def train_svm(C=0.1, grid=False):
pascal = PascalSegmentation()
files_train = pascal.get_split("kTrain")
superpixels = [slic_n(pascal.get_image(f), n_superpixels=100,
compactness=10)
for f in files_train]
bow = SiftBOW(pascal, n_words=1000, color_sift=True)
data_train = bow.fit_transform(files_train, superpixels)
data_train = add_global_descriptor(data_train)
svm = LinearSVC(C=C, dual=False, class_weight='auto')
chi2 = AdditiveChi2Sampler()
X, y = np.vstack(data_train.X), np.hstack(data_train.Y)
X = chi2.fit_transform(X)
svm.fit(X, y)
print(svm.score(X, y))
eval_on_sp(pascal, data_train, [svm.predict(chi2.transform(x)) for x in
data_train.X], print_results=True)
files_val = pascal.get_split("kVal")
superpixels_val = [slic_n(pascal.get_image(f), n_superpixels=100,
compactness=10) for f in files_val]
data_val = bow.transform(files_val, superpixels_val)
data_val = add_global_descriptor(data_val)
eval_on_sp(pascal, data_val, [svm.predict(chi2.transform(x)) for x in
data_val.X], print_results=True)
tracer()
def load_pascal(which='train', year="2010", sp_type="slic", n_jobs=-1):
pascal = PascalSegmentation()
files = pascal.get_split(which=which, year=year)
results = Parallel(n_jobs=n_jobs)(delayed(load_pascal_single)(
f, which=which, sp_type=sp_type, pascal=pascal) for f in files)
X, Y, superpixels, segments = zip(*results)
if sp_type == "slic":
return DataBunch(X, Y, files, superpixels)
else:
return HierarchicalDataBunch(X, Y, files, superpixels, segments)
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))
def train_svm(C=0.1, grid=False):
pascal = PascalSegmentation()
files_train = pascal.get_split("kTrain")
superpixels = [
slic_n(pascal.get_image(f), n_superpixels=100, compactness=10)
for f in files_train
]
bow = SiftBOW(pascal, n_words=1000, color_sift=True)
data_train = bow.fit_transform(files_train, superpixels)
data_train = add_global_descriptor(data_train)
svm = LinearSVC(C=C, dual=False, class_weight='auto')
chi2 = AdditiveChi2Sampler()
X, y = np.vstack(data_train.X), np.hstack(data_train.Y)
X = chi2.fit_transform(X)
svm.fit(X, y)
print(svm.score(X, y))
eval_on_sp(pascal,
data_train,
[svm.predict(chi2.transform(x)) for x in data_train.X],
print_results=True)
files_val = pascal.get_split("kVal")
superpixels_val = [
slic_n(pascal.get_image(f), n_superpixels=100, compactness=10)
for f in files_val
]
data_val = bow.transform(files_val, superpixels_val)
data_val = add_global_descriptor(data_val)
eval_on_sp(pascal,
data_val, [svm.predict(chi2.transform(x)) for x in data_val.X],
print_results=True)
tracer()
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()
