Hi im using Android Studio ver. 0.8.6 now i want to use andEngine, i know that i should download andengine.jar file and copy it to libs folder in my project, everything is all right, but i can find the andengine.jar GLES2 there are only files with 1st version, and the newest examples didn't work with it, can someone pls give me link to andengine.jar GLES2? or maybe im doing something wrong?
andengine download examples
We will be using the AndEngine game platform in the rest of this book, so now would be a good time for you to get a taste of what that game engine can do. Scores of games built using AndEngine are available on Android Market, but instead of downloading another game, let's download an example program that demonstrates many of the features of AndEngine.
Nicolas has generously made the source for AndEngine Examples available as well (at ). These resources are excellent references for how features can be used. If you prefer (or if you don't have access to Android Market for some reason), you can download the .apk installation file from that site, and load it onto your Android device (or the emulator) using adb (Android Debug Bridge). We'll get into building source in more detail in Chapter 12. For now, just install the app on your phone, and start it up. You will see a menu of features, as shown in Figure 1.5.
The menu items form a hierarchy of options, each of which demonstrates one aspect of the AndEngine platform. Take some time now to just play with the examples to get a taste of what AndEngine can enable your game to do.
Open up your browser and go to the AndEngine github page. You should see a link on the page to download the repository as a ZIP file. Use that to download a copy of the AndEngine project. Or, you should be able to use this link to download the file directly.
In the above code, you first create an ITexture object. ITexture is an interface. An object of this type is initialized to a BitmapTexture object, which, you guessed it, is used to to load a bitmap into VRAM. The above code creates ITexture objects for all the assets you downloaded, and loads them into VRAM by calling the load method on each object.
AndEngine - Examples is a free app for Android published in the Components & Libraries list of apps, part of Development.The company that develops AndEngine - Examples is Nicolas Gramlich. The latest version released by its developer is 1.3.6. This app was rated by 6 users of our site and has an average rating of 5.0.To install AndEngine - Examples on your Android device, just click the green Continue To App button above to start the installation process. The app is listed on our website since 2010-09-13 and was downloaded 754 times. We have already checked if the download link is safe, however for your own protection we recommend that you scan the downloaded app with your antivirus. Your antivirus may detect the AndEngine - Examples as malware as malware if the download link to org.anddev.andengine.examples is broken.How to install AndEngine - Examples on your Android device:Click on the Continue To App button on our website. This will redirect you to Google Play.
Once the AndEngine - Examples is shown in the Google Play listing of your Android device, you can start its download and installation. Tap on the Install button located below the search bar and to the right of the app icon.
A pop-up window with the permissions required by AndEngine - Examples will be shown. Click on Accept to continue the process.
AndEngine - Examples will be downloaded onto your device, displaying a progress. Once the download completes, the installation will start and you'll get a notification after the installation is finished.
Now navigate to the folder where you downloaded Andengine. You need to go into src/org/andengine. Copy all of the files to you Android Studio projects folder src/main/java/org/andengine. See screenshot above.
Hi. I try to setup like you mention, but when building it appear error like this. how to solve it? ThanksC:\Users\USER\AndroidStudioProjects\MyProject\AndEngine\src\main\java\org\andengine\opengl\texture\atlas\bitmap\source\decorator\ColorSwapBitmapTextureAtlasSourceDecorator.java:7: error: cannot find symbolimport android.graphics.AvoidXfermode; ^ symbol: class AvoidXfermode location: package android.graphics
Microsoft Defender Antivirus uses cloud-delivered protection (also called the Microsoft Advanced Protection Service or MAPS) and periodically downloads dynamic security intelligence updates to provide more protection. These dynamic updates don't take the place of regular security intelligence updates via security intelligence update KB2267602.
Minibuses, scooters, and emergency vehicles using audible warning devices were excluded from our research. Also bicycles were excluded, as they produce almost no sound (we recorded several examples). Tractor units without trailers or semitrailers were also excluded.
After having our observation recorded, we search for audio segments with the data corresponding to our target classes, i.e. representing the sounds of the target vehicle classes. This means that we can ignore for now the ambulance siren, which is very loud and masks other sounds, and the bicycle (it also produces audible data), as they do not represent any of the target classes, as well as the segments with no vehicles recorded at all. We look for segments representing positive and negative examples for each target class. Next, we divide the selected segments into 330 ms frames, without overlapping. In our example, we will have positive examples for car, small truck, big truck, and bus:
If in the first second of the remaining segmented recording we have only the bus recorded, we will have 3 frames (3 330 ms = 990 ms) of positive examples for bus. The remaining 10 ms will be discarded.
If in the next segment, say from 04.670 s to 05.630 s, i.e. 960 ms, we have two cars, one following the other one, so we will have 2 frames (2 330 ms = 660 ms) of positive examples for car, and the remaining 300 ms will be ignored. These positive examples can be used as negative examples for other classes, as we know that they are not present here.
If in the next segment, say from 10.630 s to 12.710 s, i.e. 2080 ms, we have a car one coming from the left and a small truck coming a bit later (say 0.350 s later it is certainly audible) from the right, then we will have 6 frames of positive example for car. These examples could be negative for other classes except small truck, because we are not sure whether it is present or not in this segment. The remaining 100ms will be ignored.
If in the next segment, say from 21.020 s to 22.510 s we have a car, but we are not sure whether other approaching vehicles are audible, we can use data from this segment as positive examples for car, but we cannot use it as negative examples for other classes.
Continuous recordings contain audio data representing background noise and sounds of vehicles approaching the microphone (and camera) from the left and the right hand side, passing by, and then receding. We selected sections which we could clearly label as positive or negative examples for each target class. Video data were used to guide this manual selection.
The on-road data used in our experiments contained carefully selected examples representing the target classes. Each positive example represents a 330 ms long segment of audio data with a vehicle from the target class passing by in front of the microphone, possibly accompanied by another vehicle. Negative examples may contain audio material of a vehicle or vehicles from classes other than the target class (or background noise); negative examples outnumber the positive ones for each class. Ground-truth labeling is a demanding task, as we must take into account the vehicles that are not visible, but can be heard in each segment. Video information is used for ground-truth labeling only, whereas each 330 ms audio frame is used for calculating the feature vector, which is next used in further experiments. The data contain:
Actually, we had much more samples at our disposal, especially for the car class. However, since we had only a few seconds of recordings for tractors and motorcycles, we decided to limit other data, in order to have comparable amounts of positive examples for each class.
The data were divided into 3 folds, with different vehicles data used for training and for testing, in 3-fold cross-validation (CV-3; approximately 2/3 for training and 1/3 for testing in each validation run). The data representing each particular vehicle were always put together in the same fold. The audio data represented sound of a single vehicle, or multiple vehicles. Positive examples contained sounds of the target class (possibly accompanied with other sounds), and negative examples represented any other classes (single or multiple vehicles), or background noise.
Now we can perform training of classifiers on our data from Example 1.2. The frames representing positive examples for car, small truck, big truck, and bus in Example 1.1 are now used as positive examples in training classifiers for car, small truck, big truck, and bus, respectively. Segments where we are sure that we have car only are taken as negative examples for big truck, small truck, and bus. The segments ignored in Example 1.1 with no vehicles recorded can now be used as negative examples for all 4 classes. Therefore, the frames representing ER ambulance with siren, and bicycle as well, can be used as negative examples, if no other vehicles are present in these data.
The error and F-measure for our data using the binary relevance approach are shown in Fig. 3. The classification error is defined as the number of incorrectly classified instances divided by the number of all classified instances. SVM with RBF kernel was applied in this experiment. F-measure in 2 cases could not be calculated, as no positive examples were indicated, or precision and recall were both equal to zero. The error is usually small, with the highest error for car classification using SVM, but still much better than random choice. 2ff7e9595c
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