javacpp-presets | The missing Java distribution of native C++ libraries | Wrapper library
kandi X-RAY | javacpp-presets Summary
kandi X-RAY | javacpp-presets Summary
The JavaCPP Presets modules contain Java configuration and interface classes for widely used C/C libraries. The configuration files in the org.bytedeco..presets packages are used by the Parser to create from C/C header files the Java interface files targeting the org.bytedeco. packages, which in turn are used by the Generator and the native C++ compiler to produce the required JNI libraries. Moreover, helper classes make their functionality easier to use on the Java platform, including Android.
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Top functions reviewed by kandi - BETA
- Utility method to set an image acquisition .
- Helper method to set the init params .
- Handle video sequence
- Uses an int8 to compute the topology
- Optimize BERT model
- Build the module .
- Copy a frame to a device frame
- Map information to InfoMap .
- Encodes CUDA in memory .
- Decode a media file .
javacpp-presets Key Features
javacpp-presets Examples and Code Snippets
License : Strong Copyleft (CC BY-SA 4.0)
Copy### set border in different colors
reset session
set multiplot
set margins 0,0,-1,-1 # l, r, b, t
set origin 0.10,0.10
set size 0.60,0.90
set border lw 2
set grid x,y
plot sin(x)
set origin 0.70,0.10
import tensorflow as tf
from tensorflow.python.ops import resource_variable_ops
class MyModule(tf.Module):
def __init__(self):
pass
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=ffmpeg -threads:v 2 -threads:a 8 -filter_threads 2 -thread_queue_size 1080 \
-loop 1 -re -i ./image.png \
-stream_loop -1 -i ./track.mp3 \
...
void fireBullet() {
var enemies = gameRef.children.query();
if (enemies.isEmpty) return;
var nearestEnemy = enemies.first;
var nearestDistance = nearestEnemy.position.distance2(playerComponent);
enemies.forEach((elffmpeg -i pth_in.mp4 \
-movflags faststart \
-filter_complex [0:v]thumbnail,trim=end_frame=1,scale=360:-1[thumb] \
-map 0:v -c:v:0 hevc_nvenc -preset slow \
-map [thumb] -frames:v:1 1 -c:v:1 mjpeg -disposition:vimport tensorflow.keras as keras
import tensorflow as tf
from tensorflow.keras.utils import Sequence
feature_format = {"a": tf.io.FixedLenFeature((1,), dtype=tf.int64),"b": tf.io.FixedLenFeature((1,), dtype=tf.int64)}
label_format = { "lffmpeg -pixel_format nv12 -video_size 1944x1204 -i myImage.yuv output.png
ffmpeg -r 60 -an -i /dev/video0 -vcodec copy test.mp4
ffmpeg -i samplevideo.mp4 -i sampleaudio.aac \
-filter_complex "[0:a]volume=0:enable=between(t\,5\,12)[a0];\
[1:a]atrim=0:7,adelay=5s:all=1[a1];\
[a0][a1]amix=normalize=0:duration=firffmpeg -i "$i" -sample_fmt s16 -ar 44100 -f segment -segment_time 10800 "${name}__%03d.flac"
Community Discussions
Trending Discussions on javacpp-presets
QUESTION
After several weeks of development, I have finally created an AI in an Android App that works using the matrix manipulation API provided by the Nd4j library. These were imported into the project with gradle, following this documentation.
Unfortunately, I'm finding out that Nd4j depends on some release-killingly large run time libraries, especially libnd4jcpu.so, which is about 150mb per abi platform, leading to apk sizes upwards of half a gigabyte! The average app size you'll find on the Google Play store is about 11.5MB.
The compressed download size limit of Android App Bundles on Google Play is 150MB.
The problem of how to reduce the size of the Dl4j dependencies was raised in a previous StackOverflow question. No solution could be offered however, except just to be more selective about what platforms you support. Again, per abi platform, that still means a minimum APK size of at minimum ~200MB.
One has to wonder why the Deeplearning4J community has gone to the effort of supporting Android mobile development in the first place, and why the inevitable problem of runtime library dependency sizes isn't so much as mentioned anywhere in the documentation.
Surely I am missing something here?
...ANSWER
Answered 2021-Mar-02 at 13:17"Nd4j" is actually libraries + self contained c++ library compiled to native binaries per platform bundled in to the jar for fast performance. You typically want to strip down those dependencies in your build. You can see how to do that here:
https://github.com/bytedeco/javacpp-presets/wiki/Reducing-the-Number-of-Dependencies
Nd4j relies on javacpp for packaging. In short, you can either specify -Dplatform=android-x86_64 or android-arm64 (depending on the architecture) in your maven/gradle build if you use nd4j-native-platform or you can just use the nd4j-native dependency (no classifier) + the classifier for your platform.
Editing my response a bit sorry I didn't get the time to fully read your question this morning. Let me respond point by point.
First of all this: "One has to wonder why the Deeplearning4J community has gone to the effort of supporting Android mobile development in the first place.."
First of all fair point and I want to work on this. Please be open minded when working with us here a bit. Generally people have specific requirements and work with us on their specific circumstances. Sometimes we help them minimize binary size via #2.
Regarding this: "especially libnd4jcpu.so, which is about 150mb per abi platform, " as it stands generally folks come with different spins of their apps. We've admittedly focused more on op coverage than binary size. There's a minifier we have that could help: https://github.com/eclipse/deeplearning4j/tree/master/libnd4j/minifier I'm happy to help with your use case if you can be more specific, but it's not quite a "just read the docs and go about it on your own" experience.
Surely I am missing something here?
QUESTION
I have a code which was previously parallelised in python using multiprocess and it worked okay (although slow and memory hungry). I've decided to try to convert it to cython. I'm new to cython and not much experience in c. The below example is as simplified as I can get it, and it worked serially, but as soon as I parallelised it, it no longer works. Due to the nature of running in parallel I've gone through all my code and turned off gil.
The code relies on an external C library https://github.com/astro-informatics/ssht/ (compilation instructions in the README) which uses fftw under the hood. This library has its own cython file which calls the same c function that I'm using (ssht_core_mw_inverse_sov_sym_ss). The function which closely resembles mine (in the cython file in that repo) looks like this
ANSWER
Answered 2020-Nov-28 at 17:13So as @DavidW pointed out that I'm running issues due to the fact that FFTW can't be run multithreaded (but works in python with multiprocessing). The problem is related to the external code I'm using which relies on FFTW. I've raised an issue to see if we can force the FFTW bits to be single threaded https://github.com/astro-informatics/ssht/issues/44
QUESTION
There is an application written with Spring Boot 1.5.21. I'm asked to upgrade it to Spring Boot 2.3.4, and fix the errors the upgrade brings. Apparently, this didn't go well since I'm merely a beginner on many things. I've been able to solve some errors due to dependency changes, by googling. But the one I'm going to ask took me hours yet I didn't even see a clue of it.
Directly this is the error message shown by IntelliJ:
...ANSWER
Answered 2020-Oct-05 at 09:31The solution is simple and also really silly IMHO.
In src/test/resources/application.yml, previously it was
QUESTION
I have a sample code from https://ratiler.wordpress.com/2014/09/08/detection-de-mouvement-avec-javacv/ with small difference in loading the open cv library in the static block using nu.pattern.OpenCV.loadShared(); :
ANSWER
Answered 2020-Sep-30 at 08:26It was a licensing issue that made impossible to include ffmpeg to
QUESTION
I have to build a program that takes in skewed forms (images that have been scanned) for image processing. The first step is to get rid of the skeweness. I'm successfully getting the contours of the image, and I'm attempting to do a four_point_transform as presented in this post Remove top section of image above border line to detect text document . However, my code is failing due to:
Error
...ANSWER
Answered 2020-Sep-26 at 23:23getPerspectiveTransform() is working some other way (see my comment). However, I found minAreaRect() as more suitable method here. I have no prepared java enviroment so here is python code. I hope you will have no difficulties while converting it.
QUESTION
I want to build a fat jar for tesseract. With the following build settings I get a jar of about 68 MB with dependencies for all supported platforms:
...ANSWER
Answered 2020-May-15 at 15:20I'll have to update that guide a little bit, thanks for reporting! We now also need javacpp-platform, which would give us something like this for windows-x86_64 in this case:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install javacpp-presets
In the Projects window, right-click the Libraries node of your project, and select "Add JAR/Folder…".
Locate the JAR files, select them, and click OK.
Navigate to Project > Properties > Java Build Path > Libraries and click "Add External JARs…".
Locate the JAR files, select them, and click OK.
Follow the instructions on this page: http://developer.android.com/training/basics/firstapp/
Copy all the JAR files into the app/libs subdirectory.
Navigate to File > Project Structure > app > Dependencies, click +, and select "2 File dependency".
Select all the JAR files from the libs subdirectory.
[OpenCV documentation](http://docs.opencv.org/master/)
[FFmpeg documentation](http://ffmpeg.org/doxygen/trunk/)
If the binary files available above are not enough for your needs, you might need to rebuild them from the source code. To this end, project files on the Java side were created as [Maven modules](#the-maven-modules). By default, the Maven build also installs the native libraries on the native C/C++ side with the [cppbuild.sh scripts](#the-cppbuildsh-scripts), but they can also be installed by other means. Additionally, one can find on the wiki page additional information about the recommended [build environments](https://github.com/bytedeco/javacpp-presets/wiki/Build-Environments) for the major platforms.
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