def get_data_testing_overlap(test_imgs_original, test_groudTruth, Imgs_to_test, patch_height, patch_width,
stride_height, stride_width):
### test
test_imgs_original = load_hdf5(test_imgs_original)
test_masks = load_hdf5(test_groudTruth)
test_imgs = my_PreProc(test_imgs_original)
test_masks = test_masks / 255.
# extend both images and masks so they can be divided exactly by the patches dimensions
test_imgs = test_imgs[0:Imgs_to_test, :, :, :]
test_masks = test_masks[0:Imgs_to_test, :, :, :]
test_imgs = paint_border_overlap(test_imgs, patch_height, patch_width, stride_height, stride_width)
# check masks are within 0-1
assert (np.max(test_masks) == 1 and np.min(test_masks) == 0)
print("\ntest images shape:")
print(test_imgs.shape)
print("\ntest mask shape:")
print(test_masks.shape)
print("test images range (min-max): " + str(np.min(test_imgs)) + ' - ' + str(np.max(test_imgs)))
print("test masks are within 0-1\n")
# extract the TEST patches from the full images
patches_imgs_test = extract_ordered_overlap(test_imgs, patch_height, patch_width, stride_height, stride_width)
print("\ntest PATCHES images shape:")
print(patches_imgs_test.shape)
print("test PATCHES images range (min-max): " + str(np.min(patches_imgs_test)) + ' - ' + str(
np.max(patches_imgs_test)))
patches_imgs_test = np.transpose(patches_imgs_test, (0, 2, 3, 1))
test_masks = np.transpose(test_masks, (0, 2, 3, 1))
return patches_imgs_test, test_imgs.shape[2], test_imgs.shape[3], test_masks
def get_data_training(train_imgs_original,
train_groudTruth,
patch_height,
patch_width,
N_subimgs,
inside_FOV, dataset,
path_experiment, fcn=True):
train_imgs_original = load_hdf5(train_imgs_original)
train_masks = load_hdf5(train_groudTruth) # masks always the same
visualize(group_images(train_imgs_original[:, :, :, :], 5),
path_experiment + 'imgs_train') # check original imgs train
# TODO: preprocessing
train_imgs = my_PreProc(train_imgs_original)
train_masks = train_masks / 255.
if dataset == 'DRIVE':
train_imgs = train_imgs[:, :, 9:574, :] # cut bottom and top so now it is 565*565
train_masks = train_masks[:, :, 9:574, :] # cut bottom and top so now it is 565*565
elif dataset == 'STARE':
train_imgs = train_imgs[:, :, :, 15:685]
train_masks = train_masks[:, :, :, 15:685]
else:
train_imgs = train_imgs[:, :, :, 19:979]
train_masks = train_masks[:, :, :, 19:979]
data_consistency_check(train_imgs, train_masks)
# check masks are within 0-1
assert (np.min(train_masks) == 0 and np.max(train_masks) == 1)
print("\ntrain images/masks shape:")
print(train_imgs.shape)
print("train images range (min-max): " + str(np.min(train_imgs)) + ' - ' + str(np.max(train_imgs)))
print("train masks are within 0-1\n")
# extract the TRAINING patches from the full images
if fcn:
patches_imgs_train, patches_masks_train = extract_random_patches(train_imgs_original, train_imgs, train_masks,
patch_height, patch_width,
N_subimgs,
inside=inside_FOV)
data_consistency_check(patches_imgs_train, patches_masks_train)
else:
patches_imgs_train, patches_masks_train = extract_random(train_imgs_original, train_imgs, train_masks,
patch_height, patch_width,
N_subimgs,
inside=inside_FOV)
##Fourier transform of patches
# TODO add hessian grangi
# patches_imgs_train = my_PreProc_patches(patches_imgs_train)
print("\ntrain PATCHES images/masks shape:")
print(patches_imgs_train.shape)
print("train PATCHES images range (min-max): " + str(np.min(patches_imgs_train)) + ' - ' + str(
np.max(patches_imgs_train)))
return patches_imgs_train, patches_masks_train # , patches_imgs_test, patches_masks_test
# pre_processing.py
if len(sys.argv) > 1:
config_file = sys.argv[1]
else:
config_file = './configuration.txt'
# ========= CONFIG FILE TO READ FROM =======
config = hf.parse_config(config_file)
# ===========================================
# run the training on invariant or local
dataset = config['dataset']
# original test images
test_images_file = config['test_images_file']
test_images = hf.load_hdf5(test_images_file)
full_img_height = test_images.shape[2]
full_img_width = test_images.shape[3]
# the masks
test_masks_file = config['test_masks_file']
test_border_masks = hf.load_hdf5(test_masks_file)
test_gt_file = config['test_gt_file']
# dimension of the patches
patch_height = config['patch_height']
patch_width = config['patch_width']
# the stride in case output with average
stride_height = config['stride_height']
stride_width = config['stride_width']