!285 reconstruct output of natural robustness serving

Merge pull request !285 from ZhidanLiu/serving

README.md

@@ -93,7 +93,12 @@ The architecture is shown as follow：
     cd mindarmour
     python setup.py install
     ```
+3. Natural Robustness method in MindArmour use third-party package: Perlin-numpy. This package can be installed by command:
 
+    ```bash
+    pip3 install git+https://github.com/pvigier/perlin-numpy
+    ```
+   
 #### Installation by pip
 
 ```bash

README_CN.md

@@ -90,6 +90,12 @@ Fuzz Testing模块的架构图如下：
     cd mindarmour
     python setup.py install
     ```
+ 
+3. 图片自然扰动算法使用到Perlin-numpy，安装：
+
+    ```bash
+    pip3 install git+https://github.com/pvigier/perlin-numpy
+    ```
 
 #### pip安装
 

examples/natural_robustness/natural_robustness_example.py

@@ -0,0 +1,176 @@
+# 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.
+
+"""Example for natural robustness methods."""
+
+import numpy as np
+import cv2
+
+from mindarmour.natural_robustness.natural_noise import Perlin, Perspective, Scale, Shear, SaltAndPepperNoise, \
+    BackgroundWord, BackShadow, MotionBlur, GaussianBlur, GradientBlur, Rotate, Contrast, Translate, Curve, \
+    GradientLuminance, NaturalNoise
+
+
+def test_perspective(image):
+    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(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_constract(image):
+    trans = Contrast(alpha=1.5, beta=0)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_gaussian_blur(image):
+    trans = GaussianBlur(ksize=5)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_salt_and_pepper_noise(image):
+    trans = SaltAndPepperNoise(factor=0.01)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_translate(image):
+    trans = Translate(x_bias=0.1, y_bias=0.1)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_scale(image):
+    trans = Scale(factor_x=0.7, factor_y=0.7)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_shear(image):
+    trans = Shear(factor=0.2)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_rotate(image):
+    trans = Rotate(angle=20)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_background_word(image):
+    trans = BackgroundWord(shade=0.1)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_curve(image):
+    trans = Curve(curves=1.5, depth=1.5, mode='horizontal')
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_natural_noise(image):
+    trans = NaturalNoise(ratio=0.0001, k_x_range=(1, 30), k_y_range=(1, 10))
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_back_shadow(image):
+    image = np.array(image)
+    template_path = 'test_data/template/leaf'
+    shade = 0.2
+    trans = BackShadow(template_path, shade=shade)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_perlin(image):
+    image = np.array(image)
+    shade = 0.5
+    ratio = 0.3
+    trans = Perlin(ratio, shade)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_gradient_luminance(image):
+    height, width = image.shape[:2]
+    point = (height // 4, width // 2)
+    start = (255, 255, 255)
+    end = (0, 0, 0)
+    scope = 0.3
+    bright_rate = 0.4
+    trans = GradientLuminance(start, end, start_point=point, scope=scope, pattern='dark', bright_rate=bright_rate,
+                              mode='horizontal')
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_motion_blur(image):
+    angle = -10.5
+    i = 3
+    trans = MotionBlur(degree=i, angle=angle)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+def test_gradient_blur(image):
+    number = 4
+    h, w = image.shape[:2]
+    point = (int(h / 5), int(w / 5))
+    center = True
+    trans = GradientBlur(point, number, center)
+    dst = trans(image)
+    cv2.imshow('dst', dst)
+    cv2.waitKey()
+
+
+if __name__ == '__main__':
+    img = cv2.imread('test_data/1.png')
+    img = np.array(img)
+    test_motion_blur(img)
+    test_gradient_blur(img)
+    test_gradient_luminance(img)
+    test_perlin(img)
+    test_back_shadow(img)
+    test_natural_noise(img)
+    test_curve(img)
+    test_background_word(img)
+    test_rotate(img)
+    test_shear(img)
+    test_scale(img)
+    test_translate(img)
+    test_salt_and_pepper_noise(img)
+    test_gaussian_blur(img)
+    test_constract(img)
+    test_perspective(img)

examples/natural_robustness/serving/README.md

@@ -0,0 +1,232 @@
+# 自然扰动样本生成serving
+
+提供自然扰动样本生成在线服务。客户端传入图片和扰动参数，服务端返回扰动后的图片数据。
+
+## 环境准备
+
+硬件环境：Ascend 910，GPU
+
+操作系统：Linux-x86_64
+
+软件环境：
+
+1. python 3.7.5或python 3.9.0
+
+2. 安装MindSpore 1.5.0可以参考[MindSpore安装页面](https://www.mindspore.cn/install)
+
+3. 安装MindSpore Serving 1.5.0可以参考[MindSpore Serving 安装页面](https://www.mindspore.cn/serving/docs/zh-CN/r1.5/serving_install.html)
+
+4. 安装serving分支的MindArmour:
+
+   - 从Gitee下载源码
+
+     `git clone https://gitee.com/mindspore/mindarmour.git`
+
+   - 在源码根目录下，切换到serving分支
+
+     `cd mindarmour`
+
+     `git checkout -b serving origin/serving`
+
+   - 编译并安装MindArmour
+
+     `python setup.py install`
+
+5. 安装Perlin-numpy:
+
+    `pip3 install git+https://github.com/pvigier/perlin-numpy`
+
+### 文件结构说明
+
+```bash
+serving
+├── server
+│   ├── serving_server.py   # 启动serving服务脚本
+│   ├── export_model
+│   │   └── add_model.py    # 生成模型文件脚本
+│   └── perturbation
+│       └── serverable_config.py    # 服务端接收客户端数据后的处理脚本
+└── client
+    ├── serving_client.py   # 启动客户端脚本
+    └── perturb_config.py   # 扰动方法配置文件
+```
+
+## 脚本说明及使用
+
+### 导出模型
+
+在`server/export_model`目录下，使用[add_model.py](https://gitee.com/mindspore/serving/blob/r1.5/example/tensor_add/export_model/add_model.py)，构造了一个只有Add算子的tensor加法网络。使用命令
+
+```bash
+python add_model.py
+```
+
+在`perturbation`模型文件夹下生成`tensor_add.mindir`模型文件。
+
+该服务实际上并没有使用到模型，但目前版本的serving需要有一个模型，serving升级后这部分会删除。
+
+### 部署Serving推理服务
+
+1. #### `servable_config.py`说明。
+
+   ```python
+   ···
+
+   # Path of template images
+   TEMPLATE_LEAF_PATH = '/root/mindarmour/example/test_data/template/leaf'
+   TEMPLATE_WINDOW_PATH = '/root/mindarmour/example/test_data/template/window'
+   TEMPLATE_PERSON_PATH = '/root/mindarmour/example/test_data/template/person'
+   TEMPLATE_BACKGROUND_PATH = '/root/mindarmour/example/test_data/template/dirt_background'
+
+   ···
+
+   # 客户端可以请求的方法，包含4个返回值："results", "file_names", "file_length", "names_dict"
+
+   @register.register_method(output_names=["results", "file_names", "file_length", "names_dict"])
+   def natural_perturbation(img, perturb_config, methods_number, outputs_number):
+       """method natural_perturbation data flow definition, only preprocessing and call model"""
+       res = register.add_stage(perturb, img, perturb_config, methods_number, outputs_number, outputs_count=4)
+       return res
+   ```
+
+   方法`natural_perturbation`为对外提供服务的接口。
+
+   **输入：**
+
+   - img：输入为图片，格式为bytes。
+   - perturb_config：扰动配置项，具体配置参考`perturb_config.py`。
+   - methods_number：每次扰动随机从配置项中选择方法的个数。
+   - outputs_number：对于每张图片，生成的扰动图片数量。
+
+   **输出**res中包含4个参数：
+
+   - results：拼接后的图像bytes；
+
+   - file_names：图像名，格式为`xxx.png`，其中‘xxx’为A-Za-z中随机选择20个字符构成的字符串。
+
+   - file_length：每张图片的bytes长度。
+
+   - names_dict: 图片名和图片使用扰动方法构成的字典。格式为：
+
+     ```bash
+     {
+     picture1.png: [[method1, parameters of method1], [method2, parameters of method2], ...]],
+     picture2.png: [[method3, parameters of method3], [method4, parameters of method4], ...]],
+     ...
+     }
+     ```
+
+   启动server服务前请将TEMPLATE_LEAF_PATH、TEMPLATE_WINDOW_PATH、TEMPLATE_PERSON_PATH、TEMPLATE_BACKGROUND_PATH的路径换成用户本地模板图片路径。
+
+2. #### 启动server。
+
+   ```python
+   ···
+
+   def start():
+       servable_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
+       # 服务配置
+       servable_config = server.ServableStartConfig(servable_directory=servable_dir, servable_name="perturbation", device_ids=(0, 1), num_parallel_workers=4)
+       # 启动服务
+       server.start_servables(servable_configs=servable_config)
+
+       # 启动启动gRPC服务，用于客户端和服务端之间通信
+       server.start_grpc_server(address="0.0.0.0:5500", max_msg_mb_size=200)   # ip和最大的传输数据量，单位MB
+       # 启动启动Restful服务，用于客户端和服务端之间通信
+       server.start_restful_server(address="0.0.0.0:5500")
+   ```
+
+   gRPC传输性能更好，Restful更适合用于web服务，根据需要选择。
+
+   执行命令`python serverong_server.py`启动服务。
+
+   当服务端打印日志`Serving RESTful server start success, listening on 0.0.0.0:5500`时，表示Serving RESTful服务启动成功，推理模型已成功加载。
+
+### 客户端进行推理
+
+1. 在`perturb_config.py`中设置扰动方法及参数。下面是个例子：
+
+   ```python
+   PerturbConfig = [{"method": "Contrast", "params": {"alpha": 1.5, "beta": 0}},
+                    {"method": "GaussianBlur", "params": {"ksize": 5}},
+                    {"method": "SaltAndPepperNoise", "params": {"factor": 0.05}},
+                    {"method": "Translate", "params": {"x_bias": 0.1, "y_bias": -0.2}},
+                    {"method": "Scale", "params": {"factor_x": 0.7, "factor_y": 0.7}},
+                    {"method": "Shear", "params": {"factor": 2, "director": "horizontal"}},
+                    {"method": "Rotate", "params": {"angle": 40}},
+                    {"method": "MotionBlur", "params": {"degree": 5, "angle": 45}},
+                    {"method": "GradientBlur", "params": {"point": [50, 100], "kernel_num": 3, "center": True}},
+                    {"method": "GradientLuminance",
+                     "params": {"color_start": [255, 255, 255],
+                                "color_end": [0, 0, 0],
+                                "start_point": [100, 150], "scope": 0.3,
+                                "bright_rate": 0.3, "pattern": "light",
+                                "mode": "circle"}},
+                    {"method": "Perlin", "params": {"ratio": 0.5, "shade": 0.1}},
+                    {"method": "Curve", "params": {"curves": 10, "depth": 10,
+                                                    "mode": "vertical"}},
+                    {"method": "BackgroundWord", "params": {"shade": 0.1}},
+                    {"method": "Perspective",
+                     "params": {"ori_pos": [[0, 0], [0, 800], [800, 0], [800, 800]],
+                                "dst_pos": [[50, 0], [0, 800], [780, 0], [800, 800]]}},
+                    {"method": "BackShadow",
+                    "params": {"back_type": 'leaf', "shade": 0.2}},
+                   ]
+   ```
+
+   其中`method`为扰动方法名，`params`为对应方法的参数。可用的扰动方法及对应参数可在`mindarmour/natural_robustness/natural_noise.py`中查询。
+
+   其中，`BackShadow`方法的参数较为特别，在`natural_noise.py`中的参数为template_path，但是这里配置时需改为back_type，参数值取值范围：'leaf'、'window'、'person'、'background'。
+
+2. 在`serving_client.py`中写客户端的处理脚本，包含输入输出的处理、服务端的调用，可以参考下面的例子。
+
+   ```python
+   ···
+
+   def perturb(perturb_config):
+       """invoke servable perturbation method natural_perturbation"""
+
+       # 请求的服务端ip及端口、请求的服务名、请求的方法名
+       client = Client("10.175.122.87:5500", "perturbation", "natural_perturbation")
+
+       # 输入数据
+       instances = []
+       img_path = '/root/mindarmour/example/adversarial/test_data/1.png'
+       result_path = '/root/mindarmour/example/adv/result/'
+       methods_number = 2
+       outputs_number = 3
+       img = cv2.imread(img_path)
+       img = cv2.imencode('.png', img)[1].tobytes()    # 图片传输用bytes格式，不支持numpy.ndarray格式
+       perturb_config = json.dumps(perturb_config) # 配置方法转成json格式
+       instances.append({"img": img, 'perturb_config': perturb_config, "methods_number": methods_number,
+                      "outputs_number": outputs_number})   # instances中可添加多个输入
+
+       # 请求服务，返回结果
+       result = client.infer(instances)
+
+       # 对服务请求得到的结果进行处理，将返回的图片字节流存成图片
+       file_names = result[0]['file_names'].split(';')
+       length = result[0]['file_length'].tolist()
+       before = 0
+       for name, leng in zip(file_names, length):
+           res_img = result[0]['results']
+           res_img = res_img[before:before + leng]
+           before = before + leng
+           print('name: ', name)
+           image = Image.open(BytesIO(res_img))
+           image.save(os.path.join(result_path, name))
+
+       names_dict = result[0]['names_dict']
+       with open('names_dict.json', 'w') as file:
+           file.write(names_dict)
+   ```
+
+   启动client前，需将服务端的IP地址改成部署server的IP地址，图片路径、结果存储路基替换成用户数据路径。
+
+   目前serving数据传输支持的数据类型包括：python的int、float、bool、str、bytes，numpy number, numpy array object。
+
+   输入命令`python serving_client.py`开启客户端，如果对应目录下生成扰动样本图片则说明serving服务正确执行。
+
+   ### 其他
+
+   在`serving_logs`目录下可以查看运行日志，辅助debug。

examples/natural_robustness/serving/client/perturb_config.py

@@ -0,0 +1,46 @@
+# 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.
+
+"""
+Configuration of natural robustness methods for server.
+"""
+
+PerturbConfig = [{"method": "Contrast", "params": {"alpha": 1.5, "beta": 0}},
+                 {"method": "GaussianBlur", "params": {"ksize": 5}},
+                 {"method": "SaltAndPepperNoise", "params": {"factor": 0.05}},
+                 {"method": "Translate", "params": {"x_bias": 0.1, "y_bias": -0.2}},
+                 {"method": "Scale", "params": {"factor_x": 0.7, "factor_y": 0.7}},
+                 {"method": "Shear", "params": {"factor": 2, "direction": "horizontal"}},
+                 {"method": "Rotate", "params": {"angle": 40}},
+                 {"method": "MotionBlur", "params": {"degree": 5, "angle": 45}},
+                 {"method": "GradientBlur", "params": {"point": [50, 100], "kernel_num": 3, "center": True}},
+                 {"method": "GradientLuminance", "params": {"color_start": [255, 255, 255], "color_end": [0, 0, 0],
+                                                            "start_point": [100, 150], "scope": 0.3,
+                                                            "bright_rate": 0.3, "pattern": "light", "mode": "circle"}},
+                 {"method": "GradientLuminance", "params": {"color_start": [255, 255, 255],
+                                                            "color_end": [0, 0, 0], "start_point": [150, 200],
+                                                            "scope": 0.3, "pattern": "light", "mode": "horizontal"}},
+                 {"method": "GradientLuminance", "params": {"color_start": [255, 255, 255], "color_end": [0, 0, 0],
+                                                            "start_point": [150, 200], "scope": 0.3,
+                                                            "pattern": "light", "mode": "vertical"}},
+                 {"method": "Perlin", "params": {"ratio": 0.5, "shade": 0.1}},
+                 {"method": "Curve", "params": {"curves": 10, "depth": 10, "mode": "vertical"}},
+                 {"method": "BackgroundWord", "params": {"shade": 0.1}},
+                 {"method": "Perspective", "params": {"ori_pos": [[0, 0], [0, 800], [800, 0], [800, 800]],
+                                                      "dst_pos": [[50, 0], [0, 800], [780, 0], [800, 800]]}},
+                 {"method": "BackShadow", "params": {"back_type": 'leaf', "shade": 0.2}},
+                 {"method": "BackShadow", "params": {"back_type": 'window', "shade": 0.2}},
+                 {"method": "BackShadow", "params": {"back_type": 'person', "shade": 0.1}},
+                 {"method": "BackShadow", "params": {"back_type": 'background', "shade": 0.1}},
+                 ]

examples/natural_robustness/serving/client/serving_client.py

@@ -0,0 +1,59 @@
+# 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.
+# ============================================================================
+"""The client of example add."""
+import os
+import json
+from io import BytesIO
+
+import cv2
+from PIL import Image
+from mindspore_serving.client import Client
+
+from perturb_config import PerturbConfig
+
+
+def perturb(perturb_config):
+    """Invoke servable perturbation method natural_perturbation"""
+    client = Client("10.175.244.87:5500", "perturbation", "natural_perturbation")
+    instances = []
+    img_path = '/root/mindarmour/example/adversarial/test_data/1.png'
+    result_path = '/root/mindarmour/example/adv/result/'
+    methods_number = 2
+    outputs_number = 3
+    img = cv2.imread(img_path)
+    img = cv2.imencode('.png', img)[1].tobytes()
+    perturb_config = json.dumps(perturb_config)
+    instances.append({"img": img, 'perturb_config': perturb_config, "methods_number": methods_number,
+                      "outputs_number": outputs_number})
+
+    result = client.infer(instances)
+
+    file_names = result[0]['file_names'].split(';')
+    length = result[0]['file_length'].tolist()
+    before = 0
+    for name, leng in zip(file_names, length):
+        res_img = result[0]['results']
+        res_img = res_img[before:before + leng]
+        before = before + leng
+        print('name: ', name)
+        image = Image.open(BytesIO(res_img))
+        image.save(os.path.join(result_path, name))
+    names_dict = result[0]['names_dict']
+    with open('names_dict.json', 'w') as file:
+        file.write(names_dict)
+
+
+if __name__ == '__main__':
+    perturb(PerturbConfig)

examples/natural_robustness/serving/server/export_model/add_model.py

@@ -0,0 +1,58 @@
+# 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.
+# ============================================================================
+"""add model generator"""
+import os
+from shutil import copyfile
+import numpy as np
+
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.ops as ops
+import mindspore as ms
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    """Define Net of add"""
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.add = ops.Add()
+
+    def construct(self, x_, y_):
+        """construct add net"""
+        return self.add(x_, y_)
+
+
+def export_net():
+    """Export add net of 2x2 + 2x2, and copy output model `tensor_add.mindir` to directory ../add/1"""
+    x = np.ones([2, 2]).astype(np.float32)
+    y = np.ones([2, 2]).astype(np.float32)
+    add = Net()
+    ms.export(add, ms.Tensor(x), ms.Tensor(y), file_name='tensor_add', file_format='MINDIR')
+    dst_dir = '../perturbation/1'
+    try:
+        os.mkdir(dst_dir)
+    except OSError:
+        pass
+
+    dst_file = os.path.join(dst_dir, 'tensor_add.mindir')
+    copyfile('tensor_add.mindir', dst_file)
+    print("copy tensor_add.mindir to " + dst_dir + " success")
+
+
+if __name__ == "__main__":
+    export_net()

examples/natural_robustness/serving/server/perturbation/servable_config.py

@@ -0,0 +1,129 @@
+# 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.
+# ============================================================================
+"""perturbation servable config"""
+import json
+import copy
+import random
+from io import BytesIO
+import cv2
+from PIL import Image
+from mindspore_serving.server import register
+from mindarmour.natural_robustness.natural_noise import *
+
+# Path of template images
+TEMPLATE_LEAF_PATH = '/root/mindarmour/example/adv/test_data/template/leaf'
+TEMPLATE_WINDOW_PATH = '/root/mindarmour/example/adv/test_data/template/window'
+TEMPLATE_PERSON_PATH = '/root/mindarmour/example/adv/test_data/template/person'
+TEMPLATE_BACKGROUND_PATH = '/root/mindarmour/example/adv/test_data//template/dirt_background'
+
+CHARACTERS = [chr(i) for i in range(65, 91)]+[chr(j) for j in range(97, 123)]
+
+path_dict = {'leaf': TEMPLATE_LEAF_PATH,
+             'window': TEMPLATE_WINDOW_PATH,
+             'person': TEMPLATE_PERSON_PATH,
+             'background': TEMPLATE_BACKGROUND_PATH}
+
+methods_dict = {'Contrast': Contrast,
+                'GaussianBlur': GaussianBlur,
+                'SaltAndPepperNoise': SaltAndPepperNoise,
+                'Translate': Translate,
+                'Scale': Scale,
+                'Shear': Shear,
+                'Rotate': Rotate,
+                'MotionBlur': MotionBlur,
+                'GradientBlur': GradientBlur,
+                'GradientLuminance': GradientLuminance,
+                'Perlin': Perlin,
+                'BackShadow': BackShadow,
+                'NaturalNoise': NaturalNoise,
+                'Curve': Curve,
+                'BackgroundWord': BackgroundWord,
+                'Perspective': Perspective}
+
+
+def check_inputs(img, perturb_config, methods_number, outputs_number):
+    """Check inputs."""
+    if not np.any(img):
+        raise ValueError("img cannot be empty.")
+    img = Image.open(BytesIO(img))
+    img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
+
+    config = json.loads(perturb_config)
+    if not config:
+        raise ValueError("perturb_config cannot be empty.")
+    for item in config:
+        if item['method'] not in methods_dict.keys():
+            raise ValueError("{} is not a valid method.".format(item['method']))
+        if item['method'] == 'BackShadow':
+            item['params']['template_path'] = path_dict[item['params']['back_type']]
+            del item['params']['back_type']
+
+    methods_number = int(methods_number)
+    if methods_number < 1:
+        raise ValueError("methods_number must more than 0.")
+    outputs_number = int(outputs_number)
+    if outputs_number < 1:
+        raise ValueError("outputs_number must more than 0.")
+
+    return img, config, methods_number, outputs_number
+
+
+def perturb(img, perturb_config, methods_number, outputs_number):
+    """Perturb given image."""
+    img, config, methods_number, outputs_number = check_inputs(img, perturb_config, methods_number, outputs_number)
+    res_img_bytes = b''
+    file_names = []
+    file_length = []
+    names_dict = {}
+    for _ in range(outputs_number):
+        dst = copy.deepcopy(img)
+        used_methods = []
+        for _ in range(methods_number):
+            item = np.random.choice(config)
+            method_name = item['method']
+            method = methods_dict[method_name]
+            params = item['params']
+            dst = method(**params)(img)
+
+            if method_name == 'BackShadow':
+                method_params = copy.deepcopy(params)
+                method_params['back_type'] = method_params['template_path'].split('/')[-1]
+                del method_params['template_path']
+            else:
+                method_params = params
+
+            used_methods.append([method_name, method_params])
+        name = ''.join(random.sample(CHARACTERS, 20))
+        name += '.png'
+        file_names.append(name)
+        names_dict[name] = used_methods
+
+        res_img = cv2.imencode('.png', dst)[1].tobytes()
+        res_img_bytes += res_img
+        file_length.append(len(res_img))
+
+    names_dict = json.dumps(names_dict)
+
+    return res_img_bytes, ';'.join(file_names), file_length, names_dict
+
+
+model = register.declare_model(model_file="tensor_add.mindir", model_format="MindIR", with_batch_dim=False)
+
+
+@register.register_method(output_names=["results", "file_names", "file_length", "names_dict"])
+def natural_perturbation(img, perturb_config, methods_number, outputs_number):
+    """method natural_perturbation data flow definition, only preprocessing and call model"""
+    res = register.add_stage(perturb, img, perturb_config, methods_number, outputs_number, outputs_count=4)
+    return res

examples/natural_robustness/serving/server/serving_server.py

@@ -0,0 +1,35 @@
+# 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.
+# ============================================================================
+"""The server of example perturbation"""
+
+import os
+import sys
+from mindspore_serving import server
+
+
+def start():
+    """Start server."""
+    servable_dir = os.path.dirname(os.path.realpath(sys.argv[0]))
+
+    servable_config = server.ServableStartConfig(servable_directory=servable_dir, servable_name="perturbation",
+                                                 device_ids=(0, 1), num_parallel_workers=4)
+    server.start_servables(servable_configs=servable_config)
+
+    server.start_grpc_server(address="0.0.0.0:5500", max_msg_mb_size=200)
+    # server.start_restful_server(address="0.0.0.0:5500")
+
+
+if __name__ == "__main__":
+    start()

mindarmour/adv_robustness/__init__.py

@@ -14,6 +14,5 @@
 """
 Adversarial Robustness.
 
-This module is a tool box to enhance model security and against adversarial
-examples.
+This module is a tool box to enhance model security and against adversarial examples.
 """

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.
+"""

mindarmour/natural_robustness/natural_noise.py

@@ -0,0 +1,916 @@
+# 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. Suggested value range in [0.2, 2].
+        beta (Union[float, int]): Delta added to alpha. Default: 0.
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> alpha = 0.1
+        >>> beta = 1
+        >>> trans = Contrast(alpha, beta)
+        >>> dst = trans(img)
+    """
+
+    def __init__(self, alpha=1, beta=0):
+        super(Contrast, self).__init__()
+        self.alpha = check_param_multi_types('factor', alpha, [int, float])
+        self.beta = check_param_multi_types('factor', beta, [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]): Size of  gaussian kernel.
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> ksize = 0.1
+        >>> trans = GaussianBlur(ksize)
+        >>> dst = trans(img)
+    """
+
+    def __init__(self, ksize=5):
+        super(GaussianBlur, self).__init__()
+        ksize = check_int_positive('ksize', ksize)
+        self.ksize = (ksize, 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): Noise density, the proportion of noise points per unit pixel area. Suggested value range in
+            [0.001, 0.15].
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> factor = 0.1
+        >>> trans = SaltAndPepperNoise(factor)
+        >>> dst = trans(img)
+    """
+
+    def __init__(self, factor=0):
+        super(SaltAndPepperNoise, self).__init__()
+        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. Suggested value range
+            in [-0.1, 0.1].
+        y_bias (Union[int, float]): Y-direction translation,  y = y + y_bias*image_wide. Suggested value range
+            in [-0.1, 0.1].
+
+    Example:
+        >>> 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):
+        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])
+
+    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. 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.
+
+    Example:
+        >>> 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):
+        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])
+
+    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 (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'.
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> factor = 0.2
+        >>> trans = Shear(factor, direction='horizontal')
+        >>> dst = trans(img)
+    """
+
+    def __init__(self, factor, direction='horizontal'):
+        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
+
+    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.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))
+        return new_img
+
+
+class Rotate:
+    """
+    Rotate an image of counter clockwise around its center.
+
+    Args:
+        angle (Union[float, int]): Degrees of counter clockwise. Suggested value range in [-60, 60].
+
+    Example:
+        >>> img = cv2.imread('1.png')
+        >>> img = np.array(img)
+        >>> angle = 20
+        >>> trans = Rotate(angle)
+        >>> dst = trans(img)
+    """
+    def __init__(self, angle=20):
+        super(Rotate, self).__init__()
+        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])
+
+        # 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))
+        return rotate
+
+
+class Perspective:
+    """
+    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.
+
+    Example:
+        >>> 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):
+        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. Suggested value range in [1, 15].
+        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. Suggested value range in [1, 8].
+        center (bool): Blurred or clear at the center of a specified point.
+
+    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): Range of the gradient. A larger value indicates a larger gradient range.
+        point (union([tuple, list]): Gradient 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.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.
+        start_pos (union[tuple, list]): 2D coordinate of gradient center.
+        start_color (union([tuple, list])): Color of circle gradient center.
+        end_color (union([tuple, list])): Color of circle gradient edge.
+        mode　(str): Direction of gradient. Optional value is 'vertical' or 'horizontal'.
+
+    Returns:
+        numpy.ndarray, gradients mask.
+    """
+    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.
+        pattern (str): Dark or light, this value must be in ['light', 'dark'].
+        bright_rate (float): Control brightness of . A larger value indicates a larger gradient range. If parameter
+            'pattern' is 'light', Suggested value range in [0.1, 0.7], if parameter 'pattern' is 'dark', Suggested value
+            range in [0.1, 0.9].
+        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
+        >>> pattern='light'
+        >>> bright_rate = 0.3
+        >>> trans = GradientLuminance(start, end, point, scope, pattern, bright_rate, 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):
+        """
+        Gradient adjusts the luminance of picture.
+
+        Args:
+            image (numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, image with perlin noise.
+        """
+        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. Suggested value range in [0.05, 0.9].
+        shade (float): The degree of background shade color. Suggested value range in [0.1, 0.5].
+
+    Examples:
+        >>> img = cv2.imread('xx.png')
+        >>> img = np.array(img)
+        >>> ratio = 0.2
+        >>> shade = 0.1
+        >>> 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):
+        """
+        Add perlin noise to given 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 background picture to given image.
+
+    Args:
+        template_path (str): Path of template pictures file.
+        shade (float): The weight of background. Suggested value range in [0.1, 0.7].
+
+    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):
+        """
+        Add background picture to given 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)
+        shadow = cv2.resize(shadow, (image.shape[1], image.shape[0]))
+        dst = cv2.addWeighted(image, 1 - self.shade, shadow, self.shade, 0)
+        return dst
+
+
+class NaturalNoise:
+    """
+    Add natural noise to an image.
+
+    Args:
+        ratio (float): Noise density, the proportion of noise blocks per unit pixel area. Suggested value range in
+            [0.00001, 0.001].
+        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)
+        >>> ratio = 0.0002
+        >>> k_x_range = (1, 5)
+        >>> k_y_range = (3, 25)
+        >>> trans = NaturalNoise(ratio, k_x_range, k_y_range)
+        >>> 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.　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'.
+
+    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): The weight of background. Suggested value range in [0.05, 0.3].
+        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):
+        """
+        Overlay the background image on the original image.
+
+        Args:
+            image (numpy.ndarray): Original image.
+
+        Returns:
+            numpy.ndarray, curved image.
+        """
+        image = check_numpy_param('image', image)
+        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, 0)
+        return dst