新增 images自适应二值化
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@@ -1,14 +1,14 @@
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"ui";
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var url = "https://www.autojs.org/assets/uploads/profile/3-profileavatar.png";
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var url = "https://www.autojs.org/assets/uploads/files/1540386817060-918021-20160416200702191-185324559.jpg";
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var logo = null;
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var currentImg = null;
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ui.layout(
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<vertical>
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<img id="img" w="150" h="150" url="{{url}}" />
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<img id="img" w="250" h="250" url="{{url}}" />
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<button id="grayscale" text="灰度化" />
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<button id="binary" text="二值化" />
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<button id="adaptiveBinary" text="自适应二值化" />
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<button id="hsv" text="RGB转HSV" />
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<button id="blur" text="模糊" />
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<button id="medianBlur" text="中值滤波" />
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@@ -19,14 +19,7 @@ ui.layout(
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//把一张图片设置到图片控件中
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function setImage(img) {
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ui.run(() => {
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var curImg = currentImg;
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if(oldImg != null){
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ui.post(()=>{
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oldImg.recycle();
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});
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}
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ui.img.setImageBitmap(img.bitmap);
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currentImg = img;
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});
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}
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@@ -57,8 +50,21 @@ ui.grayscale.on("click", () => {
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ui.binary.on("click", () => {
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processImg(img => {
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var g = images.grayscale(img);
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//二值化,取灰度为30到200之间的图片
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return images.threshold(images.grayscale(img), 100, 200);
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var result = images.threshold(g, 100, 200);
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g.recycle();
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return result;
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});
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});
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ui.adaptiveBinary.on("click", () => {
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processImg(img => {
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var g = images.grayscale(img);
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//自适应二值化,最大值为200,块大小为25
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var result = images.adaptiveThreshold(g, 200, "MEAN_C", "BINARY", 25, 10);
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g.recycle();
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return result;
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});
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});
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@@ -82,6 +88,7 @@ ui.medianBlur.on("click", () => {
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return images.medianBlur(img, 5);
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});
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});
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ui.gaussianBlur.on("click", () => {
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processImg(img => {
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//高斯模糊
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@@ -68,12 +68,20 @@ module.exports = function (__runtime__, scope) {
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images.threshold = function (img, threshold, maxVal, type) {
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var mat = newMat();
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type = type || "binary";
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type = Imgproc["THRESH_" + type.toUpperCase()];
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type = type || "BINARY";
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type = Imgproc["THRESH_" + type];
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Imgproc.threshold(img.mat, mat, threshold, maxVal, type);
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return matToImage(mat);
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}
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images.adaptiveThreshold = function(img, maxValue, adaptiveMethod, thresholdType, blockSize, C){
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var mat = newMat();
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adaptiveMethod = Imgproc["ADAPTIVE_THRESH_" + adaptiveMethod];
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thresholdType = Imgproc["THRESH_" + thresholdType];
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Imgproc.adaptiveThreshold(img.mat, mat, maxValue, adaptiveMethod, thresholdType, blockSize, C);
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return matToImage(mat);
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}
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images.blur = function (img, size, point, type) {
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var mat = newMat();
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size = newSize(size);
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@@ -9,6 +9,7 @@ import com.afollestad.materialdialogs.MaterialDialog;
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import org.opencv.android.InstallCallbackInterface;
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import org.opencv.android.LoaderCallbackInterface;
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import org.opencv.android.OpenCVLoader;
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import org.opencv.imgproc.Imgproc;
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/**
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@@ -46,7 +47,6 @@ public class OpenCVHelper {
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callback.onInitFinish();
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return;
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}
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mInitialized = true;
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OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_13, activity.getApplicationContext(), new LoaderCallback(activity) {
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@@ -1,4 +1,3 @@
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// ResourceMonitor$onFinalize$1.java
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package com.stardust.util;
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import android.os.Handler;
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@@ -12,7 +11,8 @@ import java.util.concurrent.ConcurrentHashMap;
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public final class ResourceMonitor {
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private static final String LOG_TAG = "ResourceTracer";
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private static final String LOG_TAG = "ResourceMonitor";
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private static final ConcurrentHashMap<Class<?>, SparseArray<UnclosedResourceException>> mResources;
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private static Handler mHandler;
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private static boolean mEnabled;
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