face-detection/index.html at master · moqi-y/face-detection · GitHub

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<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>人脸识别</title>
    <!--
        face-api可以放在自己的服务器上使用url引入加载，例如：
        <script src="http://127.0.0.1:8081/face-api.min.js"></script>
     -->
    <script src="./dist/face-api.js"></script>
    <style>
        #image {
            width: 500px;
            height: auto;
            position: absolute;
            left: 100px;
            top: 100px;
        }

        #btn {
            width: 90px;
            height: 40px;
            border-radius: 5px;
            background: #40a3ef;
            color: #fff;
            cursor: pointer;
            font-size: 16px;
        }
        .btn-area{
            margin: 5px 10px;
            border: 2px solid #ccc;
        }
        .btn-area > button{
            padding: 10px;
            border-radius: 5px;
            background: #40a3ef;
            color: #fff;
            cursor: pointer;
            font-size: 16px;
            border-radius: 8px;
            margin-right: 5px;
        }

        #canvas,
        #video {
            position: absolute;
            left: 100px;
            top: 100px;
        }
    </style>
</head>

<body>
    <button id="btn">开始检测</button>
    <canvas id="canvas" width="1200" height="800" style="border:1px solid #d3d3d3;">
        您的浏览器不支持canvas
    </canvas>

    <div class="btn-area">
        <span style="color: orange;"><b>其他示例：</b></span>
        <button><a href="./input-face.html">自定义图片检测示例</a></button>
        <button><a href="./video-face.html">摄像头人脸识别检测示例</a></button>
        <button><a href="./faceapi-draw.html">faceapi内置方法绘制识别框示例</a></button>
    </div>
    <script>
        const image = document.getElementById('image');
        const canvas = document.getElementById('canvas');
        const ctx = canvas.getContext('2d');
        const btn = document.getElementById('btn');
        // 窗口加载触发
        window.onload = function () {
            // 将图片绘制到canvas
            drawImage()
            btn.onclick = function () {
                loadModel()
            }
        }

        // 将图片绘制到canvas
        function drawImage(params) {
            var img = new Image();
            img.onload = function () {
                ctx.drawImage(img, 0, 0);
            }
            img.src = './MR.jpg'; // 替换为你的图片源
        }

        // 绘制识别框的方法
        function drawRect(x, y, width, height, color, weight) {
            ctx.strokeStyle = color
            ctx.lineWidth = weight
            ctx.strokeRect(x, y, width, height)
        }

        // 绘制文本
        function drawText(text, x, y, fontSize) {
            ctx.font = fontSize + " " + "Arial";
            ctx.strokeText(text, x, y)
        }

        // 置信度处理
        function financial(x) {
            return Number.parseFloat(x).toFixed(4);
        }

        async function loadModel(params) {
            // 加载模型,模型的Json和shard文件必须位于同一目录
            await faceapi.loadSsdMobilenetv1Model('./weights/')
            // 全局神经网络实例
            console.log(faceapi.nets)
            // 检测图像中的所有人脸
            const detections = await faceapi.detectAllFaces(canvas)
            console.log("detections", detections);
            for (let index = 0; index < detections.length; index++) {
                const item = detections[index];
                console.log("item", item);
                drawRect(item._box._x, item._box._y, item._box._width, item._box._height, "orange", 2)
                drawText(`置信度:${financial(item._score)}`, item._box._x + 1, item._box._y - 5, "20px")
            }

            // 检测图像中置信度得分最高的人脸
            const detection = await faceapi.detectSingleFace(canvas)
            console.log("detection", detection);
            console.log(`得分：${detection._score}`);
            drawRect(detection._box._x, detection._box._y, detection._box._width, detection._box._height, "red", 2)
            drawText(`置信度:${financial(detection._score)}`, detection._box._x + 1, detection._box._y - 5, "20px")
        }


    </script>
</body>

</html>