def preprocessing_fn_segmentation(image, mask, out_shape):
return segmentation_preprocessing.segmentation_preprocessing(
image, mask, out_shape, is_training)
def evulate_dir_nii_weakly():
'''
新版本的评估代码
:return:
'''
import os
from metrics import dice, IoU
from datasets.medicalImage import convertCase2PNGs, image_expand
case_names = util.io.ls(FLAGS.dataset_dir)
case_names.sort()
restore_path = '/home/give/PycharmProjects/weakly_label_segmentation/logs/DSW/DSW-upsampling-True-1-False-True-True/model.ckpt-10450'
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3])
input_shape_placeholder = tf.placeholder(tf.int32, shape=[2])
processed_image = segmentation_preprocessing.segmentation_preprocessing(image, None, None,
out_shape=[256, 256],
is_training=False)
b_image = tf.expand_dims(processed_image, axis=0)
net = UNetBlocksMS.UNet(b_image, None, None, is_training=False, decoder=FLAGS.decoder,
update_center_flag=FLAGS.update_center,
batch_size=2, init_center_value=None, update_center_strategy=1,
full_annotation_flag=False,
output_shape_tensor=input_shape_placeholder)
# print slim.get_variables_to_restore()
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver()
checkpoint = restore_path
# pixel_recovery_features = tf.image.resize_images(net.pixel_recovery_features, image_shape_placeholder)
with tf.Session(config=sess_config) as sess:
saver.restore(sess, checkpoint)
global_gt = []
global_pred = []
global_pred_kmeans = []
global_pred_centers = []
case_dices = []
case_IoUs = []
case_dices_kmeans = [] # kmeans
case_IoUs_kmeans = [] # kmeans
case_dices_centers = [] # net centers
case_IoUs_centers = [] # net centers
for iter, case_name in enumerate(case_names):
# image_data = util.img.imread(
# glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'images', '*.png'))[0])
# gt = util.img.imread(util.io.join_path(FLAGS.dataset_dir, case_name, 'weakly_label_whole_mask.png'))[:, :, 1]
image_data = cv2.imread(
glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'images', '*.png'))[0])
gt = cv2.imread(glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'whole_mask.png'))[0])[
:, :, 1]
gt = cv2.resize(gt, (256, 256), interpolation=cv2.INTER_NEAREST)
# if case_name == '0402a81e75262469925ea893b6706183832e85324f7b1e08e634129f5d522cdd':
# gt = np.transpose(gt, axes=[1, 0])
print(np.shape(image_data), np.shape(gt))
recover_image, pixel_recover_feature, pixel_cls_scores, b_image_v, global_step_v, net_centers = sess.run(
[net.pixel_recovery_logits, net.pixel_recovery_features, net.pixel_cls_scores, b_image, global_step, net.centers],
feed_dict={
image: image_data,
input_shape_placeholder: [256, 256],
})
pred = np.asarray(pixel_cls_scores[0, :, :, 1] > 0.5, np.uint8)
pixel_recover_feature = pixel_recover_feature[0]
# pred = cv2.resize(pred, tuple(np.shape(image_data)[:2][::-1]))
# pixel_recover_feature = cv2.resize(pixel_recover_feature, tuple(np.shape(image_data)[:2][::-1]))
print(np.shape(pred), case_name)
cv2.imwrite('./tmp/%s_gt.png' % (case_name), np.asarray(gt * 200, np.uint8))
cv2.imwrite('./tmp/%s_img.png' % (case_name), np.asarray(cv2.resize(image_data, (256, 256)), np.uint8))
cv2.imwrite('./tmp/%s_recover_img.png' % (case_name), np.asarray(recover_image[0] * 255., np.uint8))
cv2.imwrite('./tmp/%s_pred.png' % (case_name), np.asarray(pred * 200, np.uint8))
# 开操作 先腐蚀，后膨胀
# 闭操作 先膨胀，后腐蚀
# pred = close_operation(pred, kernel_size=3)
# pred = open_operation(pred, kernel_size=3)
# pred = fill_region(pred)
# 再计算kmeans 的结果
pred_kmeans = np.asarray(image_expand(pred, kernel_size=5), np.uint8)
# pred_seg = image_expand(pred_seg, 5)
print(np.shape(pred_kmeans), np.shape(gt), np.shape(pixel_recover_feature))
pred_kmeans = cluster_postprocessing(pred_kmeans, gt, pixel_recover_feature, k=2)
# pred_kmeans[image_data[:, :, 1] < (10./255.)] = 0
# pred_kmeans = close_operation(pred_kmeans, kernel_size=5)
pred_kmeans = fill_region(pred_kmeans)
# 计算根据center得到的结果
# pixel_recover_feature, net_centers
# pred_centers = np.asarray(image_expand(pred, kernel_size=5), np.uint8)
pred_centers = np.asarray(pred, np.uint8)
pred_centers = net_center_posprocessing(pred_centers, centers=net_centers,
pixel_wise_feature=pixel_recover_feature, gt=gt)
# pred_centers[image_data[:, :, 1] < (10. / 255.)] = 0
# pred_centers = close_operation(pred_centers, kernel_size=5)
pred_centers = fill_region(pred_centers)
cv2.imwrite('./tmp/%s_pred_center.png' % (case_name), np.asarray(pred_centers * 200, np.uint8))
cv2.imwrite('./tmp/%s_pred_kmeans.png' % (case_name), np.asarray(pred_kmeans * 200, np.uint8))
global_gt.append(gt)
global_pred.append(pred)
global_pred_kmeans.append(pred_kmeans)
global_pred_centers.append(pred_centers)
case_dice = dice(gt, pred)
case_IoU = IoU(gt, pred)
case_dice_kmeans = dice(gt, pred_kmeans)
case_IoU_kmeans = IoU(gt, pred_kmeans)
case_dice_centers = dice(gt, pred_centers)
case_IoU_centers = IoU(gt, pred_centers)
print('case dice: ', case_dice)
print('case IoU: ', case_IoU)
print('case dice kmeans: ', case_dice_kmeans)
print('case IoU kmeans: ', case_IoU_kmeans)
print('case dice centers: ', case_dice_centers)
print('case IoU centers: ', case_IoU_centers)
case_dices.append(case_dice)
case_IoUs.append(case_IoU)
case_dices_kmeans.append(case_dice_kmeans)
case_IoUs_kmeans.append(case_IoU_kmeans)
case_dices_centers.append(case_dice_centers)
case_IoUs_centers.append(case_IoU_centers)
print 'global dice is ', dice(global_gt, global_pred)
print 'global IoU is ', IoU(global_gt, global_pred)
print('mean of case dice is ', np.mean(case_dices))
print('mean of case IoU is ', np.mean(case_IoUs))
print 'global dice (kmeans) is ', dice(global_gt, global_pred_kmeans)
print 'global IoU (kmeans) is ', IoU(global_gt, global_pred_kmeans)
print 'mean of case dice (kmeans) is ', np.mean(case_dices_kmeans)
print 'mean of case IoU (kmenas) is ', np.mean(case_IoUs_kmeans)
print 'global dice (centers) is ', dice(global_gt, global_pred_centers)
print 'global IoU (centers) is ', IoU(global_gt, global_pred_centers)
print 'mean of case dice (centers) is ', np.mean(case_dices_centers)
print 'mean of case IoU (centers) is ', np.mean(case_IoUs_centers)
def test():
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.int32, shape = [None, None, 3])
image_shape = tf.placeholder(dtype = tf.int32, shape = [3, ])
processed_image = segmentation_preprocessing.segmentation_preprocessing(image, None, out_shape=[256, 256],
is_training=False)
b_image = tf.expand_dims(processed_image, axis = 0)
net = UNet.UNet(b_image, None, is_training=False, decoder=FLAGS.decoder)
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement = False, allow_soft_placement = True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction;
checkpoint_dir = util.io.get_dir(FLAGS.checkpoint_path)
logdir = util.io.join_path(checkpoint_dir, 'test', FLAGS.dataset_name + '_' +FLAGS.dataset_split_name)
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(var_list = variables_to_restore)
case_names = util.io.ls(FLAGS.dataset_dir)
case_names.sort()
checkpoint = FLAGS.checkpoint_path
checkpoint_name = util.io.get_filename(str(checkpoint))
IoUs = []
dices = []
with tf.Session(config = sess_config) as sess:
saver.restore(sess, checkpoint)
centers = sess.run(net.centers)
for iter, case_name in enumerate(case_names):
image_data = util.img.imread(
glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'images', '*.png'))[0], rgb=True)
pixel_cls_scores, pixel_recovery_feature_map = sess.run(
[net.pixel_cls_scores, net.pixel_recovery_features[-1]],
feed_dict = {
image: image_data
})
print '%d/%d: %s'%(iter + 1, len(case_names), case_name), np.shape(pixel_cls_scores)
pos_score = np.asarray(pixel_cls_scores > 0.5, np.uint8)[0, :, :, 1]
pred = cv2.resize(pos_score, tuple(np.shape(image_data)[:2][::-1]), interpolation=cv2.INTER_NEAREST)
gt = util.img.imread(util.io.join_path(FLAGS.dataset_dir, case_name, 'weakly_label_whole_mask.png'))[:, :, 1]
intersection = np.sum(np.logical_and(gt != 0, pred != 0))
union = np.sum(gt != 0) + np.sum(pred != 0)
IoU = (1.0 * intersection) / (1.0 * union - 1.0 * intersection) * 100
dice = (2.0 * intersection) / (1.0 * union) * 100
IoUs.append(IoU)
dices.append(dice)
cv2.imwrite(util.io.join_path(FLAGS.pred_path, case_name + '.png'), pred)
cv2.imwrite(util.io.join_path(FLAGS.pred_vis_path, case_name + '.png'),
np.asarray(pred * 200))
assign_label = get_assign_label(centers, pixel_recovery_feature_map)[0]
assign_label = assign_label * pos_score
assign_label += 1
cv2.imwrite(util.io.join_path(FLAGS.pred_assign_label_path, case_name + '.png'),
np.asarray(assign_label * 100, np.uint8))
print('total mean of IoU is ', np.mean(IoUs))
print('total mean of dice is ', np.mean(dices))
def generate_recovery_image_feature_map():
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3])
image_shape_placeholder = tf.placeholder(tf.int32, shape=[2])
processed_image = segmentation_preprocessing.segmentation_preprocessing(image, None,
out_shape=[FLAGS.eval_image_width,
FLAGS.eval_image_height],
is_training=False)
b_image = tf.expand_dims(processed_image, axis=0)
print('the decoder is ', FLAGS.decoder)
net = UNet.UNet(b_image, None, is_training=False, decoder=FLAGS.decoder, update_center_flag=FLAGS.update_center,
batch_size=1)
# print slim.get_variables_to_restore()
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver()
case_names = util.io.ls(FLAGS.dataset_dir)
case_names.sort()
checkpoint = FLAGS.checkpoint_path
checkpoint_name = util.io.get_filename(str(checkpoint))
IoUs = []
dices = []
pixel_recovery_features = tf.image.resize_images(net.pixel_recovery_features, image_shape_placeholder)
with tf.Session(config=sess_config) as sess:
saver.restore(sess, checkpoint)
centers = sess.run(net.centers)
print('sum of centers is ', np.sum(centers))
for iter, case_name in enumerate(case_names):
image_data = util.img.imread(
glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'images', '*.png'))[0], rgb=True)
mask = cv2.imread(glob(util.io.join_path(FLAGS.dataset_dir, case_name, 'whole_mask.png'))[0])[
:, :, 0]
pixel_cls_scores, recovery_img, recovery_feature_map, b_image_v, global_step_v = sess.run(
[net.pixel_cls_scores, net.pixel_recovery_value, pixel_recovery_features, b_image, global_step],
feed_dict={
image: image_data,
image_shape_placeholder: np.shape(image_data)[:2]
})
print global_step_v
print '%d / %d: %s' % (iter + 1, len(case_names), case_name), np.shape(pixel_cls_scores), np.max(
pixel_cls_scores[:, :, :, 1]), np.min(pixel_cls_scores[:, :, :, 1]), np.shape(
recovery_img), np.max(recovery_img), np.min(recovery_img), np.max(b_image_v), np.min(
b_image_v), np.shape(b_image_v)
print np.shape(recovery_feature_map), np.shape(mask)
pred_vis_path = util.io.join_path(FLAGS.pred_vis_dir, case_name + '.png')
pred_path = util.io.join_path(FLAGS.pred_dir, case_name + '.png')
pos_score = np.asarray(pixel_cls_scores > 0.5, np.uint8)[0, :, :, 1]
pred = cv2.resize(pos_score, tuple(np.shape(image_data)[:2][::-1]), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(pred_vis_path, np.asarray(pred * 200, np.uint8))
cv2.imwrite(pred_path, np.asarray(pred, np.uint8))
recovery_img_path = util.io.join_path(FLAGS.recovery_img_dir, case_name + '.png')
cv2.imwrite(recovery_img_path, np.asarray(recovery_img[0] * 255, np.uint8))
recovery_feature_map_path = util.io.join_path(FLAGS.recovery_feature_map_dir, case_name + '.npy')
xs, ys = np.where(mask == 1)
features = recovery_feature_map[0][xs, ys, :]
print 'the size of feature map is ', np.shape(np.asarray(features, np.float32))
np.save(recovery_feature_map_path, np.asarray(features, np.float32))
def evulate_dir_nii_weakly_new():
'''
新版本的评估代码
:return:
'''
from metrics import dice, IoU
from datasets.medicalImage import convertCase2PNGs, image_expand
nii_dir = '/home/give/Documents/dataset/ISBI2017/Training_Batch_1'
save_dir = '/home/give/Documents/dataset/ISBI2017/weakly_label_segmentation_V4/Batch_1/DLSC_0/niis'
# restore_path = '/home/give/PycharmProjects/weakly_label_segmentation/logs/ISBI2017_V2/1s_agumentation_weakly-upsampling-2/model.ckpt-168090'
restore_path = '/home/give/PycharmProjects/weakly_label_segmentation/logs/ISBI2017_V2/1s_agumentation_weakly_V3-upsampling-1/model.ckpt-167824'
nii_parent_dir = os.path.dirname(nii_dir)
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.float32, shape=[None, None, 3])
image_shape_placeholder = tf.placeholder(tf.int32, shape=[2])
input_shape_placeholder = tf.placeholder(tf.int32, shape=[2])
processed_image = segmentation_preprocessing.segmentation_preprocessing(
image,
None,
None,
out_shape=input_shape_placeholder,
is_training=False)
b_image = tf.expand_dims(processed_image, axis=0)
net = UNetBlocksMS.UNet(b_image,
None,
None,
is_training=False,
decoder=FLAGS.decoder,
update_center_flag=FLAGS.update_center,
batch_size=2,
init_center_value=None,
update_center_strategy=1,
num_centers_k=FLAGS.num_centers_k,
full_annotation_flag=False,
output_shape_tensor=input_shape_placeholder)
# print slim.get_variables_to_restore()
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver()
nii_pathes = glob(os.path.join(nii_dir, 'volume-*.nii'))
checkpoint = restore_path
# pixel_recovery_features = tf.image.resize_images(net.pixel_recovery_features, image_shape_placeholder)
with tf.Session(config=sess_config) as sess:
saver.restore(sess, checkpoint)
global_gt = []
global_pred = []
global_pred_grabcut = []
case_dices = []
case_IoUs = []
case_dices_grabcut = []
case_IoUs_grabcut = []
for iter, nii_path in enumerate(nii_pathes):
# if os.path.basename(nii_path) in ['volume-15.nii', 'volume-25.nii']:
# continue
if os.path.basename(nii_path) != 'volume-0.nii':
continue
nii_path_basename = os.path.basename(nii_path)
pred_dir = os.path.join(
save_dir,
nii_path_basename.split('.')[0].split('-')[1], 'pred')
pred_vis_dir = os.path.join(
save_dir,
nii_path_basename.split('.')[0].split('-')[1], 'pred_vis')
recovery_img_dir = os.path.join(
save_dir,
nii_path_basename.split('.')[0].split('-')[1], 'recovery_img')
if not os.path.exists(pred_dir):
os.makedirs(pred_dir)
if not os.path.exists(pred_vis_dir):
os.makedirs(pred_vis_dir)
if not os.path.exists(recovery_img_dir):
os.makedirs(recovery_img_dir)
seg_path = os.path.join(
nii_dir, 'segmentation-' +
nii_path.split('.')[0].split('-')[1] + '.nii')
case_preds = []
case_gts = []
case_preds_grabcut = []
# case_recover_features = []
print(nii_path, seg_path)
imgs, tumor_masks, liver_masks, tumor_weak_masks = convertCase2PNGs(
nii_path, seg_path, save_dir=None)
print(len(imgs), len(tumor_masks), len(liver_masks),
len(tumor_masks))
for slice_idx, (image_data, liver_mask, whole_mask) in enumerate(
zip(imgs, liver_masks, tumor_masks)):
pixel_cls_scores_ms = []
pixel_recover_feature_ms = []
for single_scale in scales:
pixel_recover_feature, pixel_cls_scores, b_image_v, global_step_v, net_centers = sess.run(
[
net.pixel_recovery_features, net.pixel_cls_scores,
b_image, global_step, net.centers
],
feed_dict={
image: image_data,
image_shape_placeholder: np.shape(image_data)[:2],
input_shape_placeholder: single_scale
})
pixel_cls_scores_ms.append(
cv2.resize(pixel_cls_scores[0, :, :, 1],
tuple(np.shape(image_data)[:2][::-1])))
pixel_recover_feature_ms.append(pixel_recover_feature[0])
del pixel_recover_feature
pixel_cls_scores = np.mean(pixel_cls_scores_ms, axis=0)
pixel_recover_feature = np.mean(pixel_recover_feature_ms,
axis=0)
# case_recover_features.append(pixel_recover_feature)
if np.sum(whole_mask) != 0:
pred = np.asarray(pixel_cls_scores > 0.6, np.uint8)
# 开操作 先腐蚀，后膨胀
# 闭操作 先膨胀，后腐蚀
# pred = close_operation(pred, kernel_size=3)
pred = open_operation(pred, kernel_size=3)
pred = fill_region(pred)
from grabcut import grabcut
pred_grabcut = np.asarray(
image_expand(pred, kernel_size=5), np.uint8)
xs, ys = np.where(pred_grabcut == 1)
print(np.shape(pred_grabcut))
if len(xs) == 0:
pred_grabcut = np.zeros_like(whole_mask)
print(np.min(pred_grabcut), np.max(pred_grabcut),
np.sum(pred_grabcut))
else:
min_xs = np.min(xs)
max_xs = np.max(xs)
min_ys = np.min(ys)
max_ys = np.max(ys)
pred_grabcut = grabcut(
np.asarray(image_data * 255., np.uint8),
[min_xs, min_ys, max_xs, max_ys])
pred_grabcut = np.asarray(pred_grabcut == 255,
np.uint8)
print(np.unique(pred_grabcut))
cv2.imwrite('./tmp/%d_gt.png' % slice_idx,
np.asarray(whole_mask * 200, np.uint8))
cv2.imwrite('./tmp/%d_pred.png' % slice_idx,
np.asarray(pred * 200, np.uint8))
cv2.imwrite('./tmp/%d_pred_grabcut.png' % slice_idx,
np.asarray(pred_grabcut * 200, np.uint8))
else:
pred = np.zeros_like(whole_mask)
pred_grabcut = np.zeros_like(whole_mask)
global_gt.append(whole_mask)
case_gts.append(whole_mask)
case_preds.append(pred)
case_preds_grabcut.append(pred_grabcut)
global_pred.append(pred)
global_pred_grabcut.append(pred_grabcut)
print '%d / %d: %s' % (slice_idx + 1, len(
imgs), os.path.basename(nii_path)), np.shape(
pixel_cls_scores), np.max(pixel_cls_scores), np.min(
pixel_cls_scores), np.shape(pixel_recover_feature)
del pixel_recover_feature, pixel_recover_feature_ms
gc.collect()
case_dice = dice(case_gts, case_preds)
case_IoU = IoU(case_gts, case_preds)
case_dice_grabcut = dice(case_gts, case_preds_grabcut)
case_IoU_grabcut = IoU(case_gts, case_preds_grabcut)
print('case dice: ', case_dice)
print('case IoU: ', case_IoU)
print('case dice grabcut: ', case_dice_grabcut)
print('case IoU grabcut: ', case_IoU_grabcut)
case_dices.append(case_dice)
case_IoUs.append(case_IoU)
case_dices_grabcut.append(case_dice_grabcut)
case_IoUs_grabcut.append(case_IoU_grabcut)
print 'global dice is ', dice(global_gt, global_pred)
print 'global IoU is ', IoU(global_gt, global_pred)
print('mean of case dice is ', np.mean(case_dices))
print('mean of case IoU is ', np.mean(case_IoUs))
print('mean of case dice (grabcut) is ', np.mean(case_dices_grabcut))
print('mean of case IoU (grabcut) is ', np.mean(case_IoUs_grabcut))
print('global dice (grabcut) is ', dice(global_gt,
global_pred_grabcut))
print('global IoU (grabcut) is ', IoU(global_gt, global_pred_grabcut))
def test():
with tf.name_scope('test'):
image = tf.placeholder(dtype=tf.int32, shape=[None, None, 3])
image_shape = tf.placeholder(dtype=tf.int32, shape=[
3,
])
processed_image = segmentation_preprocessing.segmentation_preprocessing(
image, None, out_shape=[256, 256], is_training=False)
b_image = tf.expand_dims(processed_image, axis=0)
net = UNet.UNet(b_image, None, is_training=False)
global_step = slim.get_or_create_global_step()
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction
checkpoint_dir = util.io.get_dir(FLAGS.checkpoint_path)
logdir = util.io.join_path(
checkpoint_dir, 'test',
FLAGS.dataset_name + '_' + FLAGS.dataset_split_name)
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(var_list=variables_to_restore)
case_names = util.io.ls(FLAGS.dataset_dir)
case_names.sort()
checkpoint = FLAGS.checkpoint_path
checkpoint_name = util.io.get_filename(str(checkpoint))
with tf.Session(config=sess_config) as sess:
saver.restore(sess, checkpoint)
for iter, case_name in enumerate(case_names):
image_data = util.img.imread(glob(
util.io.join_path(FLAGS.dataset_dir, case_name, 'images',
'*.png'))[0],
rgb=True)
pixel_cls_scores, = sess.run([net.pixel_cls_scores],
feed_dict={image: image_data})
print '%d/%d: %s' % (iter + 1, len(case_names),
case_name), np.shape(pixel_cls_scores)
pos_score = np.asarray(pixel_cls_scores > 0.5, np.uint8)[0, :, :,
1]
pos_score = cv2.resize(pos_score,
tuple(np.shape(image_data)[:2]),
interpolation=cv2.INTER_NEAREST)
cv2.imwrite(util.io.join_path(FLAGS.pred_path, case_name + '.png'),
pos_score)
cv2.imwrite(
util.io.join_path(FLAGS.pred_vis_path, case_name + '.png'),
np.asarray(pos_score * 200))