使用Flutter将http后请求包含图像发送到Flask API

如何解决使用Flutter将http后请求包含图像发送到Flask API

我已经在CIFAR10数据集中训练了CNN（占位符，稍后将替换为其他模型），并将该模型集成到flask API中。该API托管在Heroku上，我现在想使用Flutter / Dart在手机上拍照，将其发送到Flask API，在其上运行经过训练的模型，然后返回预测。

使用python，我可以轻松地向我的API发出发布请求并返回预测。这是我的简单python代码：

import requests
import json

img = open('some-picture.jpg','rb')

files = {'image': img}
response = requests.post("url_to_api",files=files)

print(response.text)

我已经很长时间没有使用Flutter / Dart了，我发现发出htpp请求的过程比在python中复杂一些。 有人可以给我一些指针或代码，让我用相机拍照，将其上传到我的API并将响应存储在变量中吗？这是我的flask API的（简化）python代码：

from flask import Flask,request
import os
import numpy as np
from PIL import Image
from tensorflow import keras

app = Flask(__name__)
app.config["DEBUG"] = True

model = keras.models.load_model('cifar10_cnn.h5')
labels = ["Airplane","Automobile","Bird","Cat","Deer","Dog","Frog","Horse","Ship","Truck"]


@app.route('/',methods=["POST"])
def predict():
    
    # stuff not relevant to question,left out for conciseness #
    
    file = request.files['image']

    image = Image.open(file).resize((32,32))
    image = np.array(image)
    image = image / 255
    image = image.reshape(-1,32,3)

    predictions = model.predict([image])
    index = np.argmax(predictions)
    results = {'Prediction:': labels[index]}

    return results

if __name__ == '__main__':
    app.run()

到目前为止，我知道Multipart文件似乎很可行，而且Dio包可能值得研究。如果能提供更多提示或代码，我将不胜感激。

解决方法

您可能已经知道如何从图库/相机中选择图像（例如，使用image_picker库）。使用该选择器的结果填充一个File image;之类的全局变量。这可能很简单：

import 'dart:io';
import 'package:image_picker/image_picker.dart';
class _MyHomePageState extends State<MyHomePage> {
  File image;
  final picker = ImagePicker();

  pickImageFromGallery(ImageSource source) async {
    final image = await picker.getImage(source: source);

    setState(() {
      this.image = File(image.path);
    });
  }
}

（更改ImageSource source使其符合您的需求：相机或图库）

然后，您可以将该文件上传到api。 使用http库：

import 'package:http/http.dart' as http;
import 'package:http_parser/http_parser.dart';

class _MyHomePageState extends State<MyHomePage> {
doUpload(){
    var request = http.MultipartRequest(
      'POST',Uri.parse("url_to_api"),);
    Map<String,String> headers = {"Content-type": "multipart/form-data"};
    request.files.add(
      http.MultipartFile(
        'image',image.readAsBytes().asStream(),image.lengthSync(),filename: "filename",contentType: MediaType('image','jpeg'),),);
    request.headers.addAll(headers);
    print("request: " + request.toString());
    request.send().then((value) => print(value.statusCode));
}
}

对于这些库中的每一个，都必须将它们作为依赖项添加到flutter项目的pubspec.yaml中：

cupertino_icons: ^0.1.3
http: ^0.12.2
image_picker: ^0.6.7

,

我对Django和Flutter做过类似的工作。我使用image_picker选择图像，并使用dio上传图像。

这是上传功能：

  _upLoadImage(File image) async {
    setState(() {
      loadingdone = false;
    });
    String path = image.path;
    var name = path.substring(path.lastIndexOf("/") + 1,path.length);
    var suffix = name.substring(name.lastIndexOf(".") + 1,name.length);
    FormData formData = FormData.fromMap({
        "img": await MultipartFile.fromFile(path,filename: name)
    });
    

    Dio dio = new Dio();
    var respone = await dio.post<String>("http://192.168.1.104:8000/uploadImg/",data: formData);
    if (respone.statusCode == 200) {
      Fluttertoast.showToast(
          msg: 'Done!',gravity: ToastGravity.BOTTOM,textColor: Colors.grey);
      setState(() {
          _label = jsonDecode(respone.data.toString())['label'];
          _score = jsonDecode(respone.data.toString())['score'];
          loadingdone = true;
      });
      
    }
  }

选择图片后运行上传功能：

  Future getImage() async {
    var image = await ImagePicker.pickImage(source: ImageSource.gallery);
    _upLoadImage(image);
    setState(() {
      _image = image;
    });
  }