use_colors_predict=True):

if not os.path.exists(folder):

os.mkdir(folder)

import matplotlib.colors as cl

np.random.seed(0)

random_colormap = cl.ListedColormap(np.random.uniform(size=(100, 3)))

for image_name, superpixels, y, y_pred in zip(data.file_names,

data.superpixels, data.Y,

Y_pred):

image = dataset.get_image(image_name)

fig, axes = plt.subplots(2, 3, figsize=(12, 6))

axes[0, 0].imshow(image)

axes[0, 1].set_title("ground truth")

axes[0, 1].imshow(image)

gt = dataset.get_ground_truth(image_name)

axes[0, 1].imshow(gt, alpha=.7, cmap=dataset.cmap)

axes[1, 0].set_title("sp ground truth")

axes[1, 0].imshow(image)

axes[1, 0].imshow(y[superpixels], vmin=0, vmax=dataset.cmap.N,

alpha=.7, cmap=dataset.cmap)

axes[1, 1].set_title("prediction")

axes[1, 1].imshow(image)

if use_colors_predict:

axes[1, 1].imshow(y_pred[superpixels], alpha=.7, vmin=0,

vmax=dataset.cmap.N, cmap=dataset.cmap)

else:

vmax = np.max(np.hstack(Y_pred))

axes[1, 1].imshow(y_pred[superpixels], vmin=0, vmax=vmax, alpha=.9,

cmap=random_colormap)

if use_colors_predict:

present_y = np.unique(np.hstack([y, y_pred]))

else:

present_y = np.unique(y)

present_y = present_y[present_y != dataset.void_label]

axes[0, 2].imshow(present_y[:, np.newaxis], interpolation='nearest',

cmap=dataset.cmap, vmin=0, vmax=dataset.cmap.N)

for i, c in enumerate(present_y):

axes[0, 2].text(1, i, dataset.classes[c])

for ax in axes.ravel():

ax.set_xticks(())

ax.set_yticks(())

axes[1, 2].set_visible(False)

fig.savefig(folder + "/%s.png" % image_name, bbox_inches="tight")

plt.figure(figsize=(10, 10))

bounary_image = mark_boundaries(image, superpixels)

plt.imshow(bounary_image)

centers = get_superpixel_centers(superpixels)

for i, edge in enumerate(edges):

e0, e1 = edge

if len(colors) == len(edges):

color = colors[i]

else:

color = colors

plt.plot([centers[e0][0], centers[e1][0]],

[centers[e0][1], centers[e1][1]],

c=color)

plt.scatter(centers[:, 0], centers[:, 1], s=100)

plt.tight_layout()

plt.xlim(0, superpixels.shape[1])

plt.ylim(superpixels.shape[0], 0)