How to call tensorflow_model_server from Nodejs, with an image

Question

This is the metadata of my serving model

"metadata": {"signature_def": {
 "signature_def": {
  "serving_default": {
   "inputs": {
    "vgg16_input": {
     "dtype": "DT_FLOAT",
     "tensor_shape": {
      "dim": [
       {
        "size": "-1",
        "name": ""
       },
       {
        "size": "224",
        "name": ""
       },
       {
        "size": "224",
        "name": ""
       },
       {
        "size": "3",
        "name": ""
       }
      ],
      "unknown_rank": false
     },
     "name": "serving_default_vgg16_input:0"
    }
   }...

Sadly I don't know how to talk to it from NodeJs. How to transorm a local image to a valid 224,224,3 DT_FLOAT Tensor ...

In python, i can do it with this code, but I would like the nodejs version

import numpy as np
import requests
from keras.preprocessing import image

image_path = './data/black/fhb2l97vdi8qc0rt5ow3.jpg'
img = image.img_to_array(image.load_img(image_path, target_size=(224, 224))) / 255.
img = img.astype('float16')

payload = {
    "instances": [{'vgg16_input': img.tolist()}]
}

r = requests.post('http://ip:port/v1/models/color:predict', json=payload)
print(r.content)

So far my code

var request = require('request');
var fs = require('fs');
var myImg = __dirname + '/../tensorflow2/data/black/0a13y2gtunswi8ox4bjf.jpg';



var options = {
    method: 'POST',
    url: `http://ip:port/v1/models/color:predict`,
    json:{ 
          instances: [{'vgg16_input': ??????}]
        }

};

request(options, function (err, resp, body) {
    if (err)
      cb(err);

      console.log(body);
  });

Maybe i could use some function from tensorflowjs ...

Answer 1

The image must be passed in JSON as list of lists of floats (pixel is list of 3 RGB values, row is list of pixels and image is list of rows).

We need to decode and resize the JPEG image. Install sharp package with npm install sharp.

Image preparation is as follows:

const fs = require('fs');
const sharp = require('sharp');

function imgToJson(buffer) {
  var decoded = [];

  var h;
  var w;
  var line;
  var pixel;

  var b = 0;
  for (h = 0; h < 224; h++) {
      var line = [];
      for (w = 0; w < 224; w++) {
          var pixel = [];

          pixel.push(buffer[b++] / 255.0); /* r */
          pixel.push(buffer[b++] / 255.0); /* g */
          pixel.push(buffer[b++] / 255.0); /* b */

          line.push(pixel);
      }
      decoded.push(line);
  }

  return decoded;
}

async function prepare_image(imagePath) {
  var jpegData = fs.readFileSync(imagePath); /* for example sake synchronous */
  const buffer = await sharp(jpegData).resize(224, 224).raw().toBuffer();
  return imgToJson(buffer);
}

The result of prepare_image is future returning the list of lists of floats representing image. The last step is to perform request:

var request = require('request');

async function perform_request(imagePath) {
  var decoded = await prepare_image(imagePath);
  var options = {
      method: 'POST',
      url: 'http://ip:port/v1/models/color:predict',
      json: { 
            instances: [{'vgg16_input': decoded}]
      }
  };

  request(options, function (err, resp, body) {
      if (err)
        cb(err);

        console.log(body);
    });
}

perform_request("image.jpeg");