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Idea from: iOS GPUImage framework and Android GPUImage framework
The GPUImage-x framework is a cross-platform (for both Android and iOS) library, which aims to have something similar to GPUImage that let you apply GPU-accelerated filters to images, live camera video. Part of vertex and fragment shaders is taken from GPUImage.
The greatest strength of GPUImage-x is that it enables you to develop your Android and iOS project with one library. The core code of this framework is written in C++, and is exactly the same for both iOS and Android projects, which locates in GPUImage-x/proj.iOS/GPUImage-x/GPUImage-x/*.cpp
and GPUImage-x/proj.android/GPUImage-x/library/src/main/cpp/*.cpp
respectively. Also, you can extend your customized filters easily.
- Android 2.2 or higher
- iPhone 4 or later
- OpenGL ES 2.0
Following frameworks are required to be added to your project.
AVFoundation.framework
CoreMedia.framework
GPUImage::SourceImage* sourceImage;
GPUImage::Filter* filter;
GPUImageView* filterView = (GPUImageView*)self.view;
UIImage* inputImage = [UIImage imageNamed:@"test.jpg"];
GPUImage::Context::getInstance()->runSync([&]{
// 1. create image source
sourceImage = GPUImage::SourceImage::create(inputImage);
// 2. create a filter
filter = GPUImage::GaussianBlurFilter::create();
// 3. build pipeline
sourceImage->addTarget(filter)->addTarget(filterView);
// 4. proceed
sourceImage->proceed();
});
This will filter an image with Gaussian Blur effect. GPUImage-x function calls must be embraced between GPUImage::Context::getInstance()->runSync([&]{
and });
, as GPUImage-x code should run in a seperate thread.
GPUImage::SourceCamera* camera;
GPUImage::Filter* filter;
GPUImageView* filterView = (GPUImageView*)self.view;
GPUImage::Context::getInstance()->runSync([&]{
// 1. create camera source
camera = GPUImage::SourceCamera::create();
// 2. create a filter
filter = GPUImage::BeautifyFilter::create();
// 3. build pipeline
camera->addTarget(filter)->addTarget(filterView);
// 4. start the camera and proceed
camera->start();
});
This will filter a camera video in real time with Beautify Effect.
repositories {
jcenter()
}
dependencies {
compile 'com.jin.gpuimage-x:gpuimage-x:1.0.1'
}
// 1. create image source
Bitmap bmp = BitmapFactory.decodeStream(getAssets().open("test.jpg"));
GPUImageSourceImage sourceImage = new GPUImageSourceImage(bmp);
// 2. create a filter
GPUImageFilter filter = GPUImageFilter.create("GrayscaleFilter");
// 3. build the pipeline
sourceImage.addTarget(filter).addTarget((GPUImageView) findViewById(R.id.gpuimagexview));
// 4. let the GPUImage-x know which source to use
GPUImage.getInstance().setSource(sourceImage);
// 5. proceed
sourceImage.proceed();
This will filter an image with Graysacle effect. More filters can be applied in sequence if you want, e.g. sourceImage.addTarget(filter1).addTarget(filter2). ... .addTarget(filterN).addTarget((GPUImageView) findViewById(R.id.gpuimagexview));
// 1. create the camera source
GPUImageSourceCamera sourceCamera = new GPUImageSourceCamera(CameraSampleActivity.this);
// 2. create a filter
GPUImageFilter filter = GPUImageFilter.create("EmbossFilter");
// 3. build the pipeline
sourceCamera.addTarget(filter).addTarget((GPUImageView) findViewById(R.id.gpuimagexview));
// 4. let the GPUImage-x know which source to use
GPUImage.getInstance().setSource(sourceCamera);
This will filter a camera video in real time with Emboss Effect.
Here is a few samples of images applied by filters:
Copyright (C) 2017 Yijin Wang, Yiqian Wang
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
- More filters and features will be added.
- More platforms will be supported.
Your donation will be greatly appreciated :)
- Email: [email protected]
- More: http://yijin.wang