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random_transform_mask.py
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random_transform_mask.py
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import random
import keras.backend as K
import numpy as np
from keras.preprocessing.image import transform_matrix_offset_center, apply_transform, random_channel_shift, flip_axis, \
load_img, img_to_array
class ImageWithMaskFunction:
def __init__(self, out_size, mask_dir, mask_suffix="_mask.gif", crop_size=None):
super().__init__()
self.out_size = out_size
self.mask_dir = mask_dir
self.mask_suffix = mask_suffix
self.crop_size = crop_size
def random_transform(self,
x,
mask,
rotation_range=None,
height_shift_range=None,
width_shift_range=None,
shear_range=None,
zoom_range=None,
channel_shift_range=None,
horizontal_flip=None, vertical_flip=None, fill_mode='constant', cval=0):
"""Randomly augment a image tensor and mask.
# Arguments
x: 3D tensor, single image.
# Returns
A randomly transformed version of the input (same shape).
"""
# x is a single image, so it doesn't have image number at index 0
img_row_axis = 0
img_col_axis = 1
img_channel_axis = 2
# use composition of homographies
# to generate final transform that needs to be applied
if rotation_range:
theta = np.pi / 180 * np.random.uniform(-rotation_range, rotation_range)
else:
theta = 0
if height_shift_range:
uniform = np.random.uniform(-height_shift_range, height_shift_range)
tx = uniform * x.shape[img_row_axis]
tmx = uniform * mask.shape[img_row_axis]
else:
tx = 0
tmx = 0
if width_shift_range:
random_uniform = np.random.uniform(-width_shift_range, width_shift_range)
ty = random_uniform * x.shape[img_col_axis]
tmy = random_uniform * mask.shape[img_col_axis]
else:
ty = 0
tmy = 0
if shear_range:
shear = np.random.uniform(-shear_range, shear_range)
else:
shear = 0
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
transform_matrix = None
transform_matrix_mask = None
if theta != 0:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
transform_matrix = rotation_matrix
transform_matrix_mask = rotation_matrix
if tx != 0 or ty != 0:
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
shift_matrix_mask = np.array([[1, 0, tmx],
[0, 1, tmy],
[0, 0, 1]])
transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)
transform_matrix_mask = shift_matrix_mask if transform_matrix_mask is None else np.dot(
transform_matrix_mask,
shift_matrix_mask)
if shear != 0:
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix)
transform_matrix_mask = shear_matrix if transform_matrix_mask is None else np.dot(transform_matrix_mask,
shear_matrix)
if zx != 1 or zy != 1:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)
transform_matrix_mask = zoom_matrix if transform_matrix_mask is None else np.dot(transform_matrix_mask,
zoom_matrix)
if transform_matrix is not None:
h, w = x.shape[img_row_axis], x.shape[img_col_axis]
transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
x = apply_transform(x, transform_matrix, img_channel_axis,
fill_mode=fill_mode, cval=cval)
if transform_matrix_mask is not None:
h, w = mask.shape[img_row_axis], mask.shape[img_col_axis]
transform_matrix_mask = transform_matrix_offset_center(transform_matrix_mask, h, w)
mask[:, :, 0:1] = apply_transform(mask[:, :, 0:1], transform_matrix_mask, img_channel_axis,
fill_mode='constant', cval=0.)
if channel_shift_range != 0:
x = random_channel_shift(x, channel_shift_range,
img_channel_axis)
if horizontal_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_col_axis)
mask = flip_axis(mask, img_col_axis)
if vertical_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_row_axis)
mask = flip_axis(mask, img_row_axis)
return x, mask
def mask_pred(self, batch_x, filenames, index_array, aug=False):
mask_pred = np.zeros((len(batch_x), self.out_size[0], self.out_size[1], 1), dtype=K.floatx())
mask_pred[:, :, :, :] = 0.
for i, j in enumerate(index_array):
fname = filenames[j]
mask = self.mask_dir + "/" + fname.split('/')[-1].replace(".jpg", self.mask_suffix)
mask_pred[i, :, :, :] = img_to_array(
load_img(mask, grayscale=True, target_size=(self.out_size[0], self.out_size[1]))) / 255.
if aug:
batch_x[i, :, :, :], mask_pred[i, :, :, :] = self.random_transform(x=batch_x[i, :, :, :],
mask=mask_pred[i, :, :, :],
height_shift_range=0.0,
width_shift_range=0.0,
shear_range=0.0,
rotation_range=0,
zoom_range=[0.95, 1.05],
channel_shift_range=0.1,
horizontal_flip=True)
if self.crop_size:
height = self.crop_size[0]
width = self.crop_size[1]
ori_height = self.out_size[0]
ori_width = self.out_size[1]
if aug:
h_start = random.randint(0, ori_height - height - 1)
w_start = random.randint(0, ori_width - width - 1)
else:
# validate on center crops
h_start = (ori_height - height) // 2
w_start = (ori_width - width) // 2
MASK_CROP = mask_pred[:, h_start:h_start + height, w_start:w_start + width, :]
return batch_x[:, h_start:h_start + height, w_start:w_start + width, :], MASK_CROP
else:
return batch_x, mask_pred
def mask_pred_train(self, batch_x, filenames, index_array, l):
return self.mask_pred(batch_x, filenames, index_array, True)
def mask_pred_val(self, batch_x, filenames, index_array, l):
return self.mask_pred(batch_x, filenames, index_array, False)
def random_transform_two_masks(x,
mask1,
mask2,
rotation_range=None,
height_shift_range=None,
width_shift_range=None,
shear_range=None,
zoom_range=None,
channel_shift_range=None,
horizontal_flip=None, vertical_flip=None, fill_mode='constant', cval=0):
"""Randomly augment a image tensor and masks.
# Arguments
x: 3D tensor, single image.
# Returns
A randomly transformed version of the input (same shape).
"""
# x is a single image, so it doesn't have image number at index 0
img_row_axis = 0
img_col_axis = 1
img_channel_axis = 2
# use composition of homographies
# to generate final transform that needs to be applied
if rotation_range:
theta = np.pi / 180 * np.random.uniform(-rotation_range, rotation_range)
else:
theta = 0
if height_shift_range:
uniform = np.random.uniform(-height_shift_range, height_shift_range)
tx = uniform * x.shape[img_row_axis]
tmx1 = uniform * mask1.shape[img_row_axis]
tmx2 = uniform * mask2.shape[img_row_axis]
else:
tx = 0
tmx1 = 0
tmx2 = 0
if width_shift_range:
random_uniform = np.random.uniform(-width_shift_range, width_shift_range)
ty = random_uniform * x.shape[img_col_axis]
tmy1 = random_uniform * mask1.shape[img_col_axis]
tmy2 = random_uniform * mask2.shape[img_col_axis]
else:
ty = 0
tmy1 = 0
tmy2 = 0
if shear_range:
shear = np.random.uniform(-shear_range, shear_range)
else:
shear = 0
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
transform_matrix = None
transform_matrix_mask1 = None
transform_matrix_mask2 = None
if theta != 0:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
transform_matrix = rotation_matrix
transform_matrix_mask1 = rotation_matrix
transform_matrix_mask2 = rotation_matrix
if tx != 0 or ty != 0:
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
shift_matrix_mask1 = np.array([[1, 0, tmx1],
[0, 1, tmy1],
[0, 0, 1]])
shift_matrix_mask2 = np.array([[1, 0, tmx2],
[0, 1, tmy2],
[0, 0, 1]])
transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)
transform_matrix_mask1 = shift_matrix_mask1 if transform_matrix_mask1 is None else np.dot(
transform_matrix_mask1,
shift_matrix_mask1)
transform_matrix_mask2 = shift_matrix_mask1 if transform_matrix_mask2 is None else np.dot(
transform_matrix_mask2,
shift_matrix_mask2)
if shear != 0:
shear_matrix = np.array([[1, -np.sin(shear), 0],
[0, np.cos(shear), 0],
[0, 0, 1]])
transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix)
transform_matrix_mask1 = shear_matrix if transform_matrix_mask1 is None else np.dot(transform_matrix_mask1,
shear_matrix)
transform_matrix_mask2 = shear_matrix if transform_matrix_mask2 is None else np.dot(transform_matrix_mask2,
shear_matrix)
if zx != 1 or zy != 1:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)
transform_matrix_mask1 = zoom_matrix if transform_matrix_mask1 is None else np.dot(transform_matrix_mask1,
zoom_matrix)
transform_matrix_mask2 = zoom_matrix if transform_matrix_mask2 is None else np.dot(transform_matrix_mask2,
zoom_matrix)
if transform_matrix is not None:
h, w = x.shape[img_row_axis], x.shape[img_col_axis]
transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
x = apply_transform(x, transform_matrix, img_channel_axis,
fill_mode=fill_mode, cval=cval)
if transform_matrix_mask1 is not None:
h, w = mask1.shape[img_row_axis], mask1.shape[img_col_axis]
transform_matrix_mask1 = transform_matrix_offset_center(transform_matrix_mask1, h, w)
mask1[:, :, 0:1] = apply_transform(mask1[:, :, 0:1], transform_matrix_mask1, img_channel_axis,
fill_mode='constant', cval=0.)
if transform_matrix_mask2 is not None:
h, w = mask2.shape[img_row_axis], mask2.shape[img_col_axis]
transform_matrix_mask2 = transform_matrix_offset_center(transform_matrix_mask2, h, w)
mask2[:, :, 0:1] = apply_transform(mask2[:, :, 0:1], transform_matrix_mask2, img_channel_axis,
fill_mode='constant', cval=0.)
if channel_shift_range != 0:
x = random_channel_shift(x, channel_shift_range,
img_channel_axis)
if horizontal_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_col_axis)
mask1 = flip_axis(mask1, img_col_axis)
mask2 = flip_axis(mask2, img_col_axis)
if vertical_flip:
if np.random.random() < 0.5:
x = flip_axis(x, img_row_axis)
mask1 = flip_axis(mask1, img_row_axis)
mask2 = flip_axis(mask2, img_row_axis)
return x, mask1, mask2