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(
+
+);
+
+// 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(
+
+
+
+
+);
+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 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 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 wordLabels = new Vector();
+ protected Vector 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 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 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 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 postprocess(ArrayList 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 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 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 transformData(List OcrResultModelList) {
- if (OcrResultModelList == null) {
- return Collections.emptyList();
- }
- List words_result = new ArrayList<>();
- for (OcrResultModel model : OcrResultModelList) {
- List 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 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 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 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 R(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
+ public List 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 R(ImageWrapper image, int cpuThreadNum) {
- return R(image, cpuThreadNum, true);
+ public List detect(ImageWrapper image, int cpuThreadNum) {
+ return detect(image, cpuThreadNum, true);
}
- public List R(ImageWrapper image) {
- return R(image, 4, true);
+ public List detect(ImageWrapper image) {
+ return detect(image, 4, true);
}
- public String[] T(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
- List words_result = R(image, cpuThreadNum, useSlim);
+ public String[] recognizeText(ImageWrapper image, int cpuThreadNum, boolean useSlim) {
+ List 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)) {