def render_image(img = None, spd = None, rfl = None, out = 'img_hyp', \
refspd = None, D = None, cieobs = _CIEOBS, \
cspace = 'ipt', cspace_tf = {},\
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 uint8 rgb image.
| None load a default image.
:spd:
| ndarray, optional
| Light source spectrum for rendering
: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:
| 'ipt', optional
| Color space for color coordinate to rfl mapping.
:cspace_tf:
| {}, optional
| Dict with parameters for xyz_to_cspace and cspace_to_xyz transform.
:k_neighbours:
| 4 or int, optional
| Number of nearest neighbours for reflectance spectrum interpolation.
| Neighbours are found using scipy.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 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)
# Convert to 2D format:
rgb = img.reshape(img.shape[0] * img.shape[1], 3) * 1.0 # *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 Ref spd:
if refspd is None:
refspd = _CIE_ILLUMINANTS['D65'].copy()
# Convert rgb_u to xyz and lab-type values under assumed refspd:
xyz_wr = spd_to_xyz(refspd, cieobs=cieobs, relative=True)
xyz_ur = colortf(rgb_u, tf='srgb>xyz')
# 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,\
k_neighbours = k_neighbours, verbosity = verbosity)
# Get default test spd if none supplied:
if spd is None:
spd = _CIE_ILLUMINANTS['F4']
# 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
# Reconstruct original locations for rendered image rgbs:
img_ren = rgbti[rgb_indices]
img_ren.shape = img.shape # reshape back to 3D size of original
# For output:
if show_ref_img == True:
rgb_ref = colortf(xyzri, tf='srgb') / 255
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 / 255
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_str)
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)
_CMF[cieobs]['M'] = M
#return _CMF
if __name__ == '__main__':
outcmf = 'lms'
out = outcmf + ',trans_lens,trans_macula,sens_photopig,LMSa'
LMS, trans_lens, trans_macula, sens_photopig, LMSa = cie2006cmfsEx(out=out)
plt.figure()
plt.plot(LMS[0], LMS[1], color='r', linestyle='--')
plt.plot(LMS[0], LMS[2], color='g', linestyle='--')
plt.plot(LMS[0], LMS[3], color='b', linestyle='--')
plt.title('cie2006cmfsEx(...)')
plt.show()
out = outcmf + ',var_age,vAll'
LMS_All, var_age, vAll = genMonteCarloObs(n_obs=10,
fieldsize=10,
list_Age=[32],
out=out)
plt.figure()
plt.plot(LMS_All[0], LMS_All[1], color='r', linestyle='-')
plt.plot(LMS_All[0], LMS_All[2], color='g', linestyle='-')
plt.plot(LMS_All[0], LMS_All[3], color='b', linestyle='-')
plt.title('genMonteCarloObs(...)')
plt.show()
def demo_opt(f, args=(), xrange=None, options={}):
"""
DEMO_OPT: Multi-objective optimization using the DEMO
This function uses the Differential Evolution for Multi-objective
Optimization (a.k.a. DEMO) to solve a multi-objective problem. The
result is a set of nondominated points that are (hopefully) very close
to the true Pareto front.
Args:
:f:
| handle to objective function.
| The output must be, for each point, a column vector of size m x 1,
| with m > 1 the number of objectives.
:args: (), optional
| Input arguments required for f.
:xrange: None, optional
| ndarray with lower and upperbounds.
| If n is the dimension, it will be a n x 2 matrix, such that the
| first column contains the lower bounds,
| and the second, the upper bounds.
| None defaults to no bounds ( [-Inf, Inf] ndarray).
:options:
| None, optional
| dict with internal parameters of the algorithm.
| None initializes default values.
| keys:
| - 'F': the scale factor to be used in the mutation (default: 0.5);
| - 'CR': the crossover factor used in the recombination (def.: 0.3);
| - 'mu': the population size (number of individuals) (def.: 100);
| - 'kmax': maximum number of iterations (def.: 300);
| - 'display': 'on' to display the population while the algorithm is
| being executed, and 'off' to not (default: 'off');
| If any of the parameters is not set, the default ones are used
| instead.
Returns: fopt, xopt
:fopt: the m x mu_opt ndarray with the mu_opt best objectives
:xopt: the n x mu_opt ndarray with the mu_opt best individuals
"""
# Initialize the parameters of the algorithm with values contained in dict:
options = init_options(options=options)
# Initial considerations
n = xrange.shape[0] #dimension of the problem
P = {'x': np.random.rand(n, options['mu'])} #initial decision variables
P['f'] = fobjeval(f, P['x'], args,
xrange) #evaluates the initial population
m = P['f'].shape[0] #number of objectives
k = 0 #iterations counter
# Beginning of the main loop
Pfirst = P.copy()
axh = None
while (k <= options['kmax']):
# Plot the current population (if desired):
if options['display'] == True:
if (k == 0) & (m < 4):
fig = plt.gcf()
fig.show()
fig.canvas.draw()
if m == 2:
# fig = plt.figure()
axh = plt.axes()
plt.plot(P['f'][0], P['f'][1], 'o')
plt.title('Objective values during the execution')
plt.xlabel('f_1')
plt.ylabel('f_2')
fig.canvas.draw()
del axh.lines[0]
elif m == 3:
# fig = plt.figure()
axh = plt.axes(projection='3d')
# print(P['f'])
axh.plot3D(P['f'][0], P['f'][1], P['f'][2], 'o')
plt.title('Objective values during the execution')
axh.set_xlabel('f_1')
axh.set_ylabel('f_2')
axh.set_zlabel('f_3')
fig.canvas.draw()
plt.pause(0.01)
del axh.lines[0]
# Perform the variation operation (mutation and recombination):
O = {'x': mutation(P['x'], options)} #mutation
O['x'] = recombination(P['x'].copy(), O['x'], options) #recombination
O['x'] = repair(
O['x']) #assure the offspring do not cross the search limits
O['f'] = fobjeval(f, O['x'], args,
xrange) #compute objective functions
# Selection and updates
P = selection(P, O, options)
print('Iteration #{:1.0f} of {:1.0f}'.format(k, options['kmax']))
k += 1
# Return the final population:
# First, unnormalize it
Xmin = xrange[:,
0][:,
None] # * np.ones(options['mu']) #replicate lower bound
Xmax = xrange[:,
1][:,
None] # * np.ones(options['mu']) #replicate upper limit
Xun = (Xmax - Xmin) * P['x'] + Xmin
# Then, return the nondominated front:
ispar = ndset(P['f'])
fopt = P['f'][:, ispar]
xopt = Xun[:, ispar]
return fopt, xopt
