def create_gist_database(data_dir, img_dirs):
"""Create a GIST database"""
if (not os.path.isfile(gists_db_name)) or (not os.path.isfile(f_db_name)):
gist_data = []
file_names = []
for dir in img_dirs:
scene_dir = os.path.join(data_dir, dir)
img_files = [f for f in os.listdir(scene_dir)
if os.path.isfile(os.path.join(scene_dir, f))
and 'jpg' in f]
for f in img_files:
img_file = os.path.join(scene_dir, f)
print img_file
gist, param \
= lmgist.lmgist(img_file, param)
gist_data.append(gist)
file_names.append(img_file)
np.save(gists_db_name, gist_data)
np.save(f_db_name, file_names)
with open('gistparams', 'w') as f:
pickle.dump(param, f)
else:
print '%s and %s already exist' % (gists_db_name, f_db_name)
return
def get_gist(query_name, img_query, img_mask, param):
'''
Get the gist descriptor for the query image with mask
'''
# get the gist parameter of the source file
gist, param = lmgist.lmgist(query_name, param)
# resize the query and mask images to match the one used to compute
# the GIST descriptor
img_query = transform.resize(img_query, (param.img_size, param.img_size),
clip=True, order=1)
img_mask = transform.resize(img_mask, (param.img_size, param.img_size),
clip=True, order=1)
# make sure the mask is 2d and in [0, 1]
if len(img_mask.shape) != 2:
raise TypeError('Mask image needs to be 2D!')
elif not ((np.min(img_mask) == 0) and (np.max(img_mask == 1))):
img_mask = (img_mask - np.min(img_mask)) \
/ (np.max(img_mask) - np.min(img_mask))
# GIST weights are the average value of mask pixels within each block
s = (np.linspace(0, param.img_size, param.number_blocks + 1)).astype(int)
block_weight = np.zeros((param.number_blocks, param.number_blocks))
for y in range(param.number_blocks):
for x in range(param.number_blocks):
block = img_mask[s[y]:s[y + 1], s[x]:s[x + 1]]
block_weight[y, x] = np.mean(block)
block_weight = 1 - block_weight
n_filters = sum(param.orientations_per_scale)
block_weight = matlib.repmat(
np.reshape(block_weight.T, [param.number_blocks ** 2, 1]), n_filters, 1)
return gist, block_weight
