commit 3c735d9643cac9059a622b702a3f6d11dd24ecb1 Author: taoya7 <569781231@qq.com> Date: Wed Nov 20 16:05:29 2024 +0800 feat: Image diff --git a/.bolt/config.json b/.bolt/config.json new file mode 100644 index 0000000..a4df855 --- /dev/null +++ b/.bolt/config.json @@ -0,0 +1,3 @@ +{ + "template": "node" +} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3c3629e --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +node_modules diff --git a/README.md b/README.md new file mode 100644 index 0000000..5136adf --- /dev/null +++ b/README.md @@ -0,0 +1,80 @@ +# 图片灰度处理工具 + +这个程序可以处理本地图片和网络图片，提供两种灰度处理算法，并支持生成 TSPL 打印指令： +1. OTSU 自动阈值法 +2. 平均灰度法 + +## 功能特点 + +- 支持本地图片和网络图片处理 +- 提供 OTSU 和平均灰度两种处理算法 +- 自动创建输出目录 +- 保留原始文件名 +- 生成 TSPL 打印指令 +- 提供详细的处理信息输出 + +## 算法说明 + +1. **OTSU 算法**： + - 自动计算最优阈值 + - 适用于双峰直方图图像 + - 输出文件名后缀为 `_otsu.jpg` + - 生成对应的 `_otsu.tspl` 打印指令文件 + +2. **平均灰度算法**： + - 使用图像的平均灰度值作为阈值 + - 简单快速，适用于对比度较好的图像 + - 输出文件名后缀为 `_average.jpg` + - 生成对应的 `_average.tspl` 打印指令文件 + +## TSPL 打印指令 + +程序会为每个处理后的图像生成对应的 TSPL 打印指令文件： +- 自动计算适合的打印参数 +- 支持自定义打印浓度和速度 +- 生成标准 TSPL 指令格式 +- 可直接发送到支持 TSPL 的打印机 + +## 使用方法 + +1. 安装依赖： +```bash +npm install +``` + +2. 运行程序： +```bash +npm start +``` + +## 输出说明 + +- 程序会在 `output` 目录下保存： + - 处理后的图片（.jpg） + - TSPL 打印指令文件（.tspl） +- 控制台会显示: + - 图片尺寸 + - 计算得到的阈值 + - 图片输出路径 + - TSPL 指令文件路径 + +## 自定义使用 + +你可以修改 `src/index.js` 中的参数来处理其他图片： + +```javascript +// 处理网络图片 +const imageUrl = 'https://你的图片URL'; + +// 选择处理方法: 'otsu' 或 'average' +const result = await processor.processImage(imageUrl, outputDir, 'otsu'); +``` + +你也可以自定义 TSPL 转换器的参数： + +```javascript +const tsplConverter = new TSPLConverter({ + density: 10, // 打印浓度 (0-15) + speed: 3, // 打印速度 (1-4) + width: 384 // 打印宽度（点） +}); \ No newline at end of file diff --git a/index.js b/index.js new file mode 100644 index 0000000..39ce866 --- /dev/null +++ b/index.js @@ -0,0 +1,3 @@ +// run `node index.js` in the terminal + +console.log(`Hello Node.js v${process.versions.node}!`); diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..df8ce57 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,570 @@ +{ + "name": "image-grayscale", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "image-grayscale", + "version": "1.0.0", + "dependencies": { + "axios": "^1.6.2", + "sharp": "^0.33.2" + } + }, + "node_modules/@emnapi/runtime": { + "version": "1.3.1", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.3.1.tgz", + "integrity": "sha512-kEBmG8KyqtxJZv+ygbEim+KCGtIq1fC22Ms3S4ziXmYKm8uyoLX0MHONVKwp+9opg390VaKRNt4a7A9NwmpNhw==", + 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const response = await axios.get(url, { responseType: 'arraybuffer' }); + return Buffer.from(response.data); + } catch (error) { + throw new Error(`下载图片失败: ${error.message}`); + } + } + + async ensureDirectoryExists(dir) { + if (!existsSync(dir)) { + await mkdir(dir, { recursive: true }); + } + } + + calculateOtsuThreshold(histogram, total) { + let sum = 0; + for (let i = 0; i < 256; i++) { + sum += i * histogram[i]; + } + + let sumB = 0; + let wB = 0; + let wF = 0; + let maxVariance = 0; + let threshold = 0; + + for (let t = 0; t < 256; t++) { + wB += histogram[t]; + if (wB === 0) continue; + + wF = total - wB; + if (wF === 0) break; + + sumB += t * histogram[t]; + const mB = sumB / wB; + const mF = (sum - sumB) / wF; + + const variance = wB * wF * Math.pow(mB - mF, 2); + if (variance > maxVariance) { + maxVariance = variance; + threshold = t; + } + } + + return threshold; + } + + async processImage(input, outputDir = './output', method = 'average') { + try { + await this.ensureDirectoryExists(outputDir); + + let imageBuffer; + let filename; + + if (typeof input === 'string' && input.startsWith('http')) { + imageBuffer = await this.downloadImage(input); + filename = path.basename(new URL(input).pathname); + } else { + imageBuffer = input; + filename = 'local_image.jpg'; + } + + const image = sharp(imageBuffer); + const { data, info } = await image + .grayscale() + .raw() + .toBuffer({ resolveWithObject: true }); + + // 计算直方图 + const histogram = new Array(256).fill(0); + for (const pixel of data) { + histogram[pixel]++; + } + + let threshold; + if (method === 'otsu') { + threshold = this.calculateOtsuThreshold(histogram, info.width * info.height); + } else { + // 平均灰度法 + const sum = data.reduce((acc, val) => acc + val, 0); + threshold = Math.round(sum / (info.width * info.height)); + } + + // 应用阈值进行二值化 + const binaryData = Buffer.from(data.map(pixel => pixel >= threshold ? 255 : 0)); + + const fileExt = '.jpg'; + const baseName = path.parse(filename).name; + const methodSuffix = method === 'otsu' ? '_otsu' : '_average'; + const outputFilename = `${baseName}${methodSuffix}${fileExt}`; + const outputPath = path.join(outputDir, outputFilename); + + // 保存处理后的图片 + await sharp(binaryData, { + raw: { + width: info.width, + height: info.height, + channels: 1 + } + }) + .jpeg() + .toFile(outputPath); + + return { + dimensions: { width: info.width, height: info.height }, + threshold, + method, + outputPath + }; + } catch (error) { + throw new Error(`处理图片失败: ${error.message}`); + } + } +} \ No newline at end of file diff --git a/src/index.js b/src/index.js new file mode 100644 index 0000000..3b5890c --- /dev/null +++ b/src/index.js @@ -0,0 +1,31 @@ +import { ImageProcessor } from './imageProcessor.js'; +import path from 'path'; + +async function main() { + try { + const outputDir = path.join(process.cwd(), 'output'); + const processor = new ImageProcessor(); + const imageUrl = 'https://taoya.art/avatar.jpeg'; + + // 使用 OTSU 方法处理 + console.log('使用 OTSU 方法处理图片:', imageUrl); + const otsuResult = await processor.processImage(imageUrl, outputDir, 'otsu'); + console.log('\nOTSU 处理结果:'); + console.log(`图片尺寸: ${otsuResult.dimensions.width}x${otsuResult.dimensions.height}`); + console.log(`OTSU 阈值: ${otsuResult.threshold}`); + console.log(`输出路径: ${otsuResult.outputPath}`); + + // 使用平均灰度方法处理 + console.log('\n使用平均灰度方法处理图片:', imageUrl); + const avgResult = await processor.processImage(imageUrl, outputDir, 'average'); + console.log('\n平均灰度处理结果:'); + console.log(`图片尺寸: ${avgResult.dimensions.width}x${avgResult.dimensions.height}`); + console.log(`平均灰度阈值: ${avgResult.threshold}`); + console.log(`输出路径: ${avgResult.outputPath}`); + + } catch (error) { + console.error('程序执行失败:', error.message); + } +} + +main(); \ No newline at end of file