def compute_contrast_for_table(luminance, kernel):
contrast = numpy.ndarray(shape=luminance.shape, dtype=luminance.dtype)
contrast[:]['time'] = luminance[:]['time']
contrast[:]['frame'] = luminance[:]['frame']
contrast[:]['obj_id'] = luminance[:]['obj_id']
num = len(luminance)
pbar = progress_bar('Computing contrast', num)
for i in xrange(num):
pbar.update(i)
y = luminance[i]['value'].astype('float32')
c = intrinsic_contrast(y, kernel)
contrast[i]['value'][:] = c
return contrast
def main():
sigma_deg = 6
kernel1 = get_contrast_kernel(sigma_deg=sigma_deg, eyes_interact=True)
kernel2 = get_contrast_kernel(sigma_deg=sigma_deg, eyes_interact=False) # better
kernel1 = kernel1.astype('float32')
kernel2 = kernel2.astype('float32')
meany = Expectation()
ex1 = Expectation()
ex2 = Expectation()
cp = ClientProcess()
cp.config_use_white_arena()
cp.config_stimulus_xml(example_stim_xml)
#position = [0.15, 0.5, 0.25]
position = [0.35, 0.5, 0.25]
linear_velocity_body = [0, 0, 0]
angular_velocity_body = [0, 0, 0]
#from flydra_render.contrast import intrinsic_contrast
from fast_contrast import intrinsic_contrast #@UnresolvedImport
N = 360
pb = progress_bar('Computing contrast', N)
orientation = numpy.linspace(0, 2 * numpy.pi, N)
for i, theta in enumerate(orientation):
attitude = rotz(theta)
pb.update(i)
res = cp.render(position, attitude,
linear_velocity_body, angular_velocity_body)
y = numpy.array(res['luminance']).astype('float32')
meany.update(y)
#y = numpy.random.rand(1398)
c1 = intrinsic_contrast(y, kernel1)
c2 = intrinsic_contrast(y, kernel2)
ex1.update(c1)
ex2.update(c2)
r = Report()
r.data_rgb('meany', scale(values2retina(meany.get_value())))
r.data_rgb('mean1', plot_contrast(ex1.get_value()))
r.data_rgb('mean2', plot_contrast(ex2.get_value()))
r.data_rgb('one-y', (plot_luminance(y)))
r.data_rgb('one-c1', plot_contrast(c1))
r.data_rgb('one-c2', plot_contrast(c2))
r.data_rgb('kernel', scale(values2retina(kernel2[100, :])))
f = r.figure(shape=(2, 3))
f.sub('one-y', 'One random image')
f.sub('one-c1', 'Contrast of random image')
f.sub('one-c2', 'Contrast of random image')
f.sub('meany', 'Mean luminance')
f.sub('mean1', 'Mean over %s samples' % N)
f.sub('mean2', 'Mean over %s samples' % N)
f.sub('kernel')
filename = 'compute_contrast_demo.html'
print("Writing on %s" % filename)
r.to_html(filename)