def generate_heatingmaps(data_dir, target_label, patch_size, save_dir):
patches_arr, paths, mhd_images = extract_patch(data_dir, str(target_label),
patch_size)
from load_liver_density import load_raw_liver_density
liver_density = load_raw_liver_density()
for index, patches in enumerate(patches_arr):
path = paths[index]
basename = os.path.basename(path)
predicted_labels = []
start_index = 0
while True:
end_index = start_index + net_config.BATCH_SIZE
if end_index > len(patches):
# restart = end_index - len(patches)
end_index = len(patches)
cur_patches = patches[start_index:end_index]
expand_patches = patches[start_index:end_index]
# expand_patches = [mhd_images[index]] * len(cur_patches) # 使用完整的ＲＯＩ作为ｅｘｐａｎｄ 的ｐａｔｃｈ
roi_images_values = resize_images(cur_patches,
net_config.ROI_SIZE_W,
rescale=(not divided_liver))
expand_roi_images_values = resize_images(
expand_patches,
net_config.EXPAND_SIZE_W,
rescale=(not divided_liver))
cur_liver_densitys = [liver_density[os.path.basename(path)]
] * len(cur_patches)
if divided_liver:
for i in range(len(roi_images_values)):
for j in range(3):
roi_images_values[
i, :, :,
j] = (1.0 * roi_images_values[i, :, :, j]) / (
1.0 * cur_liver_densitys[i][j])
expand_roi_images_values[i, :, :, j] = (
1.0 * expand_roi_images_values[i, :, :, j]) / (
1.0 * cur_liver_densitys[i][j])
predicted_label_value = sess.run(predicted_label_tensor,
feed_dict={
roi_images:
roi_images_values,
expand_roi_images:
expand_roi_images_values,
batch_size_tensor:
len(roi_images_values),
is_training_tensor:
False
})
predicted_labels.extend(predicted_label_value)
start_index = end_index
if start_index == len(patches):
break
if len(predicted_labels) == 0:
continue
heatingmap_size = int(math.sqrt(len(predicted_labels)))
heatingmap_image = np.zeros([heatingmap_size, heatingmap_size, 3],
np.uint8)
for i in range(heatingmap_size):
for j in range(heatingmap_size):
heatingmap_image[i, j] = net_config.color_maping[
predicted_labels[i * heatingmap_size + j]]
print index, np.shape(heatingmap_image), len(predicted_labels)
img = Image.fromarray(np.asarray(heatingmap_image))
img.save(os.path.join(save_dir, str(target_label), basename + '.jpg'))
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=5,
point_phase=[2],
is_training=is_training_tensor,
batch_size=batch_size_tensor)
model_path = '/home/give/PycharmProjects/MedicalImage/Net/ICIP/Patch_ROI/models/500.0'
# 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/MedicalImage/MedicalImage/Patches/ICIP/only-patch/test'
labels = []
paths = []
for typeid in [0, 1, 2, 3, 4]:
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=
'/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)
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, _, _, representor_tensor = 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/LSTM/parameters/0/0.0001'
# 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/0/test'
slice_dir = '/home/give/Documents/dataset/MICCAI2018/Slices/crossvalidation/0/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, representor_value, logits_value = sess.run(
[predicted_label_tensor, representor_tensor, logits],
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)
})
features.extend(representor_value)
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)
# get the feature, visualize it
# first dimension reduction
from sklearn.decomposition import PCA
dim = 2
from plot import plot_scatter, plot_scatter3D
pca_obj = PCA(n_components=dim)
visualized_data = pca_obj.fit_transform(features)
if dim == 3:
plot_scatter3D(visualized_data[:, 0],
visualized_data[:, 1],
visualized_data[:, 2],
labels=labels,
category_num=4)
else:
plot_scatter(visualized_data[:, 0],
visualized_data[:, 1],
labels=labels,
category_num=4)
dim = 3
pca_obj = PCA(n_components=dim)
visualized_data = pca_obj.fit_transform(features)
if dim == 3:
plot_scatter3D(visualized_data[:, 0],
visualized_data[:, 1],
visualized_data[:, 2],
labels=labels,
category_num=4)
else:
plot_scatter(visualized_data[:, 0],
visualized_data[:, 1],
labels=labels,
category_num=4)
