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 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 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 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