mirror of
https://github.com/TonyJiangWJ/Auto.js.git
synced 2026-06-24 21:33:16 +08:00
优化OCR接口 移除无用代码
This commit is contained in:
TonyJiangWJ
parent
bd466224b2
commit
5fd0402cd5
@ -1,19 +1,18 @@
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//目录路径(必须是完整路径)
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var dirPath = "/sdcard/脚本/";
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if(!files.isDir(dirPath)) files.create(dirPath);
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//压缩文件路径(必须是完整路径)
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var filePath = "/sdcard/脚本.7z";
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//压缩类型
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//支持的压缩类型包括:zip 7z bz2 bzip2 tbz2 tbz gz gzip tgz tar wim swm xz txz。
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// 目录路径(必须是完整路径)
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var dirPath = files.cwd();
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// 压缩文件路径(必须是完整路径)
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var filePath = org.autojs.autojs.Pref.getScriptDirPath() + '/测试.7z';
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// 压缩类型
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// 支持的压缩类型包括:zip 7z bz2 bzip2 tbz2 tbz gz gzip tgz tar wim swm xz txz。
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var type = "7z";
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//压缩密码
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// 压缩密码
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var password = "password"
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//7z加密压缩(若文件已存在则跳过)
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//zips.A(type, filePath, dirPath, password)
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// 7z加密压缩(若文件已存在则跳过)
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// $zips.addFiles(type, filePath, dirPath, password)
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//压缩
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switch (zips.A(type, filePath, dirPath)) {
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// 压缩
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switch ($zips.addFiles(type, filePath, dirPath)) {
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case 0:
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toastLog("压缩成功！文件已保存为： " + filePath)
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break;
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@ -1,16 +1,16 @@
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//压缩文件路径(必须是完整路径)
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// 压缩文件路径(必须是完整路径)
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var filePath = files.path("./bonus.rar");
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//目录路径(必须是完整路径)
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var dirPath = "/sdcard/脚本";
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//压缩密码
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// 目录路径(必须是完整路径)
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var dirPath = org.autojs.autojs.Pref.getScriptDirPath();
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// 压缩密码
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var password = "password"
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//支持的解压缩类型包括：zip、7z、bz2、bzip2、tbz2、tbz、gz、gzip、tgz、tar、wim、swm、xz、txz以及rar、chm、iso、msi等众多格式。
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//解压无加密的压缩包(若文件已存在则跳过)
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//zips.X(filePath, dirPath)
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// 支持的解压缩类型包括：zip、7z、bz2、bzip2、tbz2、tbz、gz、gzip、tgz、tar、wim、swm、xz、txz以及rar、chm、iso、msi等众多格式。
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// 解压无加密的压缩包(若文件已存在则跳过)
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// $zips.X(filePath, dirPath)
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//解压加密的压缩包(若文件已存在则跳过)
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switch (zips.X(filePath, dirPath, password)) {
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// 解压加密的压缩包(若文件已存在则跳过)
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switch ($zips.extraFiles(filePath, dirPath, password)) {
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case 0:
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toastLog("解压缩成功！请到 " + dirPath + " 目录下查看。")
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break;
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@ -1,19 +1,29 @@
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const img = images.read("./test.png");
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const cpuThreadNum = 4;
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const img = images.read("./test.png")
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console.show()
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let cpuThreadNum = 4
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// PaddleOCR 移动端提供了两种模型：ocr_v2_for_cpu与ocr_v2_for_cpu(slim)，此选项用于选择加载的模型,默认true使用v2的slim版(速度更快)，false使用v2的普通版(准确率更高）
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var useSlim = true;
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let useSlim = true
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let start = new Date()
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// 识别图片中的文字，返回完整识别信息（兼容百度OCR格式）。
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const result = ocr.R(img, cpuThreadNum, useSlim);
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// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true;
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// const result = ocr.R(img);
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toastLog("完整识别信息（兼容百度OCR格式）: " + JSON.stringify(result));
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// 识别图片中的文字，只返回文本识别信息（字符串列表）。
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const stringList = ocr.T(img, cpuThreadNum, useSlim);
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// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true;
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// const stringList = ocr.T(img);
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toastLog("文本识别信息（字符串列表）: " + JSON.stringify(stringList));
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let result = $ocr.detect(img, cpuThreadNum, useSlim)
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log('slim识别耗时：' + (new Date() - start) + 'ms')
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// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true
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// const result = $ocr.detect(img)
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toastLog("完整识别信息: " + JSON.stringify(result))
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start = new Date()
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// 识别图片中的文字，只返回文本识别信息（字符串列表）。当前版本可能存在文字顺序错乱的问题 建议先使用detect后自行排序
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const stringList = $ocr.recognizeText(img, cpuThreadNum, useSlim)
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log('slim纯文本识别耗时：' + (new Date() - start) + 'ms')
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// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true
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// const stringList = $ocr.recognizeText(img)
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toastLog("文本识别信息（字符串列表）: " + JSON.stringify(stringList))
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// 增加线程数
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cpuThreadNum = 8
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start = new Date()
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result = $ocr.detect(img, cpuThreadNum, useSlim)
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log('8线程识别耗时：' + (new Date() - start) + 'ms')
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toastLog("完整识别信息（兼容百度OCR格式）: " + JSON.stringify(result))
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// 释放模型 用于释放native内存
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ocr.release()
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$ocr.release()
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// 回收图片
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img.recycle()
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@ -2,15 +2,21 @@ importClass(com.baidu.paddle.lite.ocr.Predictor)
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console.show()
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let path = 'test.jpg'
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// 指定是否用精简版模型 速度较快
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let useSlim = false
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// 创建检测器
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let predictor = new Predictor()
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// predictor.cpuThreadNum = 4 //可以自定义使用CPU的线程数
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// 初始化模型 首次运行时会比较耗时
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let loadSuccess = predictor.init(context)
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// 内置的模型只有一个models/ocr_v2_for_cpu，初始化自定义模型请写绝对路径否则无法获取到
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// 使用自定义模型时det rec cls三个模型文件名称需要修改为，后续版本开放自定义文件名
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// ch_ppocr_mobile_v2.0_det_opt.nb
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// ch_ppocr_mobile_v2.0_rec_opt.nb
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// ch_ppocr_mobile_v2.0_cls_opt.nb
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let loadSuccess = predictor.init(context, useSlim) // predictor.init(context) 为默认不使用精简版
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// 内置默认 modelPath 为 models/ocr_v2_for_cpu，初始化自定义模型请写绝对路径否则无法获取到
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// 内置默认 labelPath 为 labels/ppocr_keys_v1.txt
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// let modelPath = files.path('./models/customize') // 指定自定义模型路径
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// let labelPath = files.path('./models/customize') // 指定自定义label路径
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// 使用自定义模型时det rec cls三个模型文件名称需要手动指定
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// predictor.detModelFilename = 'ch_ppocr_mobile_v2.0_det_opt.nb'
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// predictor.recModelFilename = 'ch_ppocr_mobile_v2.0_rec_opt.nb'
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// predictor.clsModelFilename = 'ch_ppocr_mobile_v2.0_cls_opt.nb'
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// predictor.init(context, modelPath, labelPath)
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toastLog('加载模型结果：' + loadSuccess)
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let start = new Date()
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188
app/src/main/assets/sample/OCR/PaddleOCR截图识别.js
Normal file
188
app/src/main/assets/sample/OCR/PaddleOCR截图识别.js
Normal file
@ -0,0 +1,188 @@
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let currentEngine = engines.myEngine()
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let runningEngines = engines.all()
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let currentSource = currentEngine.getSource() + ''
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if (runningEngines.length > 1) {
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runningEngines.forEach(compareEngine => {
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let compareSource = compareEngine.getSource() + ''
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if (currentEngine.id !== compareEngine.id && compareSource === currentSource) {
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// 强制关闭同名的脚本
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compareEngine.forceStop()
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}
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})
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}
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if (!requestScreenCapture()) {
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toastLog('请求截图权限失败')
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exit()
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}
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sleep(1000)
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importClass(com.baidu.paddle.lite.ocr.Predictor)
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// 指定是否用精简版模型 速度较快
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let useSlim = false
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// 创建检测器
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let predictor = new Predictor()
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// predictor.cpuThreadNum = 4 //可以自定义使用CPU的线程数
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// 初始化模型 首次运行时会比较耗时
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let loadSuccess = predictor.init(context, useSlim) // predictor.init(context) 为默认不使用精简版
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// 内置默认 modelPath 为 models/ocr_v2_for_cpu，初始化自定义模型请写绝对路径否则无法获取到
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// 内置默认 labelPath 为 labels/ppocr_keys_v1.txt
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// let modelPath = files.path('./models/customize') // 指定自定义模型路径
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// let labelPath = files.path('./models/customize') // 指定自定义label路径
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// 使用自定义模型时det rec cls三个模型文件名称需要手动指定
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// predictor.detModelFilename = 'ch_ppocr_mobile_v2.0_det_opt.nb'
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// predictor.recModelFilename = 'ch_ppocr_mobile_v2.0_rec_opt.nb'
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// predictor.clsModelFilename = 'ch_ppocr_mobile_v2.0_cls_opt.nb'
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// predictor.init(context, modelPath, labelPath)
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if (!loadSuccess) {
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toastLog('初始化ocr失败')
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exit()
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}
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// 识别结果和截图信息
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let result = []
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let img = null
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let running = true
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let capturing = true
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/**
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* 截图并识别OCR文本信息
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*/
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function captureAndOcr() {
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capturing = true
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img && img.recycle()
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img = captureScreen()
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if (!img) {
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toastLog('截图失败')
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}
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let start = new Date()
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result = predictor.runOcr(img.getBitmap())
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toastLog('耗时' + (new Date() - start) + 'ms')
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capturing = false
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}
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captureAndOcr()
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// 获取状态栏高度
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let offset = -getStatusBarHeightCompat()
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// 绘制识别结果
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let window = floaty.rawWindow(
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<canvas id="canvas" layout_weight="1" />
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);
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// 设置悬浮窗位置
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ui.post(() => {
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window.setPosition(0, offset)
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window.setSize(device.width, device.height)
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window.setTouchable(false)
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})
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// 操作按钮
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let clickButtonWindow = floaty.rawWindow(
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<vertical>
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<button id="captureAndOcr" text="截图识别" />
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<button id="closeBtn" text="退出" />
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</vertical>
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);
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ui.run(function () {
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clickButtonWindow.setPosition(device.width / 2 - ~~(clickButtonWindow.getWidth() / 2), device.height * 0.65)
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})
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// 点击识别
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clickButtonWindow.captureAndOcr.click(function () {
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result = []
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ui.run(function () {
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clickButtonWindow.setPosition(device.width, device.height)
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})
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setTimeout(() => {
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captureAndOcr()
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ui.run(function () {
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clickButtonWindow.setPosition(device.width / 2 - ~~(clickButtonWindow.getWidth() / 2), device.height * 0.65)
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})
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}, 500)
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})
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// 点击关闭
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clickButtonWindow.closeBtn.click(function () {
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exit()
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})
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let Typeface = android.graphics.Typeface
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let paint = new Paint()
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paint.setStrokeWidth(1)
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paint.setTypeface(Typeface.DEFAULT_BOLD)
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paint.setTextAlign(Paint.Align.LEFT)
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paint.setAntiAlias(true)
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paint.setStrokeJoin(Paint.Join.ROUND)
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paint.setDither(true)
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window.canvas.on('draw', function (canvas) {
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if (!running || capturing) {
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return
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}
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// 清空内容
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canvas.drawColor(0xFFFFFF, android.graphics.PorterDuff.Mode.CLEAR)
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if (result && result.length > 0) {
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for (let i = 0; i < result.length; i++) {
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let ocrResult = result[i]
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drawRectAndText(ocrResult.label, ocrResult.bounds, '#00ff00', canvas, paint)
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}
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}
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})
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setInterval(() => { }, 10000)
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events.on('exit', () => {
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// 标记停止 避免canvas导致闪退
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running = false
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// 回收图片
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img && img.recycle()
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// 撤销监听
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window.canvas.removeAllListeners()
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// 释放模型
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predictor.releaseModel()
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})
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/**
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* 绘制文本和方框
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*
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* @param {*} desc
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* @param {*} rect
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* @param {*} colorStr
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* @param {*} canvas
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* @param {*} paint
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*/
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function drawRectAndText (desc, rect, colorStr, canvas, paint) {
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let color = colors.parseColor(colorStr)
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paint.setStrokeWidth(1)
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paint.setStyle(Paint.Style.STROKE)
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// 反色
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paint.setARGB(255, 255 - (color >> 16 & 0xff), 255 - (color >> 8 & 0xff), 255 - (color & 0xff))
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canvas.drawRect(rect, paint)
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paint.setARGB(255, color >> 16 & 0xff, color >> 8 & 0xff, color & 0xff)
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paint.setStrokeWidth(1)
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paint.setTextSize(20)
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paint.setStyle(Paint.Style.FILL)
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canvas.drawText(desc, rect.left, rect.top, paint)
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paint.setTextSize(10)
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paint.setStrokeWidth(1)
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paint.setARGB(255, 0, 0, 0)
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}
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/**
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* 获取状态栏高度
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*
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* @returns
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*/
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function getStatusBarHeightCompat () {
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let result = 0
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let resId = context.getResources().getIdentifier("status_bar_height", "dimen", "android")
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if (resId > 0) {
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result = context.getResources().getDimensionPixelOffset(resId)
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}
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if (result <= 0) {
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result = context.getResources().getDimensionPixelOffset(R.dimen.dimen_25dp)
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}
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return result
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}
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@ -15,7 +15,7 @@ import java.util.Set;
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public class ExplorerFileItem implements ExplorerItem {
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private static final Set<String> sEditableFileExts = new HashSet<>(Arrays.asList(
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"js", "java", "xml", "json", "txt", "log", "ts", "html"
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"js", "java", "xml", "json", "txt", "log", "ts", "html", "css", "vue"
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));
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private PFile mFile;
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@ -48,6 +48,12 @@ str_to_cpu_mode(const std::string &cpu_mode) {
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::toupper);
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auto index = cpu_mode_map.find(upper_key);
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if (index == cpu_mode_map.end()) {
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// 可能因为大小写转换后找不到 直接通过入参查找
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index = cpu_mode_map.find(cpu_mode);
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if (index != cpu_mode_map.end()) {
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LOGI("find cpu_mode by &cpu_mode %s", cpu_mode.c_str());
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return index->second;
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}
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LOGE("cpu_mode not found %s", upper_key.c_str());
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return paddle::lite_api::LITE_POWER_HIGH;
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} else {
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@ -33,7 +33,7 @@ public class OCRPredictorNative {
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public OCRPredictorNative(Config config) {
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this.config = config;
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loadLibrary();
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nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
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nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename,
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config.cpuThreadNum, config.cpuPower);
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Log.i("OCRPredictorNative", "load success " + nativePointer);
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@ -44,8 +44,7 @@ public class OCRPredictorNative {
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Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
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float[] dims = new float[]{1, channels, height, width};
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float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
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ArrayList<OcrResultModel> results = postprocess(rawResults);
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return results;
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return postprocess(rawResults);
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}
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public static class Config {
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@ -57,15 +56,14 @@ public class OCRPredictorNative {
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}
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// 原代码中该方法名为“destory()”，此处应该是拼写错误，其它类中调用此方法的名称也已修正
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public void destroy(){
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public void destroy() {
|
||||
if (nativePointer != 0) {
|
||||
release(nativePointer);
|
||||
nativePointer = 0;
|
||||
}
|
||||
}
|
||||
|
||||
protected native long init(String detModelPath, String recModelPath,String clsModelPath, int threadNum, String cpuMode);
|
||||
protected native long init(String detModelPath, String recModelPath, String clsModelPath, int threadNum, String cpuMode);
|
||||
|
||||
protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
|
||||
|
||||
|
||||
@ -2,16 +2,11 @@ package com.baidu.paddle.lite.ocr;
|
||||
|
||||
import android.graphics.Point;
|
||||
import android.graphics.Rect;
|
||||
import android.os.Parcelable;
|
||||
|
||||
public class OcrResult {
|
||||
public float confidence;
|
||||
public float preprocessTime;
|
||||
public float inferenceTime;
|
||||
public String words;
|
||||
public Rect bounds;
|
||||
public RectLocation location;
|
||||
private String label;
|
||||
private float confidence;
|
||||
private Rect bounds;
|
||||
|
||||
public OcrResult() {
|
||||
}
|
||||
@ -63,21 +58,39 @@ public class OcrResult {
|
||||
return bounds;
|
||||
}
|
||||
|
||||
public RectLocation getLocation() {
|
||||
return new RectLocation(bounds);
|
||||
}
|
||||
|
||||
public void setBounds(Rect bounds) {
|
||||
this.bounds = bounds;
|
||||
}
|
||||
|
||||
public String getWords() {
|
||||
return label.trim().replace("\r", "");
|
||||
}
|
||||
|
||||
public static class RectLocation {
|
||||
public int left;
|
||||
public int top;
|
||||
public int width;
|
||||
public int height;
|
||||
|
||||
public RectLocation() {
|
||||
}
|
||||
|
||||
public RectLocation(int left, int top, int width, int height) {
|
||||
this.left = left;
|
||||
this.top = top;
|
||||
this.width = width;
|
||||
this.height = height;
|
||||
}
|
||||
|
||||
public RectLocation(Rect rect) {
|
||||
left = rect.left;
|
||||
top = rect.top;
|
||||
width = rect.right - rect.left;
|
||||
height = rect.bottom - rect.top;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -32,25 +32,51 @@ public class Predictor {
|
||||
protected OCRPredictorNative paddlePredictor = null;
|
||||
protected float inferenceTime = 0;
|
||||
// Only for object detection
|
||||
protected Vector<String> wordLabels = new Vector<String>();
|
||||
protected Vector<String> wordLabels = new Vector<>();
|
||||
protected String inputColorFormat = "BGR";
|
||||
protected long[] inputShape = new long[]{1, 3, 960};
|
||||
protected float[] inputMean = new float[]{0.485f, 0.456f, 0.406f};
|
||||
protected float[] inputStd = new float[]{0.229f, 0.224f, 0.225f};
|
||||
protected float scoreThreshold = 0.1f;
|
||||
protected Bitmap inputImage = null;
|
||||
protected Bitmap outputImage = null;
|
||||
protected volatile String outputResult = "";
|
||||
protected float preprocessTime = 0;
|
||||
protected float postprocessTime = 0;
|
||||
protected boolean useSlim = true;;
|
||||
protected boolean useSlim = true;
|
||||
|
||||
/**
|
||||
* 检测模型
|
||||
*/
|
||||
public String detModelFilename = "ch_ppocr_mobile_v2.0_det_opt.nb";
|
||||
/**
|
||||
* 识别模型
|
||||
*/
|
||||
public String recModelFilename = "ch_ppocr_mobile_v2.0_rec_opt.nb";
|
||||
/**
|
||||
* 文本方向检测模型
|
||||
*/
|
||||
public String clsModelFilename = "ch_ppocr_mobile_v2.0_cls_opt.nb";
|
||||
|
||||
private final String defaultLabelPath = "labels/ppocr_keys_v1.txt";
|
||||
private final String defaultModelPath = "models/ocr_v2_for_cpu";
|
||||
private final String defaultModelPathSlim = "models/ocr_v2_for_cpu(slim)";
|
||||
|
||||
public Predictor() {
|
||||
}
|
||||
|
||||
public boolean init(Context appCtx) {
|
||||
return this.init(appCtx, "models/ocr_v2_for_cpu", "labels/ppocr_keys_v1.txt");
|
||||
return this.init(appCtx, defaultModelPath, defaultLabelPath);
|
||||
}
|
||||
|
||||
public boolean init(Context appCtx, boolean useSlim) {
|
||||
if (this.isLoaded && this.useSlim == useSlim) {
|
||||
return true;
|
||||
}
|
||||
this.useSlim = useSlim;
|
||||
if (useSlim) {
|
||||
return this.init(appCtx, defaultModelPathSlim, defaultLabelPath);
|
||||
} else {
|
||||
return this.init(appCtx, defaultModelPath, defaultLabelPath);
|
||||
}
|
||||
}
|
||||
|
||||
public boolean init(Context appCtx, String modelPath, String labelPath) {
|
||||
@ -64,7 +90,6 @@ public class Predictor {
|
||||
|
||||
|
||||
public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
|
||||
String inputColorFormat,
|
||||
long[] inputShape, float[] inputMean,
|
||||
float[] inputStd, float scoreThreshold) {
|
||||
if (inputShape.length != 3) {
|
||||
@ -89,15 +114,10 @@ public class Predictor {
|
||||
"channel size in your Apps!");
|
||||
return false;
|
||||
}
|
||||
if (!inputColorFormat.equalsIgnoreCase("BGR")) {
|
||||
Log.e(TAG, "Only BGR color format is supported.");
|
||||
return false;
|
||||
}
|
||||
boolean isLoaded = init(appCtx, modelPath, labelPath);
|
||||
if (!isLoaded) {
|
||||
return false;
|
||||
}
|
||||
this.inputColorFormat = inputColorFormat;
|
||||
this.inputShape = inputShape;
|
||||
this.inputMean = inputMean;
|
||||
this.inputStd = inputStd;
|
||||
@ -114,22 +134,19 @@ public class Predictor {
|
||||
return false;
|
||||
}
|
||||
String realPath = modelPath;
|
||||
if (!modelPath.substring(0, 1).equals("/")) {
|
||||
if (modelPath.charAt(0) != '/') {
|
||||
// Read model files from custom path if the first character of mode path is '/'
|
||||
// otherwise copy model to cache from assets
|
||||
realPath = appCtx.getCacheDir() + "/" + modelPath;
|
||||
Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
|
||||
}
|
||||
if (realPath.isEmpty()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
OCRPredictorNative.Config config = new OCRPredictorNative.Config();
|
||||
config.cpuThreadNum = cpuThreadNum;
|
||||
config.detModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_det_opt.nb";
|
||||
config.recModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_rec_opt.nb";
|
||||
config.clsModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_cls_opt.nb";
|
||||
Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
|
||||
config.detModelFilename = realPath + File.separator + detModelFilename;
|
||||
config.recModelFilename = realPath + File.separator + recModelFilename;
|
||||
config.clsModelFilename = realPath + File.separator + clsModelFilename;
|
||||
Log.i("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
|
||||
config.cpuPower = cpuPowerMode;
|
||||
paddlePredictor = new OCRPredictorNative(config);
|
||||
|
||||
@ -146,8 +163,6 @@ public class Predictor {
|
||||
paddlePredictor = null;
|
||||
}
|
||||
isLoaded = false;
|
||||
cpuThreadNum = 1;
|
||||
cpuPowerMode = "LITE_POWER_HIGH";
|
||||
modelPath = "";
|
||||
modelName = "";
|
||||
}
|
||||
@ -176,81 +191,6 @@ public class Predictor {
|
||||
}
|
||||
|
||||
|
||||
public boolean runModel() {
|
||||
if (inputImage == null || !isLoaded()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Pre-process image, and feed input tensor with pre-processed data
|
||||
|
||||
Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
|
||||
|
||||
Date start = new Date();
|
||||
int channels = (int) inputShape[1];
|
||||
int width = scaleImage.getWidth();
|
||||
int height = scaleImage.getHeight();
|
||||
float[] inputData = new float[channels * width * height];
|
||||
if (channels == 3) {
|
||||
int[] channelIdx = null;
|
||||
if (inputColorFormat.equalsIgnoreCase("RGB")) {
|
||||
channelIdx = new int[]{0, 1, 2};
|
||||
} else if (inputColorFormat.equalsIgnoreCase("BGR")) {
|
||||
channelIdx = new int[]{2, 1, 0};
|
||||
} else {
|
||||
Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
|
||||
"supported!");
|
||||
return false;
|
||||
}
|
||||
|
||||
int[] channelStride = new int[]{width * height, width * height * 2};
|
||||
int[] pixels = new int[width * height];
|
||||
scaleImage.getPixels(pixels, 0, scaleImage.getWidth(), 0, 0, scaleImage.getWidth(), scaleImage.getHeight());
|
||||
for (int i = 0; i < pixels.length; i++) {
|
||||
int color = pixels[i];
|
||||
float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
|
||||
(float) blue(color) / 255.0f};
|
||||
inputData[i] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
|
||||
inputData[i + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
|
||||
inputData[i + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
|
||||
}
|
||||
} else if (channels == 1) {
|
||||
int[] pixels = new int[width * height];
|
||||
scaleImage.getPixels(pixels, 0, scaleImage.getWidth(), 0, 0, scaleImage.getWidth(), scaleImage.getHeight());
|
||||
for (int i = 0; i < pixels.length; i++) {
|
||||
int color = pixels[i];
|
||||
float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
|
||||
inputData[i] = (gray - inputMean[0]) / inputStd[0];
|
||||
}
|
||||
} else {
|
||||
Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ", only channel 1 and 3 is " +
|
||||
"supported!");
|
||||
return false;
|
||||
}
|
||||
float[] pixels = inputData;
|
||||
Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
|
||||
+ " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
|
||||
Date end = new Date();
|
||||
preprocessTime = (float) (end.getTime() - start.getTime());
|
||||
|
||||
// Warm up
|
||||
for (int i = 0; i < warmupIterNum; i++) {
|
||||
paddlePredictor.runImage(inputData, width, height, channels, inputImage);
|
||||
}
|
||||
warmupIterNum = 0; // do not need warm
|
||||
// Run inference
|
||||
start = new Date();
|
||||
ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
|
||||
end = new Date();
|
||||
inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
|
||||
|
||||
results = postprocess(results);
|
||||
Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
|
||||
+ " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
|
||||
drawResults(results);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
public List<OcrResult> runOcr(Bitmap inputImage) {
|
||||
if (inputImage == null || !isLoaded()) {
|
||||
return Collections.emptyList();
|
||||
@ -322,7 +262,7 @@ public class Predictor {
|
||||
Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
|
||||
+ " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
|
||||
List<OcrResult> ocrResults = new ArrayList<>();
|
||||
for (OcrResultModel resultModel:results) {
|
||||
for (OcrResultModel resultModel : results) {
|
||||
ocrResults.add(new OcrResult(resultModel));
|
||||
}
|
||||
return ocrResults;
|
||||
@ -357,14 +297,6 @@ public class Predictor {
|
||||
return inputImage;
|
||||
}
|
||||
|
||||
public Bitmap outputImage() {
|
||||
return outputImage;
|
||||
}
|
||||
|
||||
public String outputResult() {
|
||||
return outputResult;
|
||||
}
|
||||
|
||||
public float preprocessTime() {
|
||||
return preprocessTime;
|
||||
}
|
||||
@ -373,6 +305,22 @@ public class Predictor {
|
||||
return postprocessTime;
|
||||
}
|
||||
|
||||
public String getDefaultLabelPath() {
|
||||
return defaultLabelPath;
|
||||
}
|
||||
|
||||
public String getDefaultModelPath() {
|
||||
return defaultModelPath;
|
||||
}
|
||||
|
||||
public String getDefaultModelPathSlim() {
|
||||
return defaultModelPathSlim;
|
||||
}
|
||||
|
||||
public boolean isUseSlim() {
|
||||
return useSlim;
|
||||
}
|
||||
|
||||
public void setInputImage(Bitmap image) {
|
||||
if (image == null) {
|
||||
return;
|
||||
@ -382,7 +330,7 @@ public class Predictor {
|
||||
|
||||
private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
|
||||
for (OcrResultModel r : results) {
|
||||
StringBuffer word = new StringBuffer();
|
||||
StringBuilder word = new StringBuilder();
|
||||
for (int index : r.getWordIndex()) {
|
||||
if (index >= 0 && index < wordLabels.size()) {
|
||||
word.append(wordLabels.get(index));
|
||||
@ -396,166 +344,5 @@ public class Predictor {
|
||||
return results;
|
||||
}
|
||||
|
||||
private void drawResults(ArrayList<OcrResultModel> results) {
|
||||
StringBuffer outputResultSb = new StringBuffer("");
|
||||
for (int i = 0; i < results.size(); i++) {
|
||||
OcrResultModel result = results.get(i);
|
||||
StringBuilder sb = new StringBuilder("");
|
||||
sb.append(result.getLabel());
|
||||
sb.append(" ").append(result.getConfidence());
|
||||
sb.append("; Points: ");
|
||||
for (Point p : result.getPoints()) {
|
||||
sb.append("(").append(p.x).append(",").append(p.y).append(") ");
|
||||
}
|
||||
Log.i(TAG, sb.toString()); // show LOG in Logcat panel
|
||||
outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
|
||||
}
|
||||
outputResult = outputResultSb.toString();
|
||||
outputImage = inputImage;
|
||||
Canvas canvas = new Canvas(outputImage);
|
||||
Paint paintFillAlpha = new Paint();
|
||||
paintFillAlpha.setStyle(Paint.Style.FILL);
|
||||
paintFillAlpha.setColor(Color.parseColor("#3B85F5"));
|
||||
paintFillAlpha.setAlpha(50);
|
||||
|
||||
Paint paint = new Paint();
|
||||
paint.setColor(Color.parseColor("#3B85F5"));
|
||||
paint.setStrokeWidth(5);
|
||||
paint.setStyle(Paint.Style.STROKE);
|
||||
|
||||
for (OcrResultModel result : results) {
|
||||
Path path = new Path();
|
||||
List<Point> points = result.getPoints();
|
||||
path.moveTo(points.get(0).x, points.get(0).y);
|
||||
for (int i = points.size() - 1; i >= 0; i--) {
|
||||
Point p = points.get(i);
|
||||
path.lineTo(p.x, p.y);
|
||||
}
|
||||
canvas.drawPath(path, paint);
|
||||
canvas.drawPath(path, paintFillAlpha);
|
||||
}
|
||||
}
|
||||
|
||||
public boolean init(Context appCtx, boolean useSlim) {
|
||||
if (!this.isLoaded || (this.useSlim != useSlim)) {
|
||||
loadLabel(appCtx, "labels/ppocr_keys_v1.txt");
|
||||
if (useSlim) {
|
||||
loadModel(appCtx, "models/ocr_v2_for_cpu(slim)", 4, "LITE_POWER_HIGH");
|
||||
} else {
|
||||
loadModel(appCtx, "models/ocr_v2_for_cpu", 4, "LITE_POWER_HIGH");
|
||||
}
|
||||
}
|
||||
this.isLoaded = true;
|
||||
this.useSlim = useSlim;
|
||||
Log.i(TAG, "isLoaded: " + this.isLoaded);
|
||||
return this.isLoaded;
|
||||
}
|
||||
|
||||
public List<OcrResult> transformData(List<OcrResultModel> OcrResultModelList) {
|
||||
if (OcrResultModelList == null) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
List<OcrResult> words_result = new ArrayList<>();
|
||||
for (OcrResultModel model : OcrResultModelList) {
|
||||
List<Point> pointList = model.getPoints();
|
||||
if (pointList.isEmpty()) {
|
||||
continue;
|
||||
}
|
||||
Point firstPoint = pointList.get(0);
|
||||
int left = firstPoint.x;
|
||||
int top = firstPoint.y;
|
||||
int right = firstPoint.x;
|
||||
int bottom = firstPoint.y;
|
||||
for (Point p : pointList) {
|
||||
if (p.x < left) {
|
||||
left = p.x;
|
||||
}
|
||||
if (p.x > right) {
|
||||
right = p.x;
|
||||
}
|
||||
if (p.y < top) {
|
||||
top = p.y;
|
||||
}
|
||||
if (p.y > bottom) {
|
||||
bottom = p.y;
|
||||
}
|
||||
}
|
||||
OcrResult ocrResult = new OcrResult();
|
||||
ocrResult.preprocessTime = preprocessTime;
|
||||
ocrResult.inferenceTime = inferenceTime;
|
||||
ocrResult.confidence = model.getConfidence();
|
||||
ocrResult.words = model.getLabel().trim().replace("\r", "");
|
||||
ocrResult.location = new OcrResult.RectLocation(left, top, Math.abs(right - left), Math.abs(bottom - top));
|
||||
ocrResult.bounds = new Rect(left, top, right, bottom);
|
||||
words_result.add(ocrResult);
|
||||
}
|
||||
return words_result;
|
||||
}
|
||||
|
||||
public List<OcrResult> ocr(Bitmap inputImage, int cpuThreadNum) {
|
||||
this.cpuThreadNum = cpuThreadNum;
|
||||
if (inputImage == null) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
// Pre-process image, and feed input tensor with pre-processed data
|
||||
Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
|
||||
Date start = new Date();
|
||||
int channels = (int) inputShape[1];
|
||||
int width = scaleImage.getWidth();
|
||||
int height = scaleImage.getHeight();
|
||||
float[] inputData = new float[channels * width * height];
|
||||
if (channels == 3) {
|
||||
int[] channelIdx = null;
|
||||
if (inputColorFormat.equalsIgnoreCase("RGB")) {
|
||||
channelIdx = new int[]{0, 1, 2};
|
||||
} else if (inputColorFormat.equalsIgnoreCase("BGR")) {
|
||||
channelIdx = new int[]{2, 1, 0};
|
||||
} else {
|
||||
Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
|
||||
"supported!");
|
||||
return Collections.emptyList();
|
||||
}
|
||||
int[] channelStride = new int[]{width * height, width * height * 2};
|
||||
int[] pixels = new int[width * height];
|
||||
scaleImage.getPixels(pixels, 0, scaleImage.getWidth(), 0, 0, scaleImage.getWidth(), scaleImage.getHeight());
|
||||
for (int i = 0; i < pixels.length; i++) {
|
||||
int color = pixels[i];
|
||||
float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
|
||||
(float) blue(color) / 255.0f};
|
||||
inputData[i] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
|
||||
inputData[i + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
|
||||
inputData[i + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
|
||||
}
|
||||
} else if (channels == 1) {
|
||||
int[] pixels = new int[width * height];
|
||||
scaleImage.getPixels(pixels, 0, scaleImage.getWidth(), 0, 0, scaleImage.getWidth(), scaleImage.getHeight());
|
||||
for (int i = 0; i < pixels.length; i++) {
|
||||
int color = pixels[i];
|
||||
float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
|
||||
inputData[i] = (gray - inputMean[0]) / inputStd[0];
|
||||
}
|
||||
} else {
|
||||
Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ", only channel 1 and 3 is " +
|
||||
"supported!");
|
||||
return Collections.emptyList();
|
||||
}
|
||||
float[] pixels = inputData;
|
||||
Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
|
||||
+ " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
|
||||
Date end = new Date();
|
||||
preprocessTime = (float) (end.getTime() - start.getTime());
|
||||
|
||||
// Warm up
|
||||
for (int i = 0; i < warmupIterNum; i++) {
|
||||
paddlePredictor.runImage(inputData, width, height, channels, inputImage);
|
||||
}
|
||||
warmupIterNum = 0; // do not need warm
|
||||
// Run inference
|
||||
start = new Date();
|
||||
ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
|
||||
end = new Date();
|
||||
inferenceTime = (float) (end.getTime() - start.getTime());
|
||||
results = postprocess(results);
|
||||
return transformData(results);
|
||||
}
|
||||
}
|
||||
|
||||
@ -141,7 +141,7 @@ module.exports = function(runtime, global){
|
||||
return buildTypes.release;
|
||||
}
|
||||
|
||||
global.zips = Object.create(runtime.zips);
|
||||
global.ocr = Object.create(runtime.ocr);
|
||||
global.$zips = Object.create(runtime.zips);
|
||||
global.$ocr = Object.create(runtime.ocr);
|
||||
|
||||
}
|
||||
@ -68,6 +68,12 @@ public class ImageWrapper {
|
||||
return new ImageWrapper(mat);
|
||||
}
|
||||
|
||||
public static ImageWrapper ofMat(org.opencv.core.Mat mat) {
|
||||
if (mat == null) {
|
||||
return null;
|
||||
}
|
||||
return new ImageWrapper(new Mat(mat.clone().getNativeObjAddr()));
|
||||
}
|
||||
|
||||
public static ImageWrapper ofBitmap(Bitmap bitmap) {
|
||||
if (bitmap == null) {
|
||||
|
||||
@ -13,7 +13,7 @@ public class DeleteOnFinalizeFile {
|
||||
public DeleteOnFinalizeFile(File file) {
|
||||
fileObject = file;
|
||||
}
|
||||
/*
|
||||
|
||||
@Override
|
||||
protected void finalize() throws Throwable {
|
||||
super.finalize();
|
||||
@ -23,5 +23,4 @@ public class DeleteOnFinalizeFile {
|
||||
fileObject.delete();
|
||||
}
|
||||
}
|
||||
*/
|
||||
}
|
||||
|
||||
@ -452,6 +452,7 @@ public class ScriptRuntime {
|
||||
sensors = null;
|
||||
ignoresException(timers::recycle);
|
||||
ignoresException(ui::recycle);
|
||||
ignoresException(ocr::release);
|
||||
ignoresException(() -> mTopLevelScope.get().markReleased(engines.myEngine().getSource().toString()));
|
||||
}
|
||||
|
||||
|
||||
@ -8,22 +8,26 @@ import com.baidu.paddle.lite.ocr.OcrResult;
|
||||
import com.baidu.paddle.lite.ocr.Predictor;
|
||||
import com.stardust.app.GlobalAppContext;
|
||||
import com.stardust.autojs.core.image.ImageWrapper;
|
||||
import com.stardust.concurrent.VolatileDispose;
|
||||
|
||||
import java.util.Collections;
|
||||
import java.util.List;
|
||||
|
||||
public class OCR {
|
||||
|
||||
private Predictor mPredictor = new Predictor();
|
||||
private final Predictor mPredictor = new Predictor();
|
||||
|
||||
|
||||
public synchronized boolean init(boolean useSlim) {
|
||||
if (!mPredictor.isLoaded) {
|
||||
if (!mPredictor.isLoaded || useSlim != mPredictor.isUseSlim()) {
|
||||
if (Looper.getMainLooper() == Looper.myLooper()) {
|
||||
VolatileDispose<Boolean> result = new VolatileDispose<>();
|
||||
new Thread(() -> {
|
||||
mPredictor.init(GlobalAppContext.get(), useSlim);
|
||||
result.setAndNotify(mPredictor.init(GlobalAppContext.get(), useSlim));
|
||||
}).start();
|
||||
return result.blockedGet();
|
||||
} else {
|
||||
mPredictor.init(GlobalAppContext.get(), useSlim);
|
||||
return mPredictor.init(GlobalAppContext.get(), useSlim);
|
||||
}
|
||||
}
|
||||
return mPredictor.isLoaded;
|
||||
@ -33,7 +37,7 @@ public class OCR {
|
||||
mPredictor.releaseModel();
|
||||
}
|
||||
|
||||
public List<OcrResult> R(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
|
||||
public List<OcrResult> detect(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
|
||||
if (image == null) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
@ -41,34 +45,38 @@ public class OCR {
|
||||
if (bitmap == null || bitmap.isRecycled()) {
|
||||
return Collections.emptyList();
|
||||
}
|
||||
if (mPredictor.cpuThreadNum != cpuThreadNum) {
|
||||
mPredictor.releaseModel();
|
||||
mPredictor.cpuThreadNum = cpuThreadNum;
|
||||
}
|
||||
init(useSlim);
|
||||
return mPredictor.ocr(bitmap, cpuThreadNum);
|
||||
return mPredictor.runOcr(bitmap);
|
||||
}
|
||||
|
||||
public List<OcrResult> R(ImageWrapper image, int cpuThreadNum) {
|
||||
return R(image, cpuThreadNum, true);
|
||||
public List<OcrResult> detect(ImageWrapper image, int cpuThreadNum) {
|
||||
return detect(image, cpuThreadNum, true);
|
||||
}
|
||||
|
||||
public List<OcrResult> R(ImageWrapper image) {
|
||||
return R(image, 4, true);
|
||||
public List<OcrResult> detect(ImageWrapper image) {
|
||||
return detect(image, 4, true);
|
||||
}
|
||||
|
||||
public String[] T(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
|
||||
List<OcrResult> words_result = R(image, cpuThreadNum, useSlim);
|
||||
public String[] recognizeText(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
|
||||
List<OcrResult> words_result = detect(image, cpuThreadNum, useSlim);
|
||||
String[] outputResult = new String[words_result.size()];
|
||||
for (int i = 0; i < words_result.size(); i++) {
|
||||
outputResult[i] = words_result.get(i).words;
|
||||
Log.i("outputResult", outputResult[i].toString()); // show LOG in Logcat panel
|
||||
outputResult[i] = words_result.get(i).getLabel();
|
||||
Log.i("outputResult", outputResult[i]); // show LOG in Logcat panel
|
||||
}
|
||||
return outputResult;
|
||||
}
|
||||
|
||||
public String[] T(ImageWrapper image, int cpuThreadNum) {
|
||||
return T(image, cpuThreadNum, true);
|
||||
public String[] recognizeText(ImageWrapper image, int cpuThreadNum) {
|
||||
return recognizeText(image, cpuThreadNum, true);
|
||||
}
|
||||
|
||||
public String[] T(ImageWrapper image) {
|
||||
return T(image, 4, true);
|
||||
public String[] recognizeText(ImageWrapper image) {
|
||||
return recognizeText(image, 4, true);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@ -14,7 +14,7 @@ public class SevenZip {
|
||||
}
|
||||
}
|
||||
|
||||
public int A(String type, String destFilePath, String srcPath) {
|
||||
public int addFiles(String type, String destFilePath, String srcPath) {
|
||||
String typeOption = "";
|
||||
if (!type.trim().isEmpty()) {
|
||||
typeOption = " -t" + type.trim();
|
||||
@ -32,7 +32,7 @@ public class SevenZip {
|
||||
}
|
||||
}
|
||||
|
||||
public int A(String type, String destFilePath, String srcPath, String password) {
|
||||
public int addFiles(String type, String destFilePath, String srcPath, String password) {
|
||||
String typeOption = "";
|
||||
if (!type.trim().isEmpty()) {
|
||||
typeOption = " -t" + type.trim();
|
||||
@ -50,7 +50,7 @@ public class SevenZip {
|
||||
}
|
||||
}
|
||||
|
||||
public int X(String filePath0, String dirPath1) {
|
||||
public int extraFiles(String filePath0, String dirPath1) {
|
||||
String cmdStr = "7z x -y -aos " + filePath0;
|
||||
if (PFiles.isFile(filePath0)) {
|
||||
if (PFiles.isDir(dirPath1)) {
|
||||
@ -66,10 +66,10 @@ public class SevenZip {
|
||||
}
|
||||
}
|
||||
|
||||
public int X(String filePath0, String dirPath1, String password) {
|
||||
public int extraFiles(String filePath0, String dirPath1, String password) {
|
||||
String cmdStr = "7z x -y -aos " + filePath0 + "";
|
||||
if (password == "") {
|
||||
X(filePath0, dirPath1);
|
||||
if (password.equals("")) {
|
||||
extraFiles(filePath0, dirPath1);
|
||||
} else {
|
||||
if (PFiles.isFile(filePath0)) {
|
||||
if (PFiles.isDir(dirPath1)) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user