def __init__(self, data_path, batch_size=100):
self.data_dir = data_path
self.batch_size = batch_size
self.train_image, self.train_label, self.test_image, self.test_label = read_npys(
self.data_dir)
self.train_image, self.train_label = shuffle_image_label(
self.train_image, self.train_label)
self.test_image, self.test_label = shuffle_image_label(
self.test_image, self.test_label)
split_rate = [0.8]
splited_images = split_array(self.train_image, num=2, rate=split_rate)
splited_labels = split_array(self.train_label, num=2, rate=split_rate)
self.train_image = splited_images[0]
self.train_label = splited_labels[0]
self.val_image = splited_images[1]
self.val_label = splited_labels[1]
print np.shape(self.train_image), np.shape(self.val_image), np.shape(
self.test_image)
print np.shape(self.train_label), np.shape(self.val_label), np.shape(
self.test_label)
self.train_generator = GenerateBatch(
self.train_image,
self.train_label,
self.batch_size,
epoch_num=None).generate_next_batch()
self.val_generator = GenerateBatch(
self.val_image, self.val_label, self.batch_size,
epoch_num=None).generate_next_batch()
self.test_generator = GenerateBatch(self.test_image,
self.test_label,
self.batch_size,
epoch_num=1).generate_next_batch()
def generate_paths(dir_name,
target_labels=[0, 1, 2, 3],
mapping_label={
0: 0,
1: 1,
2: 2,
3: 3
},
shuffle=True):
'''
返回dirname中的所有病灶图像的路径
:param dir_name: 父文件夹的路径
:param cross_ids: 包含的交叉的折，一般来说我们做三折交叉验证,cross_ids就是[0, 1] 或者是[2]
:param target_labels: 需要文件标注的label
:return:
'''
roi_paths = []
labels = []
cur_dir = dir_name
print cur_dir
# names = os.listdir(cur_dir)
for target_label in target_labels:
type_dir = os.path.join(cur_dir, str(target_label))
type_names = os.listdir(type_dir)
roi_paths.extend(
[os.path.join(type_dir, name) for name in type_names])
labels.extend([mapping_label[target_label]] * len(type_names))
if shuffle:
roi_paths, labels = shuffle_image_label(roi_paths, labels)
return roi_paths, roi_paths, labels
def generate_paths(dir_name,
target_labels=[0, 1, 2, 3],
mapping_label={
0: 0,
1: 1,
2: 2,
3: 3
},
shuffle=True):
'''
返回dirname中的所有病灶图像的路径
:param dir_name: 父文件夹的路径
:param cross_ids: 包含的交叉的折，一般来说我们做三折交叉验证,cross_ids就是[0, 1] 或者是[2]
:param target_labels: 需要文件标注的label
:return:
'''
roi_paths = []
labels = []
cur_dir = dir_name
print cur_dir
names = os.listdir(cur_dir)
for name in names:
if int(name[-1]) not in target_labels:
continue
type_dir = os.path.join(cur_dir, name)
roi_paths.append(type_dir)
labels.append(mapping_label[int(name[-1])])
if shuffle:
roi_paths, labels = shuffle_image_label(roi_paths, labels)
return roi_paths, roi_paths, labels
def main(_):
roi_images = tf.placeholder(shape=[
None, net_config.ROI_SIZE_W, net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='roi_input')
expand_roi_images = tf.placeholder(shape=[
None, net_config.EXPAND_SIZE_W, net_config.EXPAND_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='expand_roi_input')
batch_size_tensor = tf.placeholder(dtype=tf.int32, shape=[])
is_training_tensor = tf.placeholder(dtype=tf.bool, shape=[])
logits, _, _, _ = inference_small(roi_images,
expand_roi_images,
phase_names=['NC', 'ART', 'PV'],
num_classes=4,
is_training=is_training_tensor,
batch_size=batch_size_tensor)
model_path = '/home/give/PycharmProjects/MICCAI2018/deeplearning/Parallel/parameters/1'
# model_path = '/home/give/PycharmProjects/MedicalImage/Net/forpatch/cross_validation/model/multiscale/parallel/0/2200.0'
predictions = tf.nn.softmax(logits)
saver = tf.train.Saver(tf.all_variables())
print predictions
predicted_label_tensor = tf.argmax(predictions, axis=1)
print predicted_label_tensor
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
latest = tf.train.latest_checkpoint(model_path)
if not latest:
print "No checkpoint to continue from in", model_path
sys.exit(1)
print "resume", latest
saver.restore(sess, latest)
data_dir = '/home/give/Documents/dataset/MICCAI2018/Patches/crossvalidation/1/test'
slice_dir = '/home/give/Documents/dataset/MICCAI2018/Slices/crossvalidation/1/test'
labels = []
paths = []
for typeid in [0, 1, 2, 3]:
cur_path = os.path.join(data_dir, str(typeid))
names = os.listdir(cur_path)
labels.extend([typeid] * len(names))
paths.extend([os.path.join(cur_path, name) for name in names])
paths, labels = shuffle_image_label(paths, labels)
start_index = 0
predicted_labels = []
liver_density = load_raw_liver_density()
while True:
if start_index >= len(paths):
break
print start_index, len(paths)
end_index = start_index + net_config.BATCH_SIZE
cur_paths = paths[start_index:end_index]
cur_roi_images = [np.asarray(load_patch(path)) for path in cur_paths]
cur_expand_roi_images = [
np.asarray(load_patch(path, return_roi=True, parent_dir=slice_dir))
for path in cur_paths
]
cur_roi_images = resize_images(cur_roi_images, net_config.ROI_SIZE_W,
True)
cur_expand_roi_images = resize_images(cur_expand_roi_images,
net_config.EXPAND_SIZE_W, True)
cur_liver_densitys = [
liver_density[os.path.basename(path)
[:os.path.basename(path).rfind('_')]]
for path in cur_paths
]
# for i in range(len(cur_roi_images)):
# for j in range(3):
# cur_roi_images[i, :, :, j] = (1.0 * cur_roi_images[i, :, :, j]) / (1.0 * cur_liver_densitys[i][j])
# cur_expand_roi_images[i, :, :, j] = (1.0 * cur_expand_roi_images[i, :, :, j]) / (
# 1.0 * cur_liver_densitys[i][j])
predicted_batch_labels = sess.run(predicted_label_tensor,
feed_dict={
roi_images:
cur_roi_images,
expand_roi_images:
cur_expand_roi_images,
is_training_tensor:
False,
batch_size_tensor:
len(cur_roi_images)
})
batch_labels = labels[start_index:end_index]
predicted_labels.extend(predicted_batch_labels)
start_index = end_index
calculate_acc_error(predicted_batch_labels, batch_labels)
calculate_acc_error(predicted_labels, labels)
def main(_):
roi_images = tf.placeholder(shape=[
None, net_config.ROI_SIZE_W, net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='roi_input')
expand_roi_images = tf.placeholder(shape=[
None, net_config.EXPAND_SIZE_W, net_config.EXPAND_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='expand_roi_input')
batch_size_tensor = tf.placeholder(dtype=tf.int32, shape=[])
is_training_tensor = tf.placeholder(dtype=tf.bool, shape=[])
logits = inference_small(roi_images,
expand_roi_images,
phase_names=['NC', 'ART', 'PV'],
num_classes=4,
point_phase=[2],
is_training=is_training_tensor,
batch_size=batch_size_tensor)
# model_path = '/home/give/PycharmProjects/MedicalImage/Net/ICIP/4-class/Patch_ROI/models/300.0/'
# model_path = '/home/give/PycharmProjects/MedicalImage/Net/forpatch/cross_validation/model/multiscale/parallel/0/2200.0'
model_path = '/home/give/PycharmProjects/MedicalImage/Net/ICIP/4-class/Patch_ROI/models_7'
predictions = tf.nn.softmax(logits)
saver = tf.train.Saver(tf.all_variables())
print predictions
predicted_label_tensor = tf.argmax(predictions, axis=1)
print predicted_label_tensor
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
latest = tf.train.latest_checkpoint(model_path)
if not latest:
print "No checkpoint to continue from in", model_path
sys.exit(1)
print "resume", latest
saver.restore(sess, latest)
data_dir = '/home/give/Documents/dataset/MedicalImage/MedicalImage/Patches/ICIP/only-patch-7/val'
labels = []
paths = []
mapping_label = {0: 0, 1: 1, 2: 2, 3: 3}
for typeid in [0, 1, 2, 3]:
cur_path = os.path.join(data_dir, str(typeid))
names = os.listdir(cur_path)
labels.extend([mapping_label[typeid]] * len(names))
paths.extend([os.path.join(cur_path, name) for name in names])
paths, labels = shuffle_image_label(paths, labels)
start_index = 0
predicted_labels = []
while True:
if start_index >= len(paths):
break
print start_index, len(paths)
end_index = start_index + net_config.BATCH_SIZE
cur_paths = paths[start_index:end_index]
cur_roi_images = [np.asarray(load_patch(path)) for path in cur_paths]
cur_expand_roi_images = [
np.asarray(
load_patch(
path,
return_roi=True,
parent_dir=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/SL_TrainAndVal/val'
)) for path in cur_paths
]
cur_roi_images = resize_images(cur_roi_images, net_config.ROI_SIZE_W,
True)
cur_expand_roi_images = resize_images(cur_expand_roi_images,
net_config.EXPAND_SIZE_W, True)
predicted_batch_labels = sess.run(predicted_label_tensor,
feed_dict={
roi_images:
cur_roi_images,
expand_roi_images:
cur_expand_roi_images,
is_training_tensor:
False,
batch_size_tensor:
len(cur_roi_images)
})
batch_labels = labels[start_index:end_index]
predicted_labels.extend(predicted_batch_labels)
start_index = end_index
calculate_acc_error(predicted_batch_labels, batch_labels)
calculate_acc_error(predicted_labels, labels)
