From 5fd0402cd5d964f295701e68c6c27c51268a2cb2 Mon Sep 17 00:00:00 2001
From: TonyJiangWJ <tonyjiangwj@gmail.com>
Date: Tue, 8 Feb 2022 21:19:41 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BC=98=E5=8C=96OCR=E6=8E=A5=E5=8F=A3=20?=
 =?UTF-8?q?=E7=A7=BB=E9=99=A4=E6=97=A0=E7=94=A8=E4=BB=A3=E7=A0=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/src/main/assets/sample/7zip/7zip压缩.js   |  23 +-
 app/src/main/assets/sample/7zip/rar解压缩.js  |  18 +-
 app/src/main/assets/sample/OCR/OCR.js         |  38 ++-
 app/src/main/assets/sample/OCR/PaddleOCR.js   |  18 +-
 .../assets/sample/OCR/PaddleOCR截图识别.js    | 188 ++++++++++
 .../model/explorer/ExplorerFileItem.java      |   2 +-
 autojs-aar/paddleocr/src/main/cpp/native.cpp  |   6 +
 .../paddle/lite/ocr/OCRPredictorNative.java   |  10 +-
 .../com/baidu/paddle/lite/ocr/OcrResult.java  |  27 +-
 .../com/baidu/paddle/lite/ocr/Predictor.java  | 321 +++---------------
 autojs/src/main/assets/modules/__globals__.js |   4 +-
 .../autojs/core/image/ImageWrapper.java       |   6 +
 .../autojs/rhino/DeleteOnFinalizeFile.java    |   3 +-
 .../autojs/runtime/ScriptRuntime.java         |   1 +
 .../com/stardust/autojs/runtime/api/OCR.java  |  44 ++-
 .../stardust/autojs/runtime/api/SevenZip.java |  12 +-
 16 files changed, 371 insertions(+), 350 deletions(-)
 create mode 100644 app/src/main/assets/sample/OCR/PaddleOCR截图识别.js

diff --git a/app/src/main/assets/sample/7zip/7zip压缩.js b/app/src/main/assets/sample/7zip/7zip压缩.js
index 7a14499c..154aa35a 100644
--- a/app/src/main/assets/sample/7zip/7zip压缩.js
+++ b/app/src/main/assets/sample/7zip/7zip压缩.js
@@ -1,19 +1,18 @@
-//目录路径(必须是完整路径)
-var dirPath = "/sdcard/脚本/";
-if(!files.isDir(dirPath)) files.create(dirPath);
-//压缩文件路径(必须是完整路径)
-var filePath = "/sdcard/脚本.7z";
-//压缩类型
-//支持的压缩类型包括:zip 7z bz2 bzip2 tbz2 tbz gz gzip tgz tar wim swm xz txz。
+// 目录路径(必须是完整路径)
+var dirPath = files.cwd();
+// 压缩文件路径(必须是完整路径)
+var filePath = org.autojs.autojs.Pref.getScriptDirPath() + '/测试.7z';
+// 压缩类型
+// 支持的压缩类型包括:zip 7z bz2 bzip2 tbz2 tbz gz gzip tgz tar wim swm xz txz。
 var type = "7z";
-//压缩密码
+// 压缩密码
 var password = "password"
 
-//7z加密压缩(若文件已存在则跳过)
-//zips.A(type, filePath, dirPath, password)
+// 7z加密压缩(若文件已存在则跳过)
+// $zips.addFiles(type, filePath, dirPath, password)
 
-//压缩
-switch (zips.A(type, filePath, dirPath)) {
+// 压缩
+switch ($zips.addFiles(type, filePath, dirPath)) {
     case 0:
         toastLog("压缩成功！文件已保存为： " + filePath)
         break;
diff --git a/app/src/main/assets/sample/7zip/rar解压缩.js b/app/src/main/assets/sample/7zip/rar解压缩.js
index f4b4484a..4442d8f2 100644
--- a/app/src/main/assets/sample/7zip/rar解压缩.js
+++ b/app/src/main/assets/sample/7zip/rar解压缩.js
@@ -1,16 +1,16 @@
-//压缩文件路径(必须是完整路径)
+// 压缩文件路径(必须是完整路径)
 var filePath = files.path("./bonus.rar");
-//目录路径(必须是完整路径)
-var dirPath = "/sdcard/脚本";
-//压缩密码
+// 目录路径(必须是完整路径)
+var dirPath = org.autojs.autojs.Pref.getScriptDirPath();
+// 压缩密码
 var password = "password"
 
-//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。
-//解压无加密的压缩包(若文件已存在则跳过)
-//zips.X(filePath, dirPath)
+// 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。
+// 解压无加密的压缩包(若文件已存在则跳过)
+// $zips.X(filePath, dirPath)
 
-//解压加密的压缩包(若文件已存在则跳过)
-switch (zips.X(filePath, dirPath, password)) {
+// 解压加密的压缩包(若文件已存在则跳过)
+switch ($zips.extraFiles(filePath, dirPath, password)) {
     case 0:
         toastLog("解压缩成功！请到 " + dirPath + " 目录下查看。")
         break;
diff --git a/app/src/main/assets/sample/OCR/OCR.js b/app/src/main/assets/sample/OCR/OCR.js
index b1d9d174..005a0ef9 100644
--- a/app/src/main/assets/sample/OCR/OCR.js
+++ b/app/src/main/assets/sample/OCR/OCR.js
@@ -1,19 +1,29 @@
-const img = images.read("./test.png");
-const cpuThreadNum = 4;
+const img = images.read("./test.png")
+console.show()
+let cpuThreadNum = 4
 // 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）
-var useSlim = true;
+let useSlim = true
+let start = new Date()
 // 识别图片中的文字，返回完整识别信息（兼容百度OCR格式）。
-const result = ocr.R(img, cpuThreadNum, useSlim);
-// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true;
-// const result = ocr.R(img);
-toastLog("完整识别信息（兼容百度OCR格式）: " + JSON.stringify(result));
-
-// 识别图片中的文字，只返回文本识别信息（字符串列表）。
-const stringList = ocr.T(img, cpuThreadNum, useSlim);
-// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true;
-// const stringList = ocr.T(img);
-toastLog("文本识别信息（字符串列表）: " + JSON.stringify(stringList));
+let result = $ocr.detect(img, cpuThreadNum, useSlim)
+log('slim识别耗时：' + (new Date() - start) + 'ms')
+// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true
+// const result = $ocr.detect(img)
+toastLog("完整识别信息: " + JSON.stringify(result))
+start = new Date()
+// 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
+const stringList = $ocr.recognizeText(img, cpuThreadNum, useSlim)
+log('slim纯文本识别耗时：' + (new Date() - start) + 'ms')
+// 可以使用简化的调用命令，默认参数：cpuThreadNum = 4, useSlim = true
+// const stringList = $ocr.recognizeText(img)
+toastLog("文本识别信息（字符串列表）: " + JSON.stringify(stringList))
+// 增加线程数
+cpuThreadNum = 8
+start = new Date()
+result = $ocr.detect(img, cpuThreadNum, useSlim)
+log('8线程识别耗时：' + (new Date() - start) + 'ms')
+toastLog("完整识别信息（兼容百度OCR格式）: " + JSON.stringify(result))
 // 释放模型 用于释放native内存
-ocr.release()
+$ocr.release()
 // 回收图片
 img.recycle()
diff --git a/app/src/main/assets/sample/OCR/PaddleOCR.js b/app/src/main/assets/sample/OCR/PaddleOCR.js
index bef661b0..877f25d8 100644
--- a/app/src/main/assets/sample/OCR/PaddleOCR.js
+++ b/app/src/main/assets/sample/OCR/PaddleOCR.js
@@ -2,15 +2,21 @@ importClass(com.baidu.paddle.lite.ocr.Predictor)
 
 console.show()
 let path = 'test.jpg'
+// 指定是否用精简版模型 速度较快
+let useSlim = false
 // 创建检测器
 let predictor = new Predictor()
+// predictor.cpuThreadNum = 4 //可以自定义使用CPU的线程数
 // 初始化模型 首次运行时会比较耗时
-let loadSuccess = predictor.init(context)
-// 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
-// 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
-//    ch_ppocr_mobile_v2.0_det_opt.nb
-//    ch_ppocr_mobile_v2.0_rec_opt.nb
-//    ch_ppocr_mobile_v2.0_cls_opt.nb
+let loadSuccess = predictor.init(context, useSlim) // predictor.init(context) 为默认不使用精简版
+// 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
+// 内置默认 labelPath 为 labels/ppocr_keys_v1.txt
+// let modelPath = files.path('./models/customize') // 指定自定义模型路径
+// let labelPath = files.path('./models/customize') // 指定自定义label路径
+// 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
+// predictor.detModelFilename = 'ch_ppocr_mobile_v2.0_det_opt.nb'
+// predictor.recModelFilename = 'ch_ppocr_mobile_v2.0_rec_opt.nb'
+// predictor.clsModelFilename = 'ch_ppocr_mobile_v2.0_cls_opt.nb'
 // predictor.init(context, modelPath, labelPath)
 toastLog('加载模型结果：' + loadSuccess)
 let start = new Date()
diff --git a/app/src/main/assets/sample/OCR/PaddleOCR截图识别.js b/app/src/main/assets/sample/OCR/PaddleOCR截图识别.js
new file mode 100644
index 00000000..e88e7658
--- /dev/null
+++ b/app/src/main/assets/sample/OCR/PaddleOCR截图识别.js
@@ -0,0 +1,188 @@
+let currentEngine = engines.myEngine()
+let runningEngines = engines.all()
+let currentSource = currentEngine.getSource() + ''
+if (runningEngines.length > 1) {
+  runningEngines.forEach(compareEngine => {
+    let compareSource = compareEngine.getSource() + ''
+    if (currentEngine.id !== compareEngine.id && compareSource === currentSource) {
+      // 强制关闭同名的脚本
+      compareEngine.forceStop()
+    }
+  })
+}
+
+if (!requestScreenCapture()) {
+  toastLog('请求截图权限失败')
+  exit()
+}
+
+sleep(1000)
+
+importClass(com.baidu.paddle.lite.ocr.Predictor)
+
+// 指定是否用精简版模型 速度较快
+let useSlim = false
+// 创建检测器
+let predictor = new Predictor()
+// predictor.cpuThreadNum = 4 //可以自定义使用CPU的线程数
+// 初始化模型 首次运行时会比较耗时
+let loadSuccess = predictor.init(context, useSlim) // predictor.init(context) 为默认不使用精简版
+// 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
+// 内置默认 labelPath 为 labels/ppocr_keys_v1.txt
+// let modelPath = files.path('./models/customize') // 指定自定义模型路径
+// let labelPath = files.path('./models/customize') // 指定自定义label路径
+// 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
+// predictor.detModelFilename = 'ch_ppocr_mobile_v2.0_det_opt.nb'
+// predictor.recModelFilename = 'ch_ppocr_mobile_v2.0_rec_opt.nb'
+// predictor.clsModelFilename = 'ch_ppocr_mobile_v2.0_cls_opt.nb'
+// predictor.init(context, modelPath, labelPath)
+if (!loadSuccess) {
+  toastLog('初始化ocr失败')
+  exit()
+}
+// 识别结果和截图信息
+let result = []
+let img = null
+let running = true
+let capturing = true
+
+/**
+ * 截图并识别OCR文本信息
+ */
+function captureAndOcr() {
+  capturing = true
+  img && img.recycle()
+  img = captureScreen()
+  if (!img) {
+    toastLog('截图失败')
+  }
+  let start = new Date()
+  result = predictor.runOcr(img.getBitmap())
+  toastLog('耗时' + (new Date() - start) + 'ms')
+  capturing = false
+}
+
+captureAndOcr()
+
+// 获取状态栏高度
+let offset = -getStatusBarHeightCompat()
+
+// 绘制识别结果
+let window = floaty.rawWindow(
+  <canvas id="canvas" layout_weight="1" />
+);
+
+// 设置悬浮窗位置
+ui.post(() => {
+  window.setPosition(0, offset)
+  window.setSize(device.width, device.height)
+  window.setTouchable(false)
+})
+
+// 操作按钮
+let clickButtonWindow = floaty.rawWindow(
+  <vertical>
+    <button id="captureAndOcr" text="截图识别" />
+    <button id="closeBtn" text="退出" />
+  </vertical>
+);
+ui.run(function () {
+  clickButtonWindow.setPosition(device.width / 2 - ~~(clickButtonWindow.getWidth() / 2), device.height * 0.65)
+})
+
+// 点击识别
+clickButtonWindow.captureAndOcr.click(function () {
+  result = []
+  ui.run(function () {
+    clickButtonWindow.setPosition(device.width, device.height)
+  })
+  setTimeout(() => {
+    captureAndOcr()
+    ui.run(function () {
+      clickButtonWindow.setPosition(device.width / 2 - ~~(clickButtonWindow.getWidth() / 2), device.height * 0.65)
+    })
+  }, 500)
+})
+
+// 点击关闭
+clickButtonWindow.closeBtn.click(function () {
+  exit()
+})
+
+let Typeface = android.graphics.Typeface
+let paint = new Paint()
+paint.setStrokeWidth(1)
+paint.setTypeface(Typeface.DEFAULT_BOLD)
+paint.setTextAlign(Paint.Align.LEFT)
+paint.setAntiAlias(true)
+paint.setStrokeJoin(Paint.Join.ROUND)
+paint.setDither(true)
+window.canvas.on('draw', function (canvas) {
+  if (!running || capturing) {
+    return
+  }
+  // 清空内容
+  canvas.drawColor(0xFFFFFF, android.graphics.PorterDuff.Mode.CLEAR)
+  if (result && result.length > 0) {
+    for (let i = 0; i < result.length; i++) {
+      let ocrResult = result[i]
+      drawRectAndText(ocrResult.label, ocrResult.bounds, '#00ff00', canvas, paint)
+    }
+  }
+})
+
+setInterval(() => { }, 10000)
+events.on('exit', () => {
+  // 标记停止 避免canvas导致闪退
+  running = false
+  // 回收图片
+  img && img.recycle()
+  // 撤销监听
+  window.canvas.removeAllListeners()
+  // 释放模型
+  predictor.releaseModel()
+})
+
+/**
+ * 绘制文本和方框
+ *
+ * @param {*} desc
+ * @param {*} rect
+ * @param {*} colorStr
+ * @param {*} canvas
+ * @param {*} paint
+ */
+function drawRectAndText (desc, rect, colorStr, canvas, paint) {
+  let color = colors.parseColor(colorStr)
+
+  paint.setStrokeWidth(1)
+  paint.setStyle(Paint.Style.STROKE)
+  // 反色
+  paint.setARGB(255, 255 - (color >> 16 & 0xff), 255 - (color >> 8 & 0xff), 255 - (color & 0xff))
+  canvas.drawRect(rect, paint)
+  paint.setARGB(255, color >> 16 & 0xff, color >> 8 & 0xff, color & 0xff)
+  paint.setStrokeWidth(1)
+  paint.setTextSize(20)
+  paint.setStyle(Paint.Style.FILL)
+  canvas.drawText(desc, rect.left, rect.top, paint)
+  paint.setTextSize(10)
+  paint.setStrokeWidth(1)
+  paint.setARGB(255, 0, 0, 0)
+}
+
+/**
+ * 获取状态栏高度
+ *
+ * @returns
+ */
+function getStatusBarHeightCompat () {
+  let result = 0
+  let resId = context.getResources().getIdentifier("status_bar_height", "dimen", "android")
+  if (resId > 0) {
+    result = context.getResources().getDimensionPixelOffset(resId)
+  }
+  if (result <= 0) {
+    result = context.getResources().getDimensionPixelOffset(R.dimen.dimen_25dp)
+  }
+  return result
+}
diff --git a/app/src/main/java/org/autojs/autojs/model/explorer/ExplorerFileItem.java b/app/src/main/java/org/autojs/autojs/model/explorer/ExplorerFileItem.java
index 09beadb6..2fa4f494 100644
--- a/app/src/main/java/org/autojs/autojs/model/explorer/ExplorerFileItem.java
+++ b/app/src/main/java/org/autojs/autojs/model/explorer/ExplorerFileItem.java
@@ -15,7 +15,7 @@ import java.util.Set;
 public class ExplorerFileItem implements ExplorerItem {
 
     private static final Set<String> sEditableFileExts = new HashSet<>(Arrays.asList(
-            "js", "java", "xml", "json", "txt", "log", "ts", "html"
+            "js", "java", "xml", "json", "txt", "log", "ts", "html", "css", "vue"
     ));
 
     private PFile mFile;
diff --git a/autojs-aar/paddleocr/src/main/cpp/native.cpp b/autojs-aar/paddleocr/src/main/cpp/native.cpp
index a4bcd9b2..285f0933 100644
--- a/autojs-aar/paddleocr/src/main/cpp/native.cpp
+++ b/autojs-aar/paddleocr/src/main/cpp/native.cpp
@@ -48,6 +48,12 @@ str_to_cpu_mode(const std::string &cpu_mode) {
                  ::toupper);
   auto index = cpu_mode_map.find(upper_key);
   if (index == cpu_mode_map.end()) {
+    // 可能因为大小写转换后找不到 直接通过入参查找
+    index = cpu_mode_map.find(cpu_mode);
+    if (index != cpu_mode_map.end()) {
+      LOGI("find cpu_mode by &cpu_mode %s", cpu_mode.c_str());
+      return index->second;
+    }
     LOGE("cpu_mode not found %s", upper_key.c_str());
     return paddle::lite_api::LITE_POWER_HIGH;
   } else {
diff --git a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OCRPredictorNative.java b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OCRPredictorNative.java
index c4016238..d85d30b2 100644
--- a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OCRPredictorNative.java
+++ b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OCRPredictorNative.java
@@ -33,7 +33,7 @@ public class OCRPredictorNative {
     public OCRPredictorNative(Config config) {
         this.config = config;
         loadLibrary();
-        nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
+        nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename,
                 config.cpuThreadNum, config.cpuPower);
         Log.i("OCRPredictorNative", "load success " + nativePointer);
 
@@ -44,8 +44,7 @@ public class OCRPredictorNative {
         Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
         float[] dims = new float[]{1, channels, height, width};
         float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
-        ArrayList<OcrResultModel> results = postprocess(rawResults);
-        return results;
+        return postprocess(rawResults);
     }
 
     public static class Config {
@@ -57,15 +56,14 @@ public class OCRPredictorNative {
 
     }
 
-    // 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
-    public void destroy(){
+    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);
 
diff --git a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OcrResult.java b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OcrResult.java
index 67a66c21..64a2cd99 100644
--- a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OcrResult.java
+++ b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/OcrResult.java
@@ -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;
+        }
     }
 }
diff --git a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/Predictor.java b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/Predictor.java
index c5d3285f..56600e47 100644
--- a/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/Predictor.java
+++ b/autojs-aar/paddleocr/src/main/java/com/baidu/paddle/lite/ocr/Predictor.java
@@ -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);
-    }
 }
diff --git a/autojs/src/main/assets/modules/__globals__.js b/autojs/src/main/assets/modules/__globals__.js
index c5de3e6e..f0ae2144 100644
--- a/autojs/src/main/assets/modules/__globals__.js
+++ b/autojs/src/main/assets/modules/__globals__.js
@@ -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);
 
 }
\ No newline at end of file
diff --git a/autojs/src/main/java/com/stardust/autojs/core/image/ImageWrapper.java b/autojs/src/main/java/com/stardust/autojs/core/image/ImageWrapper.java
index dd00e763..ce1da7bb 100644
--- a/autojs/src/main/java/com/stardust/autojs/core/image/ImageWrapper.java
+++ b/autojs/src/main/java/com/stardust/autojs/core/image/ImageWrapper.java
@@ -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) {
diff --git a/autojs/src/main/java/com/stardust/autojs/rhino/DeleteOnFinalizeFile.java b/autojs/src/main/java/com/stardust/autojs/rhino/DeleteOnFinalizeFile.java
index fbdce9c2..ef839e84 100644
--- a/autojs/src/main/java/com/stardust/autojs/rhino/DeleteOnFinalizeFile.java
+++ b/autojs/src/main/java/com/stardust/autojs/rhino/DeleteOnFinalizeFile.java
@@ -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();
         }
     }
- */
 }
diff --git a/autojs/src/main/java/com/stardust/autojs/runtime/ScriptRuntime.java b/autojs/src/main/java/com/stardust/autojs/runtime/ScriptRuntime.java
index c0ecce26..f8a370dc 100644
--- a/autojs/src/main/java/com/stardust/autojs/runtime/ScriptRuntime.java
+++ b/autojs/src/main/java/com/stardust/autojs/runtime/ScriptRuntime.java
@@ -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()));
     }
 
diff --git a/autojs/src/main/java/com/stardust/autojs/runtime/api/OCR.java b/autojs/src/main/java/com/stardust/autojs/runtime/api/OCR.java
index 179c3152..8a87b986 100644
--- a/autojs/src/main/java/com/stardust/autojs/runtime/api/OCR.java
+++ b/autojs/src/main/java/com/stardust/autojs/runtime/api/OCR.java
@@ -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);
     }
 
 }
diff --git a/autojs/src/main/java/com/stardust/autojs/runtime/api/SevenZip.java b/autojs/src/main/java/com/stardust/autojs/runtime/api/SevenZip.java
index ccf3d4c9..6144a9c6 100644
--- a/autojs/src/main/java/com/stardust/autojs/runtime/api/SevenZip.java
+++ b/autojs/src/main/java/com/stardust/autojs/runtime/api/SevenZip.java
@@ -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)) {