mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 17:41:52 +08:00
[Other] Refactor js submodule (#415)
* Refactor js submodule * Remove change-log * Update ocr module * Update ocr-detection module * Update ocr-detection module * Remove change-log
This commit is contained in:
chenqianhe
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<script setup lang="ts">
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import FaceDetection from "@/pages/cv/detection/FaceDetection/FaceDetection.vue";
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</script>
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<template>
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<el-container>
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<el-header>Header</el-header>
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<el-main>
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<FaceDetection></FaceDetection>
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</el-main>
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<el-footer>Footer</el-footer>
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</el-container>
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</template>
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<style scoped lang="less">
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.el-container {
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height: 100%;
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width: 100%;
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}
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</style>
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@@ -0,0 +1,142 @@
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<template>
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<el-dialog
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v-model="isLoadingModel"
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title="提示"
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width="30%"
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center
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:lock-scroll="true"
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:show-close="false"
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:close-on-click-modal="false"
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:close-on-press-escape="false"
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>
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<span>正在加载模型，请稍等。</span>
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</el-dialog>
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<el-row :gutter="20">
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<el-col :span="12">
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<el-row class="small-title">
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<h2>上传人脸图片</h2>
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</el-row>
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<el-row>
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<el-input type="file" v-model="fileName" @change="uploadImg"></el-input>
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</el-row>
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<el-row>
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<!-- 用于展示图片 -->
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<img id="show-img" class="show-area" />
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<!-- 用于存放真实图片进行文字识别 -->
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<img id="raw-img" style="display: none" />
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</el-row>
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</el-col>
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<el-col :span="12">
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<el-row class="small-title">
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<h2>人脸区域检测</h2>
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</el-row>
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<el-row>
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<el-button type="primary" @click="predict">开始检测</el-button>
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</el-row>
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<el-row>
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<canvas id="canvas" class="show-area"></canvas>
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</el-row>
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</el-col>
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</el-row>
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</template>
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<script setup lang="ts">
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import { FaceDetector } from "@paddle-js-models/facedetect";
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import { onMounted, ref } from "vue";
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interface TransformData {
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left: number;
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width: number;
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top: number;
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height: number;
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confidence: number;
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}
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const faceDet = new FaceDetector();
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const fileName = ref(null);
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const canvas = ref(null as unknown as HTMLCanvasElement);
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const isLoadingModel = ref(true);
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onMounted(async () => {
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canvas.value = document.getElementById("canvas") as HTMLCanvasElement;
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await faceDet.init();
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isLoadingModel.value = false;
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});
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const uploadImg = () => {
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/**
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* 这里由于操作是绑定在 el-input 上；因此需要在内部重新获取 input 再拿到 file
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*/
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const reader = new FileReader();
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// 用于展示
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const showImg = document.getElementById("show-img") as HTMLImageElement;
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// 用于识别
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const rawImg = document.getElementById("raw-img") as HTMLImageElement;
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const inputElement = document
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.getElementsByClassName("el-input")[0]
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.getElementsByTagName("input")[0];
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try {
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const file = inputElement.files![0];
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reader.onload = () => {
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showImg.src = URL.createObjectURL(file);
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rawImg.src = URL.createObjectURL(file);
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};
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reader.readAsDataURL(file);
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} catch (err) {
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console.error(err);
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}
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};
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const predict = async () => {
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const img = document.getElementById("raw-img") as HTMLImageElement;
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const res = await faceDet.detect(img);
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console.log(res);
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drawBox(res);
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};
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const drawBox = (data: TransformData[]) => {
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const img = document.getElementById("raw-img") as HTMLImageElement;
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const imgHeight = img.height;
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const imgWidth = img.width;
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canvas.value.width = imgWidth;
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canvas.value.height = imgHeight;
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const ctx = canvas.value.getContext("2d") as CanvasRenderingContext2D;
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ctx.drawImage(img, 0, 0, canvas.value.width, canvas.value.height);
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data.forEach((item: TransformData) => {
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// 开始一个新的绘制路径
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ctx.beginPath();
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// 设置线条颜色为蓝色
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ctx.strokeStyle = "red";
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// 获取检测框选坐标
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const x = item.left * imgWidth;
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const y = item.top * imgHeight;
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const w = item.width * imgWidth;
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const h = item.height * imgHeight;
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ctx.beginPath();
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// 绘制检测框选矩形
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ctx.rect(x, y, w, h);
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// 绘制label
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ctx.fillText(item.confidence.toFixed(6), x, y);
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ctx.stroke();
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});
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};
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</script>
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<style scoped lang="less">
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.small-title {
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justify-content: space-between;
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align-items: center;
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}
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.show-area {
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width: 100%;
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}
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.el-row {
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margin-bottom: 20px;
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}
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</style>
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@@ -0,0 +1,14 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<script>var Module;</script>
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<meta charset="UTF-8" />
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<link rel="icon" href="/favicon.ico" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Vite App</title>
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</head>
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<body>
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<div id="app"></div>
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<script type="module" src="main.ts"></script>
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</body>
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</html>
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@@ -0,0 +1,9 @@
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import { createApp } from "vue";
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import App from "./App.vue";
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import "../../../../assets/main.css";
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const app = createApp(App);
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app.mount("#app");
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@@ -0,0 +1,20 @@
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<script setup lang="ts">
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import ScrewDetection from "@/pages/cv/detection/ScrewDetection/ScrewDetection.vue";
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</script>
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<template>
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<el-container>
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<el-header>Header</el-header>
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<el-main>
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<ScrewDetection></ScrewDetection>
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</el-main>
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<el-footer>Footer</el-footer>
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</el-container>
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</template>
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<style scoped lang="less">
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.el-container {
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height: 100%;
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width: 100%;
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}
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</style>
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@@ -0,0 +1,138 @@
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<template>
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<el-dialog
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v-model="isLoadingModel"
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title="提示"
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width="30%"
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center
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:lock-scroll="true"
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:show-close="false"
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:close-on-click-modal="false"
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:close-on-press-escape="false"
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>
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<span>正在加载模型，请稍等。</span>
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</el-dialog>
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<el-row :gutter="20">
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<el-col :span="12">
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<el-row class="small-title">
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<h2>上传图片</h2>
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</el-row>
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<el-row>
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<el-input type="file" v-model="fileName" @change="uploadImg"></el-input>
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</el-row>
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<el-row>
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<!-- 用于展示图片 -->
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<img id="show-img" class="show-area" />
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<!-- 用于存放真实图片进行文字识别 -->
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<img id="raw-img" style="display: none" />
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</el-row>
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</el-col>
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<el-col :span="12">
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<el-row class="small-title">
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<h2>螺丝螺母区域检测</h2>
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</el-row>
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<el-row>
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<el-button type="primary" @click="predict">开始检测</el-button>
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</el-row>
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<el-row>
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<canvas id="canvas" class="show-area"></canvas>
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</el-row>
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</el-col>
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</el-row>
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</template>
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<script setup lang="ts">
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import * as det from "@paddle-js-models/detect";
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import { onMounted, ref } from "vue";
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const fileName = ref(null);
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const canvas = ref(null as unknown as HTMLCanvasElement);
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const isLoadingModel = ref(true);
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const label = new Map([
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[0, "default"],
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[1, "screw"],
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[2, "nut"],
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]);
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onMounted(async () => {
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canvas.value = document.getElementById("canvas") as HTMLCanvasElement;
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await det.init();
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isLoadingModel.value = false;
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});
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const uploadImg = () => {
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/**
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* 这里由于操作是绑定在 el-input 上；因此需要在内部重新获取 input 再拿到 file
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*/
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const reader = new FileReader();
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// 用于展示
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const showImg = document.getElementById("show-img") as HTMLImageElement;
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// 用于识别
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const rawImg = document.getElementById("raw-img") as HTMLImageElement;
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const inputElement = document
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.getElementsByClassName("el-input")[0]
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.getElementsByTagName("input")[0];
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try {
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const file = inputElement.files![0];
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reader.onload = () => {
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showImg.src = URL.createObjectURL(file);
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rawImg.src = URL.createObjectURL(file);
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};
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reader.readAsDataURL(file);
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} catch (err) {
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console.error(err);
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}
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};
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const predict = async () => {
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const img = document.getElementById("raw-img") as HTMLImageElement;
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const res = await det.detect(img);
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console.log(res);
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drawBox(res);
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};
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const drawBox = (points: number[][]) => {
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const img = document.getElementById("raw-img") as HTMLImageElement;
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const imgHeight = img.height;
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const imgWidth = img.width;
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canvas.value.width = imgWidth;
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canvas.value.height = imgHeight;
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const ctx = canvas.value.getContext("2d") as CanvasRenderingContext2D;
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ctx.drawImage(img, 0, 0, canvas.value.width, canvas.value.height);
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points.forEach((point: number[]) => {
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// 开始一个新的绘制路径
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ctx.beginPath();
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// 设置线条颜色为蓝色
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ctx.strokeStyle = "red";
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// 获取检测框选坐标
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const left = Math.floor(point[2] * imgWidth);
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const top = Math.floor(point[3] * imgHeight);
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const right = Math.floor(point[4] * imgWidth);
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const bottom = Math.floor(point[5] * imgHeight);
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ctx.beginPath();
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// 绘制检测框选矩形
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ctx.rect(left, top, right - left, bottom - top);
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// 绘制label
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ctx.fillText(label.get(point[0])!, left + 10, top + 10);
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ctx.stroke();
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});
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};
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</script>
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<style scoped lang="less">
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.small-title {
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justify-content: space-between;
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align-items: center;
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}
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.show-area {
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width: 100%;
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}
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.el-row {
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margin-bottom: 20px;
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}
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</style>
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@@ -0,0 +1,14 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<script>var Module;</script>
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<meta charset="UTF-8" />
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<link rel="icon" href="/favicon.ico" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Vite App</title>
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</head>
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<body>
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<div id="app"></div>
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<script type="module" src="main.ts"></script>
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</body>
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</html>
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@@ -0,0 +1,9 @@
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import { createApp } from "vue";
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import App from "./App.vue";
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import "../../../../assets/main.css";
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const app = createApp(App);
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app.mount("#app");
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