def augment_dataset(self, params, source_samples, tagrget_dir, range = None):
patch_root = params.PATCHS_ROOT_PATH[source_samples[0]]
sample_filename = source_samples[1]
train_list = "{}/{}".format(patch_root, sample_filename)
Xtrain, Ytrain = read_csv_file(patch_root, train_list)
if range is not None:
Xtrain = Xtrain[range[0]:range[1]]
Ytrain = Ytrain[range[0]:range[1]]
target_cancer_path = "{}/{}_cancer".format(patch_root, tagrget_dir)
target_normal_path = "{}/{}_noraml".format(patch_root, tagrget_dir)
if (not os.path.exists(target_cancer_path)):
os.makedirs(target_cancer_path)
if (not os.path.exists(target_normal_path)):
os.makedirs(target_normal_path)
for K, (x, y) in enumerate(zip(Xtrain, Ytrain)):
block = Block()
block.load_img(x)
img = block.get_img()
aug_img = self.augment_images(img) * 255
block.set_img(aug_img)
block.opcode = self.opcode
if y == 0:
block.save_img(target_normal_path)
else:
block.save_img(target_cancer_path)
if (0 == K % 1000):
print("{} augmenting >>> {}".format(time.asctime(time.localtime()), K))
def normalize_dataset(self,
source_samples,
tagrget_dir,
range=None,
batch_size=20):
self.opcode = 19
# normal = ACDNormalization_tf("acd", dc_txt="dc.txt", w_txt="w.txt", template_path="template_normal")
normal = ACDNormalization("acd",
dc_txt="dc.txt",
w_txt="w.txt",
template_path="template_normal")
patch_root = self._params.PATCHS_ROOT_PATH[source_samples[0]]
sample_filename = source_samples[1]
train_list = "{}/{}".format(patch_root, sample_filename)
Xtrain, Ytrain = read_csv_file(patch_root, train_list)
if range is not None:
Xtrain = Xtrain[range[0]:range[1]]
Ytrain = Ytrain[range[0]:range[1]]
# prepare
images = []
for patch_file in Xtrain:
img = io.imread(patch_file, as_gray=False)
# imgBGR = img[:, :, (2, 1, 0)]
# images.append(imgBGR)
images.append(img)
normal.prepare(images)
target_cancer_path = "{}/{}_cancer".format(patch_root, tagrget_dir)
target_normal_path = "{}/{}_normal".format(patch_root, tagrget_dir)
if (not os.path.exists(target_cancer_path)):
os.makedirs(target_cancer_path)
if (not os.path.exists(target_normal_path)):
os.makedirs(target_normal_path)
n = 0
batch_images = []
batch_y = []
batch_blocks = []
for K, (x, y) in enumerate(zip(Xtrain, Ytrain)):
new_block = Block()
new_block.load_img(x)
img = np.array(new_block.get_img())
# imgBGR = img[:, :, (2, 1, 0)]
# batch_images.append(imgBGR)
batch_images.append(img)
batch_y.append(y)
batch_blocks.append(new_block)
n = n + 1
if n >= batch_size:
norm_images = normal.normalize_on_batch(batch_images)
for block, norm_img, y in zip(batch_blocks, norm_images,
batch_y):
# block.set_img(255 * norm_img[:, :, (2, 1, 0)])
block.set_img(255 * norm_img)
block.opcode = self.opcode
if y == 0:
block.save_img(target_normal_path)
else:
block.save_img(target_cancer_path)
batch_images = []
batch_y = []
batch_blocks = []
n = 0
if (0 == K % 1000):
print("{} normalizing >>> {}".format(
time.asctime(time.localtime()), K))
if n > 0:
norm_images = normal.normalize_on_batch(batch_images)
for block, norm_img, y in zip(batch_blocks, norm_images, batch_y):
# block.set_img(255 * norm_img[:, :, (2, 1, 0)])
block.set_img(255 * norm_img)
block.opcode = self.opcode
if y == 0:
block.save_img(target_normal_path)
else:
block.save_img(target_cancer_path)
return