def visualize_pascal(plot_probabilities=False):
data = load_pascal('val')
ds = PascalSegmentation()
for x, y, f, sps in zip(data.X, data.Y, data.file_names, data.superpixels):
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()
image = ds.get_image(f)
y_pixel = ds.get_ground_truth(f)
x_raw = load_kraehenbuehl(f)
boundary_image = mark_boundaries(image, sps)
ax[0].imshow(image)
ax[1].imshow(y_pixel, cmap=ds.cmap)
ax[2].imshow(boundary_image)
ax[3].imshow(np.argmax(x_raw, axis=-1), cmap=ds.cmap, vmin=0, vmax=256)
ax[4].imshow(y[sps], cmap=ds.cmap, vmin=0, vmax=256)
ax[5].imshow(np.argmax(x, axis=-1)[sps], cmap=ds.cmap, vmin=0,
vmax=256)
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.savefig("figures_pascal_val/%s.png" % f, bbox_inches='tight')
plt.close()
if plot_probabilities:
fig, ax = plt.subplots(3, 7)
for k in range(21):
ax.ravel()[k].matshow(x[:, :, k], vmin=0, vmax=1)
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.savefig("figures_pascal_val/%s_prob.png" % f,
bbox_inches='tight')
plt.close()
tracer()
def load_pascal_pixelwise(which='train', year="2010"):
pascal = PascalSegmentation()
if which not in ["train", "val"]:
raise ValueError("Expected 'which' to be 'train' or 'val', got %s." %
which)
split_file = pascal_path + "/ImageSets/Segmentation/%s.txt" % which
files = np.loadtxt(split_file, dtype=np.str)
files = [f for f in files if f.split("_")[0] <= year]
X, Y = [], []
for f in files:
X.append(load_kraehenbuehl(f))
Y.append(pascal.get_ground_truth(f))
return DataBunchNoSP(X, Y, files)
def eval_segment_best_possible():
ds = PascalSegmentation()
print("loading")
data = load_pascal('train')
print("getting edges")
data = add_edges(data)
print("computing segments")
segments = [get_km_segments(x, ds.get_image(image_name), sps,
n_segments=25) for x, image_name, sps in
zip(data.X, data.file_names, data.superpixels)]
print("combining superpixels")
segments = [seg[sp] for seg, sp in zip(segments, data.superpixels)]
predictions = [gt_in_sp(ds, f, seg)[seg]
for seg, f in zip(segments, data.file_names)]
Y_true = [ds.get_ground_truth(f) for f in data.file_names]
hamming, jaccard = eval_on_pixels(ds, Y_true, predictions,
print_results=True)
tracer()