├── .gitignore
├── Dockerfile
├── README.md
├── app
    ├── app_compat.py
    ├── index.html
    └── start.sh
├── deploy
    └── ImageStyleTransfer
    │   ├── hook
    │       └── index.js
    │   ├── publish.yaml
    │   ├── readme.md
    │   ├── readme_en.md
    │   ├── src
    │       └── s.yaml
    │   └── version.md
├── httpapi_test.py
├── main.py
├── result.png
├── run_batch_push.sh
├── run_build.sh
└── test.png


/.gitignore:
--------------------------------------------------------------------------------
1 | .idea/
2 | 


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/Dockerfile:
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 1 | FROM registry.cn-hongkong.aliyuncs.com/duolabmeng/pyrun:paddlehub222
 2 | 
 3 | COPY app /app
 4 | WORKDIR /app
 5 | 
 6 | ADD /app/app_compat.py /usr/local/lib/python3.7/site-packages/paddlehub/serving/app_compat.py
 7 | RUN hub install animegan_v2_hayao_99
 8 | 
 9 | EXPOSE 9000
10 | CMD ["/bin/bash","-c","hub serving start -m animegan_v2_hayao_99 -p 9000"]
11 | 


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/README.md:
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  1 | # 基于 Serverless 图像转换为宫崎骏动漫风格案例
  2 | 
  3 | 大家可以通过本项目提供的镜像，快速发布成可调用的Restful API服务。
  4 | 
  5 | # animegan_v2_hayao_99
  6 | 
  7 | [使用模型 animegan_v2_hayao_99](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.2/modules/image/Image_gan/style_transfer/animegan_v2_hayao_99)
  8 | 
  9 | |模型名称|animegan_v2_hayao_99|
 10 | | :--- | :---: |
 11 | |类别|图像 - 图像生成|
 12 | |网络|AnimeGAN|
 13 | |数据集|The Wind Rises|
 14 | |是否支持Fine-tuning|否|
 15 | |模型大小|9.4MB|
 16 | |最新更新日期|2021-07-30|
 17 | |数据指标|-|
 18 | 
 19 | ## 一、模型基本信息
 20 | 
 21 | - ### 应用效果展示
 22 |   - 样例结果示例：
 23 |     <p align="center">
 24 |     <img src="./test.png"  width = "450" height = "300" hspace='10'/>
 25 |     <br />
 26 |     输入图像
 27 |     <br />
 28 |     <img src="result.png"  width = "450" height = "300" hspace='10'/>
 29 |     <br />
 30 |     输出图像
 31 |      <br />
 32 |     </p>
 33 | 
 34 | 
 35 | - ### 模型介绍
 36 | 
 37 |   - AnimeGAN V2 图像风格转换模型, 模型可将输入的图像转换成宫崎骏动漫风格，模型权重转换自[AnimeGAN V2官方开源项目](https://github.com/TachibanaYoshino/AnimeGANv2)。
 38 | 
 39 | # 部署方法
 40 | 
 41 | # 1. 在阿里云函数计算应用中心里立即创建
 42 | 
 43 | [阿里云Serverless 应用中心一键体验 ](https://fcnext.console.aliyun.com/applications/create?template=paddleImageStyleTransfer)
 44 | 
 45 | # 2. 终端上输入命令创建
 46 | 
 47 | ```shell
 48 | 
 49 | s init paddleImageStyleTransfer  # 初始化项目
 50 | s deploy  # 部署项目
 51 | 
 52 | ```
 53 | 
 54 | # 调用方法
 55 | 
 56 | ```python
 57 | import requests
 58 | import json
 59 | import cv2
 60 | import base64
 61 | 
 62 | 
 63 | def cv2_to_base64(image):
 64 |     data = cv2.imencode('.jpg', image)[1]
 65 |     return base64.b64encode(data.tostring()).decode('utf8')
 66 | 
 67 | 
 68 | def getResult(imagePath):
 69 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
 70 |     r = requests.post("http://127.0.0.1:9000/predict/animegan_v2_hayao_99", data=data,
 71 |                       headers={'Content-Type': 'application/json'})
 72 |     return r.json()["results"]
 73 | 
 74 | 
 75 | print(getResult("./test.png"))
 76 | ```
 77 | 
 78 | # 调用失败的请看这里
 79 | 
 80 | * 500x300 分辨率 需要 7秒   2.6gb内存 所以函数设置 3gb 运行内存才可以
 81 | * 720x500 分辨率 需要 15秒  5.76gb内存 所以函数设置 8gb 运行内存才可以
 82 | * 2000x1280 分辨率 需要 30秒 6.77g内存 所以设置 8gb 运行内存才可以
 83 | 
 84 | 我是直接设置了16gb内存测试的
 85 | 
 86 | 
 87 | # 本应用的镜像开发教程
 88 | 
 89 | https://github.com/duolabmeng6/paddlehub_ppocr
 90 | 
 91 | 阅读本文你将学会：
 92 | 
 93 | 在 Serverless 架构中 docker 镜像制作的最佳实践，游刃有余的部署复杂场景下的深度学习模型
 94 | 
 95 | 熟练的使用各厂商提供的 Serverless 服务，部署。
 96 | 
 97 | 制作小巧精良的 docker 镜像
 98 | 
 99 | # 常用命令
100 | ```shell
101 | s cli registry login # 登录授权 一次就行
102 | s cli registry publish # 发布包
103 | s cli registry list # 查看子机已发布的包
104 | 
105 | s init paddleImageStyleTransfer # 自己测试应用的效果
106 | s deploy # 部署项目试试
107 | ```


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/app/app_compat.py:
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  1 | # coding:utf-8
  2 | # Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
  3 | #
  4 | # Licensed under the Apache License, Version 2.0 (the "License"
  5 | # you may not use this file except in compliance with the License.
  6 | # You may obtain a copy of the License at
  7 | #
  8 | #     http://www.apache.org/licenses/LICENSE-2.0
  9 | #
 10 | # Unless required by applicable law or agreed to in writing, software
 11 | # distributed under the License is distributed on an "AS IS" BASIS,
 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 13 | # See the License for the specific language governing permissions and
 14 | # limitations under the License.
 15 | 
 16 | import traceback
 17 | import time
 18 | 
 19 | from flask import Flask, request
 20 | 
 21 | from paddlehub.serving.model_service.base_model_service import cv_module_info
 22 | from paddlehub.serving.model_service.base_model_service import nlp_module_info
 23 | from paddlehub.serving.model_service.base_model_service import v2_module_info
 24 | from paddlehub.utils import utils, log
 25 | 
 26 | filename = 'HubServing-%s.log' % time.strftime("%Y_%m_%d", time.localtime())
 27 | 
 28 | 
 29 | def package_result(status: str, msg: str, data: dict):
 30 |     '''
 31 |     Package message of response.
 32 | 
 33 |     Args:
 34 |          status(str): Error code
 35 |             ========   ==============================================================================================
 36 |             Code       Meaning
 37 |             --------   ----------------------------------------------------------------------------------------------
 38 |             '000'      Return results normally
 39 |             '101'      An error occurred in the predicting method
 40 |             '111'      Module is not available
 41 |             '112'      Use outdated and abandoned HTTP protocol format
 42 |             ========   ===============================================================================================
 43 |          msg(str): Detailed info for error
 44 |          data(dict): Result of predict api.
 45 | 
 46 |     Returns:
 47 |         dict: Message of response
 48 | 
 49 |     Examples:
 50 |         .. code-block:: python
 51 | 
 52 |             data = {'result': 0.002}
 53 |             package_result(status='000', msg='', data=data)
 54 |     '''
 55 |     return {"status": status, "msg": msg, "results": data}
 56 | 
 57 | 
 58 | def predict_v2(module_info: dict, input: dict):
 59 |     '''
 60 | 
 61 |     Predict with `serving` API of module.
 62 | 
 63 |     Args:
 64 |          module_info(dict): Module info include module name, method name and
 65 |                             other info.
 66 |          input(dict): Data to input to predict API.
 67 | 
 68 |     Returns:
 69 |         dict: Response after packaging by func `package_result`
 70 | 
 71 |     Examples:
 72 |         .. code-block:: python
 73 | 
 74 |             module_info = {'module_name': 'lac'}}
 75 |             data = {'text': ['今天天气很好']}
 76 |             predict_v2(module_info=module_info, input=data)
 77 |     '''
 78 |     serving_method_name = module_info["method_name"]
 79 |     serving_method = getattr(module_info["module"], serving_method_name)
 80 |     predict_args = module_info["predict_args"].copy()
 81 |     predict_args.update(input)
 82 | 
 83 |     for item in serving_method.__code__.co_varnames:
 84 |         if item in module_info.keys():
 85 |             predict_args.update({item: module_info[item]})
 86 |     try:
 87 |         output = serving_method(**predict_args)
 88 |     except Exception as err:
 89 |         log.logger.error(traceback.format_exc())
 90 |         return package_result("101", str(err), "")
 91 | 
 92 |     return package_result("000", "", output)
 93 | 
 94 | 
 95 | def create_app(init_flag: bool = False, configs: dict = None):
 96 |     '''
 97 |     Start one flask instance and ready for HTTP requests.
 98 | 
 99 |     Args:
100 |          init_flag(bool): Whether the instance need to be initialized with
101 |                           `configs` or not
102 |          configs(dict): Module configs for initializing.
103 | 
104 |     Returns:
105 |         One flask instance.
106 | 
107 |     Examples:
108 |         .. code-block:: python
109 | 
110 |             create_app(init_flag=False, configs=None)
111 |     '''
112 |     if init_flag is False:
113 |         if configs is None:
114 |             raise RuntimeError("Lack of necessary configs.")
115 |         config_with_file(configs)
116 | 
117 |     app_instance = Flask(__name__)
118 |     app_instance.config["JSON_AS_ASCII"] = False
119 |     app_instance.logger = log.get_file_logger(filename)
120 | 
121 |     @app_instance.route("/", methods=["GET", "POST"])
122 |     def index():
123 |         '''
124 |         Provide index page.
125 |         '''
126 |         return open('/app/index.html').read()
127 | 
128 |         return '暂不提供可视化界面，请直接使用脚本进行请求。<br/>No visual ' \
129 |                'interface is provided for the time being, please use the' \
130 |                ' python script to make a request directly.'
131 | 
132 |     @app_instance.before_request
133 |     def before_request():
134 |         '''
135 |         Add id info to `request.data` before request.
136 |         '''
137 |         request.data = {"id": utils.md5(request.remote_addr + str(time.time()))}
138 | 
139 |     @app_instance.route("/predict/<module_name>", methods=["POST"])
140 |     def predict_serving_v2(module_name: str):
141 |         '''
142 |         Http api for predicting.
143 | 
144 |         Args:
145 |             module_name(str): Module name for predicting.
146 | 
147 |         Returns:
148 |             Result of predicting after packaging.
149 |         '''
150 |         if module_name in v2_module_info.modules:
151 |             module_info = v2_module_info.get_module_info(module_name)
152 |         else:
153 |             msg = "Module {} is not available.".format(module_name)
154 |             return package_result("111", msg, "")
155 |         inputs = request.json
156 |         if inputs is None:
157 |             results = "This usage is out of date, please use 'application/json' as content-type to post to /predict/%s. See 'https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.6/docs/tutorial/serving.md' for more details." % (
158 |                 module_name)
159 |             return package_result("112", results, "")
160 | 
161 |         results = predict_v2(module_info, inputs)
162 |         return results
163 | 
164 |     return app_instance
165 | 
166 | 
167 | def config_with_file(configs: dict):
168 |     '''
169 |     Config `cv_module_info` and `nlp_module_info` by configs.
170 | 
171 |     Args:
172 |         configs(dict): Module info and configs
173 | 
174 |     Examples:
175 |         .. code-block:: python
176 | 
177 |             configs = {'lac': {'version': 1.0.0, 'category': nlp}}
178 |             config_with_file(configs=configs)
179 |     '''
180 |     for key, value in configs.items():
181 |         if "CV" == value["category"]:
182 |             cv_module_info.add_module(key, {key: value})
183 |         elif "NLP" == value["category"]:
184 |             nlp_module_info.add_module(key, {key: value})
185 |         v2_module_info.add_module(key, {key: value})
186 |         logger = log.get_file_logger(filename)
187 |         logger.info("%s==%s" % (key, value["version"]))
188 | 
189 | 
190 | def run(configs: dict = None, port: int = 8866):
191 |     '''
192 |     Run flask instance for PaddleHub-Serving
193 | 
194 |     Args:
195 |          configs(dict): module info and configs
196 |          port(int): the port of the webserver
197 | 
198 |     Examples:
199 |         .. code-block:: python
200 | 
201 |             configs = {'lac': {'version': 1.0.0, 'category': nlp}}
202 |             run(configs=configs, port=8866)
203 |     '''
204 |     logger = log.get_file_logger(filename)
205 |     if configs is not None:
206 |         config_with_file(configs)
207 |     else:
208 |         logger.error("Start failed cause of missing configuration.")
209 |         return
210 |     my_app = create_app(init_flag=True)
211 |     my_app.run(host="0.0.0.0", port=port, debug=False, threaded=False)
212 |     log.logger.info("PaddleHub-Serving has been stopped.")
213 | 


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/app/index.html:
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  1 | <!DOCTYPE html>
  2 | <html lang="en">
  3 | <head>
  4 |     <meta charset="UTF-8">
  5 |     <title>基于 Serverless 图像转换为宫崎骏动漫风格案例</title>
  6 |     <link href="https://www.bootcss.com/p/layoutit/css/bootstrap-combined.min.css" rel="stylesheet">
  7 |     <script>
  8 |         var image = undefined
  9 |         var uri = '/predict/animegan_v2_hayao_99'
 10 |         var appName = 'paddleImageStyleTransfer'
 11 | 
 12 |         function getSentence() {
 13 |             document.getElementById("result").innerText = "预测中 ..."
 14 |             const xmlhttp = window.XMLHttpRequest ? new XMLHttpRequest() : new ActiveXObject("Microsoft.XMLHTTP");
 15 |             xmlhttp.open("POST", uri, false);
 16 |             xmlhttp.onreadystatechange = function () {
 17 |                 if (xmlhttp.readyState == 4 && xmlhttp.status == 200) {
 18 |                     res = JSON.parse(xmlhttp.responseText);
 19 |                     document.getElementById("result").src = "data:image/png;base64,"+res.results[0];
 20 |                 }
 21 |             }
 22 |             xmlhttp.setRequestHeader("Content-type", "application/json");
 23 |             image = image.substring( image.indexOf( "," ) + 1 );
 24 |             data = '{"images":["' + image + '"]}'
 25 |             xmlhttp.send(data);
 26 |         }
 27 | 
 28 |         function getImage(file) {
 29 |             const reader = new FileReader();
 30 |             const img = document.getElementById('img');
 31 | 
 32 |             //读取File对象的数据
 33 |             reader.onload = function (evt) {
 34 |                 img.src = evt.target.result;
 35 |                 image = evt.target.result
 36 |             }
 37 |             reader.readAsDataURL(file.files[0]);
 38 |         }
 39 |     </script>
 40 | </head>
 41 | <body>
 42 | <div class="container-fluid" style="margin-top: 10px">
 43 |     <div class="row-fluid">
 44 |         <div class="span12">
 45 |             <center>
 46 |                 <h3>
 47 |                     基于 Serverless 图像转换为宫崎骏动漫风格案例
 48 |                 </h3>
 49 |             </center>
 50 |         </div>
 51 |     </div>
 52 |     <div class="row-fluid">
 53 |         <div class="span2">
 54 |         </div>
 55 |         <div class="span8">
 56 |             <fieldset>
 57 |                 <legend>说明：</legend>
 58 |                 本应用基于 Serverless Devs 进行部署，可通过 <a id="appUrl">Serverless 应用中心一键体验</a>。
 59 |                 <br>
 60 |                 <br>
 61 |                 本应用的使用帮助<a href="https://github.com/duolabmeng6/paddleImageStyleTransfer">基于 Serverless 图像转换为宫崎骏动漫风格案例图像转换为宫崎骏动漫风格案例</a>
 62 |                 <br><h5>接口文档：</h5>
 63 |                 <p>地址：：</p>
 64 |                 <pre id="url"></pre>
 65 |                 <p>参数：</p>
 66 |                 <pre id="args"></pre>
 67 |                 <p>案例：</p>
 68 |                 <pre id="example"></pre>
 69 |             </fieldset>
 70 |         </div>
 71 |         <div class="span2">
 72 |         </div>
 73 |     </div>
 74 |     <div class="row-fluid">
 75 |         <div class="span2">
 76 |         </div>
 77 |         <div class="span8">
 78 |             <center style="width: 100%">
 79 |                 <img style="width: 45%" src="" id="img"/>
 80 |                 <img style="width: 45%" src="" id="result"/>
 81 |             </center>
 82 |             <fieldset>
 83 |                 <legend id="action">体验：</legend>
 84 |                 <input type="file" id="upload" onchange="getImage(this)"/>
 85 |                 <button class="btn" onclick="getSentence()">图像预测</button>
 86 |             </fieldset>
 87 | 
 88 |         </div>
 89 |         <div class="span2">
 90 |         </div>
 91 |     </div>
 92 | </div>
 93 | <script>
 94 |     document.getElementById("appUrl").href = "https://fcnext.console.aliyun.com/applications/create?template=" + appName
 95 |     document.getElementById("url").innerText = window.location.protocol + '//' + window.location.host + uri
 96 |     document.getElementById("args").innerText = `  Headers:
 97 |      Content-type: application/json
 98 |   Body:
 99 |      image: 图片Base64后的字符串`
100 |     document.getElementById("example").innerText = `import requests
101 | import json
102 | import cv2
103 | import base64
104 | 
105 | def cv2_to_base64(image):
106 |   data = cv2.imencode('.jpg', image)[1]
107 |   return base64.b64encode(data.tostring()).decode('utf8')
108 | 
109 | def getResult(imagePath):
110 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
111 |     r = requests.post("${window.location.protocol + '//' + window.location.host + uri}", data=data,
112 |                         headers={'Content-Type': 'application/json'})
113 |     return r.json()["results"]
114 | print(getResult("./test.png"))`
115 | 
116 | </script>
117 | </body>
118 | </html>


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/app/start.sh:
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1 | #!/bin/bash
2 | hub serving start -m animegan_v2_hayao_99 -p 9000


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/deploy/ImageStyleTransfer/hook/index.js:
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 1 | async function preInit(inputObj) {
 2 |     console.log("项目帮助 https://github.com/duolabmeng6/paddlehub_ppocr")
 3 | }
 4 | 
 5 | async function postInit(inputObj) {
 6 |     console.log("项目帮助 https://github.com/duolabmeng6/paddlehub_ppocr")
 7 | }
 8 | 
 9 | module.exports = {
10 |     postInit,
11 |     preInit
12 | }
13 | 


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/deploy/ImageStyleTransfer/publish.yaml:
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 1 | # 规范文档参考:
 2 | # 中文：https://github.com/Serverless-Devs/Serverless-Devs/blob/master/spec/zh/0.0.2/serverless_package_model/3.package_model.md#应用模型规范
 3 | # English: https://github.com/Serverless-Devs/Serverless-Devs/blob/master/spec/en/0.0.2/serverless_package_model/3.package_model.md#Application-model-specification
 4 | # --------------
 5 | # Package 开发发布最佳实践:
 6 | #   - [社区讨论 #62](https://github.com/Serverless-Devs/Serverless-Devs/discussions/62);
 7 | #   - [社区讨论 #407](https://github.com/Serverless-Devs/Serverless-Devs/discussions/407);
 8 | #   - [社区讨论 #439](https://github.com/Serverless-Devs/Serverless-Devs/discussions/439);
 9 | 
10 | Type: Application
11 | Name: paddleImageStyleTransfer
12 | Provider:
13 |   - 阿里云
14 | Version: 0.0.28
15 | Description: 本应用基于百度开源的 PaddlePaddle 项目，AnimeGAN V2 图像风格转换模型, 可将输入的图像转换成宫崎骏动漫风格。
16 | HomePage: https://github.com/duolabmeng6/paddleImageStyleTransfer
17 | Tags: #标签详情
18 |   - 人工智能
19 |   - 图像转换
20 | Category: 人工智能 # 基础云服务/Web框架/Web应用/人工智能/音视频处理/图文处理/监控告警/大数据/IoT/新手入门/其他
21 | Service:
22 |   函数计算:
23 |     Authorities:
24 |       - AliyunFCFullAccess
25 |       - AliyunContainerRegistryFullAccess
26 |     Runtime: custom container
27 | Parameters:
28 |   type: object
29 |   additionalProperties: false # 不允许增加其他属性
30 |   required: # 必填项
31 |     - region
32 |     - serviceName
33 |     - functionName
34 |     - memorySize
35 |   properties:
36 |     region:
37 |       title: 地域
38 |       type: string
39 |       default: cn-shenzhen
40 |       description: 创建应用所在的地区
41 |       enum:
42 |         - cn-shenzhen
43 |         - cn-beijing
44 |         - cn-hangzhou
45 |         - cn-shanghai
46 |         - cn-qingdao
47 |         - cn-zhangjiakou
48 |         - cn-huhehaote
49 |         - cn-chengdu
50 |         - cn-hongkong
51 |     serviceName:
52 |       title: 服务名
53 |       type: string
54 |       default: paddleImageStyleTransfer
55 |       description: 服务名称，只能包含字母、数字、下划线和中划线。不能以数字、中划线开头。长度在 1-128 之间
56 |     functionName:
57 |       title: 函数名
58 |       type: string
59 |       default: paddleImageStyleTransfer
60 |       description:  函数名称，只能包含字母、数字、下划线和中划线。不能以数字、中划线开头。长度在 1-64 之间
61 |     memorySize:
62 |       title: 内存规格
63 |       type: string
64 |       default: 3072
65 |       description:  内存规格，单位为 MB，默认为 3072 MB


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/deploy/ImageStyleTransfer/readme.md:
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 1 | # 基于 Serverless 图像转换为宫崎骏动漫风格案例
 2 | 
 3 | 大家可以通过本项目提供的镜像，快速发布成可调用的Restful API服务。
 4 | 
 5 | # 部署方法
 6 | 
 7 | # 1. 在阿里云函数计算应用中心里立即创建
 8 | 
 9 | [阿里云Serverless 应用中心一键体验 ](https://fcnext.console.aliyun.com/applications/create?template=paddleImageStyleTransfer)
10 | 
11 | 
12 | # 2. 终端上输入命令创建
13 | 
14 | ```shell
15 | 
16 | s init paddleImageStyleTransfer  # 初始化项目
17 | s deploy  # 部署项目
18 | 
19 | ```
20 | 
21 | # 调用方法
22 | 
23 | ```python
24 | import requests
25 | import json
26 | import cv2
27 | import base64
28 | 
29 | 
30 | def cv2_to_base64(image):
31 |     data = cv2.imencode('.jpg', image)[1]
32 |     return base64.b64encode(data.tostring()).decode('utf8')
33 | 
34 | 
35 | def getResult(imagePath):
36 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
37 |     r = requests.post("http://127.0.0.1:9000/predict/animegan_v2_hayao_99", data=data,
38 |                       headers={'Content-Type': 'application/json'})
39 |     return r.json()["results"]
40 | 
41 | 
42 | print(getResult("./test.png"))
43 | ```
44 | 
45 | 
46 | # 调用失败的请看这里
47 | 
48 | * 500x300 分辨率 需要 7秒   2.6gb内存 所以函数设置 3gb 运行内存才可以
49 | * 720x500 分辨率 需要 15秒  5.76gb内存 所以函数设置 8gb 运行内存才可以
50 | * 2000x1280 分辨率 需要 30秒 6.77g内存 所以设置 8gb 运行内存才可以
51 | 
52 | 我是直接设置了16gb内存测试的
53 | 
54 | # 本应用的镜像开发教程
55 | 
56 | https://github.com/duolabmeng6/paddlehub_ppocr
57 | 
58 | 阅读本文你将学会：
59 | 
60 | 在 Serverless 架构中 docker 镜像制作的最佳实践，游刃有余的部署复杂场景下的深度学习模型
61 | 
62 | 熟练的使用各厂商提供的 Serverless 服务，部署。
63 | 
64 | 制作小巧精良的 docker 镜像


--------------------------------------------------------------------------------
/deploy/ImageStyleTransfer/readme_en.md:
--------------------------------------------------------------------------------
 1 | # 基于 Serverless 图像转换为宫崎骏动漫风格案例
 2 | 
 3 | 大家可以通过本项目提供的镜像，快速发布成可调用的Restful API服务。
 4 | 
 5 | # 部署方法
 6 | 
 7 | # 1. 在阿里云函数计算应用中心里立即创建
 8 | 
 9 | [阿里云Serverless 应用中心一键体验 ](https://fcnext.console.aliyun.com/applications/create?template=paddleImageStyleTransfer)
10 | 
11 | 
12 | # 2. 终端上输入命令创建
13 | 
14 | ```shell
15 | 
16 | s init paddleImageStyleTransfer  # 初始化项目
17 | s deploy  # 部署项目
18 | 
19 | ```
20 | 
21 | # 调用方法
22 | 
23 | ```python
24 | import requests
25 | import json
26 | import cv2
27 | import base64
28 | 
29 | 
30 | def cv2_to_base64(image):
31 |     data = cv2.imencode('.jpg', image)[1]
32 |     return base64.b64encode(data.tostring()).decode('utf8')
33 | 
34 | 
35 | def getResult(imagePath):
36 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
37 |     r = requests.post("http://127.0.0.1:9000/predict/animegan_v2_hayao_99", data=data,
38 |                       headers={'Content-Type': 'application/json'})
39 |     return r.json()["results"]
40 | 
41 | 
42 | print(getResult("./test.png"))
43 | ```
44 | 
45 | 
46 | # 调用失败的请看这里
47 | 
48 | * 500x300 分辨率 需要 7秒   2.6gb内存 所以函数设置 3gb 运行内存才可以
49 | * 720x500 分辨率 需要 15秒  5.76gb内存 所以函数设置 8gb 运行内存才可以
50 | * 2000x1280 分辨率 需要 30秒 6.77g内存 所以设置 8gb 运行内存才可以
51 | 
52 | 我是直接设置了16gb内存测试的
53 | 
54 | # 本应用的镜像开发教程
55 | 
56 | https://github.com/duolabmeng6/paddlehub_ppocr
57 | 
58 | 阅读本文你将学会：
59 | 
60 | 在 Serverless 架构中 docker 镜像制作的最佳实践，游刃有余的部署复杂场景下的深度学习模型
61 | 
62 | 熟练的使用各厂商提供的 Serverless 服务，部署。
63 | 
64 | 制作小巧精良的 docker 镜像


--------------------------------------------------------------------------------
/deploy/ImageStyleTransfer/src/s.yaml:
--------------------------------------------------------------------------------
 1 | edition: 1.0.0
 2 | name: web-framework-app
 3 | access: "{{ access }}"
 4 | 
 5 | vars:
 6 |   region: "{{ region }}"
 7 |   memorySize: "{{ memorySize }}"
 8 |   service:
 9 |     name: "{{ serviceName }}"
10 |     description: '图像转换成宫崎骏动漫风格API 项目地址 https://github.com/duolabmeng6/paddleImageStyleTransfer'
11 | 
12 | services:
13 |   framework:
14 |     component: fc
15 |     props:
16 |       region: ${vars.region}
17 |       service:
18 |         name: ${vars.service.name}
19 |         description: ${vars.service.description}
20 |         internetAccess: true
21 |         role:
22 |           name: paddlerole
23 |           policies:
24 |             - AliyunContainerRegistryReadOnlyAccess
25 |       function:
26 |         name: "{{ functionName }}"
27 |         description: ${vars.service.description}
28 |         runtime: custom-container
29 |         memorySize: ${vars.memorySize}
30 |         caPort: 9000
31 |         codeUri: ./
32 |         timeout: 60
33 |         customContainerConfig:
34 |           image: registry-vpc.${vars.region}.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
35 |           command: '["sh","/app/start.sh"]'
36 |         environmentVariables:
37 |           testEnv: true
38 |       triggers:
39 |         - name: httpTrigger
40 |           type: http
41 |           config:
42 |             authType: anonymous
43 |             methods:
44 |               - GET
45 |               - POST
46 |               - PUT
47 |       customDomains:
48 |         - domainName: auto
49 |           protocol: HTTP
50 |           routeConfigs:
51 |             - path: /*
52 | 


--------------------------------------------------------------------------------
/deploy/ImageStyleTransfer/version.md:
--------------------------------------------------------------------------------
 1 | # 基于 Serverless 图像转换为宫崎骏动漫风格案例
 2 | 
 3 | 大家可以通过本项目提供的镜像，快速发布成可调用的Restful API服务。
 4 | 
 5 | # 部署方法
 6 | 
 7 | # 1. 在阿里云函数计算应用中心里立即创建
 8 | 
 9 | [阿里云Serverless 应用中心一键体验 ](https://fcnext.console.aliyun.com/applications/create?template=paddleImageStyleTransfer)
10 | 
11 | 
12 | # 2. 终端上输入命令创建
13 | 
14 | ```shell
15 | 
16 | s init paddleImageStyleTransfer  # 初始化项目
17 | s deploy  # 部署项目
18 | 
19 | ```
20 | 
21 | # 调用方法
22 | 
23 | ```python
24 | import requests
25 | import json
26 | import cv2
27 | import base64
28 | 
29 | 
30 | def cv2_to_base64(image):
31 |     data = cv2.imencode('.jpg', image)[1]
32 |     return base64.b64encode(data.tostring()).decode('utf8')
33 | 
34 | 
35 | def getResult(imagePath):
36 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
37 |     r = requests.post("http://127.0.0.1:9000/predict/animegan_v2_hayao_99", data=data,
38 |                       headers={'Content-Type': 'application/json'})
39 |     return r.json()["results"]
40 | 
41 | 
42 | print(getResult("./test.png"))
43 | ```
44 | 
45 | 
46 | # 调用失败的请看这里
47 | 
48 | * 500x300 分辨率 需要 7秒   2.6gb内存 所以函数设置 3gb 运行内存才可以
49 | * 720x500 分辨率 需要 15秒  5.76gb内存 所以函数设置 8gb 运行内存才可以
50 | * 2000x1280 分辨率 需要 30秒 6.77g内存 所以设置 8gb 运行内存才可以
51 | 
52 | 我是直接设置了16gb内存测试的
53 | 
54 | # 本应用的镜像开发教程
55 | 
56 | https://github.com/duolabmeng6/paddlehub_ppocr
57 | 
58 | 阅读本文你将学会：
59 | 
60 | 在 Serverless 架构中 docker 镜像制作的最佳实践，游刃有余的部署复杂场景下的深度学习模型
61 | 
62 | 熟练的使用各厂商提供的 Serverless 服务，部署。
63 | 
64 | 制作小巧精良的 docker 镜像


--------------------------------------------------------------------------------
/httpapi_test.py:
--------------------------------------------------------------------------------
 1 | import requests
 2 | import json
 3 | import cv2
 4 | import base64
 5 | 
 6 | 
 7 | def cv2_to_base64(image):
 8 |     data = cv2.imencode('.jpg', image)[1]
 9 |     return base64.b64encode(data.tostring()).decode('utf8')
10 | 
11 | 
12 | def getResult(imagePath):
13 |     data = json.dumps({'images': [cv2_to_base64(cv2.imread(imagePath))]})
14 |     r = requests.post("http://127.0.0.1:9000/predict/animegan_v2_hayao_99", data=data,
15 |                       headers={'Content-Type': 'application/json'})
16 |     return r.json()["results"]
17 | 
18 | 
19 | print(getResult("./test.png"))


--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
 1 | import paddlehub as hub
 2 | import cv2
 3 | from pyefun import *
 4 | 
 5 | model = hub.Module(name="animegan_v2_hayao_99")
 6 | 
 7 | with 计时器() as t:
 8 |     result = model.style_transfer(images=[cv2.imread('test.png')])
 9 |     cv2.imwrite('./result.png', result[0])
10 |     print(t.取总耗时())
11 | 


--------------------------------------------------------------------------------
/result.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/duolabmeng6/paddleImageStyleTransfer/c8968980de7e21d683709bbc3d6512bf9e5168e0/result.png


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/run_batch_push.sh:
--------------------------------------------------------------------------------
 1 | docker tag animegan_v2_hayao_99:1.0 registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 2 | docker push registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 3 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-beijing.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 4 | docker push registry.cn-beijing.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 5 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-hangzhou.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 6 | docker push registry.cn-hangzhou.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 7 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-shanghai.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 8 | docker push registry.cn-shanghai.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 9 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-qingdao.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
10 | docker push registry.cn-qingdao.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
11 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-zhangjiakou.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
12 | docker push registry.cn-zhangjiakou.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
13 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-huhehaote.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
14 | docker push registry.cn-huhehaote.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
15 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-chengdu.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
16 | docker push registry.cn-chengdu.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
17 | docker tag registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1 registry.cn-hongkong.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
18 | docker push registry.cn-hongkong.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1


--------------------------------------------------------------------------------
/run_build.sh:
--------------------------------------------------------------------------------
 1 | docker build -f ./Dockerfile -t animegan_v2_hayao_99:1.0 .
 2 | docker rm -f animegan_v2_hayao_99
 3 | docker run -itd --name animegan_v2_hayao_99 -p 9022:9000 animegan_v2_hayao_99:1.0
 4 | docker logs animegan_v2_hayao_99
 5 | docker diff animegan_v2_hayao_99
 6 | 
 7 | #没问题的话就可以执行推送命令
 8 | docker tag animegan_v2_hayao_99:1.0 registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
 9 | docker push registry.cn-shenzhen.aliyuncs.com/duolabmeng/animegan_v2_hayao_99:1.1
10 | 
11 | 
12 | 


--------------------------------------------------------------------------------
/test.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/duolabmeng6/paddleImageStyleTransfer/c8968980de7e21d683709bbc3d6512bf9e5168e0/test.png


--------------------------------------------------------------------------------