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)