From da807797e12721b92a1699a82e26882fec094083 Mon Sep 17 00:00:00 2001
From: lvzhangcheng <lvzhangcheng@huawei.com>
Date: Mon, 25 Oct 2021 20:45:07 +0800
Subject: [PATCH] add natural robustness methods

---
 mindarmour/natural_robustness/__init__.py      |  16 +
 mindarmour/natural_robustness/natural_noise.py | 910 +++++++++++++++++++++++++
 2 files changed, 926 insertions(+)
 create mode 100644 mindarmour/natural_robustness/__init__.py
 create mode 100644 mindarmour/natural_robustness/natural_noise.py

diff --git a/mindarmour/natural_robustness/__init__.py b/mindarmour/natural_robustness/__init__.py
new file mode 100644
index 0000000..439f375
--- /dev/null
+++ b/mindarmour/natural_robustness/__init__.py
@@ -0,0 +1,16 @@
+# Copyright 2021 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.
+"""
+This package include methods to generate natural perturbation samples.
+"""
diff --git a/mindarmour/natural_robustness/natural_noise.py b/mindarmour/natural_robustness/natural_noise.py
new file mode 100644
index 0000000..ce38176
--- /dev/null
+++ b/mindarmour/natural_robustness/natural_noise.py
@@ -0,0 +1,910 @@
+# Copyright 2019 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 transform
+"""
+import os
+import math
+import numpy as np
+import cv2
+from perlin_numpy import generate_fractal_noise_2d
+
+from mindarmour.utils._check_param import check_param_multi_types, check_param_in_range, check_numpy_param, \
+    check_int_positive, check_param_type, check_value_non_negative
+from mindarmour.utils.logger import LogUtil
+
+LOGGER = LogUtil.get_instance()
+TAG = 'Image Transformation'
+
+
+class Contrast:
+    """
+    Contrast of an image.
+
+    Args:
+        alpha(Union[float, int]): Control the contrast of an image. If 1.0,
+            gives the original image. If 0, gives a gray image. Default: 1.
+        beta(Union[float, int]): delta added to alpha. Default: 0.
+    """
+
+    def __init__(self, alpha=1, beta=0):
+        super(Contrast, self).__init__()
+        self.set_params(alpha)
+        self.beta = check_param_multi_types('factor', beta, [int, float])
+
+    def set_params(self, alpha=1, auto_param=False):
+        """
+        Set contrast parameters.
+
+        Args:
+            alpha (Union[float, int]): Control the contrast of an image. If 1.0
+                gives the original image. If 0 gives a gray image. Default: 1.
+            auto_param (bool): True if auto generate parameters. Default: False.
+        """
+        if auto_param:
+            self.alpha = np.random.uniform(-5, 5)
+        else:
+            self.alpha = check_param_multi_types('factor', alpha, [int, float])
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image (numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', image)
+        new_img = cv2.convertScaleAbs(image, alpha=self.alpha, beta=self.beta)
+        return new_img
+
+
+class GaussianBlur:
+    """
+    Blurs the image using Gaussian blur filter.
+
+    Args:
+        ksize(Union[list, tuple]): Blur radius, 0 means no blur. Default: 0.
+    """
+
+    def __init__(self, ksize=(5, 5)):
+        super(GaussianBlur, self).__init__()
+        ksize = check_param_multi_types('ksize', ksize, [list, tuple])
+        self.ksize = tuple(ksize)
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image (numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', image)
+        new_img = cv2.GaussianBlur(image, self.ksize, 0)
+        return new_img
+
+
+class SaltAndPepperNoise:
+    """
+    Add noise of an image.
+
+    Args:
+        factor (float): factor is the ratio of pixels to add noise.
+            If 0 gives the original image. Default 0.
+    """
+
+    def __init__(self, factor=0):
+        super(SaltAndPepperNoise, self).__init__()
+        self.set_params(factor)
+
+    def set_params(self, factor=0, auto_param=False):
+        """
+        Set noise parameters.
+
+        Args:
+            factor (Union[float, int]): factor is the ratio of pixels to
+                add noise. If 0 gives the original image. Default 0.
+            auto_param (bool): True if auto generate parameters. Default: False.
+        """
+        if auto_param:
+            self.factor = np.random.uniform(0, 1)
+        else:
+            self.factor = check_param_multi_types('factor', factor, [int, float])
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image (numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', image)
+        ori_dtype = image.dtype
+        noise = np.random.uniform(low=-1, high=1, size=(image.shape[0], image.shape[1]))
+        trans_image = np.copy(image)
+        threshold = 1 - self.factor
+        trans_image[noise < -threshold] = (0, 0, 0)
+        trans_image[noise > threshold] = (255, 255, 255)
+        return trans_image.astype(ori_dtype)
+
+
+class Translate:
+    """
+    Translate an image.
+
+    Args:
+        x_bias (Union[int, float]): X-direction translation, x = x + x_bias*image_length.
+            Default: 0.
+        y_bias (Union[int, float]): Y-direction translation,  y = y + y_bias*image_wide.
+            Default: 0.
+    """
+
+    def __init__(self, x_bias=0, y_bias=0):
+        super(Translate, self).__init__()
+        self.set_params(x_bias, y_bias)
+
+    def set_params(self, x_bias=0, y_bias=0, auto_param=False):
+        """
+        Set translate parameters.
+
+        Args:
+            x_bias (Union[float, int]): X-direction translation, and x_bias should be in range of (-1, 1). Default: 0.
+            y_bias (Union[float, int]): Y-direction translation, and y_bias should be in range of (-1, 1). Default: 0.
+            auto_param (bool): True if auto generate parameters. Default: False.
+        """
+        x_bias = check_param_in_range('x_bias', x_bias, -1, 1)
+        y_bias = check_param_in_range('y_bias', y_bias, -1, 1)
+        self.auto_param = auto_param
+        if auto_param:
+            self.x_bias = np.random.uniform(-0.3, 0.3)
+            self.y_bias = np.random.uniform(-0.3, 0.3)
+        else:
+            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])
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image(numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', 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))
+        return new_img
+
+
+class Scale:
+    """
+    Scale an image in the middle.
+
+    Args:
+        factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x.
+            Default: 1.
+        factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y.
+            Default: 1.
+    """
+
+    def __init__(self, factor_x=1, factor_y=1):
+        super(Scale, self).__init__()
+        self.set_params(factor_x, factor_y)
+
+    def set_params(self, factor_x=1, factor_y=1, auto_param=False):
+
+        """
+        Set scale parameters.
+
+        Args:
+            factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x.
+                Default: 1.
+            factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y.
+                Default: 1.
+            auto_param (bool): True if auto generate parameters. Default: False.
+        """
+        if auto_param:
+            self.factor_x = np.random.uniform(0.7, 3)
+            self.factor_y = np.random.uniform(0.7, 3)
+        else:
+            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])
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image(numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', 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))
+        return new_img
+
+
+class Shear:
+    """
+    Shear an image, for each pixel (x, y) in the sheared image, the new value is
+    taken from a position (x+factor_x*y, factor_y*x+y) in the origin image. Then
+    the sheared image will be rescaled to fit original size.
+
+    Args:
+        factor_x (Union[float, int]): Shear factor of horizontal direction.
+            Default: 0.
+        factor_y (Union[float, int]): Shear factor of vertical direction.
+            Default: 0.
+
+    """
+
+    def __init__(self, factor, director='horizonal'):
+        super(Shear, self).__init__()
+        self.factor = factor
+        self.director = director
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image(numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        h, w = image.shape[:2]
+        if self.director == '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))
+        return new_img
+
+
+class Rotate:
+    """
+    Rotate an image of degrees counter clockwise around its center.
+
+    Args:
+        angle(Union[float, int]): Degrees counter clockwise. Default: 0.
+    """
+
+    def __init__(self, angle=0):
+        super(Rotate, self).__init__()
+        self.set_params(angle)
+
+    def set_params(self, angle=0, auto_param=False):
+        """
+        Set rotate parameters.
+
+        Args:
+            angle(Union[float, int]): Degrees counter clockwise. Default: 0.
+            auto_param (bool): True if auto generate parameters. Default: False.
+        """
+        if auto_param:
+            self.angle = np.random.uniform(0, 360)
+        else:
+            self.angle = check_param_multi_types('angle', angle, [int, float])
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image(numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, rotated image.
+        """
+        image = check_numpy_param('image', 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])
+
+        # 计算图像旋转后的新边界
+        nw = int((h * sin) + (w * cos))
+        nh = int((h * cos) + (w * sin))
+
+        # 调整旋转矩阵的移动距离（t_{x}, t_{y}）
+        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))
+        return rotate
+
+
+class Perspective:
+    """
+    Shear an image, for each pixel (x, y) in the sheared image, the new value is
+    taken from a position (x+factor_x*y, factor_y*x+y) in the origin image. Then
+    the sheared image will be rescaled to fit original size.
+
+    Args:
+        factor_x (Union[float, int]): Shear factor of horizontal direction.
+            Default: 0.
+        factor_y (Union[float, int]): Shear factor of vertical direction.
+            Default: 0.
+
+    """
+
+    def __init__(self, ori_pos, dst_pos):
+        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)
+
+    def __call__(self, image):
+        """
+        Transform the image.
+
+        Args:
+            image(numpy.ndarray): Original image to be transformed.
+
+        Returns:
+            numpy.ndarray, transformed image.
+        """
+        image = check_numpy_param('image', image)
+        h, w = image.shape[:2]
+        matrix = cv2.getPerspectiveTransform(self.ori_pos, self.dst_pos)
+        new_img = cv2.warpPerspective(image, matrix, (w, h))
+        return new_img
+
+
+class MotionBlur:
+    """
+    Motion blur for a given image.
+
+    Args:
+        degree (int): Degree of blur. This value must be positive. Default: 5.
+        angle: (union[float, int]): Direction of motion blur. Angle=0 means up and down motion blur. Angle is
+            counterclockwise.
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> angle = 0
+        >>> degree = 5
+        >>> trans = MotionBlur(degree=degree, angle=angle)
+        >>> new_img = trans(img)
+    """
+
+    def __init__(self, degree=5, angle=45):
+        super(MotionBlur, self).__init__()
+        self.degree = check_int_positive('degree', degree)
+        self.degree = check_param_multi_types('degree', degree, [float, int])
+        self.angle = angle - 45
+
+    def __call__(self, image):
+        """
+        Motion blur for a given image.
+
+        Args:
+            image (numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, image after motion blur.
+        """
+        image = check_numpy_param('image', image)
+        matrix = cv2.getRotationMatrix2D((self.degree / 2, self.degree / 2), self.angle, 1)
+        motion_blur_kernel = np.diag(np.ones(self.degree))
+        motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, matrix, (self.degree, self.degree))
+        motion_blur_kernel = motion_blur_kernel / self.degree
+        blurred = cv2.filter2D(image, -1, motion_blur_kernel)
+        # convert to uint8
+        cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
+        blurred = np.array(blurred, dtype=np.uint8)
+        return blurred
+
+
+class GradientBlur:
+    """
+    Gradient blur.
+
+    Args:
+        point(union[tuple, list]): 2D coordinate of the Blur center point.
+        kernel_num(int): Number of blur kernels. Default: 5.
+        center(bool): Blurred or clear at the center of a specified point. Default: True.
+
+    Example:
+        >>> img = cv2.imread('xx.png')
+        >>> img = np.array(img)
+        >>> number = 5
+        >>> h, w = img.shape[:2]
+        >>> point = (int(h / 5), int(w / 5))
+        >>> center = True
+        >>> trans = GradientBlur(point, number,  center)
+        >>> new_img = trans(img)
+    """
+
+    def __init__(self, point, kernel_num=3, center=True):
+        super(GradientBlur).__init__()
+        point = check_param_multi_types('point', point, [list, tuple])
+        self.point = tuple(point)
+        self.kernel_num = check_int_positive('kernel_num', kernel_num)
+        self.center = check_param_type('center', center, bool)
+
+    def __call__(self, image):
+        """
+
+        Args:
+            image(numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, gradient blurred image.
+        """
+        image = check_numpy_param('image', image)
+        w, h = image.shape[:2]
+        mask = np.zeros(image.shape, dtype=np.uint8)
+        masks = []
+        radius = max(w - self.point[0], self.point[0], h - self.point[1], self.point[1])
+        radius = int(radius / self.kernel_num)
+        for i in range(self.kernel_num):
+            circle = cv2.circle(mask.copy(), self.point, radius * (1 + i), (1, 1, 1), -1)
+            masks.append(circle)
+        blurs = []
+        for i in range(3, 3 + 2 * self.kernel_num, 2):
+            ksize = (i, i)
+            blur = cv2.GaussianBlur(image, ksize, 0)
+            blurs.append(blur)
+
+        dst = image.copy()
+        if self.center:
+            for i in range(self.kernel_num):
+                dst = masks[i] * dst + (1 - masks[i]) * blurs[i]
+        else:
+            for i in range(self.kernel_num - 1, -1, -1):
+                dst = masks[i] * blurs[self.kernel_num - 1 - i] + (1 - masks[i]) * dst
+        return dst
+
+
+def _circle_gradient_mask(img_src, color_start, color_end, scope=0.5, point=None):
+    """
+    Generate circle gradient mask.
+
+    Args:
+        img_src(numpy.ndarray): Source image.
+        color_start(union([tuple, list])): Color of circle gradient center.
+        color_end(union([tuple, list])): Color of circle gradient edge.
+        scope(float):
+        point(union([tuple, list]): center point.
+
+    Returns:
+        numpy.ndarray, gradients mask.
+    """
+    if not isinstance(img_src, np.ndarray):
+        raise TypeError('`src` must be numpy.ndarray type, but got {0}.'.format(type(img_src)))
+
+    height, width = img_src.shape[:2]
+
+    if point is None:
+        point = (height // 2, width // 2)
+    x, y = point
+
+    # upper left
+    bound_upper_left = math.ceil(math.sqrt(x ** 2 + y ** 2))
+    # upper right
+    bound_upper_right = math.ceil(math.sqrt(height ** 2 + (width - y) ** 2))
+    # lower left
+    bound_lower_left = math.ceil(math.sqrt((height - x) ** 2 + y ** 2))
+    # lower right
+    bound_lower_right = math.ceil(math.sqrt((height - x) ** 2 + (width - y) ** 2))
+
+    radius = max(bound_lower_left, bound_lower_right, bound_upper_left, bound_upper_right) * scope
+
+    # img_grad = np.zeros_like(img_src, dtype=np.uint8)
+    img_grad = np.ones_like(img_src, dtype=np.uint8) * max(color_end)
+    # opencv use BGR format
+    grad_b = float(color_end[0] - color_start[0]) / radius
+    grad_g = float(color_end[1] - color_start[1]) / radius
+    grad_r = float(color_end[2] - color_start[2]) / radius
+
+    for i in range(height):
+        for j in range(width):
+            distance = math.ceil(math.sqrt((x - i) ** 2 + (y - j) ** 2))
+            if distance >= radius:
+                continue
+            img_grad[i, j, 0] = color_start[0] + distance * grad_b
+            img_grad[i, j, 1] = color_start[1] + distance * grad_g
+            img_grad[i, j, 2] = color_start[2] + distance * grad_r
+
+    return img_grad
+
+
+def _line_gradient_mask(image, start_pos=None, start_color=(0, 0, 0), end_color=(255, 255, 255), mode='horizontal'):
+    """
+    Generate liner gradient mask.
+
+    Args:
+        image(numpy.ndarray): Original image.
+        mode(str): mode must be in ['horizontal', 'vertical'].
+    """
+    h, w = image.shape[:2]
+    if start_pos is None:
+        start_pos = 0.5
+    else:
+        if mode == 'horizontal':
+            start_pos = start_pos[0] / h
+        else:
+            start_pos = start_pos[1] / w
+    start_color = np.array(start_color)
+    end_color = np.array(end_color)
+    if mode == 'horizontal':
+        w_l = int(w * start_pos)
+        w_r = w - w_l
+        if w_l > w_r:
+            r_end_color = (end_color - start_color) / start_pos * (1 - start_pos) + start_color
+            left = np.linspace(end_color, start_color, w_l)
+            right = np.linspace(start_color, r_end_color, w_r)
+        else:
+            l_end_color = (end_color - start_color) / (1 - start_pos) * start_pos + start_color
+            left = np.linspace(l_end_color, start_color, w_l)
+            right = np.linspace(start_color, end_color, w_r)
+        line = np.concatenate((left, right), axis=0)
+        mask = np.reshape(np.tile(line, (h, 1)), (h, w, 3))
+        mask = np.array(mask, dtype=np.uint8)
+    else:
+        # 'vertical'
+        h_t = int(h * start_pos)
+        h_b = h - h_t
+        if h_t > h_b:
+            b_end_color = (end_color - start_color) / start_pos * (1 - start_pos) + start_color
+            top = np.linspace(end_color, start_color, h_t)
+            bottom = np.linspace(start_color, b_end_color, h_b)
+        else:
+            t_end_color = (end_color - start_color) / (1 - start_pos) * start_pos + start_color
+            top = np.linspace(t_end_color, start_color, h_t)
+            bottom = np.linspace(start_color, end_color, h_b)
+        line = np.concatenate((top, bottom), axis=0)
+        mask = np.reshape(np.tile(line, (w, 1)), (w, h, 3))
+        mask = np.transpose(mask, [1, 0, 2])
+        mask = np.array(mask, dtype=np.uint8)
+    return mask
+
+
+class GradientLuminance:
+    """
+    Gradient adjusts the luminance of picture.
+
+    Args:
+        color_start(union[tuple, list]): Color of gradient center. Default:(0, 0, 0).
+        color_end(union[tuple, list]):  Color of gradient edge. Default:(255, 255, 255).
+        start_point(union[tuple, list]): 2D coordinate of gradient center.
+        scope(float): Range of the gradient. A larger value indicates a larger gradient range. Default: 0.3.
+        bright_rate(float): Control brightness of . A larger value indicates a larger gradient range. Default: 0.3.
+        pattern(str): Dark or light, this value must be in ['light', 'dark'].
+        mode(str): Gradient mode, value must be in ['circle', 'horizontal', 'vertical'].
+
+    Examples:
+        >>> img = cv2.imread('x.png')
+        >>> height, width = img.shape[:2]
+        >>> point = (height // 4, width // 2)
+        >>> start = (255, 255, 255)
+        >>> end = (0, 0, 0)
+        >>> scope = 0.3
+        >>> trans = GradientLuminance(start, end, point, scope, pattern='light', mode='circle')
+        >>> img_new = trans(img)
+    """
+
+    def __init__(self, color_start, color_end, start_point, scope=0.5, pattern='light', bright_rate=0.3, mode='circle'):
+        self.color_start = check_param_multi_types('color_start', color_start, [list, tuple])
+        self.color_end = check_param_multi_types('color_end', color_end, [list, tuple])
+        self.start_point = check_param_multi_types('start_point', start_point, [list, tuple])
+        self.scope = check_value_non_negative('scope', scope)
+        self.bright_rate = check_param_type('bright_rate', bright_rate, float)
+        self.bright_rate = check_param_in_range('bright_rate', bright_rate, 0, 1)
+
+        if pattern in ['light', 'dark']:
+            self.pattern = pattern
+        else:
+            msg = "Value of param pattern must be in ['light', 'dark']"
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+        if mode in ['circle', 'horizontal', 'vertical']:
+            self.mode = mode
+        else:
+            msg = "Value of param mode must be in ['circle', 'horizontal', 'vertical']"
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+
+    def __call__(self, image):
+        image = check_numpy_param('image', image)
+        if self.mode == 'circle':
+            mask = _circle_gradient_mask(image, self.color_start, self.color_end, self.scope, self.start_point)
+        else:
+            mask = _line_gradient_mask(image, self.start_point, self.color_start, self.color_end, mode=self.mode)
+
+        if self.pattern == 'light':
+            img_new = cv2.addWeighted(image, 1, mask, self.bright_rate, 0.0)
+        else:
+            img_new = cv2.addWeighted(image, self.bright_rate, mask, 1 - self.bright_rate, 0.0)
+        return img_new
+
+
+class Perlin:
+    """
+    Add perlin noise to given image.
+
+    Args:
+        ratio(float): Noise density. Default: 0.3.
+        shade(float): The degree of background shade color. Default: 0.1.
+
+    Examples:
+        >>> img = cv2.imread('xx.png')
+        >>> img = np.array(img)
+        >>> shade = 0.1
+        >>> ratio = 0.2
+        >>> trans = Perlin(ratio, shade)
+        >>> new_img = trans(img)
+    """
+
+    def __init__(self, ratio, shade=0.1):
+        super(Perlin).__init__()
+        ratio = check_param_type('ratio', ratio, float)
+        ratio = check_param_in_range('ratio', ratio, 0, 1)
+        if ratio > 0.7:
+            self.ratio = 7
+        else:
+            self.ratio = int(ratio * 10)
+        shade = check_param_type('shade', shade, float)
+        self.shade = check_param_in_range('shade', shade, 0, 1)
+
+    def __call__(self, image):
+        """
+        Args:
+            image(numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, image with perlin noise.
+        """
+        image = check_numpy_param('image', image)
+        noise = generate_fractal_noise_2d((1024, 1024), (2 ** self.ratio, 2 ** self.ratio), 4)
+        noise[noise < 0] = 0
+        noise[noise > 1] = 1
+        back = np.array((1 - noise) * 255, dtype=np.uint8)
+        back = cv2.resize(back, (image.shape[1], image.shape[0]))
+        back = np.resize(np.repeat(back, 3), image.shape)
+        dst = cv2.addWeighted(image, 1 - self.shade, back, self.shade, 0)
+        return dst
+
+
+class BackShadow:
+    """
+    Add back ground picture to given image.
+
+    Args:
+        template_path(str): Path of template pictures file.
+        shade(float): TWeight of background. Default: 0.1.
+
+    Examples:
+        >>> img = cv2.imread('xx.png')
+        >>> img = np.array(img)
+        >>> template_path = 'template/leaf'
+        >>> shade = 0.2
+        >>> trans = BackShadow(template_path, shade=shade)
+        >>> new_img = trans(img)
+    """
+
+    def __init__(self, template_path, shade=0.1):
+        super(BackShadow).__init__()
+        if os.path.exists(template_path):
+            self.template_path = template_path
+        else:
+            msg = "Template_path is not exist"
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+        shade = check_param_type('shade', shade, float)
+        self.shade = check_param_in_range('shade', shade, 0, 1)
+
+    def __call__(self, image):
+        """
+        Args:
+            image(numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, image with background shadow.
+        """
+        image = check_numpy_param('image', image)
+        file = os.listdir(self.template_path)
+        file_path = os.path.join(self.template_path, np.random.choice(file))
+        shadow = cv2.imread(file_path)
+        beta = 0
+        shadow = cv2.resize(shadow, (image.shape[1], image.shape[0]))
+        dst = cv2.addWeighted(image, 1 - self.shade, shadow, self.shade, beta)
+        return dst
+
+
+class NaturalNoise:
+    """
+    Add natural noise to an image.
+
+    Args:
+        ratio(float): Noise density. Default: 0.0002.
+        k_x_range(union[list, tuple]): Value range of the noise block length.
+        k_y_range(union[list, tuple]): Value range of the noise block width.
+
+    Examples:
+        >>> img = cv2.imread('xx.png')
+        >>> img = np.array(img)
+        >>> k_x_range=(1, 5)
+        >>> k_y_range=(3, 25)
+        >>> trans = NaturalNoise(range=0.0002)
+        >>> new_img = trans(img)
+
+    """
+
+    def __init__(self, ratio=0.0002, k_x_range=(1, 5), k_y_range=(3, 25)):
+        super(NaturalNoise).__init__()
+        self.ratio = check_param_type('ratio', ratio, float)
+        k_x_range = check_param_multi_types('k_x_range', k_x_range, [list, tuple])
+        k_y_range = check_param_multi_types('k_y_range', k_y_range, [list, tuple])
+        self.k_x_range = tuple(k_x_range)
+        self.k_y_range = tuple(k_y_range)
+
+    def __call__(self, image):
+        """
+        Add natural noise to given image.
+        Args:
+            image(numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, image with natural noise.
+        """
+        image = check_numpy_param('image', image)
+        randon_range = 100
+        w, h = image.shape[:2]
+        dst = np.ones((w, h, 3), dtype=np.uint8) * 255
+        for _ in range(5):
+            noise = np.ones((w, h, 3), dtype=np.uint8) * 255
+            rate = self.ratio / 5
+            mask = np.random.uniform(size=(w, h)) < rate
+            noise[mask] = np.random.randint(0, randon_range)
+
+            k_x, k_y = np.random.randint(*self.k_x_range), np.random.randint(*self.k_y_range)
+            kernel = np.ones((k_x, k_y), np.uint8)
+            erode = cv2.erode(noise, kernel, iterations=1)
+            dst = erode * (erode < randon_range) + dst * (1 - erode < randon_range)
+            # Add black point
+            for _ in range(np.random.randint(k_x * k_y / 2)):
+                x = np.random.randint(-k_x, k_x)
+                y = np.random.randint(-k_y, k_y)
+                matrix = np.array([[1, 0, y], [0, 1, x]], dtype=np.float)
+                affine = cv2.warpAffine(noise, matrix, (h, w))
+                dst = affine * (affine < randon_range) + dst * (1 - affine < randon_range)
+            # Add white point
+            for _ in range(int(k_x * k_y / 2)):
+                x = np.random.randint(-k_x / 2 - 1, k_x / 2 + 1)
+                y = np.random.randint(-k_y / 2 - 1, k_y / 2 + 1)
+                matrix = np.array([[1, 0, y], [0, 1, x]], dtype=np.float)
+                affine = cv2.warpAffine(noise, matrix, (h, w))
+                white = affine < randon_range
+                dst[white] = 255
+
+        mask = dst < randon_range
+        dst = image * (1 - mask) + dst * mask
+        dst = np.array(dst, dtype=np.uint8)
+        return dst
+
+
+class Curve:
+    """
+    Curve picture using sin method.
+
+    Args:
+        curves(union[float, int]): Divide width to curves of `2*math.pi`, which means how many curve cycles.
+        depth(union[float, int]): Amplitude of sin method.
+        mode(str): Direction of deformation. Optional value is 'vertical' or 'horizontal'.
+
+    Examples:
+        >>> img = cv2.imread('x.png')
+        >>> curves =1
+        >>> depth = 10
+        >>> trans = Curve(curves, depth, mode='vertical')
+        >>> img_new = trans(img)
+    """
+
+    def __init__(self, curves=10, depth=10, mode='vertical'):
+        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)
+
+    def __call__(self, image):
+        """
+        Curve picture using sin method.
+
+        Args:
+            image(numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, curved image.
+        """
+        image = check_numpy_param('image', image)
+        if self.mode == 'vertical':
+            image = np.transpose(image, [1, 0, 2])
+        heights, widths = image.shape[:2]
+
+        src_x = np.zeros((heights, widths), np.float32)
+        src_y = np.zeros((heights, widths), np.float32)
+
+        for y in range(heights):
+            for x in range(widths):
+                src_x[y, x] = x
+                src_y[y, x] = y + self.depth * math.sin(x / (widths / self.curves / 2 / math.pi))
+        img_new = cv2.remap(image, src_x, src_y, cv2.INTER_LINEAR)
+        if self.mode == 'vertical':
+            img_new = np.transpose(img_new, [1, 0, 2])
+        return img_new
+
+
+class BackgroundWord:
+    """
+    Overlay the background image on the original image.
+
+    Args:
+        shade(float): Weight of background.
+        back(numpy.ndarray): Background Image. If none, mean background image is the as original image.
+
+    Examples:
+        >>> img = cv2.imread('x.png')
+        >>> back = cv2.imread('x.png')
+        >>> shade=0.2
+        >>> trans = BackgroundWord(shade, back)
+        >>> img_new = trans(img)
+    """
+
+    def __init__(self, shade=0.1, back=None):
+        super(BackgroundWord).__init__()
+        self.shade = shade
+        self.back = back
+
+    def __call__(self, image):
+        image = check_numpy_param('image', image)
+        beta = 0
+        width, height = image.shape[:2]
+        x = np.random.randint(0, int(width / 5))
+        y = np.random.randint(0, int(height / 5))
+        matrix = np.array([[1, 0, y], [0, 1, x]], dtype=np.float)
+        affine = cv2.warpAffine(image.copy(), matrix, (height, width))
+        back = image.copy()
+        back[x:, y:] = affine[x:, y:]
+        dst = cv2.addWeighted(image, 1 - self.shade, back, self.shade, beta)
+        return dst