def visualize_segments():
pascal = PascalSegmentation()
train_files = pascal.get_split()
for image_file in train_files:
print(image_file)
image = pascal.get_image(image_file)
segments, superpixels = superpixels_segments(image_file)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
marked = mark_boundaries(image, clean_regions)
edges = create_segment_sp_graph(segments, clean_regions)
edges = np.array(edges)
n_segments = segments.shape[2]
segment_centers = [
regionprops(segments.astype(np.int)[:, :, i],
['Centroid'])[0]['Centroid'] for i in range(n_segments)
]
segment_centers = np.vstack(segment_centers)[:, ::-1]
superpixel_centers = get_superpixel_centers(clean_regions)
grr = min(n_segments, 10)
fig, axes = plt.subplots(3, grr // 3, figsize=(30, 30))
for i, ax in enumerate(axes.ravel()):
ax.imshow(mark_boundaries(marked, segments[:, :, i], (1, 0, 0)))
ax.scatter(segment_centers[:, 0],
segment_centers[:, 1],
color='red')
ax.scatter(superpixel_centers[:, 0],
superpixel_centers[:, 1],
color='blue')
this_edges = edges[edges[:, 1] == i]
for edge in this_edges:
ax.plot([
superpixel_centers[edge[0]][0], segment_centers[edge[1]][0]
], [
superpixel_centers[edge[0]][1], segment_centers[edge[1]][1]
],
c='black')
ax.set_xlim(0, superpixels.shape[1])
ax.set_ylim(superpixels.shape[0], 0)
ax.set_xticks(())
ax.set_yticks(())
#plt.show()
#imsave("segments_test/%s.png" % image_file, marked)
plt.savefig("segments_test/%s.png" % image_file)
plt.close()
def visualize_segments():
pascal = PascalSegmentation()
train_files = pascal.get_split()
for image_file in train_files:
print(image_file)
image = pascal.get_image(image_file)
segments, superpixels = superpixels_segments(image_file)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
marked = mark_boundaries(image, clean_regions)
edges = create_segment_sp_graph(segments, clean_regions)
edges = np.array(edges)
n_segments = segments.shape[2]
segment_centers = [regionprops(segments.astype(np.int)[:, :, i],
['Centroid'])[0]['Centroid'] for i in
range(n_segments)]
segment_centers = np.vstack(segment_centers)[:, ::-1]
superpixel_centers = get_superpixel_centers(clean_regions)
grr = min(n_segments, 10)
fig, axes = plt.subplots(3, grr // 3, figsize=(30, 30))
for i, ax in enumerate(axes.ravel()):
ax.imshow(mark_boundaries(marked, segments[:, :, i], (1, 0, 0)))
ax.scatter(segment_centers[:, 0], segment_centers[:, 1],
color='red')
ax.scatter(superpixel_centers[:, 0], superpixel_centers[:, 1],
color='blue')
this_edges = edges[edges[:, 1] == i]
for edge in this_edges:
ax.plot([superpixel_centers[edge[0]][0],
segment_centers[edge[1]][0]],
[superpixel_centers[edge[0]][1],
segment_centers[edge[1]][1]], c='black')
ax.set_xlim(0, superpixels.shape[1])
ax.set_ylim(superpixels.shape[0], 0)
ax.set_xticks(())
ax.set_yticks(())
#plt.show()
#imsave("segments_test/%s.png" % image_file, marked)
plt.savefig("segments_test/%s.png" % image_file)
plt.close()
def test_remove_small_segments():
pascal = PascalSegmentation()
train_files = pascal.get_split()
idx = 10
image = pascal.get_image(train_files[idx])
segments, superpixels = superpixels_segments(train_files[idx])
new_regions, correspondences = merge_small_sp(image, superpixels)
new_counts = np.bincount(new_regions.ravel())
if np.any(new_counts < 50):
raise ValueError("Stupid thing!")
def visualize_sps():
pascal = PascalSegmentation()
train_files = pascal.get_split()
for image_file in train_files:
print(image_file)
image = pascal.get_image(image_file)
segments, superpixels = superpixels_segments(image_file)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
imsave("segment_sp_fixed/%s.png"
% image_file, mark_boundaries(image, clean_regions))
def visualize_sps():
pascal = PascalSegmentation()
train_files = pascal.get_split()
for image_file in train_files:
print(image_file)
image = pascal.get_image(image_file)
segments, superpixels = superpixels_segments(image_file)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
imsave("segment_sp_fixed/%s.png" % image_file,
mark_boundaries(image, clean_regions))