20行代码：Serverless架构下用Python轻松搞定图像分类

图像分类是人工智能领域的一个热门话题，同样在生产环境中也会经常会遇到类似的需求，那么怎么快速搭建一个图像分类，或者图像内容是别的API呢？

首先，给大家推荐一个图像相关的库：ImageAI

通过官方给的代码，我们可以看到一个简单的Demo：

from imageai.Prediction import ImagePrediction
import os
execution_path = os.getcwd()

prediction = ImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
prediction.loadModel()

predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "1.jpg"), result_count=5 )
for eachPrediction, eachProbability in zip(predictions, probabilities):
    print(eachPrediction + " : " + eachProbability)

通过这个Demo我们可以考虑将这个模块部署到云函数：

首先，我们在本地创建一个Python的项目：

mkdir imageDemo

然后新建文件：vim index.py

from imageai.Prediction import ImagePrediction
import os, base64, random

execution_path = os.getcwd()

prediction = ImagePrediction()
prediction.setModelTypeAsSqueezeNet()
prediction.setModelPath(os.path.join(execution_path, "squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
prediction.loadModel()


def main_handler(event, context):
    imgData = base64.b64decode(event["body"])
    fileName = '/tmp/' + "".join(random.sample('zyxwvutsrqponmlkjihgfedcba', 5))
    with open(fileName, 'wb') as f:
        f.write(imgData)
    resultData = {}
    predictions, probabilities = prediction.predictImage(fileName, result_count=5)
    for eachPrediction, eachProbability in zip(predictions, probabilities):
        resultData[eachPrediction] =  eachProbability
    return resultData

创建完成之后，我们需要下载一下我们所依赖的模型：

- SqueezeNet（文件大小：4.82 MB，预测时间最短，精准度适中）
- ResNet50 by Microsoft Research （文件大小：98 MB，预测时间较快，精准度高）
- InceptionV3 by Google Brain team （文件大小：91.6 MB，预测时间慢，精度更高）
- DenseNet121 by Facebook AI Research （文件大小：31.6 MB，预测时间较慢，精度最高）

我们先用第一个SqueezeNet来做测试：

在官方文档复制模型文件地址：

使用wget直接安装：

wget https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5

接下来，我们就需要进行安装依赖了，这里面貌似安装的内容蛮多的：

而且这些依赖有一些需要编译的，这就需要我们在centos + python2.7/3.6的版本下打包才可以，这样就显得非常复杂，尤其是mac/windows用户，伤不起。

所以这时候，直接用我之前的打包网址：

直接下载解压，然后放到自己的项目中：

最后，一步了，我们创建serverless.yaml

imageDemo:
  component: "@serverless/tencent-scf"
  inputs:
    name: imageDemo
    codeUri: ./
    handler: index.main_handler
    runtime: Python3.6
    region: ap-guangzhou
    description: 图像识别/分类Demo
    memorySize: 256
    timeout: 10
    events:
      - apigw:
          name: imageDemo_apigw_service
          parameters:
            protocols:
              - http
            serviceName: serverless
            description: 图像识别/分类DemoAPI
            environment: release
            endpoints:
              - path: /image
                method: ANY

完成之后，执行我们的sls --debug部署，部署过程中会有扫码的登陆，登陆之后等待即可，完成之后，我们可以复制生成的URL：

通过Python语言进行测试，url就是我们刚才复制的+/image：

import urllib.request
import base64

with open("1.jpg", 'rb') as f:
    base64_data = base64.b64encode(f.read())
    s = base64_data.decode()

url = 'http://service-9p7hbgvg-1256773370.gz.apigw.tencentcs.com/release/image'

print(urllib.request.urlopen(urllib.request.Request(
    url = url,
    data=s.encode("utf-8")
)).read().decode("utf-8"))

通过网络搜索一张图片，例如我找了这个：

得到运行结果：

{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}

将代码修改一下，进行一下简单的耗时测试：

import urllib.request
import base64, time

for i in range(0,10):
    start_time = time.time()
    with open("1.jpg", 'rb') as f:
        base64_data = base64.b64encode(f.read())
        s = base64_data.decode()

    url = 'http://service-hh53d8yz-1256773370.bj.apigw.tencentcs.com/release/test'

    print(urllib.request.urlopen(urllib.request.Request(
        url = url,
        data=s.encode("utf-8")
    )).read().decode("utf-8"))
    print("cost: ", time.time() - start_time)

输出结果：

{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  2.1161561012268066
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.1259253025054932
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.3322770595550537
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.3562259674072266
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.0180821418762207
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.4290671348571777
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.5917718410491943
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.1727900505065918
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  2.962592840194702
{"cheetah": 83.12643766403198, "Irish_terrier": 2.315458096563816, "lion": 1.8476998433470726, "teddy": 1.6655176877975464, "baboon": 1.5562783926725388}
cost:  1.2248001098632812

这个数据，整体性能基本是在我可以接受的范围内。

至此，我们通过Serveerless架构搭建的Python版本的图像识别/分类小工具做好了。

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