def test_vst_estimation_movie(movie, idx=None, gt=None, filename=None):
if idx is None:
movie_train = movie[::200]
else:
movie_train = movie[idx]
block_size = 8
stride = 8
t = timeit.default_timer()
blocks_train = []
for img in movie_train:
blocks_train.append(im2col(img, block_size, stride))
blocks_train = np.vstack(blocks_train)
sigma_sq_init, alpha_init = est.initial_estimate_sigma_alpha(blocks_train)
print('\tTime', timeit.default_timer() - t)
print('\talpha = {}; sigma^2 = {}'.format(alpha_init, sigma_sq_init))
t = timeit.default_timer()
res = est.estimate_vst_movie(movie_train, stride=stride)
print('\tTime', timeit.default_timer() - t)
blocks = []
for img in movie:
blocks.append(im2col(img, block_size, stride))
blocks = np.vstack(blocks)
plot_vst_estimation(movie,
blocks,
sigma_sq_init,
alpha_init,
res,
0,
gt=gt,
filename=filename)
def test_vst_estimation_frame(movie, idx=0, gt=None, filename=None):
img = movie[idx]
block_size = 8
stride = 8
t = timeit.default_timer()
blocks = im2col(img, block_size, stride)
sigma_sq_init, alpha_init = est.initial_estimate_sigma_alpha(blocks)
print('\tTime', timeit.default_timer() - t)
print('\talpha = {}; sigma^2 = {}'.format(alpha_init, sigma_sq_init))
t = timeit.default_timer()
res = est.estimate_vst_image(img, stride=stride)
print('\tTime', timeit.default_timer() - t)
plot_vst_estimation(movie,
blocks,
sigma_sq_init,
alpha_init,
res,
idx,
gt=gt,
filename=filename)
def estimate_vst_image(img, block_size=8, stride=8):
blocks = regions.im2col(img, block_size, stride)
return estimate_vst_blocks(blocks)
def estimate_vst_movie(movie, block_size=8, stride=8):
blocks = []
for img in movie:
blocks.append(regions.im2col(img, block_size, stride))
blocks = np.vstack(blocks)
return estimate_vst_blocks(blocks)
def ground_truth_patchwise(dir_name, files, gt_means_means, gt_vars_means,
box=None, block_size=8, stride=8):
colors = sns.color_palette('Pastel1', len(files))
colors_means = sns.color_palette('Set1', len(files))
with sns.axes_style("white"):
fig, axes = plt.subplots(1, 1, figsize=(8, 4))
for k, fn in enumerate(files):
movie = tifffile.imread(dir_name + fn)
if box is None:
movie = movie[:, ::2, :]
else:
movie = movie[:, box[0]:box[1]:2, box[2]:box[3]]
movie = movie.astype(np.float32)
means = []
variances = []
for i in range(0, len(movie), 10):
blocks_i = im2col(movie[i], block_size, stride)
means_i, vars_i = compute_mean_var(blocks_i)
means.append(means_i)
variances.append(vars_i)
means = np.hstack(means)
variances = np.hstack(variances)
axes.plot(means, variances, '.', alpha=0.7, color=colors[k],
markeredgecolor='none', zorder=10 - k, rasterized=True)
axes.scatter(means.mean(), variances.mean(), marker='x', s=1000,
linewidth=2, color=colors_means[k],
edgecolor=colors_means[k], zorder=1000)
axes.scatter(gt_means_means[k], gt_vars_means[k], marker='+',
s=1000, linewidth=2, color=colors_means[k],
edgecolor=colors_means[k], zorder=1000)
axes.yaxis.set_major_formatter(
plt_tick.FuncFormatter(lambda t, pos: fix_scientific_notation(t)))
axes.set_xlabel('Mean', fontsize='large')
axes.set_ylabel('Variance', fontsize='large')
markers1 = [plt_lines.Line2D([], [], marker='o', color=colors[k],
linestyle='None')
for k in range(len(files))]
label1 = ['Movie {} - patch'.format(k+1)
for k in range(len(files))]
markers2 = [plt_lines.Line2D([], [], marker='x', color=colors_means[k],
mec=colors_means[k], linestyle='None',
markersize=10, markeredgewidth=2)
for k in range(len(files))]
label2 = ['Movie {} - patch AVG'.format(k+1)
for k in range(len(files))]
markers3 = [plt_lines.Line2D([], [], marker='+', color=colors_means[k],
mec=colors_means[k], linestyle='None',
markersize=10, markeredgewidth=2)
for k in range(len(files))]
label3 = ['Movie {} - pixel AVG'.format(k+1)
for k in range(len(files))]
plt.legend(markers1 + markers2 + markers3, label1 + label2 + label3,
bbox_to_anchor=(1.01, 1), loc='best', fontsize='large')
fig.tight_layout(rect=(0, 0, 0.7, 1))
plt.savefig('ground_truth_patchwise.pdf')