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_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 main():
from pascal.pascal_helpers import load_pascal
from datasets.pascal import PascalSegmentation
from utils import add_edges
from scipy.misc import imsave
from skimage.segmentation import mark_boundaries
ds = PascalSegmentation()
data = load_pascal("train1")
data = add_edges(data, independent=False)
# X, Y, image_names, images, all_superpixels = load_data(
# "train", independent=False)
for x, name, sps in zip(data.X, data.file_names, data.superpixels):
segments = get_km_segments(x, ds.get_image(name), sps, n_segments=25)
boundary_image = mark_boundaries(mark_boundaries(ds.get_image(name), sps), segments[sps], color=[1, 0, 0])
imsave("hierarchy_sp_own_25/%s.png" % name, boundary_image)
def main():
from pascal.pascal_helpers import load_pascal
from datasets.pascal import PascalSegmentation
from utils import add_edges
from scipy.misc import imsave
from skimage.segmentation import mark_boundaries
ds = PascalSegmentation()
data = load_pascal("train1")
data = add_edges(data, independent=False)
#X, Y, image_names, images, all_superpixels = load_data(
#"train", independent=False)
for x, name, sps in zip(data.X, data.file_names, data.superpixels):
segments = get_km_segments(x, ds.get_image(name), sps, n_segments=25)
boundary_image = mark_boundaries(mark_boundaries(
ds.get_image(name), sps),
segments[sps],
color=[1, 0, 0])
imsave("hierarchy_sp_own_25/%s.png" % name, boundary_image)
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 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()
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()
