def _mask_dataset():
# Load model and compile function
with open('../cache/foreground_model.pkl', 'rb') as f:
model, threshold = cPickle.load(f)
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=(y >= threshold))
# Load data
image_data = DataLoader('../data/cuhk_small.mat', verbose=True)
# Pre-processing
print "Pre-processing ..."
images = image_data.get_all_images()
images = [_input_preproc(image) for image in images]
images = imageproc.images2mat(images).astype(theano.config.floatX)
# Compute masks
print "Computing masks ..."
masks = output_func(images)
# Save masks
print "Saving data ..."
mask_data = DataSaver()
cur_index = 0
for gid in xrange(image_data.get_n_groups()):
m, v = image_data.get_n_pedes_views(gid)
mask_data.add_group(m, v)
for pid in xrange(m):
n_images = image_data.get_n_images(gid, pid)
for vid, n in enumerate(n_images):
view_masks = [0] * n
for k in xrange(n):
mask = masks[cur_index, :]
mask = mask.reshape(160, 80, 1)
orig_image = image_data.get_image(gid, pid, vid, k)
orig_image = imageproc.imresize(orig_image, (160, 80, 3))
view_masks[k] = (mask * orig_image).astype(numpy.uint8)
cur_index += 1
mask_data.set_images(gid, pid, vid, view_masks)
mask_data.save('../data/cuhk_small_masked.mat')
def _mask_dataset():
# Load model and compile function
with open('../cache/foreground_model.pkl', 'rb') as f:
model, threshold = cPickle.load(f)
x = T.matrix('x')
y = model.get_output(x)
output_func = theano.function(inputs=[x], outputs=(y >= threshold))
# Load data
image_data = DataLoader('../data/cuhk_small.mat', verbose=True)
# Pre-processing
print "Pre-processing ..."
images = image_data.get_all_images()
images = [_input_preproc(image) for image in images]
images = imageproc.images2mat(images).astype(theano.config.floatX)
# Compute masks
print "Computing masks ..."
masks = output_func(images)
# Save masks
print "Saving data ..."
mask_data = DataSaver()
cur_index = 0
for gid in xrange(image_data.get_n_groups()):
m, v = image_data.get_n_pedes_views(gid)
mask_data.add_group(m, v)
for pid in xrange(m):
n_images = image_data.get_n_images(gid, pid)
for vid, n in enumerate(n_images):
view_masks = [0] * n
for k in xrange(n):
mask = masks[cur_index, :]
mask = mask.reshape(160, 80, 1)
orig_image = image_data.get_image(gid, pid, vid, k)
orig_image = imageproc.imresize(orig_image, (160, 80, 3))
view_masks[k] = (mask * orig_image).astype(numpy.uint8)
cur_index += 1
mask_data.set_images(gid, pid, vid, view_masks)
mask_data.save('../data/cuhk_small_masked.mat')
def _prepare_data(load_from_cache=False, save_to_cache=False):
if load_from_cache:
with open(_cached_datasets, 'rb') as f:
datasets = cPickle.load(f)
else:
# Setup data files
input_data = DataLoader(
'../data/parse/cuhk_large_labeled_subsampled.mat',
verbose=True)
target_data = DataLoader(
'../data/parse/cuhk_large_labeled_subsampled_parse.mat',
verbose=True)
input_images = input_data.get_all_images()
target_images = target_data.get_all_images()
# Pre-processing
print "Pre-processing ..."
inputs = [_input_preproc(image) for image in input_images]
inputs = imageproc.images2mat(inputs)
targets = [_target_preproc(image) for image in target_images]
targets = imageproc.images2mat(targets)
# Prepare the datasets
print "Prepare the datasets ..."
datasets = Datasets(inputs, targets)
datasets.split(train_ratio=0.5, valid_ratio=0.3)
if save_to_cache:
with open(_cached_datasets, 'wb') as f:
cPickle.dump(datasets, f, protocol=cPickle.HIGHEST_PROTOCOL)
return datasets
def _prepare_data(load_from_cache=False, save_to_cache=False):
if load_from_cache:
with open(_cached_datasets, 'rb') as f:
datasets = cPickle.load(f)
else:
# Setup data files
input_data = DataLoader(
'../data/parse/cuhk_large_labeled_subsampled.mat', verbose=True)
target_data = DataLoader(
'../data/parse/cuhk_large_labeled_subsampled_parse.mat',
verbose=True)
input_images = input_data.get_all_images()
target_images = target_data.get_all_images()
# Pre-processing
print "Pre-processing ..."
inputs = [_input_preproc(image) for image in input_images]
inputs = imageproc.images2mat(inputs)
targets = [_target_preproc(image) for image in target_images]
targets = imageproc.images2mat(targets)
# Prepare the datasets
print "Prepare the datasets ..."
datasets = Datasets(inputs, targets)
datasets.split(train_ratio=0.5, valid_ratio=0.3)
if save_to_cache:
with open(_cached_datasets, 'wb') as f:
cPickle.dump(datasets, f, protocol=cPickle.HIGHEST_PROTOCOL)
return datasets