def load_nyu_pixelwise(ds='train'):
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = np.arange(4)
# trigger cache.
dataset = NYUSegmentation()
file_names, X, Y = [], [], []
for file_name in dataset.get_split(ds):
print(file_name)
file_names.append(file_name)
gt = dataset.get_ground_truth(file_name)
prediction = get_probabilities(file_name, dataset.directory)
Y.append(gt)
X.append(prediction[:, :, reorder])
return DataBunchNoSP(X, Y, file_names)
def load_nyu_pixelwise(ds='train'):
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = np.arange(4)
# trigger cache.
dataset = NYUSegmentation()
file_names, X, Y = [], [], []
for file_name in dataset.get_split(ds):
print(file_name)
file_names.append(file_name)
gt = dataset.get_ground_truth(file_name)
prediction = get_probabilities(file_name, dataset.directory)
Y.append(gt)
X.append(prediction[:, :, reorder])
return DataBunchNoSP(X, Y, file_names)
def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm1 = SaveLogger(file_name=argv[1]).load()
ssvm2 = SaveLogger(file_name=argv[2]).load()
data_str = 'val'
if len(argv) <= 3:
raise ValueError("Need a folder name for plotting.")
print("loading data...")
data = load_nyu(data_str, n_sp=500)
dataset = NYUSegmentation()
print("done")
data1 = add_edges(data, kind="pairwise")
data2 = add_edges(data, kind="pairwise")
data1 = add_edge_features(dataset, data1)
data2 = add_edge_features(dataset, data2, depth_diff=True)
Y_pred1 = ssvm1.predict(data1.X)
Y_pred2 = ssvm2.predict(data2.X)
folder = argv[3]
if not os.path.exists(folder):
os.mkdir(folder)
np.random.seed(0)
for image_name, superpixels, y_pred1, y_pred2 in zip(data.file_names,
data.superpixels,
Y_pred1, Y_pred2):
if np.all(y_pred1 == y_pred2):
continue
gt = dataset.get_ground_truth(image_name)
perf1 = eval_on_pixels(dataset, [gt], [y_pred1[superpixels]],
print_results=False)[0]
perf1 = np.mean(perf1[np.isfinite(perf1)])
perf2 = eval_on_pixels(dataset, [gt], [y_pred2[superpixels]],
print_results=False)[0]
perf2 = np.mean(perf2[np.isfinite(perf2)])
if np.abs(perf1 - perf2) < 2:
continue
image = dataset.get_image(image_name)
fig, axes = plt.subplots(2, 3, figsize=(12, 6))
axes[0, 0].imshow(image)
axes[0, 0].imshow((y_pred1 != y_pred2)[superpixels], vmin=0, vmax=1,
alpha=.7)
axes[0, 1].set_title("ground truth")
axes[0, 1].imshow(image)
axes[0, 1].imshow(gt, alpha=.7, cmap=dataset.cmap, vmin=0,
vmax=dataset.cmap.N)
axes[1, 0].set_title("%.2f" % perf1)
axes[1, 0].imshow(image)
axes[1, 0].imshow(y_pred1[superpixels], vmin=0, vmax=dataset.cmap.N,
alpha=.7, cmap=dataset.cmap)
axes[1, 1].set_title("%.2f" % perf2)
axes[1, 1].imshow(image)
axes[1, 1].imshow(y_pred2[superpixels], alpha=.7, cmap=dataset.cmap,
vmin=0, vmax=dataset.cmap.N)
present_y = np.unique(np.hstack([y_pred1, y_pred2, np.unique(gt)]))
present_y = np.array([y_ for y_ in present_y if 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.close(fig)
