def extract_patches_multifiles(data_dir, names, target_label, patch_size,
patch_step, save_dir):
patches = []
labeles = []
coding_labeles = []
for name in names:
if name is not None and not name.endswith(target_label):
continue
cur_data_dir = os.path.join(data_dir, name)
print 'extract patches from ', cur_data_dir, ' at ', str(os.getpid())
pv_mask_image, pv_mhd_image = read_from_dir(cur_data_dir)
coding_image = convert_coding(cur_data_dir)
[x_min, x_max, y_min, y_max] = get_boundingbox(pv_mask_image)
r = patch_size / 2
cur_patches = []
cur_coding_labeles = []
for i in range(x_min, x_max, patch_step):
for j in range(y_min, y_max, patch_step):
cur_patch = pv_mhd_image[i - r:i + r + 1, j - r:j + r + 1]
cur_mask_patch = pv_mhd_image[i - r:i + r + 1, j - r:j + r + 1]
if ((1.0 * np.sum(cur_mask_patch)) /
(1.0 * patch_size * patch_size)) < 0.1:
continue
cur_label = target_label
cur_coding_label = coding_image[i - x_min, j - y_min]
if save_dir is not None:
save_path_dir = os.path.join(save_dir,
str(cur_coding_label),
str(cur_label))
if os.path.exists(save_path_dir):
cur_id = len(os.listdir(save_path_dir))
else:
cur_id = 0
save_path = os.path.join(save_dir, str(cur_coding_label),
str(cur_label),
str(cur_id) + '.npy')
check_save_path(save_path)
np.save(save_path, cur_patch)
else:
cur_patches.append(np.array(cur_patch).flatten())
cur_coding_labeles.append(cur_coding_label)
if save_dir is None:
if len(cur_patches) == 0:
continue
patches.append(cur_patches)
coding_labeles.append(cur_coding_labeles)
labeles.append(int(target_label))
print len(patches), len(coding_labeles), len(labeles)
return patches, coding_labeles, labeles
def extract_patches_multidir(
data_dir,
subclasses=['train', 'val', 'test'],
target_labels=[0, 1, 2, 3],
patch_size=7,
patch_step=1,
save_dir='/home/give/Documents/dataset/ICPR2018/BoVW-TextSpecific-multiphase',
return_flag=False):
patches = []
labeles = []
coding_labeles = []
for subclass in subclasses:
for target_label in target_labels:
cur_data_dir = os.path.join(data_dir, subclass)
cur_patches, cur_coding_labels, cur_labeles = extract_patches_singledir(
cur_data_dir,
str(target_label),
patch_size=patch_size,
patch_step=patch_step,
save_dir=None,
multiprocess=8)
patches.extend(cur_patches)
coding_labeles.extend(cur_coding_labels)
labeles.extend(cur_labeles)
if return_flag:
return patches, coding_labeles, labeles
if save_dir is not None:
save_dict = {}
for i, cur_patches in enumerate(patches):
cur_label = labeles[i]
for j, cur_patch in enumerate(cur_patches):
cur_coding_label = coding_labeles[i][j]
if cur_coding_label not in save_dict.keys():
save_dict[cur_coding_label] = {}
if cur_label not in save_dict[cur_coding_label].keys():
save_dict[cur_coding_label][cur_label] = []
save_dict[cur_coding_label][cur_label].append(cur_patch)
# save save_dict
save_path = os.path.join(save_dir, 'save_dict.mat')
check_save_path(save_path)
# scio.savemat(save_path, save_dict)
for subkey in save_dict.keys():
for subsubkey in save_dict[subkey].keys():
save_path = os.path.join(save_dir, str(subkey), str(subsubkey),
'data.npy')
check_save_path(save_path)
np.save(save_path, save_dict[subkey][subsubkey])
def generate_representor_multidir(data_dir,
patch_dir,
reload=None,
save_dir=None):
import scipy.io as scio
if reload is None:
train_features, train_labels = generate_representor(
data_dir, patch_dir=patch_dir, subclass='train')
check_save_path(os.path.join(save_dir, 'training.mat'))
scio.savemat(os.path.join(save_dir, 'training.mat'), {
'features': train_features,
'labels': train_labels
})
test_features, test_labels = generate_representor(data_dir,
patch_dir=patch_dir,
subclass='test')
scio.savemat(os.path.join(save_dir, 'testing.mat'), {
'features': test_features,
'labels': test_labels
})
val_features, val_labels = generate_representor(data_dir,
patch_dir=patch_dir,
subclass='val')
scio.savemat(os.path.join(save_dir, 'validation.mat'), {
'features': val_features,
'labels': val_labels
})
else:
train_data = scio.loadmat(os.path.join(save_dir, 'training.mat'))
train_features = train_data['features']
train_labels = train_data['labels']
test_data = scio.loadmat(os.path.join(save_dir, 'testing.mat'))
test_features = test_data['features']
test_labels = test_data['labels']
val_data = scio.loadmat(os.path.join(save_dir, 'validation.mat'))
val_features = val_data['features']
val_labels = val_data['labels']
acc = execute_classify(train_features, train_labels, val_features,
val_labels, test_features, test_labels)
return acc
def extract_patches_multifiles(data_dir, names, target_label, patch_size, patch_step, save_dir):
patches = []
labeles = []
coding_labeles = []
for name in names:
if name is not None and not name.endswith(target_label):
continue
cur_data_dir = os.path.join(data_dir, name)
patches_save_path = os.path.join(cur_data_dir, 'patches.npy')
coding_labeles_path = os.path.join(cur_data_dir, 'coding_labeles.npy')
labeles_path = os.path.join(cur_data_dir, 'labeles.npy')
mask_images = []
mhd_images = []
if not os.path.exists(patches_save_path):
for phasename in ['NC', 'ART', 'PV']:
image_path = glob(os.path.join(data_dir, name, phasename + '_Image*.mhd'))[0]
mask_path = os.path.join(data_dir, name, phasename + '_Registration.mhd')
mhd_image = read_mhd_image(image_path, rejust=True)
mhd_image = np.squeeze(mhd_image)
# show_image(mhd_image)
mask_image = read_mhd_image(mask_path)
mask_image = np.squeeze(mask_image)
[xmin, xmax, ymin, ymax] = get_boundingbox(mask_image)
mask_image = mask_image[xmin: xmax, ymin: ymax]
mhd_image = mhd_image[xmin: xmax, ymin: ymax]
mhd_image[mask_image != 1] = 0
mask_images.append(mask_image)
mhd_images.append(mhd_image)
# show_image(mhd_image)
cur_patches = []
cur_coding_labeles = []
mask_images = convert2depthlaster(mask_images)
coding_image = convert_coding(cur_data_dir)
mhd_images = convert2depthlaster(mhd_images)
[width, height, _] = list(np.shape(mhd_images))
print 'extract patches from ', cur_data_dir, ' at ', str(
os.getpid()), ' corresponding size is [', width, height, ']'
for i in range(patch_size / 2, width - patch_size / 2, patch_step):
for j in range(patch_size / 2, height - patch_size / 2, patch_step):
cur_patch = mhd_images[i - patch_size / 2:i + patch_size / 2 + 1, j - patch_size / 2: j + patch_size / 2 + 1, :]
cur_label = target_label
cur_coding_label = coding_image[i, j]
if save_dir is not None:
save_path_dir = os.path.join(save_dir, str(cur_coding_label), str(cur_label))
if os.path.exists(save_path_dir):
cur_id = len(os.listdir(save_path_dir))
else:
cur_id = 0
save_path = os.path.join(save_dir, str(cur_coding_label), str(cur_label), str(cur_id) + '.npy')
check_save_path(save_path)
np.save(save_path, cur_patch)
else:
cur_patches.append(flatten_multiphase(np.array(cur_patch)))
cur_coding_labeles.append(cur_coding_label)
if save_dir is None:
if len(cur_patches) == 0:
continue
patches.append(cur_patches)
coding_labeles.append(cur_coding_labeles)
labeles.append(int(target_label))
np.save(patches_save_path, cur_patches)
np.save(coding_labeles_path, cur_coding_labeles)
else:
cur_patches = np.load(patches_save_path)
cur_coding_labeles = np.load(coding_labeles_path)
coding_labeles.append(cur_coding_labeles)
patches.append(cur_patches)
labeles.append(int(target_label))
print len(patches), len(coding_labeles), len(labeles)
return patches, coding_labeles, labeles
