使用 Tensorflow.js 运行对象检测 [英] Running object detection using Tensorflow.js
问题描述
我正在使用 Tensorflow.js 进行对象检测.我正在尝试在浏览器中运行自定义对象检测 tensorflow.js 模型.我可以使用以下命令将 tensorflow 模型转换为 tensorflow.js 模型(在 google colab 中):
I am working on object detection using Tensorflow.js. I am trying to run custom object detection tensorflow.js model in a browser. I could able to convert tensorflow model to tensorflow.js model (in google colab) using the following command:
!tensorflowjs_converter \
--input_format=tf_frozen_model \
--output_node_names='detection_boxes,detection_scores,detection_classes,num_detections' \
/content/frozen_inference_graph.pb \
/content/web_model
我正在分享 inference.html 文件的代码片段[更新]:
I am sharing the code snippet of inference.html file [Updated]:
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"> </script>
<!--<script src="webcam.js"></script>-->
<img id="img" src="257.jpg" width="300" height="300"/>
</head>
<body>
<button type="button" id="startPredicting" onclick="startPredicting()" >Start Predicting</button>
<button type="button" id="stopPredicting" onclick="stopPredicting()" >Stop Predicting</button>
<div id="prediction"></div>
</body>
<script src="index.js"></script>
</html>
index.js 文件的代码片段如下[更新]:
The code snippet of index.js file is as follow [Updated]:
let model;
let isPredicting = false;
async function init(){
try {
model = await tf.loadGraphModel('http://127.0.0.1:8887/uno_model/model.json');
} catch (err) {
console.log(err);
}
}
async function predict() {
console.log("executing model");
const img = document.getElementById('img');
tf_img = tf.browser.fromPixels(img);
tf.print(tf_img)
tf_img = tf_img.expandDims(0);
console.log(tf_img.shape) // Image dimension is [1, 300, 300, 3]
let output = await model.executeAsync(
{ 'image_tensor' : tf_img},
[ 'detection_boxes','detection_scores','detection_classes','num_detections']);
for (let i = 0; i < output.length; i++){
console.log(output[i].dataSync())
}
}
init()
function startPredicting(){
isPredicting = true;
predict();
}
function stopPredicting(){
isPredicting = false;
predict();
}
它产生以下输出[更新]:
我查看了上面的输出，但我无法获得类标签等.如何提取 detection_classes、detection_scores 和 detection_boxes?该模型与 python 代码一起正常工作.
I looked at the above output but I couldn't get class labels etc. How can I extract detection_classes, detection_scores, and detection_boxes? This model works properly with python code.
[更新]:看起来，我在提供 [1,300,300,3] 图像作为模型输入后得到了输出.
[Updated]: It seems like, I am getting the output after providing [1,300,300,3] image as input to the model.
你能指导我吗?我错过了什么吗?
Could you please guide me? Am I missing something?
推荐答案
终于找到了问题所在，它与输入框的大小有关.
Finally, I could figure out the problem and it was related to the size of an input frame.
SSD 模型需要 [1,300,300,3] 图像/帧的形状作为输入.我在我的代码中添加了这个并得到了解决方案.使用以下行(在 inference.html 中)，我们可以将 (300,300,3) 形状的图像作为模型的输入:
SSD model needs shape of [1,300,300,3] image/frame as input. I added this in my code and got the solution. Using the following line (in inference.html), we can feed (300,300,3) shape of image as an input to the model:
<img id="img" src="257.jpg" width="300" height="300"/>
在 index.js 中使用以下几行:
Using the following lines in index.js:
tf_img = tf_img.expandDims(0);
console.log(tf_img.shape) // Image dimension is [1, 300, 300, 3]
我们得到SSD需要的[1,300,300,3]的图像形状.
We obtain image shape of [1,300,300,3] which is needed by SSD.
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