20 行代码:Serverless 架构下用 Python 轻松搞定图像分类

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发布于: 2020-2-29

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「图像分类」是人工智能领域的一个热门话题,我们在实际生活中甚至业务的生产环境里,也经常遇到图像分类相似的需求,如何能快速搭建一个图像分类或者内容识别的 API 呢?

我们考虑使用 Serverless Framework 将图像识别模块部署到腾讯云云函数 SCF 上。

这里我们会用到一个图像相关的库:ImageAI,官方给了一个简单的 demo:

复制代码
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)

接下来分四步进行:创建项目 → 安装依赖 → 配置 yml 文件 → 部署

本地创建 Python 项目

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`

复制代码
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

下载安装依赖

项目创建完成之后,下载所依赖的模型:

- 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

接下来安装依赖,这里面貌似安装的内容蛮多的:

这里需要注意:其中一些依赖需要编译,因此要在 centos + python2.7/3.6 的版本下打包才可以,这很复杂,尤其对于 mac/windows 用户,伤不起。

这时候可以直接用我之前的打包网址:

下载解压后,直接放到自己的项目中即可:

创建 yml 文件

接着创建 serverless.yaml 配置文件

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

部署

通过 serverless 命令(可使用命令缩写 sls )进行部署,添加 --debug 参数查看部署详情:

复制代码
$ sls --debug

如果你的账号未 登陆注册 腾讯云,可以直接通过微信扫描命令行中的二维码,从而进行授权登陆和注册。

访问命令行输出的 URL,URL 就是我们刚才复制的 +/image,通过 Python 语言进行测试:

复制代码
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"))

例如我们用这张图进行测试:

得到运行结果:

复制代码
{"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:

复制代码
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,整体性能基本在可接受范围内。

基于 Serverless 架构搭建的 Python 图像识别/分类 小工具就大功告成啦!


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