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import numpy as np
from tqdm import tqdm
def augment_patch(patch):
if len(patch.shape[:-1]) == 2:
patch = np.rot90(patch, k=np.random.randint(4), axes=(0, 1))
elif len(patch.shape[:-1]) == 3:
patch = np.rot90(patch, k=np.random.randint(4), axes=(1, 2))
patch = np.flip(patch, axis=-2) if np.random.randint(2) else patch
return patch
# Below implementation of stratified sampling inherited from Noise2Void: https://github.com/juglab/n2v
# Noise2void: learning denoising from single noisy images. Krull, Alexander, Tim-Oliver Buchholz, and Florian Jug. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
def get_stratified_coords2D(coord_gen, box_size, shape):
box_count_y = int(np.ceil(shape[0] / box_size))
box_count_x = int(np.ceil(shape[1] / box_size))
x_coords = []
y_coords = []
for i in range(box_count_y):
for j in range(box_count_x):
y, x = next(coord_gen)
y = int(i * box_size + y)
x = int(j * box_size + x)
if (y < shape[0] and x < shape[1]):
y_coords.append(y)
x_coords.append(x)
return (y_coords, x_coords)
def get_stratified_coords3D(coord_gen, box_size, shape):
box_count_z = int(np.ceil(shape[0] / box_size))
box_count_y = int(np.ceil(shape[1] / box_size))
box_count_x = int(np.ceil(shape[2] / box_size))
x_coords = []
y_coords = []
z_coords = []
for i in range(box_count_z):
for j in range(box_count_y):
for k in range(box_count_x):
z, y, x = next(coord_gen)
z = int(i * box_size + z)
y = int(j * box_size + y)
x = int(k * box_size + x)
if (z < shape[0] and y < shape[1] and x < shape[2]):
z_coords.append(z)
y_coords.append(y)
x_coords.append(x)
return (z_coords, y_coords, x_coords)
def rand_float_coords2D(boxsize):
while True:
yield (np.random.rand() * boxsize, np.random.rand() * boxsize)
def rand_float_coords3D(boxsize):
while True:
yield (np.random.rand() * boxsize, np.random.rand() * boxsize, np.random.rand() * boxsize) |