需要帮助。我正在尝试检测/分类一些姿势。为了获得更好的结果，我试图通过多个/多个图像(或来自网络摄像头/视频的快照)来训练一个单一姿势的模型。
您可以在jsFiddle中检查代码，但这里也添加了代码。——https://jsfiddle.net/zahedkamal87/1v8fcuyz/6/
我尝试为相同的目标/姿势添加多个样本。但它只存储了1个(最后1个)。所以，对于一个目标，我如何添加10-20个样本?
$(document).ready(function () {
function get_new_height(width) {
var screen_width = 1920;
var screen_height = 1080;
// (original height / original width) x new width = new height
var new_height = Math.round((screen_height / screen_width) * width);
return new_height;
}
let video;
let canvas;
let poseNet;
let poseNetOptions;
let poses = [];
let pose;
let skeleton;
let targetPose;
let state = "waiting";
let brain;
let poseLabel = "Y";
let webcam_res_x = parseInt($("#tool-preview").width());
let webcam_res_y = get_new_height(webcam_res_x);
video = document.getElementById("video");
canvas = document.getElementById("canvas");
canvas.width = webcam_res_x;
canvas.height = webcam_res_y;
var ctx = canvas.getContext("2d");
var constraints = {
video: true,
audio: false
};
var streaming = false;
video.addEventListener(
"canplay",
function (ev) {
if (!streaming) {
video.setAttribute("width", webcam_res_x);
video.setAttribute("height", webcam_res_y);
streaming = true;
}
},
false
);
navigator.mediaDevices
.getUserMedia(constraints)
.then(function (stream) {
video.srcObject = stream;
video.play();
})
.catch(function (err) {
console.log("An error occurred: ", err);
});
poseNetOptions = {
// flipHorizontal: true
};
poseNet = ml5.poseNet(video, poseNetOptions, function () {
console.log("poseNet ready");
});
poseNet.on("pose", function (results) {
poses = results;
// console.log(results);
if (poses.length > 0) {
pose = poses[0].pose;
skeleton = poses[0].skeleton;
}
});
let options = {
inputs: 34,
outputs: ["poses"],
task: "classification",
debug: true
};
brain = ml5.neuralNetwork(options);
function getInputs() {
let keypoints = poses[0].pose.keypoints;
let inputs = [];
for (let i = 0; i < keypoints.length; i++) {
inputs.push(keypoints[i].position.x);
inputs.push(keypoints[i].position.y);
}
return inputs;
}
function trainModel() {
brain.normalizeData();
let options = {
epochs: 50
};
brain.train(options, finishedTraining);
}
// Begin prediction
function finishedTraining() {
classify();
}
// Classify
function classify() {
if (poses.length > 0) {
let inputs = getInputs();
brain.classify(inputs, gotResults);
}
}
function gotResults(error, results) {
console.log(results);
if (results) {
$("#classified").html(
results[0].label + " " + Math.floor(results[0].confidence * 100) + "%"
);
}
classify();
}
function drawCameraIntoCanvas() {
ctx.drawImage(video, 0, 0, webcam_res_x, webcam_res_y);
drawKeypoints();
drawSkeleton();
window.requestAnimationFrame(drawCameraIntoCanvas);
}
drawCameraIntoCanvas();
function drawKeypoints() {
for (let i = 0; i < poses.length; i += 1) {
for (let j = 0; j < poses[i].pose.keypoints.length; j += 1) {
let keypoint = poses[i].pose.keypoints[j];
if (keypoint.score > 0.2) {
ctx.beginPath();
ctx.arc(keypoint.position.x, keypoint.position.y, 10, 0, 2 * Math.PI);
ctx.stroke();
ctx.strokeStyle = "red";
ctx.lineWidth = 3;
}
}
}
}
function drawSkeleton() {
for (let i = 0; i < poses.length; i += 1) {
for (let j = 0; j < poses[i].skeleton.length; j += 1) {
let partA = poses[i].skeleton[j][0];
let partB = poses[i].skeleton[j][1];
ctx.beginPath();
ctx.moveTo(partA.position.x, partA.position.y);
ctx.lineTo(partB.position.x, partB.position.y);
ctx.stroke();
ctx.strokeStyle = "red";
ctx.lineWidth = 3;
}
}
}
$(document).on("click", ".take-snaps", function (e) {
e.preventDefault();
if (poses.length > 0) {
let target = $(this)
.closest(".tool-controls-group")
.find(".input-pose-name")
.val();
let inputs = getInputs();
brain.addData(inputs, [target]);
var image = canvas.toDataURL("image/png");
var $image = $("<img/>", {
class: "snaped-image border p-1 my-1 mx-1",
style: "max-width: 150px; transform: scaleX(-1);",
src: image
});
$(this).closest(".tool-controls-group").find(".pose-images").append($image);
}
});
$(document).on("click", "#train", function (e) {
e.preventDefault();
trainModel();
});
$(document).on("click", ".snaped-image", function (e) {
e.preventDefault();
var src = $(this).attr("src");
$("#image-preview").find("img").attr("src", src);
$("#image-preview").modal("show");
});
});.tool {
display: -webkit-box;
display: -ms-flexbox;
display: flex;
-ms-flex-wrap: wrap;
flex-wrap: wrap;
max-width: 1920px;
margin: auto;
padding-top: 3rem;
}
.tool-preview,
.tool-controls {
position: relative;
}
.tool-preview {
width: 70%;
}
.tool-preview video,
.tool-preview canvas {
-webkit-transform: scaleX(-1);
transform: scaleX(-1);
}
.tool-controls {
width: 30%;
padding-left: 2rem;
}<link href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.1.3/css/bootstrap.min.css" rel="stylesheet"/>
<script src="https://unpkg.com/ml5@0.12.1/dist/ml5.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.1.3/js/bootstrap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
<div class="px-3 py-4 text-center bg-dark text-white">
<h2 class="mb-0 fw-bold">Save Pose</h2>
</div>
<div class="container-fluid">
<div class="tool">
<div class="tool-preview" id="tool-preview">
<canvas id="canvas"></canvas>
<video id="video" autoplay style="display: none"></video>
<div class="mt-4 alert alert-info">
<p class="mb-0" id="classified">No information</p>
</div>
</div>
<div class="tool-controls">
<form method="post">
<div class="tool-controls-group mb-4">
<div class="mb-3">
<label class="form-label">Pose Name</label>
<input type="text" class="form-control input-pose-name" />
</div>
<div class="pose-images text-center"></div>
<div class="mt-3 text-end">
<button type="button" class="btn btn-primary take-snaps">Add a Snap</button>
</div>
</div>
<div class="pt-3 border-top text-end">
<button type="button" class="btn btn-success" id="train">Train</button>
</div>
</form>
</div>
</div>
</div>你的问题有点不清楚。你的代码似乎工作正常。
姿势检测只会给你几个姿势，如果有更多的人检测。否则，每一帧给你一个姿势，这是在poses[0]。
训练模型的方法是对你想学习的每个姿势尽可能多地使用addData(pose, label)。你为每个姿势添加的例子越多，你的模型就会越好。