20行程式碼：Serverless架構下用Python輕鬆搞定影像分類

• 2020 年 3 月 2 日
• 筆記

影像分類是人工智慧領域的一個熱門話題，同樣在生產環境中也會經常會遇到類似的需求，那麼怎麼快速搭建一個影像分類，或者影像內容是別的API呢？

首先，給大家推薦一個影像相關的庫：ImageAI

通過官方給的程式碼，我們可以看到一個簡單的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)

通過這個Demo我們可以考慮將這個模組部署到雲函數：

首先，我們在本地創建一個Python的項目：

mkdir imageDemo

然後新建文件：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

創建完成之後，我們需要下載一下我們所依賴的模型：

- SqueezeNet（文件大小：4.82 MB，預測時間最短，精準度適中）  - ResNet50 by Microsoft Research （文件大小：98 MB，預測時間較快，精準度高）  - InceptionV3 by Google Brain team （文件大小：91.6 MB，預測時間慢，精度更高）  - DenseNet121 by Facebook AI Research （文件大小：31.6 MB，預測時間較慢，精度最高）

我們先用第一個SqueezeNet來做測試：

在官方文檔複製模型文件地址：

使用wget直接安裝：

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

接下來，我們就需要進行安裝依賴了，這裡面貌似安裝的內容蠻多的：

而且這些依賴有一些需要編譯的，這就需要我們在centos + python2.7/3.6的版本下打包才可以，這樣就顯得非常複雜，尤其是mac/windows用戶，傷不起。

所以這時候，直接用我之前的打包網址：

直接下載解壓，然後放到自己的項目中：

最後，一步了，我們創建serverless.yaml

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

完成之後，執行我們的sls --debug部署，部署過程中會有掃碼的登陸，登陸之後等待即可，完成之後，我們可以複製生成的URL：

通過Python語言進行測試，url就是我們剛才複製的+/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"))

通過網路搜索一張圖片，例如我找了這個：

得到運行結果：

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

將程式碼修改一下，進行一下簡單的耗時測試：

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)

輸出結果：

{"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

這個數據，整體性能基本是在我可以接受的範圍內。

至此，我們通過Serveerless架構搭建的Python版本的影像識別/分類小工具做好了。