_file)
u = cv2.resize(flow_raw[..., 0], (W, H))
v = cv2.resize(flow_raw[..., 1], (W, H))
flow = np.dstack((u, v))
flow[..., 0] *= W
flow[..., 1] *= H
else:
raise IOError
return flow
def cal_ext_paras(ind_ls = (np.arange(1, params['poses_num']+1)).tolist()):
#ind_ls: Indexes of pairs used for optimization
ls = ind_ls
# res_ls = []
# pnp_ls = []
corners_in_image_all = np.array([]).reshape(0, 2)
corners_in_pcd_all = np.array([]).reshape(0, 3)
if params['camera_type'] == "panoramic":
print "Optimize the extrinsic parameters with panoramic model."
for i in ls:
imgfile = "img/" + str(i).zfill(params['file_name_digits']) + "." + params['image_format']
cords_file = params['base_dir']+"/output/img_corners/" + str(i).zfill(params['file_name_digits']) + "_img_corners.txt"
corners_in_img_arr = np.genfromtxt(cords_file, delimiter=",").astype(np.int32)
# make sure the corners are counted start from left lower
if np.linalg.norm(np.array(corners_in_img_arr[0]) - np.array([0, H])) > np.linalg.norm(
np.array(corners_in_img_arr[-1]) - np.array([0, H])):
print imgfile + " is counted in reversed order"
corners_in_img_arr = np.flipud(corners_in_img_arr)
pcd_result_file = params['base_dir']+"/output/pcd_seg/" + str(i).zfill(params['file_name_digits']) + "_pcd_result.pkl"
# print imgfile
with open(os.path.abspath(pcd_result_file), "r") as f:
pcd_result_ls = cPickle.load(f)
assert pcd_result_ls is not None
corner_arr = pcd_result_ls[4].reshape(-1, 2)
num = corner_arr.shape[0]
corner_arr = np.hstack([corner_arr, np.zeros(num).reshape(num, 1)])
# print corner_arr.shape
rot1 = pcd_result_ls[0]
t1 = pcd_result_ls[1].reshape(1, 3)
rot2 = pcd_result_ls[2]
t2 = pcd_result_ls[3].reshape(1, 3)
corners_in_pcd_arr = np.dot(np.dot(rot2.T, corner_arr.T).T - t2 + t1, rot1)
corners_in_image_all = np.vstack([corners_in_image_all, corners_in_img_arr])
corners_in_pcd_all = np.vstack([corners_in_pcd_all, corners_in_pcd_arr])
# print "corners_in_pcd_all num of rows: ", corners_in_pcd_all.shape
try:
initial_guess = calc_inintial_guess(corners_in_image_all.copy(), corners_in_pcd_all.copy(),
method="UPNP")
except:
upnp_cost = np.inf
print "upnp can not be applied"
res = opt_r_t(corners_in_image_all, corners_in_pcd_all, initial_guess=initial_guess,
save_backproj=False, ) # initial_guess=initial_guess,
# res_ls.append(convert2_ang_cm(res.x))
# pnp_ls.append(initial_guess)
print "initial guess by UPnP:", initial_guess
print
print "final extrinsic parameters:"
cal_file_name = time.strftime("%Y%m%d_%H%M%S_cali_result.txt")
np.savetxt(cal_file_name, res.x, delimiter=',')
print "refined by LM : ", res.x, " unit: [rad,rad,rad,m,m,m]. The result is Saved to ", cal_file_name
print "unit converted : ", convert2_ang_cm(res.x), "unit: [deg,deg,deg,cm,cm,cm]"
print
print
print
# print "The difference of relative_pose - res.x: ", initial_guess - res.x
# np.savetxt('intes_result', res_ls, delimiter=',')
# np.savetxt('pnp_result', pnp_ls, delimiter=',')
# back_proj = True
if params['back_proj_corners']:
# for i in range(start, end):
for i in ls:
imgfile = os.path.join(params['base_dir'], "img/") + str(i).zfill(params['file_name_digits']) + "." + \
params['image_format']
cords_file = os.path.join(params['base_dir'], "output/img_corners/") + str(i).zfill(
params['file_name_digits']) + "_img_corners.txt"
corners_in_img_arr = np.genfromtxt(cords_file, delimiter=",").astype(np.int32)
# make sure the corners are counted start from left lower
if np.linalg.norm(np.array(corners_in_img_arr[0]) - np.array([0, H])) > np.linalg.norm(
np.array(corners_in_img_arr[-1]) - np.array([0, H])):
corners_in_img_arr = np.flipud(corners_in_img_arr)
pcd_result_file = params['base_dir']+"/output/pcd_seg/" + str(i).zfill(params['file_name_digits']) + "_pcd_result.pkl"
# print imgfile
with open(os.path.abspath(pcd_result_file), "r") as f:
pcd_result_ls = cPickle.load(f)
assert pcd_result_ls is not None
corner_arr = pcd_result_ls[4].reshape(-1, 2)
num = corner_arr.shape[0]
corner_arr = np.hstack([corner_arr, np.zeros(num).reshape(num, 1)])
rot1 = pcd_result_ls[0]
t1 = pcd_result_ls[1].reshape(1, 3)
rot2 = pcd_result_ls[2]
t2 = pcd_result_ls[3].reshape(1, 3)
corners_in_pcd_arr = np.dot(np.dot(rot2.T, corner_arr.T).T - t2 + t1, rot1)
ret = back_project(res.x, cv2.imread(imgfile), corners_in_img_arr, corners_in_pcd_arr)
save_file = params['base_dir']+"/output/" + str(i).zfill(params['file_name_digits']) + "_cal_backproj." + params[
'image_format']
cv2.imwrite(save_file, ret)
elif params['camera_type'] == "perspective":
print "Optimize the extrinsic parameters with perspective model."
for i in ls:
imgfile = os.path.join(params['base_dir'], "img/") + str(i).zfill(params['file_name_digits']) + "." + \
params['image_format']
cords_file = os.path.join(params['base_dir'], "output/img_corners/") + str(i).zfill(
params['file_name_digits']) + "_img_corners.txt"
corners_in_img_arr = np.genfromtxt(cords_file, delimiter=",").astype(np.int32)
# make sure the corners are counted start from left lower
if np.linalg.norm(np.array(corners_in_img_arr[0]) - np.array([0, H])) > np.linalg.norm(
np.array(corners_in_img_arr[-1]) - np.array([0, H])):
print imgfile + " is counted in reversed order"
corners_in_img_arr = np.flipud(corners_in_img_arr)
pcd_result_file = os.path.join(params['base_dir'], "output/pcd_seg/") + str(i).zfill(
params['file_name_digits']) + "_pcd_result.pkl"
# print imgfile
with open(os.path.abspath(pcd_result_file), "r") as f:
pcd_result_ls = cPickle.load(f)
assert pcd_result_ls is not None
corner_arr = pcd_result_ls[4].reshape(-1, 2)
num = corner_arr.shape[0]
corner_arr = np.hstack([corner_arr, np.zeros(num).reshape(num, 1)])
# print corner_arr.shape
rot1 = pcd_result_ls[0]
t1 = pcd_result_ls[1].reshape(1, 3)
rot2 = pcd_result_ls[2]
t2 = pcd_result_ls[3].reshape(1, 3)
corners_in_pcd_arr = np.dot(np.dot(rot2.T, corner_arr.T).T - t2 + t1, rot1)
corners_in_image_all = np.vstack([corners_in_image_all, corners_in_img_arr])
corners_in_pcd_all = np.vstack([corners_in_pcd_all, corners_in_pcd_arr])
# print "corners_in_pcd_all num of rows: ", corners_in_pcd_all.shape
try:
initial_guess = calc_inintial_guess(corners_in_image_all.copy(), corners_in_pcd_all.copy(),
method="UPNP")
except:
upnp_cost = np.inf
initial_guess = np.zeros(6).tolist()
print "upnp can not be applied"
res = opt_r_t(corners_in_image_all, corners_in_pcd_all, initial_guess=initial_guess,
save_backproj=False, ) # initial_guess=initial_guess,
print res.cost
# res_ls.append(convert2_ang_cm(res.x))
# pnp_ls.append(initial_guess)
print "initial guess by UPnP:", initial_guess
print
print "final extrinsic parameters:"
cal_file_name = os.path.join(params['base_dir'], time.strftime("%Y%m%d_%H%M%S_cali_result.txt"))
np.savetxt(cal_file_name, res.x, delimiter=',')
print "refined by LM : ", res.x, " unit: [rad,rad,rad,m,m,m]. The result is Saved to ", cal_file_name
print "unit converted : ", convert2_ang_cm(res.x), "unit: [deg,deg,deg,cm,cm,cm]"
print
print
print
# print "The difference of relative_pose - res.x: ", initial_guess - res.x
# np.savetxt('intes_result', res_ls, delimiter=',')
# np.savetxt('pnp_result', pnp_ls, delimiter=',')
# back_proj = True
if params['back_proj_corners']:
# for i in range(start, end):
for i in ls:
imgfile = os.path.join(params['base_dir'], "img/") + str(i).zfill(params['file_name_digits']) + "." + \
params['image_format']
cords_file = os.path.join(params['base_dir'], "output/img_corners/") + str(i).zfill(
params['file_name_digits']) + "_img_corners.txt"
corners_in_img_arr = np.genfromtxt(cords_file, delimiter=",").astype(np.int32)
# make sure the corners are counted start from left lower
if np.linalg.norm(np.array(corners_in_img_arr[0]) - np.array([0, H])) > np.linalg.norm(
np.array(corners_in_img_arr[-1]) - np.array([0, H])):
corners_in_img_arr = np.flipud(corners_in_img_arr)
pcd_result_file = os.path.join(params['base_dir'], "output/pcd_seg/") + str(i).zfill(
params['file_name_digits']) + "_pcd_result.pkl"
# print imgfile
with open(os.path.abspath(pcd_result_file), "r") as f:
pcd_result_ls = cPickle.load(f)
assert pcd_result_ls is not None
corner_arr = pcd_result_ls[4].reshape(-1, 2)
num = corner_arr.shape[0]
corner_arr = np.hstack([corner_arr, np.zeros(num).reshape(num, 1)])
rot1 = pcd_result_ls[0]
t1 = pcd_result_ls[1].reshape(1, 3)
rot2 = pcd_result_ls[2]
t2 = pcd_result_ls[3].reshape(1, 3)
corners_in_pcd_arr = np.dot(np.dot(rot2.T, corner_arr.T).T - t2 + t1, rot1)
ret = back_project(res.x, cv2.imread(imgfile), corners_in_img_arr, corners_in_pcd_arr)
# ret = back_project(initial_guess, cv2.imread(imgfile), corners_in_img_arr, corners_in_pcd_arr)
save_file = os.path.join(params['base_dir'], "output/") + str(i).zfill(
params['file_name_digits']) + "_cal_backproj." + params[
'image_format']
cv2.imwrite(save_file, ret)
else:
raise "The input camera type is not implemented yet!"
def __getitem__(self, index):
if self.image_weights:
index = self.indices[index]
hyp = self.hyp
if self.mosaic:
# Load mosaic
img, labels = load_mosaic(self, index)
shapes = None
else:
# Load image
img, (h0, w0), (h, w) = load_image(self, index)
# Letterbox
shape = self.batch_shapes[self.batch[
index]] if self.rect else self.img_size # final letterboxed shape
img, ratio, pad = letterbox(img,
shape,
auto=False,
scaleup=self.augment)
shapes = (h0, w0), (
(h / h0, w / w0), pad) # for COCO mAP rescaling
# Load labels
labels = []
x = self.labels[index]
if x.size > 0:
# Normalized xywh to pixel xyxy format
labels = x.copy()
labels[:,
1] = ratio[0] * w * (x[:, 1] -
x[:, 3] / 2) + pad[0] # pad width
labels[:,
2] = ratio[1] * h * (x[:, 2] -
x[:, 4] / 2) + pad[1] # pad height
labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
if self.augment:
# Augment imagespace
if not self.mosaic:
img, labels = random_affine(img,
labels,
degrees=hyp['degrees'],
translate=hyp['translate'],
scale=hyp['scale'],
shear=hyp['shear'])
# Augment colorspace
augment_hsv(img,
hgain=hyp['hsv_h'],
sgain=hyp['hsv_s'],
vgain=hyp['hsv_v'])
# Apply cutouts
# if random.random() < 0.9:
# labels = cutout(img, labels)
nL = len(labels) # number of labels
if nL:
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
labels[:, 5:9] = xyxy2xywh(labels[:, 5:9])
# Normalize coordinates 0 - 1
labels[:, [2, 4, 6, 8]] /= img.shape[0] # height
labels[:, [1, 3, 5, 7]] /= img.shape[1] # width
if self.augment:
# random left-right flip
lr_flip = True
if lr_flip and random.random() < 0.5:
img = np.fliplr(img)
if nL:
labels[:, 1] = 1 - labels[:, 1]
labels[:, 5] = 1 - labels[:, 5]
# random up-down flip
ud_flip = False
if ud_flip and random.random() < 0.5:
img = np.flipud(img)
if nL:
labels[:, 2] = 1 - labels[:, 2]
labels[:, 6] = 1 - labels[:, 6]
labels_out = torch.zeros((nL, 10))
if nL:
labels_out[:, 1:] = torch.from_numpy(labels)
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return torch.from_numpy(img), labels_out, self.img_files[index], shapes
def plot_fields(sim,
ax=None,
fields=None,
output_plane=None,
field_parameters=None):
if not sim._is_initialized:
sim.init_sim()
if fields is None:
return ax
if field_parameters is None:
field_parameters = default_field_parameters
else:
field_parameters = dict(default_field_parameters, **field_parameters)
# user specifies a field component
if fields in [
mp.Ex, mp.Ey, mp.Ez, mp.Er, mp.Ep, mp.Dx, mp.Dy, mp.Dz, mp.Hx,
mp.Hy, mp.Hz
]:
# Get domain measurements
sim_center, sim_size = get_2D_dimensions(sim, output_plane)
xmin, xmax, ymin, ymax, zmin, zmax = box_vertices(sim_center, sim_size)
if sim_size.x == 0:
# Plot y on x axis, z on y axis (YZ plane)
extent = [ymin, ymax, zmin, zmax]
xlabel = 'Y'
ylabel = 'Z'
elif sim_size.y == 0:
# Plot x on x axis, z on y axis (XZ plane)
extent = [xmin, xmax, zmin, zmax]
if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical:
xlabel = 'R'
else:
xlabel = "X"
ylabel = 'Z'
elif sim_size.z == 0:
# Plot x on x axis, y on y axis (XY plane)
extent = [xmin, xmax, ymin, ymax]
xlabel = 'X'
ylabel = 'Y'
fields = sim.get_array(center=sim_center,
size=sim_size,
component=fields)
else:
raise ValueError(
'Please specify a valid field component (mp.Ex, mp.Ey, ...')
fields = field_parameters['post_process'](fields)
if (sim.dimensions == mp.CYLINDRICAL) or sim.is_cylindrical:
fields = np.flipud(fields)
else:
fields = np.rot90(fields)
# Either plot the field, or return the array
if ax:
if mp.am_master():
ax.imshow(fields,
extent=extent,
**filter_dict(field_parameters, ax.imshow))
return ax
else:
return fields
return ax
def train(
batch_size=128,
num_epochs=500,
sagittal_only=False,
num_frames=1,
with_maps=False,
model_new=True,
learning_rate=0.002,
lr_decay=False,
):
run_str = ''
run_str += 'NewUNet_' if model_new else 'TBMEUNet_'
run_str += 'WMaps_' if with_maps else ''
run_str += 'SagittalOnly_' if sagittal_only else 'AllImages_'
run_str += 'SingleFrame' if num_frames == 1 else str(num_frames) + 'Frames'
local_data_folder = "/home/zbaum/Baum/aigt/data"
data_arrays_folder = "DataArrays"
results_save_folder = "SavedResults"
models_save_folder = "SavedModels"
val_data_folder = "PredictionsValidation"
data_arrays_fullpath = os.path.join(local_data_folder, data_arrays_folder)
results_save_fullpath = os.path.join(local_data_folder,
results_save_folder)
models_save_fullpath = os.path.join(local_data_folder, models_save_folder)
val_data_fullpath = os.path.join(local_data_folder, val_data_folder)
if not os.path.exists(data_arrays_fullpath):
os.makedirs(data_arrays_fullpath)
print("Created folder: {}".format(data_arrays_fullpath))
if not os.path.exists(results_save_fullpath):
os.makedirs(results_save_fullpath)
print("Created folder: {}".format(results_save_fullpath))
if not os.path.exists(models_save_fullpath):
os.makedirs(models_save_fullpath)
print("Created folder: {}".format(models_save_fullpath))
if not os.path.exists(val_data_fullpath):
os.makedirs(val_data_fullpath)
print("Created folder: {}".format(val_data_fullpath))
# Learning parameters
ultrasound_size = 128
num_classes = 2
min_learning_rate = 0.00001
regularization_rate = 0.0001
filter_multiplier = 14
class_weights = np.array([0.1, 0.9])
learning_rate_decay = (learning_rate -
min_learning_rate) / num_epochs if lr_decay else 0
if with_maps:
loss_func = weighted_categorical_crossentropy_with_maps(class_weights)
preprocess_func = train_preprocess_with_maps
else:
loss_func = weighted_categorical_crossentropy(class_weights)
preprocess_func = train_preprocess
# Evaluation parameters
acceptable_margin_mm = 1.0
mm_per_pixel = 1.0
roc_thresholds = [
0.9,
0.8,
0.7,
0.65,
0.6,
0.55,
0.5,
0.45,
0.4,
0.35,
0.3,
0.25,
0.2,
0.15,
0.1,
0.08,
0.06,
0.04,
0.02,
0.01,
0.008,
0.006,
0.004,
0.002,
0.001,
]
"""
Provide NxM numpy array to schedule cross validation
N rounds of validation will be performed, leaving out M patients in each for validation data
All values should be valid patient IDs, or negative. Negative values are ignored.
Example 1: a leave-one-out cross validation with 3 patients would look like this:
validation_schedule_patient = np.array([[0],[1],[2]])
Example 2: a leave-two-out cross validation on 10 patients would look like this:
validation_schedule_patient = np.array([[0,1],[2,3],[4,5],[6,7],[8,9]])
Example 3: leave-one-out cross validation with 3 patients, then training on all available data (no validation):
validation_schedule_patient = np.array([[0],[1],[2],[-1]])
"""
# validation_schedule_patient = np.array([[-1]])
validation_schedule_patient = np.array([[0]])
# Define what data to download
girder_url = "https://pocus.cs.queensu.ca/api/v1"
girder_key = "nwv5qqqrDYn9DVakp1XnYDqjrNsowxaXisawPNRR"
queens_sagittal_csv = "QueensSagittal.csv"
verdure_axial_csv = "VerdureAxial.csv"
verdure_sagittal_csv = "VerdureSagittal.csv"
childrens_axial_csv = "Childrens1Axial.csv"
queens_ultrasound_arrays, queens_segmentation_arrays = utils.load_girder_data(
queens_sagittal_csv,
data_arrays_fullpath,
girder_url,
girder_key=girder_key)
verdure_axial_ultrasound_arrays, verdure_axial_segmentation_arrays = utils.load_girder_data(
verdure_axial_csv,
data_arrays_fullpath,
girder_url,
girder_key=girder_key)
verdure_sagittal_ultrasound_arrays, verdure_sagittal_segmentation_arrays = utils.load_girder_data(
verdure_sagittal_csv,
data_arrays_fullpath,
girder_url,
girder_key=girder_key)
childrens_ultrasound_arrays, childrens_segmentation_arrays = utils.load_girder_data(
childrens_axial_csv,
data_arrays_fullpath,
girder_url,
girder_key=girder_key)
if not sagittal_only:
ultrasound_arrays_by_patients = (queens_ultrasound_arrays +
verdure_axial_ultrasound_arrays +
verdure_sagittal_ultrasound_arrays +
childrens_ultrasound_arrays)
segmentation_arrays_by_patients = (
queens_segmentation_arrays + verdure_axial_segmentation_arrays +
verdure_sagittal_segmentation_arrays +
childrens_segmentation_arrays)
else:
ultrasound_arrays_by_patients = (queens_ultrasound_arrays +
verdure_sagittal_ultrasound_arrays)
segmentation_arrays_by_patients = (
queens_segmentation_arrays + verdure_sagittal_segmentation_arrays)
if num_frames > 1:
multiframe_ultrasound_arrays_by_patients = []
for patient_series in ultrasound_arrays_by_patients:
multiframe_patient_series = np.zeros((
patient_series.shape[0],
patient_series.shape[1],
patient_series.shape[2],
patient_series.shape[3] * num_frames,
))
for frame_idx in range(len(patient_series)):
multiframe_frame = np.zeros((
patient_series[frame_idx].shape[0],
patient_series[frame_idx].shape[1],
patient_series[frame_idx].shape[2] * num_frames,
))
for frame in range(num_frames):
if frame_idx - frame >= 0:
multiframe_frame[:, :, frame] = np.squeeze(
patient_series[frame_idx - frame])
else:
multiframe_frame[:, :, frame] = np.zeros((
patient_series[frame_idx].shape[0],
patient_series[frame_idx].shape[1],
))
multiframe_patient_series[frame_idx] = multiframe_frame
multiframe_ultrasound_arrays_by_patients.append(
multiframe_patient_series)
ultrasound_arrays_by_patients = multiframe_ultrasound_arrays_by_patients
n_patients = len(ultrasound_arrays_by_patients)
for i in range(n_patients):
print("Patient {} has {} ultrasounds and {} segmentations".format(
i,
ultrasound_arrays_by_patients[i].shape[0],
segmentation_arrays_by_patients[i].shape[0],
))
# Prepare validation rounds
if np.max(validation_schedule_patient) > (n_patients - 1):
raise Exception(
"Patient ID cannot be greater than {}".format(n_patients - 1))
num_validation_rounds = len(validation_schedule_patient)
print("Planning {} rounds of validation".format(num_validation_rounds))
for i in range(num_validation_rounds):
print("Validation on patients {} in round {}".format(
validation_schedule_patient[i], i))
# Print training parameters, to archive them together with the notebook output.
time_sequence_start = datetime.datetime.now()
print("Name for saved files: {}".format(run_str))
print("\nTraining parameters")
print("Number of epochs: {}".format(num_epochs))
print("LR: {}".format(learning_rate))
print("LR decay: {}".format(learning_rate_decay))
print("Batch size: {}".format(batch_size))
print("Regularization rate: {}".format(regularization_rate))
print("")
print("Saving validation predictions in: {}".format(val_data_fullpath))
print("Saving models in: {}".format(models_save_fullpath))
# ROC data will be saved in these containers
val_best_metrics = dict()
val_fuzzy_metrics = dict()
val_aurocs = np.zeros(num_validation_rounds)
val_best_thresholds = np.zeros(num_validation_rounds)
# Perform validation rounds
for val_round_index in range(num_validation_rounds):
# Prepare data arrays
train_ultrasound_data = np.zeros([
0,
ultrasound_arrays_by_patients[0].shape[1],
ultrasound_arrays_by_patients[0].shape[2],
ultrasound_arrays_by_patients[0].shape[3],
])
train_segmentation_data = np.zeros([
0,
segmentation_arrays_by_patients[0].shape[1],
segmentation_arrays_by_patients[0].shape[2],
segmentation_arrays_by_patients[0].shape[3],
])
val_ultrasound_data = np.zeros([
0,
ultrasound_arrays_by_patients[0].shape[1],
ultrasound_arrays_by_patients[0].shape[2],
ultrasound_arrays_by_patients[0].shape[3],
])
val_segmentation_data = np.zeros([
0,
segmentation_arrays_by_patients[0].shape[1],
segmentation_arrays_by_patients[0].shape[2],
segmentation_arrays_by_patients[0].shape[3],
])
for patient_index in range(n_patients):
if patient_index not in validation_schedule_patient[
val_round_index]:
train_ultrasound_data = np.concatenate(
(train_ultrasound_data,
ultrasound_arrays_by_patients[patient_index]))
train_segmentation_data = np.concatenate((
train_segmentation_data,
segmentation_arrays_by_patients[patient_index],
))
else:
val_ultrasound_data = np.concatenate(
(val_ultrasound_data,
ultrasound_arrays_by_patients[patient_index]))
val_segmentation_data = np.concatenate(
(val_segmentation_data,
segmentation_arrays_by_patients[patient_index]))
n_train = train_ultrasound_data.shape[0]
n_val = val_ultrasound_data.shape[0]
print("\n*** Leave-one-out round # {}".format(val_round_index))
print(" Training on {} images, validating on {} images...".format(
n_train, n_val))
train_segmentation_data_onehot = tf.keras.utils.to_categorical(
train_segmentation_data, num_classes)
val_segmentation_data_onehot = tf.keras.utils.to_categorical(
val_segmentation_data, num_classes)
# Create and train model
if not model_new:
model = old_unet(ultrasound_size, num_classes, num_frames,
filter_multiplier, regularization_rate)
else:
model = new_unet(
ultrasound_size,
num_classes=num_classes,
num_channels=num_frames,
use_batch_norm=True,
upsample_mode="deconv", # 'deconv' or 'simple'
dropout=0.0,
dropout_type="spatial",
use_attention=True,
filters=16,
num_layers=5,
output_activation="softmax",
)
model.compile(
optimizer=tf.keras.optimizers.Adam(lr=learning_rate,
decay=learning_rate_decay),
loss=loss_func,
metrics=[
"accuracy", evaluation_metrics.jaccard_coef,
evaluation_metrics.dice_coef
],
)
dataset = tf.data.Dataset.from_tensor_slices(
((train_ultrasound_data, train_segmentation_data_onehot)))
dataset = dataset.map(preprocess_func, num_parallel_calls=4)
dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)
dataset = dataset.prefetch(1)
val_dataset = tf.data.Dataset.from_tensor_slices(
((val_ultrasound_data, val_segmentation_data_onehot)))
if with_maps:
val_dataset = val_dataset.map(generate_weight_maps,
num_parallel_calls=4)
val_dataset = val_dataset.batch(batch_size)
val_dataset = val_dataset.prefetch(1)
'''
fig = plt.figure(figsize=(30, 20 * 5))
i = 0
for im, lb in dataset:
a1 = fig.add_subplot(25, 6, i * 6 + 1)
img1 = a1.imshow(np.squeeze(np.flipud(im[i, :, :, 1])), cmap='gray')
a2 = fig.add_subplot(25, 6, i * 6 + 2)
img2 = a2.imshow(np.squeeze(np.flipud(im[i, :, :, 0])), cmap='gray')
a3 = fig.add_subplot(25, 6, i * 6 + 3)
img3 = a3.imshow(np.squeeze(np.flipud(lb[i, :, :, 0])))
c = fig.colorbar(img3, fraction=0.046, pad=0.04)
a4 = fig.add_subplot(25, 6, i * 6 + 4)
img4 = a4.imshow(np.squeeze(np.flipud(lb[i, :, :, 1])))
c = fig.colorbar(img4, fraction=0.046, pad=0.04)
a5 = fig.add_subplot(25, 6, i * 6 + 5)
img5 = a5.imshow(np.squeeze(np.flipud(lb[i, :, :, -2])))
c = fig.colorbar(img5, fraction=0.046, pad=0.04)
a6 = fig.add_subplot(25, 6, i * 6 + 6)
img6 = a6.imshow(np.squeeze(np.flipud(lb[i, :, :, -1])))
c = fig.colorbar(img6, fraction=0.046, pad=0.04)
i += 1
if i >= 20:
break
plt.savefig("us_seg.png")
'''
training_time_start = datetime.datetime.now()
if n_val > 0:
training_log = model.fit(
dataset,
validation_data=val_dataset,
epochs=num_epochs,
verbose=2,
)
else:
training_log = model.fit(
training_generator,
epochs=num_epochs,
verbose=2,
)
training_time_stop = datetime.datetime.now()
# Print training log
print(" Training time: {}".format(training_time_stop -
training_time_start))
# Plot training loss and metrics
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))
axes[0].plot(training_log.history["loss"], "bo--")
if n_val > 0:
axes[0].plot(training_log.history["val_loss"], "ro-")
axes[0].set(xlabel="Epochs (n)", ylabel="Loss")
if n_val > 0:
axes[0].legend(["Training loss", "Validation loss"])
axes[1].plot(training_log.history["accuracy"], "bo--")
if n_val > 0:
axes[1].plot(training_log.history["val_accuracy"], "ro-")
axes[1].set(xlabel="Epochs (n)", ylabel="Accuracy")
if n_val > 0:
axes[1].legend(["Training accuracy", "Validation accuracy"])
fig.tight_layout()
plt.savefig(run_str + "_val-round_" + str(val_round_index) + ".png")
# Archive trained model with unique filename based on notebook name and timestamp
model_file_name = run_str + "_model-" + str(val_round_index) + ".h5"
model_fullname = os.path.join(models_save_fullpath, model_file_name)
model.save(model_fullname)
# Predict on validation data
if n_val > 0:
y_pred_val = model.predict(val_ultrasound_data)
# Saving predictions for further evaluation
val_prediction_filename = (run_str + "_prediction_" +
str(val_round_index) + ".npy")
val_prediction_fullname = os.path.join(val_data_fullpath,
val_prediction_filename)
np.save(val_prediction_fullname, y_pred_val)
# Validation results
vali_metrics_dicts, vali_best_threshold_index, vali_area = evaluation_metrics.compute_roc(
roc_thresholds, y_pred_val, val_segmentation_data,
acceptable_margin_mm, mm_per_pixel)
val_fuzzy_metrics[
val_round_index] = evaluation_metrics.compute_evaluation_metrics(
y_pred_val, val_segmentation_data, acceptable_margin_mm,
mm_per_pixel)
val_best_metrics[val_round_index] = vali_metrics_dicts[
vali_best_threshold_index]
val_aurocs[val_round_index] = vali_area
val_best_thresholds[val_round_index] = roc_thresholds[
vali_best_threshold_index]
# Display sample results
num_vali = val_ultrasound_data.shape[0]
num_show = 10
if num_vali < num_show:
num_show = 0
num_col = 4
indices = [i for i in range(num_vali)]
sample_indices = sample(indices, num_show)
threshold = 0.5
# Uncomment for comparing the same images
# sample_indices = [105, 195, 391, 133, 142]
fig = plt.figure(figsize=(18, num_show * 5))
for i in range(num_show):
a0 = fig.add_subplot(num_show, num_col, i * num_col + 1)
img0 = a0.imshow(
np.flipud(val_ultrasound_data[sample_indices[i], :, :,
0].astype(np.float32)))
a0.set_title("Ultrasound #{}".format(sample_indices[i]))
a1 = fig.add_subplot(num_show, num_col, i * num_col + 2)
img1 = a1.imshow(
np.flipud(val_segmentation_data[sample_indices[i], :, :, 0]),
vmin=0.0,
vmax=1.0,
)
a1.set_title("Segmentation #{}".format(sample_indices[i]))
c = fig.colorbar(img1, fraction=0.046, pad=0.04)
a2 = fig.add_subplot(num_show, num_col, i * num_col + 3)
img2 = a2.imshow(np.flipud(y_pred_val[sample_indices[i], :, :, 1]),
vmin=0.0,
vmax=1.0)
a2.set_title("Prediction #{}".format(sample_indices[i]))
c = fig.colorbar(img2, fraction=0.046, pad=0.04)
a3 = fig.add_subplot(num_show, num_col, i * num_col + 4)
img3 = a3.imshow(
(np.flipud(y_pred_val[sample_indices[i], :, :, 1]) >
threshold),
vmin=0.0,
vmax=1.0,
)
c = fig.colorbar(img3, fraction=0.046, pad=0.04)
a3.set_title("Thresholded #{}".format(sample_indices[i]))
plt.savefig(run_str + "-samples-" + str(val_round_index) + ".png")
# Printing total time of this validation round
print("\nTotal round time: {}".format(datetime.datetime.now() -
training_time_start))
print("")
time_sequence_stop = datetime.datetime.now()
print("\nTotal training time: {}".format(time_sequence_stop -
time_sequence_start))
# Arrange results in tables
metric_labels = [
"AUROC",
"best thresh",
"best TP",
"best FP",
"best recall",
"best precis",
"fuzzy recall",
"fuzzy precis",
"fuzzy Fscore",
]
results_labels = []
for label in metric_labels:
results_labels.append("Vali " + label)
results_df = pd.DataFrame(columns=results_labels)
for i in range(num_validation_rounds):
if i in val_best_metrics.keys():
results_df.loc[i] = [
val_aurocs[i],
val_best_thresholds[i],
val_best_metrics[i][evaluation_metrics.TRUE_POSITIVE_RATE],
val_best_metrics[i][evaluation_metrics.FALSE_POSITIVE_RATE],
val_best_metrics[i][evaluation_metrics.RECALL],
val_best_metrics[i][evaluation_metrics.PRECISION],
val_fuzzy_metrics[i][evaluation_metrics.RECALL],
val_fuzzy_metrics[i][evaluation_metrics.PRECISION],
val_fuzzy_metrics[i][evaluation_metrics.FSCORE],
]
# Save results table
csv_filename = run_str + ".csv"
csv_fullname = os.path.join(results_save_fullpath, csv_filename)
results_df.to_csv(csv_fullname)
print("Results saved to: {}".format(csv_fullname))
def _transform_samples(self, batch_index, image_index):
x_bounding_boxes = numpy.zeros((self.batch_size, 0, 4))
x_categories = numpy.zeros((self.batch_size, 0, self.n_categories))
x_images = numpy.zeros(
(self.batch_size, *self.target_size, self.channels))
x_masks = numpy.zeros((self.batch_size, 0, *self.mask_size))
x_metadata = numpy.zeros((self.batch_size, 3))
horizontal_flip = False
if self.generator.horizontal_flip:
if numpy.random.random() < 0.5:
horizontal_flip = True
vertical_flip = False
if self.generator.vertical_flip:
if numpy.random.random() < 0.5:
vertical_flip = True
try:
pathname = self.dictionary[image_index]["image"]["pathname"]
except:
raise MissingImageException
target_image = numpy.zeros((*self.target_size, self.channels))
image = skimage.io.imread(pathname)
if self.data_format == "channels_last":
image = image[..., :self.channels]
else:
image = image[:self.channels, ...]
dimensions = numpy.array([0, 0, image.shape[0], image.shape[1]])
if self.generator.crop_size:
if image.shape[0] > self.generator.crop_size[0] and image.shape[
1] > self.generator.crop_size[1]:
image, dimensions = self._crop_image(image)
dimensions = dimensions.astype(numpy.float16)
scale = self.find_scale(image)
dimensions *= scale
image = skimage.transform.rescale(image,
scale,
anti_aliasing=True,
mode="reflect",
multichannel=True)
image = self.generator.standardize(image)
if horizontal_flip:
image = numpy.fliplr(image)
if vertical_flip:
image = numpy.flipud(image)
image_r = image.shape[0]
image_c = image.shape[1]
target_image[:image_r, :image_c] = image
x_images[batch_index] = target_image
x_metadata[batch_index] = [*self.target_size, 1.0]
bounding_boxes = self.dictionary[image_index]["objects"]
n_objects = len(bounding_boxes)
if n_objects == 0:
return [
numpy.zeros((self.batch_size, 0, 4)),
numpy.zeros((self.batch_size, 0, self.n_categories)), x_images,
numpy.zeros((self.batch_size, 0, self.mask_size[0],
self.mask_size[1])), x_metadata
]
x_bounding_boxes = numpy.resize(x_bounding_boxes,
(self.batch_size, n_objects, 4))
x_masks = numpy.resize(x_masks,
(self.batch_size, n_objects, *self.mask_size))
x_categories = numpy.resize(
x_categories, (self.batch_size, n_objects, self.n_categories))
for bounding_box_index, bounding_box in enumerate(bounding_boxes):
if bounding_box["category"] not in self.categories:
continue
minimum_r = bounding_box["bounding_box"]["minimum"]["r"]
minimum_c = bounding_box["bounding_box"]["minimum"]["c"]
maximum_r = bounding_box["bounding_box"]["maximum"]["r"]
maximum_c = bounding_box["bounding_box"]["maximum"]["c"]
minimum_r *= scale
minimum_c *= scale
maximum_r *= scale
maximum_c *= scale
minimum_r = int(minimum_r)
minimum_c = int(minimum_c)
maximum_r = int(maximum_r)
maximum_c = int(maximum_c)
target_bounding_box = [minimum_r, minimum_c, maximum_r, maximum_c]
x_bounding_boxes[batch_index,
bounding_box_index] = target_bounding_box
if "mask" in bounding_box:
target_mask = skimage.io.imread(
bounding_box["mask"]["pathname"])
target_mask = skimage.transform.rescale(target_mask,
scale,
anti_aliasing=True,
mode="reflect",
multichannel=False)
target_mask = target_mask[minimum_r:maximum_r + 1,
minimum_c:maximum_c + 1]
target_mask = skimage.transform.resize(target_mask,
self.mask_size,
order=0,
mode="reflect",
anti_aliasing=True)
x_masks[batch_index, bounding_box_index] = target_mask
target_category = numpy.zeros(self.n_categories)
target_category[self.categories[bounding_box["category"]]] = 1
x_categories[batch_index, bounding_box_index] = target_category
x = self._shuffle_objects(x_bounding_boxes, x_categories, x_masks)
x_bounding_boxes, x_categories, x_masks = x
x_bounding_boxes = self._crop_bounding_boxes(x_bounding_boxes,
dimensions)
cropped = self._cropped_objects(x_bounding_boxes)
x_bounding_boxes = x_bounding_boxes[:, ~cropped]
x_categories = x_categories[:, ~cropped]
x_masks = x_masks[:, ~cropped]
for bounding_box_index, bounding_box in enumerate(x_bounding_boxes[0]):
mask = x_masks[0, bounding_box_index]
if horizontal_flip:
bounding_box = [
bounding_box[0], image.shape[1] - bounding_box[3],
bounding_box[2], image.shape[1] - bounding_box[1]
]
mask = numpy.fliplr(mask)
if vertical_flip:
bounding_box = [
image.shape[0] - bounding_box[2], bounding_box[1],
image.shape[0] - bounding_box[0], bounding_box[3]
]
mask = numpy.flipud(mask)
x_bounding_boxes[batch_index, bounding_box_index] = bounding_box
x_masks[batch_index, bounding_box_index] = mask
if self.generator.clear_border:
indices = self._clear_border(x_bounding_boxes)
x_bounding_boxes = x_bounding_boxes[:, indices]
x_categories = x_categories[:, indices]
x_masks = x_masks[:, indices]
if x_bounding_boxes.shape == (self.batch_size, 0, 4):
raise BoundingBoxException
x_masks[x_masks > 0.5] = 1.0
x_masks[x_masks < 0.5] = 0.0
return [x_bounding_boxes, x_categories, x_images, x_masks, x_metadata]