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_single_file(dataset, file_name, n_sp=300, sp='rgb', reorder=None):
print(file_name)
image = dataset.get_image(file_name)
if sp == 'rgb':
sps = slic_n(image, n_superpixels=n_sp, compactness=10)
elif sp == 'rgb-skimage':
sps = slic(image,
n_segments=n_sp,
compactness=10,
multichannel=True,
sigma=0.1)
sps = merge_small_sp(image, morphology.label(sps))[0]
elif sp == 'rgbd':
depth = dataset.get_depth(file_name)
#depth -= depth.min()
#depth /= depth.max()
rgbd = np.dstack([image / 255., depth])
sps = slic(rgbd,
n_segments=n_sp,
compactness=.1,
convert2lab=False,
multichannel=True,
sigma=0)
sps = merge_small_sp(image, morphology.label(sps))[0]
else:
raise ValueError("Expected sp to be 'rgb' or 'rgbd' got %d" % sp)
gt = gt_in_sp(dataset, file_name, sps)
probs = get_probabilities(file_name, dataset.directory)
if reorder is not None:
probs = probs[:, :, reorder]
probs_sp = probabilities_on_sp(dataset, probs, sps)
return probs_sp, gt, sps
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_single(f, sp_type, which, pascal):
print(f)
image = pascal.get_image(f)
if sp_type == "slic":
sp = slic_n(image, n_superpixels=100, compactness=10)
segments = None
elif sp_type == "cpmc":
segments, sp = superpixels_segments(f)
sp, _ = merge_small_sp(image, sp)
sp = morphological_clean_sp(image, sp, 4)
else:
raise ValueError("Expected sp to be 'slic' or 'cpmc', got %s" %
sp_type)
x = get_kraehenbuehl_pot_sp(f, sp)
if which != "test":
y = gt_in_sp(pascal, f, sp)
return x, y, sp, segments
def load_single_file(dataset, file_name, n_sp=300, sp='rgb', reorder=None):
print(file_name)
image = dataset.get_image(file_name)
if sp == 'rgb':
sps = slic_n(image, n_superpixels=n_sp, compactness=10)
elif sp == 'rgb-skimage':
sps = slic(image, n_segments=n_sp, compactness=10, multichannel=True, sigma=0.1)
sps = merge_small_sp(image, morphology.label(sps))[0]
elif sp == 'rgbd':
depth = dataset.get_depth(file_name)
#depth -= depth.min()
#depth /= depth.max()
rgbd = np.dstack([image / 255., depth])
sps = slic(rgbd, n_segments=n_sp, compactness=.1, convert2lab=False, multichannel=True, sigma=0)
sps = merge_small_sp(image, morphology.label(sps))[0]
else:
raise ValueError("Expected sp to be 'rgb' or 'rgbd' got %d" % sp)
gt = gt_in_sp(dataset, file_name, sps)
probs = get_probabilities(file_name, dataset.directory)
if reorder is not None:
probs = probs[:, :, reorder]
probs_sp = probabilities_on_sp(dataset, probs, sps)
return probs_sp, gt, sps