# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image transformation.
"""
import math
import numpy as np
import cv2
from mindarmour.natural_robustness.transform.image.natural_perturb import _NaturalPerturb
from mindarmour.utils._check_param import check_param_multi_types, check_param_type, check_value_non_negative
from mindarmour.utils.logger import LogUtil
LOGGER = LogUtil.get_instance()
TAG = 'Image Transformation'
[文档]class Translate(_NaturalPerturb):
"""
Translate an image.
Args:
x_bias (Union[int, float]): X-direction translation, x = x + x_bias*image_width. Suggested value range
in [-0.1, 0.1].
y_bias (Union[int, float]): Y-direction translation, y = y + y_bias*image_length. Suggested value range
in [-0.1, 0.1].
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('1.png')
>>> img = np.array(img)
>>> x_bias = 0.1
>>> y_bias = 0.1
>>> trans = Translate(x_bias, y_bias)
>>> dst = trans(img)
"""
def __init__(self, x_bias=0, y_bias=0, auto_param=False):
super(Translate, self).__init__()
self.x_bias = check_param_multi_types('x_bias', x_bias, [int, float])
self.y_bias = check_param_multi_types('y_bias', y_bias, [int, float])
if auto_param:
self.x_bias = np.random.uniform(-0.1, 0.1)
self.y_bias = np.random.uniform(-0.1, 0.1)
def __call__(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
h, w = image.shape[:2]
matrix = np.array([[1, 0, self.x_bias * w], [0, 1, self.y_bias * h]], dtype=np.float)
new_img = cv2.warpAffine(image, matrix, (w, h))
new_img = self._original_format(new_img, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)
[文档]class Scale(_NaturalPerturb):
"""
Scale an image in the middle.
Args:
factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x. Suggested value range in [0.5, 1] and
abs(factor_y - factor_x) < 0.5.
factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y. Suggested value range in [0.5, 1] and
abs(factor_y - factor_x) < 0.5.
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('1.png')
>>> img = np.array(img)
>>> factor_x = 0.7
>>> factor_y = 0.6
>>> trans = Scale(factor_x, factor_y)
>>> dst = trans(img)
"""
def __init__(self, factor_x=1, factor_y=1, auto_param=False):
super(Scale, self).__init__()
self.factor_x = check_param_multi_types('factor_x', factor_x, [int, float])
self.factor_y = check_param_multi_types('factor_y', factor_y, [int, float])
auto_param = check_param_type('auto_param', auto_param, bool)
if auto_param:
self.factor_x = np.random.uniform(0.5, 1)
self.factor_y = np.random.uniform(0.5, 1)
def __call__(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
h, w = image.shape[:2]
matrix = np.array([[self.factor_x, 0, 0], [0, self.factor_y, 0]], dtype=np.float)
new_img = cv2.warpAffine(image, matrix, (w, h))
new_img = self._original_format(new_img, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)
[文档]class Shear(_NaturalPerturb):
"""
Shear an image, the mapping between sheared image and origin image is (new_x, new_y) = (x+factor_x*y, factor_y*x+y).
Then the sheared image will be rescaled to fit original size.
Args:
factor (Union[float, int]): Shear rate in shear direction. Suggested value range in [0.05, 0.5].
direction (str): Direction of deformation. Optional value is 'vertical' or 'horizontal'.
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('1.png')
>>> img = np.array(img)
>>> factor = 0.2
>>> trans = Shear(factor, direction='horizontal')
>>> dst = trans(img)
"""
def __init__(self, factor=0.2, direction='horizontal', auto_param=False):
super(Shear, self).__init__()
self.factor = check_param_multi_types('factor', factor, [int, float])
if direction not in ['horizontal', 'vertical']:
msg = "'direction must be in ['horizontal', 'vertical'], but got {}".format(direction)
raise ValueError(msg)
self.direction = direction
auto_param = check_param_type('auto_params', auto_param, bool)
if auto_param:
self.factor = np.random.uniform(0.05, 0.5)
self.direction = np.random.choice(['horizontal', 'vertical'])
def __call__(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
h, w = image.shape[:2]
if self.direction == 'horizontal':
matrix = np.array([[1, self.factor, 0], [0, 1, 0]], dtype=np.float)
nw = int(w + self.factor * h)
nh = h
else:
matrix = np.array([[1, 0, 0], [self.factor, 1, 0]], dtype=np.float)
nw = w
nh = int(h + self.factor * w)
new_img = cv2.warpAffine(image, matrix, (nw, nh))
new_img = cv2.resize(new_img, (w, h))
new_img = self._original_format(new_img, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)
[文档]class Rotate(_NaturalPerturb):
"""
Rotate an image of counter clockwise around its center.
Args:
angle (Union[float, int]): Degrees of counter clockwise. Suggested value range in [-60, 60].
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('1.png')
>>> img = np.array(img)
>>> angle = 20
>>> trans = Rotate(angle)
>>> dst = trans(img)
"""
def __init__(self, angle=20, auto_param=False):
super(Rotate, self).__init__()
self.angle = check_param_multi_types('angle', angle, [int, float])
auto_param = check_param_type('auto_param', auto_param, bool)
if auto_param:
self.angle = np.random.uniform(0, 360)
def __call__(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, rotated image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
h, w = image.shape[:2]
center = (w // 2, h // 2)
matrix = cv2.getRotationMatrix2D(center, -self.angle, 1.0)
cos = np.abs(matrix[0, 0])
sin = np.abs(matrix[0, 1])
# Calculate new edge after rotated
nw = int((h * sin) + (w * cos))
nh = int((h * cos) + (w * sin))
# Adjust move distance of rotate matrix.
matrix[0, 2] += (nw / 2) - center[0]
matrix[1, 2] += (nh / 2) - center[1]
rotate = cv2.warpAffine(image, matrix, (nw, nh))
rotate = cv2.resize(rotate, (w, h))
new_img = self._original_format(rotate, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)
[文档]class Perspective(_NaturalPerturb):
"""
Perform perspective transformation on a given picture.
Args:
ori_pos (list): Four points in original image.
dst_pos (list): The point coordinates of the 4 points in ori_pos after perspective transformation.
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('1.png')
>>> img = np.array(img)
>>> ori_pos = [[0, 0], [0, 800], [800, 0], [800, 800]]
>>> dst_pos = [[50, 0], [0, 800], [780, 0], [800, 800]]
>>> trans = Perspective(ori_pos, dst_pos)
>>> dst = trans(img)
"""
def __init__(self, ori_pos, dst_pos, auto_param=False):
super(Perspective, self).__init__()
ori_pos = check_param_type('ori_pos', ori_pos, list)
dst_pos = check_param_type('dst_pos', dst_pos, list)
self.ori_pos = np.float32(ori_pos)
self.dst_pos = np.float32(dst_pos)
self.auto_param = check_param_type('auto_param', auto_param, bool)
def _set_auto_param(self, w, h):
self.ori_pos = [[h * 0.25, w * 0.25], [h * 0.25, w * 0.75], [h * 0.75, w * 0.25], [h * 0.75, w * 0.75]]
self.dst_pos = [[np.random.uniform(0, h * 0.5), np.random.uniform(0, w * 0.5)],
[np.random.uniform(0, h * 0.5), np.random.uniform(w * 0.5, w)],
[np.random.uniform(h * 0.5, h), np.random.uniform(0, w * 0.5)],
[np.random.uniform(h * 0.5, h), np.random.uniform(w * 0.5, w)]]
self.ori_pos = np.float32(self.ori_pos)
self.dst_pos = np.float32(self.dst_pos)
def __call__(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
h, w = image.shape[:2]
if self.auto_param:
self._set_auto_param(w, h)
matrix = cv2.getPerspectiveTransform(self.ori_pos, self.dst_pos)
new_img = cv2.warpPerspective(image, matrix, (w, h))
new_img = self._original_format(new_img, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)
[文档]class Curve(_NaturalPerturb):
"""
Curve picture using sin method.
Args:
curves (union[float, int]): Number of curve cycles. Suggested value range in [0.1, 5].
depth (union[float, int]): Amplitude of sin method. Suggested value not exceed 1/10 of the length of the
picture.
mode (str): Direction of deformation. Optional value is 'vertical' or 'horizontal'.
auto_param (bool): Auto selected parameters. Selected parameters will preserve semantics of image.
Default: False.
Examples:
>>> img = cv2.imread('x.png')
>>> curves =1
>>> depth = 10
>>> trans = Curve(curves, depth, mode='vertical')
>>> img_new = trans(img)
"""
def __init__(self, curves=3, depth=10, mode='vertical', auto_param=False):
super(Curve).__init__()
self.curves = check_value_non_negative('curves', curves)
self.depth = check_value_non_negative('depth', depth)
if mode in ['vertical', 'horizontal']:
self.mode = mode
else:
msg = "Value of param mode must be in ['vertical', 'horizontal']"
LOGGER.error(TAG, msg)
raise ValueError(msg)
self.auto_param = check_param_type('auto_param', auto_param, bool)
def _set_auto_params(self, height, width):
if self.auto_param:
self.curves = np.random.uniform(1, 5)
self.mode = np.random.choice(['vertical', 'horizontal'])
if self.mode == 'vertical':
self.depth = np.random.uniform(1, 0.1 * width)
else:
self.depth = np.random.uniform(1, 0.1 * height)
def __call__(self, image):
"""
Curve picture using sin method.
Args:
image (numpy.ndarray): Original image.
Returns:
numpy.ndarray, curved image.
"""
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
shape = image.shape
height, width = shape[:2]
if self.mode == 'vertical':
if len(shape) == 3:
image = np.transpose(image, [1, 0, 2])
else:
image = np.transpose(image, [1, 0])
src_x = np.zeros((height, width), np.float32)
src_y = np.zeros((height, width), np.float32)
for y in range(height):
for x in range(width):
src_x[y, x] = x
src_y[y, x] = y + self.depth * math.sin(x / (width / self.curves / 2 / math.pi))
img_new = cv2.remap(image, src_x, src_y, cv2.INTER_LINEAR)
if self.mode == 'vertical':
if len(shape) == 3:
img_new = np.transpose(img_new, [1, 0, 2])
else:
img_new = np.transpose(image, [1, 0])
new_img = self._original_format(img_new, chw, normalized, gray3dim)
return new_img.astype(ori_dtype)