功能简介
本文基于TensorFlow.js与Web Worker实现了常用的“证件照”功能，可以对照片实现抠图并替换背景。值得一提的是，正常抠图的操作应该由后端进行，这里只是主要演示该功能实现步骤，并不建议该功能由前端全权处理。
限于个人技术能力有限，当前功能实现的并不怎么良好，抠图不够精细且缺少对图片处理后的画质修复等操作，这些还请诸位大佬见谅了。
效果演示
原图
## 效果图
功能亮点
- 智能人像分割：基于BodyPix模型实现精准人像抠图
- 实时背景替换：支持动态颜色选择和边缘优化算法
- 多尺寸适配：预设常用证件照尺寸+自定义毫米级精度
- 高性能处理：Web Worker独立线程保障主线程流畅性
- 模型加载优化：按需加载MobileNetV1量化模型
- 边缘平滑算法：卷积核模糊处理提升证件照专业度
主要逻辑详解
加载模型
// 初始化模型
async function loadModel() {
try {
console.log('开始加载模型...');
if (!bodyPixModel) {
bodyPixModel = await bodyPix.load({
architecture: 'MobileNetV1',
outputStride: 16,
multiplier: 0.75,
quantBytes: 2
});
console.log('模型加载成功');
}
return bodyPixModel;
} catch (error) {
console.error('模型加载失败:', error);
throw error;
}
}
该方法的主要作用便是加载bodyPixModel模型，这里用到的模型为MobileNetV1。关于bodyPix模型下load方法的各种参数可以自行搜索一下官方文档查看。
人像分割核心流程
// 使用 BodyPix 进行人像分割
console.log('开始人像分割...');
const segmentation = await model.segmentPerson(imgDataForSegmentation, {
flipHorizontal: false,
internalResolution: 'medium',
segmentationThreshold: config.segmentationThreshold || 0.7
});
console.log('人像分割完成');
// 创建输出 Canvas
const outputCanvas = new OffscreenCanvas(img.width, img.height);
const outputCtx = outputCanvas.getContext('2d');
// 绘制原始图片
outputCtx.drawImage(img, 0, 0);
// 应用背景色
const backgroundColor = hexToRgb(config.bgColor);
const outputImageData = outputCtx.getImageData(0, 0, img.width, img.height);
const pixelData = outputImageData.data;
// 应用分割结果
for (let i = 0; i < segmentation.data.length; i++) {
const n = i * 4;
if (segmentation.data[i] === 0) { // 背景部分
pixelData[n] = backgroundColor.r;
pixelData[n + 1] = backgroundColor.g;
pixelData[n + 2] = backgroundColor.b;
pixelData[n + 3] = 255;
}
}
outputCtx.putImageData(outputImageData, 0, 0);
这里的主要逻辑是分割图像及应用选择的背景色。
边缘平滑算法
// 边缘平滑处理
async function smoothEdges(ctx, width, height) {
const imageData = ctx.getImageData(0, 0, width, height);
const data = imageData.data;
const kernel = [
[0.075, 0.124, 0.075],
[0.124, 0.204, 0.124],
[0.075, 0.124, 0.075]
];
const tempData = new Uint8ClampedArray(data);
for (let y = 1; y < height - 1; y++) {
for (let x = 1; x < width - 1; x++) {
const idx = (y * width + x) * 4;
if (isEdgePixel(data, idx, width)) {
let r = 0, g = 0, b = 0, a = 0;
for (let ky = -1; ky <= 1; ky++) {
for (let kx = -1; kx <= 1; kx++) {
const offset = ((y + ky) * width + (x + kx)) * 4;
const weight = kernel[ky + 1][kx + 1];
r += tempData[offset] * weight;
g += tempData[offset + 1] * weight;
b += tempData[offset + 2] * weight;
a += tempData[offset + 3] * weight;
}
}
data[idx] = r;
data[idx + 1] = g;
data[idx + 2] = b;
data[idx + 3] = a;
}
}
}
ctx.putImageData(imageData, 0, 0);
}
这里使用3*3卷积核对边缘像素进行了加权平均处理，从而消除锯齿效果。这里用到的isEdgePixel（）方法目的是判断一个像素是否为图像的边缘像素，方法是通过比较该像素与其相邻像素的alpha通道值（也就是透明度）。
function isEdgePixel(data, idx, width) {
const alpha = data[idx + 3];
const leftAlpha = data[idx - 4 + 3];
const rightAlpha = data[idx + 4 + 3];
const topAlpha = data[idx - width * 4 + 3];
const bottomAlpha = data[idx + width * 4 + 3];
return (alpha !== leftAlpha ||
alpha !== rightAlpha ||
alpha !== topAlpha ||
alpha !== bottomAlpha);
}
完整代码
IDPhoto.vue
<template>
<div class="id-photo-container">
<!-- 左侧工具栏 -->
<div class="tools-panel">
<el-form :model="photoConfig" label-position="top">
<!-- 预设尺寸选择 -->
<el-form-item label="证件照尺寸">
<el-select v-model="photoConfig.selectedSize" placeholder="选择尺寸">
<el-option
v-for="size in presetSizes"
:key="size.value"
:label="size.label"
:value="size.value"
/>
</el-select>
</el-form-item>
<!-- 自定义尺寸输入 -->
<el-form-item label="自定义尺寸(mm)">
<div class="custom-size">
<el-input-number
v-model="photoConfig.customWidth"
:min="20"
:max="1000"
placeholder="宽度"
/>
<span class="separator">×</span>
<el-input-number
v-model="photoConfig.customHeight"
:min="20"
:max="1000"
placeholder="高度"
/>
</div>
</el-form-item>
<!-- 背景颜色选择 -->
<el-form-item label="背景颜色">
<el-color-picker v-model="photoConfig.bgColor" />
</el-form-item>
<!-- 操作按钮 -->
<div class="action-buttons">
<el-button type="primary" @click="uploadImage">
上传图片
</el-button>
<el-button
type="success"
:disabled="!hasImage"
@click="downloadPhoto"
>
下载证件照
</el-button>
</div>
</el-form>
</div>
<!-- 右侧预览区域 -->
<div class="preview-panel">
<div
class="preview-area"
:style="{ backgroundColor: photoConfig.bgColor }"
>
<img
v-if="previewUrl"
:src="previewUrl"
ref="previewImage"
@load="handleImageLoad"
/>
<div v-else class="placeholder">
请上传图片
</div>
</div>
</div>
<!-- 隐藏的文件输入框 -->
<input
type="file"
ref="fileInput"
accept="image/*"
style="display: none"
@change="handleFileChange"
/>
<el-loading
v-model:visible="loading"
text="处理中..."
background="rgba(255, 255, 255, 0.8)"
/>
</div>
</template>
<script setup>
import { ref, reactive, onMounted, onUnmounted, watch } from 'vue'
import { ElMessage } from 'element-plus'
// 预设尺寸选项
const presetSizes = [
{ label: '一寸照片 (25×35mm)', value: '25x35' },
{ label: '二寸照片 (35×49mm)', value: '35x49' },
{ label: '小二寸 (35×45mm)', value: '35x45' },
{ label: '大二寸 (35×53mm)', value: '35x53' }
]
// 照片配置
const photoConfig = reactive({
selectedSize: '35x45',
customWidth: 35,
customHeight: 45,
bgColor: '#FFFFFF',
modelQuality: 'medium', // 可选: 'low', 'medium', 'high'
segmentationThreshold: 0.7, // 分割阈值，可调整精度
edgeBlur: 3 // 边缘模糊半径
})
// 组件引用
const fileInput = ref(null)
const previewImage = ref(null)
// 状态变量
const previewUrl = ref('')
const hasImage = ref(false)
// 图片处理 Worker
let imageWorker = null
// 添加加载状态
const loading = ref(false)
// 初始化 Worker
onMounted(() => {
try {
// 使用 ?worker 查询参数来告诉 Vite 这是一个 worker 文件
imageWorker = new Worker(
new URL('../../workers/idphoto.worker.js?worker', import.meta.url),
{ type: 'module' }
)
imageWorker.onmessage = (e) => {
console.log('收到Worker响应:', e.data)
if (e.data.status === 'success') {
const blobUrl = URL.createObjectURL(e.data.result)
previewUrl.value = blobUrl
loading.value = false // 确保加载状态被重置
} else {
console.error('Worker处理失败:', e.data.error)
ElMessage.error(`处理图片时出错: ${e.data.error}`)
loading.value = false
}
}
imageWorker.onerror = (error) => {
console.error('Worker错误:', error)
ElMessage.error('图片处理服务初始化失败')
loading.value = false
}
} catch (error) {
console.error('创建Worker失败:', error)
ElMessage.error('初始化图片处理服务失败')
loading.value = false
}
})
// 清理 Worker
onUnmounted(() => {
if (imageWorker) {
imageWorker.terminate()
}
})
// 上传图片
const uploadImage = () => {
fileInput.value.click()
}
// 处理文件选择
const handleFileChange = async (event) => {
const file = event.target.files[0]
if (!file) return
if (!file.type.startsWith('image/')) {
ElMessage.error('请上传图片文件')
return
}
if (file.size > 10 * 1024 * 1024) { // 10MB 限制
ElMessage.error('图片大小不能超过10MB')
return
}
loading.value = true
ElMessage.info('正在处理图片，首次使用可能需要加载模型...')
const reader = new FileReader()
reader.onload = (e) => {
const img = new Image()
img.onload = () => {
console.log('图片加载成功，尺寸:', img.width, 'x', img.height)
try {
imageWorker.postMessage({
imageData: e.target.result,
config: {
width: img.width,
height: img.height,
bgColor: photoConfig.bgColor,
segmentationThreshold: photoConfig.segmentationThreshold,
modelQuality: photoConfig.modelQuality
}
})
} catch (error) {
loading.value = false
console.error('发送数据到Worker时出错:', error)
ElMessage.error('处理图片时出错')
}
}
img.onerror = (error) => {
loading.value = false
console.error('图片加载失败:', error)
ElMessage.error('图片加载失败')
}
img.src = e.target.result
hasImage.value = true
}
reader.onerror = (error) => {
loading.value = false
console.error('读取文件失败:', error)
ElMessage.error('读取文件失败')
}
reader.readAsDataURL(file)
}
// 处理图片加载
const handleImageLoad = () => {
// 这里可以添加图片加载后的处理逻辑
}
// 下载证件照
const downloadPhoto = async () => {
if (!hasImage.value) return
try {
const response = await fetch(previewUrl.value)
const blob = await response.blob()
const link = document.createElement('a')
link.download = '证件照.png'
link.href = URL.createObjectURL(blob)
link.click()
ElMessage.success('下载成功')
} catch (error) {
ElMessage.error('下载图片时出错')
console.error(error)
}
}
// 添加背景色变化监听
watch(() => photoConfig.bgColor, (newColor) => {
if (hasImage.value && previewUrl.value) {
const img = new Image()
img.onload = () => {
imageWorker.postMessage({
imageData: previewUrl.value,
config: {
width: img.width,
height: img.height,
bgColor: newColor
}
})
}
img.src = previewUrl.value
}
})
</script>
<style scoped>
.id-photo-container {
display: flex;
gap: 20px;
padding: 20px;
height: 100%;
}
.tools-panel {
width: 300px;
padding: 20px;
background: #f5f7fa;
border-radius: 8px;
}
.preview-panel {
flex: 1;
display: flex;
justify-content: center;
align-items: center;
background: #f5f7fa;
border-radius: 8px;
overflow: hidden;
}
.preview-area {
width: 80%;
height: 80%;
display: flex;
justify-content: center;
align-items: center;
background: #fff;
border-radius: 4px;
box-shadow: 0 2px 12px rgba(0, 0, 0, 0.1);
}
.preview-area img {
max-width: 100%;
max-height: 100%;
object-fit: contain;
}
.placeholder {
color: #909399;
font-size: 16px;
}
.custom-size {
display: flex;
align-items: center;
gap: 10px;
}
.separator {
color: #909399;
}
.action-buttons {
display: flex;
gap: 10px;
margin-top: 20px;
}
</style>
idphoto.workder.js
import * as tf from '@tensorflow/tfjs'
import * as bodyPix from '@tensorflow-models/body-pix'
let bodyPixModel = null;
// 初始化模型
async function loadModel() {
try {
console.log('开始加载模型...');
if (!bodyPixModel) {
bodyPixModel = await bodyPix.load({
architecture: 'MobileNetV1',
outputStride: 16,
multiplier: 0.75,
quantBytes: 2
});
console.log('模型加载成功');
}
return bodyPixModel;
} catch (error) {
console.error('模型加载失败:', error);
throw error;
}
}
// 处理图片的 Worker
self.onmessage = async function(e) {
console.log('Worker 收到消息:', e.data);
const { imageData, config } = e.data;
try {
if (!imageData || !config) {
throw new Error('缺少必要的参数')
}
// 加载模型
const model = await loadModel();
console.log('模型准备就绪');
// 创建图片元素
const img = await createImageBitmap(
await fetch(imageData).then(r => r.blob())
);
// 创建离屏 Canvas
const canvas = new OffscreenCanvas(img.width, img.height);
const ctx = canvas.getContext('2d');
if (!ctx) {
throw new Error('无法创建Canvas上下文')
}
// 绘制原始图片
ctx.drawImage(img, 0, 0);
console.log('图片绘制完成');
// 获取图片数据
const imgDataForSegmentation = ctx.getImageData(0, 0, img.width, img.height);
// 使用 BodyPix 进行人像分割
console.log('开始人像分割...');
const segmentation = await model.segmentPerson(imgDataForSegmentation, {
flipHorizontal: false,
internalResolution: 'medium',
segmentationThreshold: config.segmentationThreshold || 0.7
});
console.log('人像分割完成');
// 创建输出 Canvas
const outputCanvas = new OffscreenCanvas(img.width, img.height);
const outputCtx = outputCanvas.getContext('2d');
// 绘制原始图片
outputCtx.drawImage(img, 0, 0);
// 应用背景色
const backgroundColor = hexToRgb(config.bgColor);
const outputImageData = outputCtx.getImageData(0, 0, img.width, img.height);
const pixelData = outputImageData.data;
// 应用分割结果
for (let i = 0; i < segmentation.data.length; i++) {
const n = i * 4;
if (segmentation.data[i] === 0) { // 背景部分
pixelData[n] = backgroundColor.r;
pixelData[n + 1] = backgroundColor.g;
pixelData[n + 2] = backgroundColor.b;
pixelData[n + 3] = 255;
}
}
outputCtx.putImageData(outputImageData, 0, 0);
// 优化边缘
await smoothEdges(outputCtx, img.width, img.height);
// 转换为 Blob
const resultBlob = await outputCanvas.convertToBlob({
type: 'image/png'
});
console.log('处理完成，发送结果');
self.postMessage({
status: 'success',
result: resultBlob
});
} catch (error) {
console.error('Worker处理错误:', error);
self.postMessage({
status: 'error',
error: error.message || '处理图片时发生未知错误'
});
}
};
// 边缘平滑处理
async function smoothEdges(ctx, width, height) {
const imageData = ctx.getImageData(0, 0, width, height);
const data = imageData.data;
const kernel = [
[0.075, 0.124, 0.075],
[0.124, 0.204, 0.124],
[0.075, 0.124, 0.075]
];
const tempData = new Uint8ClampedArray(data);
for (let y = 1; y < height - 1; y++) {
for (let x = 1; x < width - 1; x++) {
const idx = (y * width + x) * 4;
if (isEdgePixel(data, idx, width)) {
let r = 0, g = 0, b = 0, a = 0;
for (let ky = -1; ky <= 1; ky++) {
for (let kx = -1; kx <= 1; kx++) {
const offset = ((y + ky) * width + (x + kx)) * 4;
const weight = kernel[ky + 1][kx + 1];
r += tempData[offset] * weight;
g += tempData[offset + 1] * weight;
b += tempData[offset + 2] * weight;
a += tempData[offset + 3] * weight;
}
}
data[idx] = r;
data[idx + 1] = g;
data[idx + 2] = b;
data[idx + 3] = a;
}
}
}
ctx.putImageData(imageData, 0, 0);
}
function isEdgePixel(data, idx, width) {
const alpha = data[idx + 3];
const leftAlpha = data[idx - 4 + 3];
const rightAlpha = data[idx + 4 + 3];
const topAlpha = data[idx - width * 4 + 3];
const bottomAlpha = data[idx + width * 4 + 3];
return (alpha !== leftAlpha ||
alpha !== rightAlpha ||
alpha !== topAlpha ||
alpha !== bottomAlpha);
}
function hexToRgb(hex) {
const result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex);
return result ? {
r: parseInt(result[1], 16),
g: parseInt(result[2], 16),
b: parseInt(result[3], 16)
} : null;
}
注
以上便是证件照功能的全部逻辑，其实我这里写的相当简陋且具有很大的扩展空间。各位大佬如果有精力，可以在这个基础上 增加服装替换、美颜等功能，以及可以进一步优化UI界面（我这里样式写的不是特别好）。
总之，感谢阅读了，愿你我都能在技术之路上更进一步！
