def generate_vector_field(poly_model, pmodel, \
axr = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \
bxr = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \
make_grid = True, limit_grid_radius = 0,color = 'k'):
"""
Generates a field of vectors using the base color shift model.
| Has the option to plot vector field.
Args:
:poly_model:
| function handle to model
:pmodel:
| ndarray with model parameters.
:axr:
| np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), optional
| Ndarray specifying the a-coordinates at which to apply the model.
:bxr:
| np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), optional
| Ndarray specifying the b-coordinates at which to apply the model.
:make_grid:
| True, optional
| True: generate a 2d-grid from :axr:, :bxr:.
:limit_grid_radius:
| 0, optional
| A value of zeros keeps grid as specified by axr,bxr.
| A value > 0 only keeps (a,b) coordinates within :limit_grid_radius:
:color:
| 'k', optional
| For plotting the vector field.
| If :color: == 0, no plot will be generated.
Returns:
:returns:
| If :color: == 0: ndarray of axt,bxt,axr,bxr
| Else: handle to axes used for plotting.
"""
# Generate grid from axr, bxr:
if make_grid == True:
axr, bxr = generate_grid(ax=axr,
bx=bxr,
out='ax,bx',
limit_grid_radius=limit_grid_radius)
# Apply model at ref. coordinates:
axt, bxt, Cxt, hxt, axr, bxr, Cxr, hxr = apply_poly_model_at_x(
poly_model, pmodel, axr, bxr)
# Plot vectorfield:
if color is not 0:
#plt.plot(axr, bxr,'ro',markersize=2)
plt.quiver(axr, bxr, axt - axr, bxt - bxr, headlength=1, color=color)
plt.xlabel("a'")
plt.ylabel("b'")
return plt.gca() #plt.show(plot1)
else:
return axt, bxt, axr, bxr
def plotceruleanline(cieobs=_CIEOBS,
cspace=_CSPACE,
axh=None,
formatstr='ko-',
cspace_pars={}):
"""
Plot cerulean (yellow (577 nm) - blue (472 nm)) line
| Kuehni, CRA, 2014:
| Table II: spectral lights.
Args:
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| 'k-' or str, optional
| Format str for plotting (see ?matplotlib.pyplot.plot)
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
:returns:
| handle to cerulean line
References:
1. `Kuehni, R. G. (2014).
Unique hues and their stimuli—state of the art.
Color Research & Application, 39(3), 279–287.
<https://doi.org/10.1002/col.21793>`_
(see Table II, IV)
"""
if isinstance(cieobs, str):
cmf = _CMF[cieobs]['bar']
else:
cmf = cieobs
p_y = cmf[0] == 577.0 #Kuehni, CRA 2013 (mean, table IV)
p_b = cmf[0] == 472.0 #Kuehni, CRA 2013 (mean, table IV)
xyz_y = cmf[1:, p_y].T
xyz_b = cmf[1:, p_b].T
lab = colortf(np.vstack((xyz_b, xyz_y)), tf=cspace, tfa0=cspace_pars)
if axh is None:
axh = plt.gca()
hcerline = axh.plot(lab[:, 1], lab[:, 2], formatstr, label='Cerulean line')
return hcerline
def plot(self, ylabel='Spectrum', wavelength_bar=True, *args, **kwargs):
"""
Make a plot of the spectral data in SPD instance.
Returns:
:returns:
| handle to current axes.
"""
plt.plot(self.wl, self.value.T, *args, **kwargs)
if wavelength_bar == True:
Smax = np.nanmax(self.value)
axh = plot_spectrum_colors(spd=None,
spdmax=Smax,
axh=plt.gca(),
wavelength_height=-0.05)
plt.xlabel('Wavelength (nm)')
plt.ylabel(ylabel)
return plt.gca()
def plot(self, plt_type='3d', ax=None, title=None, **kwargs):
"""
Plot color coordinates.
Args:
:plt_type:
| '3d' or 3 or '2d or 2, optional
| -'3d' or 3: plot all 3 dimensions (lightness and chromaticity)
| -'2d' or 2: plot only chromaticity dimensions.
:ax:
| None or axes handles, optional
| None: create new figure axes, else use :ax: for plotting.
:title:
| None or str, optional
| Give plot a title.
:**kwargs:
| additional arguments for use with
| matplotlib.pyplot.scatter
Returns:
:gca:
| handle to current axes.
"""
L, a, b = self.split_()
if ax is None:
fig = plt.figure()
if (plt_type == '2d') | (plt_type == 2):
if ax is None:
ax = fig.add_subplot(111)
ax.scatter(a, b, **kwargs)
else:
if ax is None:
ax = fig.add_subplot(111, projection='3d')
ax.scatter(a, b, L, **kwargs)
ax.set_zlabel(_CSPACE_AXES[self.dtype][0])
ax.set_xlabel(_CSPACE_AXES[self.dtype][1])
ax.set_ylabel(_CSPACE_AXES[self.dtype][2])
if title is not None:
ax.set_title(title)
return plt.gca()
def plot(self, ax=None, title=None, **kwargs):
"""
Plot tristimulus or cone fundamental values.
Args:
:ax:
| None or axes handles, optional
| None: create new figure axes, else use :ax: for plotting.
:title:
| None or str, optional
| Give plot a title.
:**kwargs:
| additional arguments for use with
| matplotlib.pyplot.scatter
Returns:
:gca:
| handle to current axes.
"""
X, Y, Z = self.split_()
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
if self.dtype == 'xyz':
ax.scatter(X, Z, Y, **kwargs)
ax.set_xlabel(_CSPACE_AXES[self.dtype][0])
ax.set_ylabel(_CSPACE_AXES[self.dtype][2])
ax.set_zlabel(_CSPACE_AXES[self.dtype][1])
elif self.dtype == 'lms':
ax.scatter(X, Y, Z, **kwargs)
ax.set_xlabel(_CSPACE_AXES[self.dtype][0])
ax.set_ylabel(_CSPACE_AXES[self.dtype][1])
ax.set_zlabel(_CSPACE_AXES[self.dtype][2])
if title is not None:
ax.set_title(title)
return plt.gca()
def render_image(img = None, spd = None, rfl = None, out = 'img_hyp', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'xyz', cspace_tf = {}, CSF = None,\
interp_type = 'nd', k_neighbours = 4, show = True,
verbosity = 0, show_ref_img = True,\
stack_test_ref = 12,\
write_to_file = None):
"""
Render image under specified light source spd.
Args:
:img:
| None or str or ndarray with float (max = 1) rgb image.
| None load a default image.
:spd:
| ndarray, optional
| Light source spectrum for rendering
| If None: use CIE illuminant F4
:rfl:
| ndarray, optional
| Reflectance set for color coordinate to rfl mapping.
:out:
| 'img_hyp' or str, optional
| (other option: 'img_ren': rendered image under :spd:)
:refspd:
| None, optional
| Reference spectrum for color coordinate to rfl mapping.
| None defaults to D65 (srgb has a D65 white point)
:D:
| None, optional
| Degree of (von Kries) adaptation from spd to refspd.
:cieobs:
| _CIEOBS, optional
| CMF set for calculation of xyz from spectral data.
:cspace:
| 'xyz', optional
| Color space for color coordinate to rfl mapping.
| Tip: Use linear space (e.g. 'xyz', 'Yuv',...) for (interp_type == 'nd'),
| and perceptually uniform space (e.g. 'ipt') for (interp_type == 'nearest')
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:CSF:
| None, optional
| RGB camera response functions.
| If None: input :xyz: contains raw rgb values. Override :cspace:
| argument and perform estimation directly in raw rgb space!!!
:interp_type:
| 'nd', optional
| Options:
| - 'nd': perform n-dimensional linear interpolation using Delaunay triangulation.
| - 'nearest': perform nearest neighbour interpolation.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.spatial.cKDTree
:show:
| True, optional
| Show images.
:verbosity:
| 0, optional
| If > 0: make a plot of the color coordinates of original and
rendered image pixels.
:show_ref_img:
| True, optional
| True: shows rendered image under reference spd. False: shows
| original image.
:write_to_file:
| None, optional
| None: do nothing, else: write to filename(+path) in :write_to_file:
:stack_test_ref:
| 12, optional
| - 12: left (test), right (ref) format for show and imwrite
| - 21: top (test), bottom (ref)
| - 1: only show/write test
| - 2: only show/write ref
| - 0: show both, write test
Returns:
:returns:
| img_hyp, img_ren,
| ndarrays with float hyperspectral image and rendered images
"""
# Get image:
#imread = lambda x: plt.imread(x) #matplotlib.pyplot
if img is not None:
if isinstance(img, str):
img = plt.imread(img) # use matplotlib.pyplot's imread
else:
img = plt.imread(_HYPSPCIM_DEFAULT_IMAGE)
if isinstance(img, np.uint8):
img = img / 255
elif isinstance(img, np.uint16):
img = img / (2**16 - 1)
# Convert to 2D format:
rgb = img.reshape(img.shape[0] * img.shape[1], 3) # *1.0: make float
rgb[rgb == 0] = _EPS # avoid division by zero for pure blacks.
# Get unique rgb values and positions:
rgb_u, rgb_indices = np.unique(rgb, return_inverse=True, axis=0)
# get rfl set:
if rfl is None: # use IESTM30['4880'] set
rfl = _CRI_RFL['ies-tm30']['4880']['5nm']
wlr = rfl[
0] # spectral reflectance set determines wavelength range for estimation (xyz_to_rfl())
# get Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
refspd = cie_interp(
refspd, wlr,
kind='linear') # force spd to same wavelength range as rfl
# Convert rgb_u to xyz and lab-type values under assumed refspd:
if CSF is None:
xyz_wr = spd_to_xyz(refspd, cieobs=cieobs, relative=True)
xyz_ur = colortf(rgb_u * 255, tf='srgb>xyz')
else:
xyz_ur = rgb_u # for input in xyz_to_rfl (when CSF is not None: this functions assumes input is indeed rgb !!!)
# Estimate rfl's for xyz_ur:
rfl_est, xyzri = xyz_to_rfl(xyz_ur, rfl = rfl, out = 'rfl_est,xyz_est', \
refspd = refspd, D = D, cieobs = cieobs, \
cspace = cspace, cspace_tf = cspace_tf, CSF = CSF,\
interp_type = interp_type, k_neighbours = k_neighbours,
verbosity = verbosity)
# Get default test spd if none supplied:
if spd is None:
spd = _CIE_ILLUMINANTS['F4']
if CSF is None:
# calculate xyz values under test spd:
xyzti, xyztw = spd_to_xyz(spd, rfl=rfl_est, cieobs=cieobs, out=2)
# Chromatic adaptation from test spd to refspd:
if D is not None:
xyzti = cat.apply(xyzti, xyzw1=xyztw, xyzw2=xyz_wr, D=D)
# Convert xyzti under test spd to srgb:
rgbti = colortf(xyzti, tf='srgb') / 255
else:
# Calculate rgb coordinates from camera sensitivity functions under spd:
rgbti = rfl_to_rgb(rfl_est, spd=spd, CSF=CSF, wl=None)
# Chromatic adaptation from test spd to refspd:
if D is not None:
white = np.ones_like(spd)
white[0] = spd[0]
rgbwr = rfl_to_rgb(white, spd=refspd, CSF=CSF, wl=None)
rgbwt = rfl_to_rgb(white, spd=spd, CSF=CSF, wl=None)
rgbti = cat.apply_vonkries2(rgbti,
rgbwt,
rgbwr,
xyzw0=np.array([[1.0, 1.0, 1.0]]),
in_='rgb',
out_='rgb',
D=1)
# Reconstruct original locations for rendered image rgbs:
img_ren = rgbti[rgb_indices]
img_ren.shape = img.shape # reshape back to 3D size of original
img_ren = img_ren
# For output:
if show_ref_img == True:
rgb_ref = colortf(xyzri, tf='srgb') / 255 if (
CSF is None
) else xyzri # if CSF not None: xyzri contains rgbri !!!
img_ref = rgb_ref[rgb_indices]
img_ref.shape = img.shape # reshape back to 3D size of original
img_str = 'Rendered (under ref. spd)'
img = img_ref
else:
img_str = 'Original'
img = img
if (stack_test_ref > 0) | show == True:
if stack_test_ref == 21:
img_original_rendered = np.vstack(
(img_ren, np.ones((4, img.shape[1], 3)), img))
img_original_rendered_str = 'Rendered (under test spd)\n ' + img_str
elif stack_test_ref == 12:
img_original_rendered = np.hstack(
(img_ren, np.ones((img.shape[0], 4, 3)), img))
img_original_rendered_str = 'Rendered (under test spd) | ' + img_str
elif stack_test_ref == 1:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
elif stack_test_ref == 2:
img_original_rendered = img
img_original_rendered_str = img_str
elif stack_test_ref == 0:
img_original_rendered = img_ren
img_original_rendered_str = 'Rendered (under test spd)'
if write_to_file is not None:
# Convert from RGB to BGR formatand write:
#print('Writing rendering results to image file: {}'.format(write_to_file))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
imsave(write_to_file, img_original_rendered)
if show == True:
# show images using pyplot.show():
plt.figure()
plt.imshow(img_original_rendered)
plt.title(img_original_rendered_str)
plt.gca().get_xaxis().set_ticklabels([])
plt.gca().get_yaxis().set_ticklabels([])
if stack_test_ref == 0:
plt.figure()
plt.imshow(img)
plt.title(img_str)
plt.axis('off')
if 'img_hyp' in out.split(','):
# Create hyper_spectral image:
rfl_image_2D = rfl_est[
rgb_indices +
1, :] # create array with all rfls required for each pixel
img_hyp = rfl_image_2D.reshape(img.shape[0], img.shape[1],
rfl_image_2D.shape[1])
# Setup output:
if out == 'img_hyp':
return img_hyp
elif out == 'img_ren':
return img_ren
else:
return eval(out)
def plotUH(xyz0=None,
uhues=[0, 1, 2, 3],
cieobs=_CIEOBS,
cspace=_CSPACE,
axh=None,
formatstr=['yo-.', 'bo-.', 'ro-.', 'go-.'],
excludefromlegend='',
cspace_pars={}):
"""
Plot unique hue lines from color space center point xyz0.
| Kuehni, CRA, 2014:
| uY,uB,uG: Table II: spectral lights;
| uR: Table IV: Xiao data.
Args:
:xyz0:
| None, optional
| Center of color space (unique hue lines are expected to cross here)
| None defaults to equi-energy-white.
:uhues:
| [0,1,2,3], optional
| Unique hue lines to plot [0:'yellow',1:'blue',2:'red',3:'green']
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:cspace:
| luxpy._CSPACE or str, optional
| Determines color space / chromaticity diagram to plot data in.
| Note that data is expected to be in specified :cspace:
:formatstr:
| ['yo-.','bo-.','ro-.','go-.'] or list[str], optional
| Format str for plotting the different unique lines
| (see also ?matplotlib.pyplot.plot)
:excludefromlegend:
| '' or str, optional
| To exclude certain hues from axes legend.
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
Returns:
:returns:
| list[handles] to unique hue lines
References:
1. `Kuehni, R. G. (2014).
Unique hues and their stimuli—state of the art.
Color Research & Application, 39(3), 279–287.
<https://doi.org/10.1002/col.21793>`_
(see Table II, IV)
"""
hues = ['yellow', 'blue', 'red', 'green']
if isinstance(cieobs, str):
cmf = _CMF[cieobs]['bar']
else:
cmf = cieobs
p_y = cmf[
0] == 577.0 #unique yellow,#Kuehni, CRA 2013 (mean, table IV: spectral data)
p_b = cmf[
0] == 472.0 #unique blue,Kuehni, CRA 2013 (mean, table IV: spectral data)
p_g = cmf[
0] == 514.0 #unique green, Kuehni, CRA 2013 (mean, table II: spectral data)
p_r = cmf[
0] == 650.0 #unique red, Kuehni, CRA 2013 (Xiao data, table IV: display data)
xyz_y = 100.0 * cmf[1:, p_y].T
xyz_b = 100.0 * cmf[1:, p_b].T
xyz_g = 100.0 * cmf[1:, p_g].T
xyz_r = 100.0 * cmf[1:, p_r].T
xyz_uh = np.vstack((xyz_y, xyz_b, xyz_r, xyz_g))
huniquehues = []
if xyz0 is None:
xyz0 = np.array([100.0, 100.0, 100.0])
if axh is None:
axh = plt.gca()
for huenr in uhues:
lab = colortf(np.vstack((xyz0, xyz_uh[huenr])),
tf=cspace,
tfa0=cspace_pars)
huh = axh.plot(lab[:, 1],
lab[:, 2],
formatstr[huenr],
label=excludefromlegend + 'Unique ' + hues[huenr])
huniquehues = [huniquehues, huh]
return huniquehues