用html编一个网页程序,实现调用摄像头拍照,显示图片可对图片文字识别,图片来源于拍照或打开本地图片
时间: 2023-03-04 17:33:51 浏览: 126
抱歉,HTML本身不支持摄像头操作或图像识别,需要使用JavaScript或其他编程语言来实现这些功能。以下是一个使用JavaScript和HTML5中的`<input>`元素实现调用摄像头拍照或上传本地图片的示例代码:
```html
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Camera and OCR Example</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier"></script>
</head>
<body>
<h1>Camera and OCR Example</h1>
<div>
<video id="video" width="640" height="480" autoplay></video>
<button id="startButton">Take Photo</button>
<input type="file" id="imageLoader" name="imageLoader">
</div>
<div>
<canvas id="canvas" width="640" height="480"></canvas>
</div>
<div>
<h2>OCR Results:</h2>
<p id="ocrResults"></p>
</div>
<script>
const video = document.getElementById('video');
const canvas = document.getElementById('canvas');
const imageLoader = document.getElementById('imageLoader');
const startButton = document.getElementById('startButton');
const ocrResults = document.getElementById('ocrResults');
// 设置摄像头
navigator.mediaDevices.getUserMedia({ video: true })
.then((stream) => {
video.srcObject = stream;
})
.catch((error) => {
console.error(error);
});
// 当点击"Take Photo"按钮时,将当前视频帧转换为图像,并进行OCR识别
startButton.addEventListener('click', () => {
canvas.getContext('2d').drawImage(video, 0, 0, canvas.width, canvas.height);
recognizeImage(canvas.toDataURL());
});
// 当用户选择本地图像文件时,将其转换为图像,并进行OCR识别
imageLoader.addEventListener('change', (event) => {
const file = event.target.files[0];
const reader = new FileReader();
reader.onload = (event) => {
recognizeImage(event.target.result);
};
reader.readAsDataURL(file);
});
// 使用TensorFlow.js进行OCR识别
async function recognizeImage(imageUrl) {
ocrResults.innerHTML = 'Loading OCR model...';
// 加载预训练模型和分类器
const mobilenet = await mobilenet.load();
const classifier = await knnClassifier.create();
const model = await tf.loadGraphModel('model.json');
// 将图像转换为Tensor对象,并预处理
const img = new Image();
img.src = imageUrl;
await img.decode();
const tensor = tf.browser.fromPixels(img)
.resizeNearestNeighbor([224, 224])
.toFloat()
.div(tf.scalar(255))
.expandDims();
// 使用预训练模型提取特征向量,并使用分类器进行分类
const features = await mobilenet.infer(tensor, true);
const logits =
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