Ubuntu
92 lines
2.6 KiB
JavaScript
92 lines
2.6 KiB
JavaScript
#!/usr/bin/env node
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/**
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* 测试 paper_recognizer WASM 模块
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* 用法: node test.js
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*/
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const path = require('path');
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const fs = require('fs');
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async function test() {
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console.log('=== 测试 PaperRecognizer WASM 模块 ===\n');
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// 加载模块
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const Module = require('./web/paper_recognizer.js');
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const m = await Module();
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console.log('✅ 模块加载成功');
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console.log(` 导出函数:`);
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const exports = [
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'_recognize', '_getResultJson', '_getAnnotatedWidth',
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'_getAnnotatedHeight', '_getAnnotatedPixels', '_freeResult',
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'_malloc', '_free'
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];
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for (const name of exports) {
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const fn = m[name];
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if (typeof fn === 'function') {
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console.log(` ✅ ${name}`);
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} else {
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console.log(` ❌ ${name} 未导出`);
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}
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}
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// 创建一个测试图像（纯灰色图像）
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const W = 640, H = 480;
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const pixels = new Uint8Array(W * H * 4);
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for (let y = 0; y < H; y++) {
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for (let x = 0; x < W; x++) {
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const idx = (y * W + x) * 4;
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pixels[idx + 0] = 128; // R
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pixels[idx + 1] = 128; // G
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pixels[idx + 2] = 128; // B
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pixels[idx + 3] = 255; // A
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}
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}
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console.log('\n=== 测试识别功能 ===');
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// 分配 WASM 内存并复制图像数据
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const ptr = m._malloc(pixels.length);
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m.HEAPU8.set(pixels, ptr);
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// 调用识别
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const ret = m._recognize(ptr, W, H);
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console.log(` 识别返回值: ${ret}`);
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// 获取结果 JSON
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const resultPtr = m._getResultJson();
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const resultStr = m.UTF8ToString(resultPtr);
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console.log(` 结果 JSON:\n${JSON.stringify(JSON.parse(resultStr), null, 2)}`);
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// 获取标注图尺寸
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const aw = m._getAnnotatedWidth();
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const ah = m._getAnnotatedHeight();
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console.log(` 标注图尺寸: ${aw} x ${ah}`);
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// 获取标注图像素
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const ap = m._getAnnotatedPixels();
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if (ap && aw > 0 && ah > 0) {
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const size = aw * ah * 4;
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const annotatedData = new Uint8Array(m.HEAPU8.buffer, ap, size);
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console.log(` 标注图像素数据大小: ${annotatedData.length} bytes`);
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// 写入文件供查看
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// 保存为原始 RGBA 数据
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fs.writeFileSync('/tmp/test_result.rgba', annotatedData);
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console.log(` 已保存到 /tmp/test_result.rgba (${aw}x${ah} RGBA)`);
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}
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// 清理
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m._freeResult();
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m._free(ptr);
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console.log('\n✅ 测试完成');
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}
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test().catch(err => {
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console.error('❌ 测试失败:', err.message);
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process.exit(1);
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});
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