def _create_matrices(self, shapes, random_state):
"""Create the transformation matrix
# Arguments
shapes [list of tuples]: list of image shapes
random_state [numpy Random state]: some random state
# Returns
[list of np array]: list of transformation matrices
[list of ints]: list of heights
[list of ints]: list of widths
"""
matrices = []
max_heights = []
max_widths = []
nb_images = len(shapes)
seeds = ia.copy_random_state(random_state).randint(
0, 10**6, (nb_images, ))
for _idx in range(nb_images):
height, width = shapes[_idx][:2]
pts1 = np.float32([[0, 0], [0, height - 1], [width - 1, 0],
[width - 1, height - 1]])
transition = self.jitter.draw_samples(
(4, 2), random_state=ia.new_random_state(seeds[_idx]))
transition[:, 0] = transition[:, 0] * np.min([height, width])
transition[:, 1] = transition[:, 1] * np.min([height, width])
transition = transition.astype(np.int32)
transition[:,
0] = transition[:, 0] + np.abs(np.min(transition[:, 0]))
transition[:,
1] = transition[:, 1] + np.abs(np.min(transition[:, 1]))
pts2 = np.float32(
[[transition[0, 0], transition[0, 1]],
[transition[1, 0], height - 1 + transition[1, 1]],
[width - 1 + transition[2, 0], transition[2, 1]],
[width - 1 + transition[3, 0],
height - 1 + transition[3, 1]]])
height = np.max(pts2[:, 1])
width = np.max(pts2[:, 0])
matrices.append(cv2.getPerspectiveTransform(pts1, pts2))
max_heights.append(height)
max_widths.append(width)
return matrices, max_heights, max_widths
def _augment_images(self, images, random_state, parents, hooks):
"""Augment the images
# Arguments
images [list of np array]: the list of images
# Returns
[list of np array]: the list of augmented images
"""
result = images
seed = random_state.randint(0, 10**6, 1)[0]
angle_values = self.angle.draw_samples(
(len(images), ), random_state=ia.new_random_state(seed + 90))
for _idx, image in enumerate(result):
angle = angle_values[_idx]
if angle == 0:
continue
result[_idx] = rotate(image, angle, order=1, cval=self.cval)
return result
def _augment_images(self, images, random_state, parents, hooks):
"""Augment the images
# Arguments
images [list of np array]: the list of images
# Returns
[list of np array]: the list of augmented images
"""
result = images
seed = random_state.randint(0, 10**6, 1)[0]
shear_values = self.shear.draw_samples(
(len(images), ), random_state=ia.new_random_state(seed + 80))
for _idx, image in enumerate(result):
angle = shear_values[_idx]
if angle == 0:
continue
if self.vertical:
# use horizontal italicization method
image = rotate(image, -90, order=1, cval=self.cval)
height, original_width, _ = image.shape
distance = int(height * math.tan(math.radians(math.fabs(angle))))
if angle > 0:
point1 = np.array([[0, 0], [0, height], [5, 0]],
dtype=np.float32)
point2 = np.array(
[[distance, 0], [0, height], [5 + distance, 0]],
dtype=np.float32)
image = np.concatenate([
image,
np.ones((height, distance, 1), dtype=np.uint8) * self.cval
],
axis=1)
else:
point1 = np.array(
[[distance, 0], [distance, height], [distance + 5, 0]],
dtype=np.float32)
point2 = np.array([[0, 0], [distance, height], [5, 0]],
dtype=np.float32)
image = np.concatenate([
np.ones((height, distance, 1), dtype=np.uint8) * self.cval,
image
],
axis=1)
height, width, _ = image.shape
matrix = cv2.getAffineTransform(point1, point2)
image = cv2.warpAffine(image,
matrix, (width, height),
borderValue=self.cval)
if self.vertical:
# use horizontal intalicization method
image = rotate(image, 90, order=1, cval=self.cval)
if image.ndim == 2:
image = image[..., np.newaxis]
result[_idx] = image
return result