def hsi_to_rgb(hsi,
spd=None,
cieobs=_CIEOBS,
srgb=False,
linear_rgb=False,
CSF=None,
wl=[380, 780, 1]):
"""
Convert HyperSpectral Image to rgb.
Args:
:hsi:
| ndarray with hyperspectral image [M,N,L]
:spd:
| None, optional
| ndarray with illumination spectrum
:cieobs:
| _CIEOBS, optional
| CMF set to convert spectral data to xyz tristimulus values.
:srgb:
| False, optional
| If False: Use xyz_to_srgb(spd_to_xyz(...)) to convert to srgb values
| If True: use camera sensitivity functions.
:linear_rgb:
| False, optional
| If False: use gamma = 2.4 in xyz_to_srgb, if False: use gamma = 1.
:CSF:
| None, optional
| ndarray with camera sensitivity functions
| If None: use Nikon D700
:wl:
| [380,780,1], optional
| Wavelength range and spacing or ndarray with wavelengths of HSI image.
Returns:
:rgb:
| ndarray with rgb image [M,N,3]
"""
if spd is None:
spd = _CIE_E.copy()
wlr = getwlr(wl)
spd = cie_interp(spd, wl, kind='linear')
hsi_2d = np.reshape(hsi, (hsi.shape[0] * hsi.shape[1], hsi.shape[2]))
if srgb:
xyz = spd_to_xyz(spd,
cieobs=cieobs,
relative=True,
rfl=np.vstack((wlr, hsi_2d)))
gamma = 1 if linear_rgb else 2.4
rgb = xyz_to_srgb(xyz, gamma=gamma) / 255
else:
if CSF is None: CSF = _CSF_NIKON_D700
rgb = rfl_to_rgb(hsi_2d, spd=spd, CSF=CSF, wl=wl)
return np.reshape(rgb, (hsi.shape[0], hsi.shape[1], 3))
def plot_rfl_color_patches(rfl,
spd=None,
cieobs='1931_2',
patch_shape=(100, 100),
patch_layout=None,
ax=None,
show=True):
"""
Create (and plot) an image with colored patches representing a set of reflectance spectra illuminated by a specified illuminant.
Args:
:rfl:
| ndarray with reflectance spectra
:spd:
| None, optional
| ndarray with illuminant spectral power distribution
| If None: _CIE_D65 is used.
:cieobs:
| '1931_2', optional
| CIE standard observer to use when converting rfl to xyz.
:patch_shape:
| (100,100), optional
| shape of each of the patches in the image
:patch_layout:
| None, optional
| If None: layout is calculated automatically to give a 'good' aspect ratio
:ax:
| None, optional
| Axes to plot the image in. If None: a new axes is created.
:show:
| True, optional
| If True: plot image in axes and return axes handle; else: return ndarray with image.
Return:
:ax: or :imagae:
| Axes is returned if show == True, else: ndarray with rgb image is returned.
"""
if spd is None:
spd = _CIE_D65
xyz = spd_to_xyz(spd, rfl=rfl, cieobs=cieobs)[:, 0, :]
rgb = xyz_to_srgb(xyz).astype('uint8')
return plot_rgb_color_patches(rgb,
ax=ax,
patch_shape=patch_shape,
patch_layout=patch_layout,
show=show)
def to_srgb(self, gamma=2.4):
"""
Calculates IEC:61966 sRGB values from xyz.
Args:
:xyz:
| ndarray with relative tristimulus values.
:gamma:
| 2.4, optional
| compression in sRGB
Returns:
:rgb:
| ndarray with R,G,B values (uint8).
"""
return LAB(value=xyz_to_srgb(self.value, gamma=gamma),
relative=self.relative,
cieobs=self.cieobs,
dtype='srgb')
labw = lx.xyz_to_Vrb_mb(xyzw) lab = lx.xyz_to_Vrb_mb(xyz) xyzw_ = lx.Vrb_mb_to_xyz(labw) xyz_ = lx.Vrb_mb_to_xyz(lab) labw = lx.xyz_to_Ydlep(xyzw) lab = lx.xyz_to_Ydlep(xyz) xyzw_ = lx.Ydlep_to_xyz(labw) xyz_ = lx.Ydlep_to_xyz(lab) labw = lx.xyz_to_lms(xyzw) lab = lx.xyz_to_lms(xyz) xyzw_ = lx.lms_to_xyz(labw) xyz_ = lx.lms_to_xyz(lab) labw = lx.xyz_to_srgb(xyzw) lab = lx.xyz_to_srgb(xyz) xyzw_ = lx.srgb_to_xyz(labw) xyz_ = lx.srgb_to_xyz(lab) print(labw.shape) print(lab.shape) print(xyzw - xyzw_) print(xyz - xyz_) # cam_sww6: xyz, xyzw = lx.spd_to_xyz(spds, rfl=rfls, cieobs='2006_10', out=2) jabw = lx.xyz_to_lab_cam_sww16(xyzw, xyzw=xyzw.copy()) jab = lx.xyz_to_lab_cam_sww16(xyz, xyzw=xyzw) xyzw_ = lx.lab_cam_sww16_to_xyz(jabw, xyzw=xyzw) xyz_ = lx.lab_cam_sww16_to_xyz(jab, xyzw=xyzw)
def plot_spectrum_colors(spd = None, spdmax = None,\
wavelength_height = -0.05, wavelength_opacity = 1.0, wavelength_lightness = 1.0,\
cieobs = _CIEOBS, show = True, axh = None,\
show_grid = False,ylabel = 'Spectral intensity (a.u.)',xlim=None,\
**kwargs):
"""
Plot the spectrum colors.
Args:
:spd:
| None, optional
| Spectrum
:spdmax:
| None, optional
| max ylim is set at 1.05 or (1+abs(wavelength_height)*spdmax)
:wavelength_opacity:
| 1.0, optional
| Sets opacity of wavelength rectangle.
:wavelength_lightness:
| 1.0, optional
| Sets lightness of wavelength rectangle.
:wavelength_height:
| -0.05 or 'spd', optional
| Determine wavelength bar height
| if not 'spd': x% of spd.max()
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines CMF set to calculate spectrum locus or other.
:show_grid:
| False, optional
| Show grid (True) or not (False)
:ylabel:
| 'Spectral intensity (a.u.)' or str, optional
| Set y-axis label.
:xlim:
| None, optional
| list or ndarray with xlimits.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
if isinstance(cieobs, str):
cmfs = _CMF[cieobs]['bar']
else:
cmfs = cieobs
cmfs = cmfs[:, cmfs[1:].sum(axis=0) >
0] # avoid div by zero in xyz-to-Yxy conversion
wavs = cmfs[0:1].T
SL = cmfs[1:4].T
srgb = xyz_to_srgb(wavelength_lightness * 100 * SL)
srgb = srgb / srgb.max()
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
if (wavelength_height == 'spd') & (spd is not None):
if spdmax is None:
spdmax = np.nanmax(spd[1:, :])
y_min, y_max = 0.0, spdmax * (1.05)
if xlim is None:
x_min, x_max = spd[0, :].min(), spd[0, :].max()
else:
x_min, x_max = xlim
SLrect = np.vstack([
(x_min, 0.0),
spd.T,
(x_max, 0.0),
])
wavelength_height = y_max
spdmax = 1
else:
if (spdmax is None) & (spd is not None):
spdmax = np.nanmax(spd[1:, :])
y_min, y_max = wavelength_height * spdmax, spdmax * (
1 + np.abs(wavelength_height))
elif (spdmax is None) & (spd is None):
spdmax = 1
y_min, y_max = wavelength_height, 0
elif (spdmax is not None):
y_min, y_max = wavelength_height * spdmax, spdmax #*(1 + np.abs(wavelength_height))
if xlim is None:
x_min, x_max = wavs.min(), wavs.max()
else:
x_min, x_max = xlim
SLrect = np.vstack([
(x_min, 0.0),
(x_min, wavelength_height * spdmax),
(x_max, wavelength_height * spdmax),
(x_max, 0.0),
])
axh.set_xlim([x_min, x_max])
axh.set_ylim([y_min, y_max])
polygon = Polygon(SLrect, facecolor=None, edgecolor=None)
axh.add_patch(polygon)
padding = 0.1
axh.bar(x=wavs - padding,
height=wavelength_height * spdmax,
width=1 + padding,
color=srgb,
align='edge',
linewidth=0,
clip_path=polygon)
if spd is not None:
axh.plot(spd[0:1, :].T, spd[1:, :].T, color='k', label='spd')
if show_grid == True:
plt.grid(True)
axh.set_xlabel('Wavelength (nm)', kwargs)
axh.set_ylabel(ylabel, kwargs)
#plt.show()
return axh
else:
return None
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\
cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\
show = True, axh = None,\
show_grid = False, label_fontname = 'Times New Roman', label_fontsize = 12,\
**kwargs):
"""
Plot the chromaticity diagram colors.
Args:
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
: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:
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:show_grid:
| False, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
if isinstance(cieobs, str):
SL = _CMF[cieobs]['bar'][1:4].T
else:
SL = cieobs[1:4].T
SL = 100.0 * SL / (SL[:, 1, None] + _EPS)
SL = SL[SL.sum(axis=1) >
0, :] # avoid div by zero in xyz-to-Yxy conversion
SL = colortf(SL, tf=cspace, tfa0=cspace_pars)
plambdamax = SL[:, 1].argmax()
SL = np.vstack(
(SL[:(plambdamax + 1), :], SL[0])
) # add lowest wavelength data and go to max of gamut in x (there is a reversal for some cmf set wavelengths >~700 nm!)
Y, x, y = asplit(SL)
SL = np.vstack((x, y)).T
# create grid for conversion to srgb
offset = _EPS
min_x = min(offset, x.min())
max_x = max(1, x.max())
min_y = min(offset, y.min())
max_y = max(1, y.max())
ii, jj = np.meshgrid(
np.linspace(min_x - offset, max_x + offset, int(diagram_samples)),
np.linspace(max_y + offset, min_y - offset, int(diagram_samples)))
ij = np.dstack((ii, jj))
ij[ij == 0] = offset
ij2D = ij.reshape((diagram_samples**2, 2))
ij2D = np.hstack((diagram_lightness * 100 * np.ones(
(ij2D.shape[0], 1)), ij2D))
xyz = colortf(ij2D, tf=cspace + '>xyz', tfa0=cspace_pars)
xyz[xyz < 0] = 0
xyz[np.isinf(xyz.sum(axis=1)), :] = np.nan
xyz[np.isnan(xyz.sum(axis=1)), :] = offset
srgb = xyz_to_srgb(xyz)
srgb = srgb / srgb.max()
srgb = srgb.reshape((diagram_samples, diagram_samples, 3))
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
polygon = Polygon(SL, facecolor='none', edgecolor='none')
axh.add_patch(polygon)
image = axh.imshow(srgb,
interpolation='bilinear',
extent=(min_x, max_x, min_y - 0.05, max_y),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
axh.plot(x, y, color='darkgray')
if (cspace == 'Yxy') & (isinstance(cieobs, str)):
axh.set_xlim([0, 1])
axh.set_ylim([0, 1])
elif (cspace == 'Yuv') & (isinstance(cieobs, str)):
axh.set_xlim([0, 0.6])
axh.set_ylim([0, 0.6])
if (cspace is not None):
xlabel = _CSPACE_AXES[cspace][1]
ylabel = _CSPACE_AXES[cspace][2]
if (label_fontname is not None) & (label_fontsize is not None):
axh.set_xlabel(xlabel,
fontname=label_fontname,
fontsize=label_fontsize)
axh.set_ylabel(ylabel,
fontname=label_fontname,
fontsize=label_fontsize)
if show_grid == True:
axh.grid(True)
#plt.show()
return axh
else:
return None
def test_srgb(self, getvars):
S, spds, rfls, xyz, xyzw = getvars
lab = lx.xyz_to_srgb(
xyz[1:, :]) # first xyz row out of srgb gamut, so non-reversible!
xyz_ = lx.srgb_to_xyz(lab)
assert np.isclose(xyz[1:, :], xyz_).all()
def plot_tm30_Rfi(spd, cri_type = 'ies-tm30', axh = None,
font_size = _TM30_FONT_SIZE, **kwargs):
"""
Plot Sample Color Fidelity values (Rfi).
Args:
:spd:
| ndarray or dict
| If ndarray: single spectral power distribution.
| If dict: dictionary with pre-computed parameters (using _tm30_process_spd()).
| required keys:
| 'Rf','Rg','cct','duv','Sr','cri_type','xyzri','xyzrw',
| 'hbinnrs','Rfi','Rfhi','Rcshi','Rhshi',
| 'jabt_binned','jabr_binned',
| 'nhbins','start_hue','normalize_gamut','normalized_chroma_ref'
| see cri.spd_to_cri() for more info on parameters.
:cri_type:
| _CRI_TYPE_DEFAULT or str or dict, optional
| -'str: specifies dict with default cri model parameters
| (for supported types, see luxpy.cri._CRI_DEFAULTS['cri_types'])
| - dict: user defined model parameters
| (see e.g. luxpy.cri._CRI_DEFAULTS['cierf']
| for required structure)
| Note that any non-None input arguments (in kwargs)
| to the function will override default values in cri_type dict.
:axh:
| None, optional
| If None: create new figure with single axes, else plot on specified axes.
:font_size:
| _TM30_FONT_SIZE, optional
| Font size of text, axis labels and axis values.
:kwargs:
| Additional optional keyword arguments,
| the same as in cri.spd_to_cri()
Returns:
:axh:
| handle to figure axes.
:data:
| dictionary with required parameters for plotting functions.
"""
data = _tm30_process_spd(spd, cri_type = 'ies-tm30',**kwargs)
Rfi = data['Rfi']
# get rgb values representing each sample:
N = data['xyzri'].shape[0]
xyzw = spd_to_xyz(_CIE_D65, relative = True, cieobs = data['cri_type']['cieobs']['xyz'])
xyzri = cat.apply(data['xyzri'][:,0,:],xyzw1 = data['xyzrw'], xyzw2 = xyzw)
rgbri = xyz_to_srgb(xyzri)/255
# Create color map:
cmap = []
for i in range(N):
cmap.append(rgbri[i,...])
# Plot sample color fidelity, Rfi:
if axh is None:
fig, axh = plt.subplots(nrows = 1, ncols = 1)
for j in range(N):
axh.bar(j,Rfi[j,0], color = cmap[j], width = 1,edgecolor = None, alpha = 0.9)
#axh.text(j,Rfi[j,0]*1.1, '{:1.0f}'.format(Rfi[j,0]) ,fontsize = 9,horizontalalignment='center',verticalalignment='center',color = np.array([1,1,1])*0.3)
xticks = np.arange(0,N,step=2)
xtickslabels = ['CES{:1.0f}'.format(ii+1) for ii in range(0,N,2)]
axh.set_xticks(xticks)
axh.set_xticklabels(xtickslabels, fontsize = font_size, rotation = 90)
axh.set_ylabel(r'Color Sample Fidelity $(R_{f,CESi})$', fontsize = font_size)
axh.set_ylim([0,100])
axh.set_xlim([-0.5,N-0.5])
return axh, data
def _get_hue_map(hbins = 16, start_hue = 0.0,
hbinnrs = None, xyzri = None, xyzrw = None, cri_type = None):
"""
Generate color map for hue bins.
Args:
:hbins:
| 16 or ndarray with sorted hue bin centers (°), optional
:start_hue:
| 0.0, optional
:hbinnrs:
| None, optional
| ndarray with hue bin number of each sample.
| If hbinnrs, xyzri, xyzrw and cri_type are all not-None:
| use these to calculate color map, otherwise just use number of
| hue bins :hbins: and :start_hue:
:xyzri:
| None, optional
| relative xyz tristimulus values of samples under ref. illuminant.
| see :hbinnrs: for more info when this is used.
:xyzrw:
| None, optional
| relative xyz tristimulus values of ref. illuminant white point.
| see :hbinnrs: for more info when this is used.
:cri_type:
| None, optional
| Specifies dict with default cri model parameters
| (needed to get correct :cieobs:)
| see :hbinnrs: for more info when this is used.
Returns:
:cmap:
| list with rgb values (one for each hue bin) for plotting.
"""
# Setup hbincenters and hsv_hues:
if isinstance(hbins,float) | isinstance(hbins,int):
nhbins = hbins
dhbins = 360/(nhbins) # hue bin width
hbincenters = np.arange(start_hue + dhbins/2, 360, dhbins)
hbincenters = np.sort(hbincenters)
else:
hbincenters = hbins
idx = np.argsort(hbincenters)
hbincenters = hbincenters[idx]
nhbins = hbincenters.shape[0]
cmap = []
if (hbinnrs is not None) & (xyzri is not None) & (xyzrw is not None) & (cri_type is not None):
xyzw = spd_to_xyz(_CIE_D65, relative = True, cieobs = cri_type['cieobs']['xyz'])
xyzri = cat.apply(xyzri[:,0,:],xyzw1 = xyzrw, xyzw2 = xyzw)
# Create color from xyz average:
for i in range(nhbins):
xyzrhi = xyzri[hbinnrs[:,0] == i,:].mean(axis=0,keepdims=True)
rgbrhi = xyz_to_srgb(xyzrhi)/255
cmap.append(rgbrhi)
else:
# Create color from hue angle:
# Setup color for plotting hue bins:
hbincenters = hbincenters*np.pi/180
hsv_hues = hbincenters - 30*np.pi/180
hsv_hues = hsv_hues/hsv_hues.max()
for i in range(nhbins):
#c = np.abs(np.array(colorsys.hsv_to_rgb(hsv_hues[i], 0.75, 0.85)))
c = np.abs(np.array(colorsys.hls_to_rgb(hsv_hues[i], 0.45, 0.5)))
cmap.append(c)
return cmap
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\
cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\
show = True, axh = None,\
show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\
**kwargs):
"""
Plot the chromaticity diagram colors.
Args:
:diagram_samples:
| 256, optional
| Sampling resolution of color space.
:diagram_opacity:
| 1.0, optional
| Sets opacity of chromaticity diagram
:diagram_lightness:
| 0.25, optional
| Sets lightness of chromaticity diagram
:axh:
| None or axes handle, optional
| Determines axes to plot data in.
| None: make new figure.
:show:
| True or False, optional
| Invoke matplotlib.pyplot.show() right after plotting
: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:
:cspace_pars:
| {} or dict, optional
| Dict with parameters required by color space specified in :cspace:
| (for use with luxpy.colortf())
:show_grid:
| True, optional
| Show grid (True) or not (False)
:label_fontname:
| 'Times New Roman', optional
| Sets font type of axis labels.
:label_fontsize:
| 12, optional
| Sets font size of axis labels.
:kwargs:
| additional keyword arguments for use with matplotlib.pyplot.
Returns:
"""
offset = _EPS
ii, jj = np.meshgrid(np.linspace(offset, 1 + offset, diagram_samples), np.linspace(1+offset, offset, diagram_samples))
ij = np.dstack((ii, jj))
SL = _CMF[cieobs]['bar'][1:4].T
SL = np.vstack((SL,SL[0]))
SL = 100.0*SL/SL[:,1,None]
SL = colortf(SL, tf = cspace, tfa0 = cspace_pars)
Y,x,y = asplit(SL)
SL = np.vstack((x,y)).T
ij2D = ij.reshape((diagram_samples**2,2))
ij2D = np.hstack((diagram_lightness*100*np.ones((ij2D.shape[0],1)), ij2D))
xyz = colortf(ij2D, tf = cspace + '>xyz', tfa0 = cspace_pars)
xyz[xyz < 0] = 0
xyz[np.isinf(xyz.sum(axis=1)),:] = np.nan
xyz[np.isnan(xyz.sum(axis=1)),:] = offset
srgb = xyz_to_srgb(xyz)
srgb = srgb/srgb.max()
srgb = srgb.reshape((diagram_samples,diagram_samples,3))
if show == True:
if axh is None:
fig = plt.figure()
axh = fig.add_subplot(111)
polygon = Polygon(SL, facecolor='none', edgecolor='none')
axh.add_patch(polygon)
image = axh.imshow(
srgb,
interpolation='bilinear',
extent = (0.0, 1, -0.05, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
plt.plot(x,y, color = 'darkgray')
if cspace == 'Yxy':
plt.xlim([0,1])
plt.ylim([0,1])
elif cspace == 'Yuv':
plt.xlim([0,0.6])
plt.ylim([0,0.6])
if (cspace is not None):
xlabel = _CSPACE_AXES[cspace][1]
ylabel = _CSPACE_AXES[cspace][2]
if (label_fontname is not None) & (label_fontsize is not None):
plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize)
plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize)
if show_grid == True:
plt.grid()
#plt.show()
return axh
else:
return None
