The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def get_points():
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1 , 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('calibration_wide/GO*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (8,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (8,6), corners, ret)
#write_name = 'corners_found'+str(idx)+'.jpg'
#cv2.imwrite(write_name, img)
cv2.imshow('img', img)
cv2.waitKey(500)
cv2.destroyAllWindows()
return objpoints, imgpoints Example 2
def find_points(images):
pattern_size = (9, 6)
obj_points = []
img_points = []
# Assumed object points relation
a_object_point = np.zeros((PATTERN_SIZE[1] * PATTERN_SIZE[0], 3),
np.float32)
a_object_point[:, :2] = np.mgrid[0:PATTERN_SIZE[0],
0:PATTERN_SIZE[1]].T.reshape(-1, 2)
# Termination criteria for sub pixel corners refinement
stop_criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER,
30, 0.001)
print('Finding points ', end='')
debug_images = []
for (image, color_image) in images:
found, corners = cv.findChessboardCorners(image, PATTERN_SIZE, None)
if found:
obj_points.append(a_object_point)
cv.cornerSubPix(image, corners, (11, 11), (-1, -1), stop_criteria)
img_points.append(corners)
print('.', end='')
else:
print('-', end='')
if DEBUG:
cv.drawChessboardCorners(color_image, PATTERN_SIZE, corners, found)
debug_images.append(color_image)
sys.stdout.flush()
if DEBUG:
display_images(debug_images, DISPLAY_SCALE)
print('\nWas able to find points in %s images' % len(img_points))
return obj_points, img_points
# images is a lis of tuples: (gray_image, color_image) Example 3
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1)
fx, fy = flow[y,x].T
m = np.bitwise_and(np.isfinite(fx), np.isfinite(fy))
lines = np.vstack([x[m], y[m], x[m]+fx[m], y[m]+fy[m]]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis Example 4
def draw_flow(img, flow, step=8):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
fx, fy = flow[y,x].T
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
lines = np.int32(lines + 0.5)
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0))
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
return vis
#####################################################################
# define video capture object Example 5
def calculateExtrinsics(self, cameraParameters):
'''
Inputs:
cameraParameters is CameraParameters object
Calculate: rotate vector and transform vector
>>> marker.calculateExtrinsics(camera_matrix, dist_coeff)
>>> print(marker.rvec, marker.tvec)
'''
object_points = np.zeros((4,3), dtype=np.float32)
object_points[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)
# Test Code.
# object_points[:] -= 0.5
marker_points = self.corners
if marker_points is None: raise TypeError('The marker.corners is None')
camera_matrix = cameraParameters.camera_matrix
dist_coeff = cameraParameters.dist_coeff
ret, rvec, tvec = cv2.solvePnP(object_points, marker_points,
camera_matrix, dist_coeff)
if ret: self.rvec, self.tvec = rvec, tvec
return ret Example 6
def __init__(self, pos, size):
"""
pos is (...,3) array of the bar positions (the corner of each bar)
size is (...,3) array of the sizes of each bar
"""
nCubes = reduce(lambda a,b: a*b, pos.shape[:-1])
cubeVerts = np.mgrid[0:2,0:2,0:2].reshape(3,8).transpose().reshape(1,8,3)
cubeFaces = np.array([
[0,1,2], [3,2,1],
[4,5,6], [7,6,5],
[0,1,4], [5,4,1],
[2,3,6], [7,6,3],
[0,2,4], [6,4,2],
[1,3,5], [7,5,3]]).reshape(1,12,3)
size = size.reshape((nCubes, 1, 3))
pos = pos.reshape((nCubes, 1, 3))
verts = cubeVerts * size + pos
faces = cubeFaces + (np.arange(nCubes) * 8).reshape(nCubes,1,1)
md = MeshData(verts.reshape(nCubes*8,3), faces.reshape(nCubes*12,3))
GLMeshItem.__init__(self, meshdata=md, shader='shaded', smooth=False) Example 7
def __init__(self, pos, size):
"""
pos is (...,3) array of the bar positions (the corner of each bar)
size is (...,3) array of the sizes of each bar
"""
nCubes = reduce(lambda a,b: a*b, pos.shape[:-1])
cubeVerts = np.mgrid[0:2,0:2,0:2].reshape(3,8).transpose().reshape(1,8,3)
cubeFaces = np.array([
[0,1,2], [3,2,1],
[4,5,6], [7,6,5],
[0,1,4], [5,4,1],
[2,3,6], [7,6,3],
[0,2,4], [6,4,2],
[1,3,5], [7,5,3]]).reshape(1,12,3)
size = size.reshape((nCubes, 1, 3))
pos = pos.reshape((nCubes, 1, 3))
verts = cubeVerts * size + pos
faces = cubeFaces + (np.arange(nCubes) * 8).reshape(nCubes,1,1)
md = MeshData(verts.reshape(nCubes*8,3), faces.reshape(nCubes*12,3))
GLMeshItem.__init__(self, meshdata=md, shader='shaded', smooth=False) Example 8
def make3dplot(ax, sample, density):
ax.scatter(sample[:,0], sample[:,1], zdir='z')
ax.set_aspect('equal', 'datalim')
xlim = ax.get_xlim()
ylim = ax.get_ylim()
gridsize=50
xs, ys = np.mgrid[xlim[0]:xlim[1]:(xlim[1]-xlim[0])/float(gridsize), ylim[0]:ylim[1]:(ylim[1]-ylim[0])/float(gridsize)]
pos = np.empty(xs.shape + (2,))
pos[:, :, 0] = xs; pos[:, :, 1] = ys
zs = density(pos)
surf = ax.plot_surface(xs, ys, zs, rstride=1, cstride=1, linewidth=0, antialiased=False, alpha=.3)
ax.set_xlabel('x')
ax.set_ylabel('y') Example 9
def nufft_T(N, J, K, alpha, beta):
'''
equation (29) and (26)Fessler's paper
create the overlapping matrix CSSC (diagonal dominent matrix)
of J points
and then find out the pseudo-inverse of CSSC '''
# import scipy.linalg
L = numpy.size(alpha) - 1
# print('L = ', L, 'J = ',J, 'a b', alpha,beta )
cssc = numpy.zeros((J, J))
[j1, j2] = numpy.mgrid[1:J + 1, 1:J + 1]
overlapping_mat = j2 - j1
for l1 in range(-L, L + 1):
for l2 in range(-L, L + 1):
alf1 = alpha[abs(l1)]
# if l1 < 0: alf1 = numpy.conj(alf1)
alf2 = alpha[abs(l2)]
# if l2 < 0: alf2 = numpy.conj(alf2)
tmp = overlapping_mat + beta * (l1 - l2)
tmp = dirichlet(1.0 * tmp / (1.0 * K / N))
cssc = cssc + alf1 * numpy.conj(alf2) * tmp
return mat_inv(cssc) Example 10
def nufft_T(N, J, K, alpha, beta):
'''
The Equation (29) and (26) in Fessler and Sutton 2003.
Create the overlapping matrix CSSC (diagonal dominent matrix)
of J points and find out the pseudo-inverse of CSSC '''
# import scipy.linalg
L = numpy.size(alpha) - 1
# print('L = ', L, 'J = ',J, 'a b', alpha,beta )
cssc = numpy.zeros((J, J))
[j1, j2] = numpy.mgrid[1:J + 1, 1:J + 1]
overlapping_mat = j2 - j1
for l1 in range(-L, L + 1):
for l2 in range(-L, L + 1):
alf1 = alpha[abs(l1)]
# if l1 < 0: alf1 = numpy.conj(alf1)
alf2 = alpha[abs(l2)]
# if l2 < 0: alf2 = numpy.conj(alf2)
tmp = overlapping_mat + beta * (l1 - l2)
tmp = dirichlet(1.0 * tmp / (1.0 * K / N))
cssc = cssc + alf1 * alf2 * tmp
return mat_inv(cssc) Example 11
def x_frame2D(X, plot_limits=None, resolution=None):
"""
Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
"""
assert X.shape[1] == 2, \
'x_frame2D is defined for two-dimensional inputs'
if plot_limits is None:
(xmin, xmax) = (X.min(0), X.max(0))
(xmin, xmax) = (xmin - 0.2 * (xmax - xmin), xmax + 0.2 * (xmax
- xmin))
elif len(plot_limits) == 2:
(xmin, xmax) = plot_limits
else:
raise ValueError, 'Bad limits for plotting'
resolution = resolution or 50
(xx, yy) = np.mgrid[xmin[0]:xmax[0]:1j * resolution, xmin[1]:
xmax[1]:1j * resolution]
Xnew = np.vstack((xx.flatten(), yy.flatten())).T
return (Xnew, xx, yy, xmin, xmax) Example 12
def test_pdf(self):
'''
Tests the probability density function.
'''
# Calculate probability density function on lattice
bnds = np.empty((3), dtype=object)
bnds[0] = [-1, 1]
bnds[1] = [0, 2]
bnds[2] = [0.5, 2]
(x0g, x1g, x2g) = np.mgrid[bnds[0][0]:bnds[0][1],
bnds[1][0]:bnds[1][1],
bnds[2][0]:bnds[2][1]]
points = np.array([x0g.ravel(), x1g.ravel(), x2g.ravel()]).T
r_logpdf = np.array([-6.313469, -17.406428, -4.375992, -6.226508,
-8.836115, -20.430739, -5.107053, -6.687987])
p_logpdf = self.vine.logpdf(points)
assert_allclose(p_logpdf, r_logpdf)
r_pdf = np.array([1.811738e-03, 2.757302e-08, 1.257566e-02,
1.976342e-03, 1.453865e-04, 1.339808e-09,
6.053895e-03, 1.245788e-03])
p_pdf = self.vine.pdf(points)
assert_allclose(p_pdf, r_pdf, rtol=1e-5) Example 13
def plotImage(dta, saveFigName):
plt.clf()
dx, dy = 1, 1
# generate 2 2d grids for the x & y bounds
with np.errstate(invalid='ignore'):
y, x = np.mgrid[
slice(0, len(dta) , dx),
slice(0, len(dta[0]), dy)
]
z = dta
z_min, z_max = -np.abs(z).max(), np.abs(z).max()
#try:
c = plt.pcolormesh(x, y, z, cmap='hsv', vmin=z_min, vmax=z_max)
#except ??? as err: # data not regular?
# c = plt.pcolor(x, y, z, cmap='hsv', vmin=z_min, vmax=z_max)
d = plt.colorbar(c, orientation='vertical')
lx = plt.xlabel("index")
ly = plt.ylabel("season length")
plt.savefig(str(saveFigName)) Example 14
def areaxy(self, lowerbound=-np.inf, upperbound=np.inf, spacing=0.1):
mask = (self.coord[:,2] > lowerbound) & (self.coord[:,2] < upperbound)
points = self.coord[mask, :2]
# The magic number factor 1.1 is not critical at all
# Just a number to set a margin to the bounding box and
# have all points fall within the boundaries
bbmin, bbmax = 1.1*points.min(axis=0), 1.1*points.max(axis=0)
size = bbmax - bbmin
cells = (size / spacing + 0.5).astype('int')
# Grid points over bounding box with specified spacing
grid = np.mgrid[bbmin[0]:bbmax[0]:(cells[0]*1j),
bbmin[1]:bbmax[1]:(cells[1]*1j)].reshape((2,-1)).T
# Occupied cells is approximately equal to grid points within
# gridspacing distance of points
occupied = occupancy(grid, points, spacing)
# The occupied area follows from the fraction of occupied
# cells times the area spanned by the bounding box
return size[0]*size[1]*sum(occupied > 0)/occupied.size Example 15
def generate_hills(width, height, nhills):
'''
@param width float, terrain width
@param height float, terrain height
@param nhills int, #hills to gen. #hills actually generted is sqrt(nhills)^2
'''
# setup coordinate grid
xmin, xmax = -width/2.0, width/2.0
ymin, ymax = -height/2.0, height/2.0
x, y = np.mgrid[xmin:xmax:STEP, ymin:ymax:STEP]
pos = np.empty(x.shape + (2,))
pos[:, :, 0] = x; pos[:, :, 1] = y
# generate hilltops
xm, ym = np.mgrid[xmin:xmax:width/np.sqrt(nhills), ymin:ymax:height/np.sqrt(nhills)]
mu = np.c_[xm.flat, ym.flat]
sigma = float(width*height)/(nhills*8)
for i in range(mu.shape[0]):
mu[i] = multivariate_normal.rvs(mean=mu[i], cov=sigma)
# generate hills
sigma = sigma + sigma*np.random.rand(mu.shape[0])
rvs = [ multivariate_normal(mu[i,:], cov=sigma[i]) for i in range(mu.shape[0]) ]
hfield = np.max([ rv.pdf(pos) for rv in rvs ], axis=0)
return x, y, hfield Example 16
def joint_density(X, Y, bounds=None): """ Plots joint distribution of variables. Inherited from method in src/graphics.py module in project git://github.com/aflaxman/pymc-example-tfr-hdi.git """ if bounds: X_min, X_max, Y_min, Y_max = bounds else: X_min = X.min() X_max = X.max() Y_min = Y.min() Y_max = Y.max() pylab.plot(X, Y, linestyle='none', marker='o', color='green', mec='green', alpha=.2, zorder=-99) gkde = scipy.stats.gaussian_kde([X, Y]) x,y = pylab.mgrid[X_min:X_max:(X_max-X_min)/25.,Y_min:Y_max:(Y_max-Y_min)/25.] z = pylab.array(gkde.evaluate([x.flatten(), y.flatten()])).reshape(x.shape) pylab.contour(x, y, z, linewidths=2) pylab.axis([X_min, X_max, Y_min, Y_max])
Example 17
def hyperball(ndim, radius):
"""Return a binary morphological filter containing pixels within `radius`.
Parameters
----------
ndim : int
The number of dimensions of the filter.
radius : int
The radius of the filter.
Returns
-------
ball : array of bool, shape [2 * radius + 1,] * ndim
The required structural element
"""
size = 2 * radius + 1
center = [(radius,) * ndim]
coords = np.mgrid[[slice(None, size),] * ndim].reshape(ndim, -1).T
distances = np.ravel(spatial.distance_matrix(coords, center))
selector = distances <= radius
ball = np.zeros((size,) * ndim, dtype=bool)
ball.ravel()[selector] = True
return ball Example 18
def _generate_random_grids(self):
if self.num_grids > 40:
starter = np.random.randint(0, 20)
random_sample = np.mgrid[starter:len(self.grids)-1:20j].astype("int32")
# We also add in a bit to make sure that some of the grids have
# particles
gwp = self.grid_particle_count > 0
if np.any(gwp) and not np.any(gwp[(random_sample,)]):
# We just add one grid. This is not terribly efficient.
first_grid = np.where(gwp)[0][0]
random_sample.resize((21,))
random_sample[-1] = first_grid
mylog.debug("Added additional grid %s", first_grid)
mylog.debug("Checking grids: %s", random_sample.tolist())
else:
random_sample = np.mgrid[0:max(len(self.grids),1)].astype("int32")
return self.grids[(random_sample,)] Example 19
def test_linear_interpolator_2d():
random_data = np.random.random((64, 64))
# evenly spaced bins
fv = dict((ax, v) for ax, v in zip("xyz",
np.mgrid[0.0:1.0:64j, 0.0:1.0:64j]))
bfi = lin.BilinearFieldInterpolator(random_data,
(0.0, 1.0, 0.0, 1.0), "xy", True)
assert_array_equal(bfi(fv), random_data)
# randomly spaced bins
size = 64
bins = np.linspace(0.0, 1.0, size)
shifts = dict((ax, (1. / size) * np.random.random(size) - (0.5 / size)) \
for ax in "xy")
fv["x"] += shifts["x"][:, np.newaxis]
fv["y"] += shifts["y"]
bfi = lin.BilinearFieldInterpolator(random_data,
(bins + shifts["x"], bins + shifts["y"]), "xy", True)
assert_array_almost_equal(bfi(fv), random_data, 15) Example 20
def test_linear_interpolator_3d():
random_data = np.random.random((64, 64, 64))
# evenly spaced bins
fv = dict((ax, v) for ax, v in zip("xyz",
np.mgrid[0.0:1.0:64j, 0.0:1.0:64j, 0.0:1.0:64j]))
tfi = lin.TrilinearFieldInterpolator(random_data,
(0.0, 1.0, 0.0, 1.0, 0.0, 1.0), "xyz", True)
assert_array_almost_equal(tfi(fv), random_data)
# randomly spaced bins
size = 64
bins = np.linspace(0.0, 1.0, size)
shifts = dict((ax, (1. / size) * np.random.random(size) - (0.5 / size)) \
for ax in "xyz")
fv["x"] += shifts["x"][:, np.newaxis, np.newaxis]
fv["y"] += shifts["y"][:, np.newaxis]
fv["z"] += shifts["z"]
tfi = lin.TrilinearFieldInterpolator(random_data,
(bins + shifts["x"], bins + shifts["y"],
bins + shifts["z"]), "xyz", True)
assert_array_almost_equal(tfi(fv), random_data, 15) Example 21
def partition_index_2d(self, axis):
if not self._distributed:
return False, self.index.grid_collection(self.center,
self.index.grids)
xax = self.ds.coordinates.x_axis[axis]
yax = self.ds.coordinates.y_axis[axis]
cc = MPI.Compute_dims(self.comm.size, 2)
mi = self.comm.rank
cx, cy = np.unravel_index(mi, cc)
x = np.mgrid[0:1:(cc[0]+1)*1j][cx:cx+2]
y = np.mgrid[0:1:(cc[1]+1)*1j][cy:cy+2]
DLE, DRE = self.ds.domain_left_edge.copy(), self.ds.domain_right_edge.copy()
LE = np.ones(3, dtype='float64') * DLE
RE = np.ones(3, dtype='float64') * DRE
LE[xax] = x[0] * (DRE[xax]-DLE[xax]) + DLE[xax]
RE[xax] = x[1] * (DRE[xax]-DLE[xax]) + DLE[xax]
LE[yax] = y[0] * (DRE[yax]-DLE[yax]) + DLE[yax]
RE[yax] = y[1] * (DRE[yax]-DLE[yax]) + DLE[yax]
mylog.debug("Dimensions: %s %s", LE, RE)
reg = self.ds.region(self.center, LE, RE)
return True, reg Example 22
def init_fill(self):
rext = 1.0
Ns = 51
x, y = np.mgrid[ -rext:rext:1j*Ns, -rext:rext:1j*Ns ]
z = np.zeros_like(x)
self.controller.ax_xstress.pcolor(x,y,z, cmap=plt.cm.coolwarm)
self.controller.ax_ystress.pcolor(x,y,z, cmap=plt.cm.coolwarm)
self.controller.ax_xystress.pcolor(x,y,z, cmap=plt.cm.coolwarm)
self.controller.ax_rstress.pcolor(x,y,z, cmap=plt.cm.coolwarm)
self.controller.ax_tstress.pcolor(x,y,z, cmap=plt.cm.coolwarm)
return
# ======================= Example 23
def evaluate_model(self, model):
"""
This function ...
:param model:
:return:
"""
# Make a local copy of the model so that we can adapt its position to be relative to this box
rel_model = fitting.shifted_model(model, -self.x_min, -self.y_min)
# Create x and y meshgrid for evaluating
y_values, x_values = np.mgrid[:self.ysize, :self.xsize]
# Evaluate the model
data = rel_model(x_values, y_values)
# Return a new box
return Box(data, self.x_min, self.x_max, self.y_min, self.y_max)
# ----------------------------------------------------------------- Example 24
def evaluate_model(self, model):
"""
This function ...
:param model:
:return:
"""
# Make a local copy of the model so that we can adapt its position to be relative to this box
rel_model = fitting.shifted_model(model, -self.x_min, -self.y_min)
# Create x and y meshgrid for evaluating
y_values, x_values = np.mgrid[:self.ysize, :self.xsize]
# Evaluate the model
data = rel_model(x_values, y_values)
# Return a new box
return Box(data, self.x_min, self.x_max, self.y_min, self.y_max)
# ----------------------------------------------------------------- Example 25
def polarToLinearMaps(orig_shape, out_shape=None, center=None):
s0, s1 = orig_shape
if out_shape is None:
out_shape = (int(round(2 * s0 / 2**0.5)) - (1 - s0 % 2),
int(round(2 * s1 / (2 * np.pi) / 2**0.5)))
ss0, ss1 = out_shape
if center is None:
center = ss1 // 2, ss0 // 2
yy, xx = np.mgrid[0:ss0:1., 0:ss1:1.]
r, phi = _cart2polar(xx, yy, center)
# scale-pi...pi->0...s1:
phi = (phi + np.pi) / (2 * np.pi) * (s1 - 2)
return phi.astype(np.float32), r.astype(np.float32) Example 26
def calculateExtrinsics(self, cameraParameters):
'''
Inputs:
cameraParameters is CameraParameters object
Calculate: rotate vector and transform vector
>>> marker.calculateExtrinsics(camera_matrix, dist_coeff)
>>> print(marker.rvec, marker.tvec)
'''
object_points = np.zeros((4,3), dtype=np.float32)
object_points[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)
# Test Code.
# object_points[:] -= 0.5
marker_points = self.corners
if marker_points is None: raise TypeError('The marker.corners is None')
camera_matrix = cameraParameters.camera_matrix
dist_coeff = cameraParameters.dist_coeff
ret, rvec, tvec = cv2.solvePnP(object_points, marker_points,
camera_matrix, dist_coeff)
if ret: self.rvec, self.tvec = rvec, tvec
return ret Example 27
def calculateExtrinsics(self, cameraParameters):
'''
Inputs:
cameraParameters is CameraParameters object
Calculate: rotate vector and transform vector
>>> marker.calculateExtrinsics(camera_matrix, dist_coeff)
>>> print(marker.rvec, marker.tvec)
'''
object_points = np.zeros((4,3), dtype=np.float32)
object_points[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)
# Test Code.
# object_points[:] -= 0.5
marker_points = self.corners
if marker_points is None: raise TypeError('The marker.corners is None')
camera_matrix = cameraParameters.camera_matrix
dist_coeff = cameraParameters.dist_coeff
ret, rvec, tvec = cv2.solvePnP(object_points, marker_points,
camera_matrix, dist_coeff)
if ret: self.rvec, self.tvec = rvec, tvec
return ret Example 28
def calculateExtrinsics(self, cameraParameters):
'''
Inputs:
cameraParameters is CameraParameters object
Calculate: rotate vector and transform vector
>>> marker.calculateExtrinsics(camera_matrix, dist_coeff)
>>> print(marker.rvec, marker.tvec)
'''
object_points = np.zeros((4,3), dtype=np.float32)
object_points[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)
# Test Code.
# object_points[:] -= 0.5
marker_points = self.corners
if marker_points is None: raise TypeError('The marker.corners is None')
camera_matrix = cameraParameters.camera_matrix
dist_coeff = cameraParameters.dist_coeff
ret, rvec, tvec = cv2.solvePnP(object_points, marker_points,
camera_matrix, dist_coeff)
if ret: self.rvec, self.tvec = rvec, tvec
return ret Example 29
def _compute_gaussian_kernel(histogram_shape, relative_bw):
"""Compute a gaussian kernel double the size of the histogram matrix"""
if len(histogram_shape) == 2:
kernel_shape = [2 * n for n in histogram_shape]
# Create a scaled grid in which the kernel is symmetric to avoid matrix
# inversion problems when the bandwiths are very different
bw_ratio = relative_bw[0] / relative_bw[1]
bw = relative_bw[0]
X, Y = np.mgrid[-bw_ratio:bw_ratio:kernel_shape[0] * 1j,
-1:1:kernel_shape[1] * 1j]
grid_points = np.vstack([X.ravel(), Y.ravel()]).T
Cov = np.array(((bw, 0), (0, bw)))**2
K = stats.multivariate_normal.pdf(grid_points, mean=(0, 0), cov=Cov)
return K.reshape(kernel_shape)
else:
grid = np.mgrid[-1:1:histogram_shape[0] * 2j]
return stats.norm.pdf(grid, loc=0, scale=relative_bw) Example 30
def draw_flow(img, flow, step=16):
h, w = img.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2, -1).astype(int) # ????????????????????????16?reshape?2??array
fx, fy = flow[y, x].T # ???????????????
lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2) # ????????????2*2???
lines = np.int32(lines + 0.5) # ????????????
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.polylines(vis, lines, 0, (0, 255, 0)) # ???????????????
for (x1, y1), (x2, y2) in lines:
cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1) # ???????????????????
return vis Example 31
def flow2parallax(u,v,q):
"""
Given the flow fields (after correction!) and the epipole,
return:
- The normalized parallax (HxW array)
- The vectors pointing to the epipoles (HxWx2 array)
- The distances of all points to the epipole (HxW array)
"""
h,w = u.shape
y,x = np.mgrid[:h,:w]
u_f = q[0] - x
v_f = q[1] - y
dists = np.sqrt(u_f**2 + v_f**2)
u_f_n = u_f / np.maximum(dists,1e-3)
v_f_n = v_f / np.maximum(dists,1e-3)
parallax = u * u_f_n + v * v_f_n
return parallax, np.dstack((u_f_n, v_f_n)), dists Example 32
def create_test_dataset(image_shape, n, circle_radius, donut_radius):
img = np.zeros((image_shape[0], image_shape[1]))
y_pixels = np.arange(0, image_shape[0], 1)
x_pixels = np.arange(0, image_shape[1], 1)
cell_y_coords = np.random.choice(y_pixels, n, replace=False)
cell_x_coords = np.random.choice(x_pixels, n, replace=False)
for x, y in zip(cell_x_coords, cell_y_coords):
xx, yy = np.mgrid[:512, :512] # create mesh grid of image dimensions
circle = (xx - x) ** 2 + (yy - y) ** 2 # apply circle formula
donut = np.logical_and(circle < (circle_radius+donut_radius),
circle > (circle_radius-5)) # donuts are thresholded circles
thresholded_circle = circle < circle_radius
img[np.where(thresholded_circle)] = 1
img[np.where(donut)] = 2
return img Example 33
def test_square_grid():
X = np.mgrid[0:16, 0:16]
X = X.reshape((len(X), -1)).T
name = 'square'
D, Q = test_toy_embedding(X, 32, 2, name, palette='hls')
def plot_mat_on_data(mat, sample):
plt.figure()
plot_data_embedded(X, palette='w')
alpha = np.maximum(mat[sample], 0) / mat[sample].max()
plot_data_embedded(X, palette='#FF0000', alpha=alpha)
pdf_file_name = '{}{}_plot_{}_on_data_{}{}'
plot_mat_on_data(D, 7 * 16 + 7)
plt.savefig(pdf_file_name.format(dir_name, name, 'D', 'middle', '.pdf'))
plot_mat_on_data(Q, 7 * 16 + 7)
plt.savefig(pdf_file_name.format(dir_name, name, 'Q', 'middle', '.pdf'))
# for s in range(len(X)):
# plot_mat_on_data(Q, s)
# plt.savefig(pdf_file_name.format(dir_name, name, 'Q', s, '.png'))
# plt.close() Example 34
def plot(self, ax, idx1, idx2, range1, range2, n=100):
assert len(range1) == len(range2) == 2 and idx1 != idx2
x, y = np.mgrid[range1[0]:range1[1]:(n+0j), range2[0]:range2[1]:(n+0j)]
if isinstance(self.action_space, ContinuousSpace):
points_B_Doa = np.zeros((n*n, self.obsfeat_space.storage_size + self.action_space.storage_size))
points_B_Doa[:,idx1] = x.ravel()
points_B_Doa[:,idx2] = y.ravel()
obsfeat_B_Df, a_B_Da = points_B_Doa[:,:self.obsfeat_space.storage_size], points_B_Doa[:,self.obsfeat_space.storage_size:]
assert a_B_Da.shape[1] == self.action_space.storage_size
t_B = np.zeros(a_B_Da.shape[0]) # XXX make customizable
z = self.compute_reward(obsfeat_B_Df, a_B_Da, t_B).reshape(x.shape)
else:
obsfeat_B_Df = np.zeros((n*n, self.obsfeat_space.storage_size))
obsfeat_B_Df[:,idx1] = x.ravel()
obsfeat_B_Df[:,idx2] = y.ravel()
a_B_Da = np.zeros((obsfeat_B_Df.shape[0], 1), dtype=np.int32) # XXX make customizable
t_B = np.zeros(a_B_Da.shape[0]) # XXX make customizable
z = self.compute_reward(obsfeat_B_Df, a_B_Da, t_B).reshape(x.shape)
ax.pcolormesh(x, y, z, cmap='viridis')
ax.contour(x, y, z, levels=np.log(np.linspace(2., 3., 10)))
# ax.contourf(x, y, z, levels=[np.log(2.), np.log(2.)+.5], alpha=.5) # high-reward region is highlighted Example 35
def calculate_scalar_matrix(values_a, values_b):
"""
convenience function wrapper of py:function:`calculate_scalar_product_matrix` for the case of scalar elements.
:param values_a:
:param values_b:
:return:
"""
return calculate_scalar_product_matrix(np.multiply,
sanitize_input(values_a, Number),
sanitize_input(values_b, Number))
# i, j = np.mgrid[0:values_a.shape[0], 0:values_b.shape[0]]
# vals_i = values_a[i]
# vals_j = values_b[j]
# return np.multiply(vals_i, vals_j) Example 36
def gabor_2d(M, N, sigma, theta, xi, slant=1.0, offset=0, fft_shift=None):
gab = np.zeros((M, N), np.complex64)
R = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]], np.float32)
R_inv = np.array([[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]], np.float32)
D = np.array([[1, 0], [0, slant * slant]])
curv = np.dot(R, np.dot(D, R_inv)) / ( 2 * sigma * sigma)
for ex in [-2, -1, 0, 1, 2]:
for ey in [-2, -1, 0, 1, 2]:
[xx, yy] = np.mgrid[offset + ex * M:offset + M + ex * M, offset + ey * N:offset + N + ey * N]
arg = -(curv[0, 0] * np.multiply(xx, xx) + (curv[0, 1] + curv[1, 0]) * np.multiply(xx, yy) + curv[
1, 1] * np.multiply(yy, yy)) + 1.j * (xx * xi * np.cos(theta) + yy * xi * np.sin(theta))
gab = gab + np.exp(arg)
norm_factor = (2 * 3.1415 * sigma * sigma / slant)
gab = gab / norm_factor
if (fft_shift):
gab = np.fft.fftshift(gab, axes=(0, 1))
return gab Example 37
def test():
import PIL.Image
y, x = np.mgrid[0:256, 0:256]
z = np.ones((256,256)) * 128
img0 = np.dstack((x, y, z)).astype(np.uint8)
img1 = y.astype(np.uint8)
img2 = np.arange(256, dtype=np.uint8)
img3 = PIL.Image.open("pics/RGB.png")
img3 = np.array(img3)[:,:,0:3]
img4 = PIL.Image.open("pics/banff.jpg")
img4 = np.array(img4)[:,:,0:3]
img5, _ = (np.mgrid[0:1242, 0:1276] / 1242. * 255.).astype(np.uint8)
img6, _ = (np.mgrid[0:1007, 0:12] / 1007. * 255.).astype(np.uint8)
for i in (1, 2, 4, 8):
write_tiff("Test0_" + str(i) + ".TIF", img0, bit_depth=i)
write_tiff("Test1_" + str(i) + ".TIF", img1, bit_depth=i)
write_tiff("Test2_" + str(i) + ".TIF", img2, bit_depth=i)
write_tiff("Test3_" + str(i) + ".TIF", img3, bit_depth=i)
write_tiff("Test4_" + str(i) + ".TIF", img4, bit_depth=i)
write_tiff("Test5_" + str(i) + ".TIF", img5, bit_depth=i)
write_tiff("Test6_" + str(i) + ".TIF", img6, bit_depth=i) Example 38
def gauss_kernel(size, sigma=None, size_y=None, sigma_y=None):
"""
Generates a 2D Gaussian kernel as a numpy array
Args:
size (int): 1/2 the width of the kernel; total width := 2*size+1
sigma (float): spread of the gaussian in the width direction
size_y (int): 1/2 the height of the kernel; defaults to size
sigma_y (float): spread of the gaussian in the height direction; defaults to sigma
Returns:
numpy array: normalized 2D gaussian array
"""
size = int(size)
if not size_y:
size_y = size
else:
size_y = int(size_y)
if not sigma:
sigma = 0.5 * size + .1
if not sigma_y:
sigma_y = sigma
x, y = np.mgrid[-size:size+1, -size_y:size_y+1]
g = np.exp(-0.5 * (x ** 2 / sigma ** 2 + y ** 2 / sigma_y ** 2))
return g / g.sum() Example 39
def __init__(self, im, sigma_spatial=12, sigma_luma=4, sigma_chroma=4):
im_yuv = rgb2yuv(im)
# Compute 5-dimensional XYLUV bilateral-space coordinates
Iy, Ix = np.mgrid[:im.shape[0], :im.shape[1]]
x_coords = (Ix / sigma_spatial).astype(int)
y_coords = (Iy / sigma_spatial).astype(int)
luma_coords = (im_yuv[..., 0] /sigma_luma).astype(int)
chroma_coords = (im_yuv[..., 1:] / sigma_chroma).astype(int)
coords = np.dstack((x_coords, y_coords, luma_coords, chroma_coords))
coords_flat = coords.reshape(-1, coords.shape[-1])
self.npixels, self.dim = coords_flat.shape
# Hacky "hash vector" for coordinates,
# Requires all scaled coordinates be < MAX_VAL
self.hash_vec = (MAX_VAL**np.arange(self.dim))
# Construct S and B matrix
self._compute_factorization(coords_flat) Example 40
def get_local_mesh(self):
# Create the mesh
X = np.mgrid[self.rank*self.Np[0]:(self.rank+1)*self.Np[0], :self.N[1]].astype(self.float)
X[0] *= self.L[0]/self.N[0]
X[1] *= self.L[1]/self.N[1]
return X Example 41
def get_local_mesh(self):
xyrank = self.comm0.Get_rank() # Local rank in xz-plane
yzrank = self.comm1.Get_rank() # Local rank in xy-plane
# Create the physical mesh
x1 = slice(xyrank * self.N1[0], (xyrank+1) * self.N1[0], 1)
x2 = slice(yzrank * self.N2[1], (yzrank+1) * self.N2[1], 1)
X = np.mgrid[x1, x2, :self.N[2]].astype(self.float)
X[0] *= self.L[0]/self.N[0]
X[1] *= self.L[1]/self.N[1]
X[2] *= self.L[2]/self.N[2]
return X Example 42
def get_tform_coords(im_size):
coords0, coords1, coords2 = np.mgrid[:im_size[0], :im_size[1], :im_size[2]]
coords = np.array([coords0 - im_size[0] / 2, coords1 - im_size[1] / 2, coords2 - im_size[2] / 2])
return np.append(coords.reshape(3, -1), np.ones((1, np.prod(im_size))), axis=0) Example 43
def _FSpecialGauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function."""
radius = size // 2
offset = 0.0
start, stop = -radius, radius + 1
if size % 2 == 0:
offset = 0.5
stop -= 1
x, y = np.mgrid[offset + start:stop, offset + start:stop]
assert len(x) == size
g = np.exp(-((x ** 2 + y ** 2) / (2.0 * sigma ** 2)))
return g / g.sum() Example 44
def replace_field(f, mask):
"""Interpolates positions in field according to mask with a 2D cubic interpolator"""
lx, ly = f.shape
x, y = np.mgrid[0:lx, 0:ly]
C = CT_intp((x[~mask],y[~mask]),f[~mask], fill_value=0)
return C(x, y) Example 45
def get_frame(self, i, j):
"""
Perform interpolation to produce the deformed window for correlation.
This function takes the previously set displacement and interpolates the image for these coordinates.
If the cubic interpolation method is chosen, the cubic interpolation of this API is use.
For the bilinear method the build in scipy method `map_coordinates <https://goo.gl/wucmUO>`_ is used with *order* set to 1.
:param int i: first index in grid coordinates
:param int j: second index in grid coordinates
:returns: interpolated window for the grid coordinates i,j and the image set in initialization
"""
dws = self._shape[-1]
offset_x, offset_y = np.mgrid[-dws/2+0.5:dws/2+0.5, -dws/2+0.5:dws/2+0.5]
gx, gy = np.mgrid[0:dws, 0:dws]
grid_x = gx + self._distance*i
grid_y = gy + self._distance*j
ptsax = (grid_x + self._u_disp(i, j, offset_x, offset_y)).ravel()
ptsay = (grid_y + self._v_disp(i, j, offset_x, offset_y)).ravel()
p, q = self._shape[-2:]
if self._ipmethod == 'bilinear':
return map_coordinates(self._frame, [ptsax, ptsay], order=1).reshape(p, q)
if self._ipmethod == 'cubic':
return self._cube_ip.interpolate(ptsax, ptsay).reshape(p, q) Example 46
def makeMTX(spat_coeffs, radial_filter, kr_IDX, viz_order=None, stepsize_deg=1):
"""Returns a plane wave decomposition over a full sphere
Parameters
----------
spat_coeffs : array_like
Spatial fourier coefficients
radial_filter : array_like
Modal radial filters
kr_IDX : int
Index of kr to be computed
viz_order : int, optional
Order of the spatial fourier transform [Default: Highest available]
stepsize_deg : float, optional
Integer Factor to increase the resolution. [Default: 1]
Returns
-------
mtxData : array_like
Plane wave decomposition (frequency domain)
Note
----
The file generates a Matrix of 181x360 pixels for the
visualisation with visualize3D() in 1[deg] Steps (65160 plane waves).
"""
if not viz_order:
viz_order = _np.int(_np.ceil(_np.sqrt(spat_coeffs.shape[0]) - 1))
angles = _np.mgrid[0:360:stepsize_deg, 0:181:stepsize_deg].reshape((2, -1)) * _np.pi / 180
Y = plane_wave_decomp(viz_order, angles, spat_coeffs[:, kr_IDX], radial_filter[:, kr_IDX])
return Y.reshape((360, -1)).T # Return pwd data as [181, 360] matrix Example 47
def plot3Dgrid(rows, cols, viz_data, style, normalize=True, title=None):
if len(viz_data) > rows * cols:
raise ValueError('Number of plot data is more than the specified rows and columns.')
fig = tools.make_subplots(rows, cols, specs=[[{'is_3d': True}] * cols] * rows, print_grid=False)
if style == 'flat':
layout_3D = dict(
xaxis=dict(range=[0, 360]),
yaxis=dict(range=[0, 181]),
aspectmode='manual',
aspectratio=dict(x=3.6, y=1.81, z=1)
)
else:
layout_3D = dict(
xaxis=dict(range=[-1, 1]),
yaxis=dict(range=[-1, 1]),
zaxis=dict(range=[-1, 1]),
aspectmode='cube'
)
rows, cols = _np.mgrid[1:rows + 1, 1: cols + 1]
rows = rows.flatten()
cols = cols.flatten()
for IDX in range(0, len(viz_data)):
cur_row = rows[IDX]
cur_col = cols[IDX]
fig.append_trace(genVisual(viz_data[IDX], style=style, normalize=normalize), cur_row, cur_col)
fig.layout['scene' + str(IDX + 1)].update(layout_3D)
if title is not None:
fig.layout.update(title=title)
filename = title + '.html'
else:
filename = str(current_time()) + '.html'
if env_info() == 'jupyter_notebook':
plotly_off.iplot(fig)
else:
plotly_off.plot(fig, filename=filename) Example 48
def gk(c1,r1,c2,r2):
# First, create X and Y arrays indicating distance to the boundaries of the paintbrush
# In this current context, im is the ordinal number of pixels (64 typically)
sigma = 0.3
im = 64
x = np.repeat([np.concatenate([np.mgrid[-c1:0],np.zeros(c2-c1),np.mgrid[1:1+im-c2]])],im,axis=0)
y = np.repeat(np.vstack(np.concatenate([np.mgrid[-r1:0],np.zeros(r2-r1),np.mgrid[1:1+im-r2]])),im,axis=1)
g = np.exp(-(x**2/float(im)+y**2/float(im))/(2*sigma**2))
return np.repeat([g],3,axis=0) # remove the 3 if you want to apply this to mask rather than an RGB channel
# This function reduces the likelihood of a change based on how close each individual pixel is to a maximal value.
# Consider conditioning this based on the gK value and the requested color. I.E. instead of just a flat distance from 128,
# have it be a difference from the expected color at a given location. This could also be used to "weight" the image towards staying the same. Example 49
def fcn_FDEM_InductionSpherePlaneWidget(xtx,ytx,ztx,m,orient,x0,y0,z0,a,sig,mur,xrx,yrx,zrx,logf,Comp,Phase):
sig = 10**sig
f = 10**logf
fvec = np.logspace(0,8,41)
xmin, xmax, dx, ymin, ymax, dy = -30., 30., 0.3, -30., 30., 0.4
X,Y = np.mgrid[xmin:xmax+dx:dx, ymin:ymax+dy:dy]
X = np.transpose(X)
Y = np.transpose(Y)
Obj = SphereFEM(m,orient,xtx,ytx,ztx)
Hx,Hy,Hz,Habs = Obj.fcn_ComputeFrequencyResponse(f,sig,mur,a,x0,y0,z0,X,Y,zrx)
Hxi,Hyi,Hzi,Habsi = Obj.fcn_ComputeFrequencyResponse(fvec,sig,mur,a,x0,y0,z0,xrx,yrx,zrx)
fig1 = plt.figure(figsize=(17,6))
Ax1 = fig1.add_axes([0.04,0,0.43,1])
Ax2 = fig1.add_axes([0.6,0,0.4,1])
if Comp == 'x':
Ax1 = plotAnomalyXYplane(Ax1,f,X,Y,ztx,Hx,Comp,Phase)
Ax1 = plotPlaceTxRxSphereXY(Ax1,xtx,ytx,xrx,yrx,x0,y0,a)
Ax2 = plotResponseFEM(Ax2,f,fvec,Hxi,Comp)
elif Comp == 'y':
Ax1 = plotAnomalyXYplane(Ax1,f,X,Y,ztx,Hy,Comp,Phase)
Ax1 = plotPlaceTxRxSphereXY(Ax1,xtx,ytx,xrx,yrx,x0,y0,a)
Ax2 = plotResponseFEM(Ax2,f,fvec,Hyi,Comp)
elif Comp == 'z':
Ax1 = plotAnomalyXYplane(Ax1,f,X,Y,ztx,Hz,Comp,Phase)
Ax1 = plotPlaceTxRxSphereXY(Ax1,xtx,ytx,xrx,yrx,x0,y0,a)
Ax2 = plotResponseFEM(Ax2,f,fvec,Hzi,Comp)
elif Comp == 'abs':
Ax1 = plotAnomalyXYplane(Ax1,f,X,Y,ztx,Habs,Comp,Phase)
Ax1 = plotPlaceTxRxSphereXY(Ax1,xtx,ytx,xrx,yrx,x0,y0,a)
Ax2 = plotResponseFEM(Ax2,f,fvec,Habsi,Comp)
plt.show(fig1) Example 50
def rebin(a, newshape):
"""Rebin an array to a new shape."""
assert len(a.shape) == len(newshape)
slices = [slice(0, old, float(old) / new)
for old, new in zip(a.shape, newshape)]
coordinates = np.mgrid[slices]
indices = coordinates.astype('i')
return a[tuple(indices)] 