TensorFlow_Android | 基于TensorFlow官方Android端示例，对选择的图片进行风格迁移

by SimonCherryGZ Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TensorFlow_Android Summary

TensorFlow_Android is a Java library. TensorFlow_Android has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

TensorFlow_Android

Support

Quality

Security

License

Reuse

Support

TensorFlow_Android has a low active ecosystem.

It has 11 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

TensorFlow_Android has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlow_Android is current.

Quality

TensorFlow_Android has 0 bugs and 0 code smells.

Security

TensorFlow_Android has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TensorFlow_Android code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TensorFlow_Android does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

TensorFlow_Android releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

TensorFlow_Android saves you 1925 person hours of effort in developing the same functionality from scratch.

It has 4240 lines of code, 273 functions and 46 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed TensorFlow_Android and discovered the below as its top functions. This is intended to give you an instant insight into TensorFlow_Android implemented functionality, and help decide if they suit your requirements.

Called when an image is available
Handle the detection of an object
Process a list of results
Triggers tracking
Returns a list of recognize images for the specified bitmap
Calculate softmax
End a split
Initialize preview size
Creates a TensorFlow session
Poll the flow data for the last frame time
Convert size list to a string
Override this method to show the photo
Call media scanner
Searches for a measured dimension
Handle key down
Resume the inference thread
Override this to draw on the canvas
Returns the simple name of the caller
Gets the bitmap from assetPath
Styles a bitmap
Sets the preview size
Returns a list of recognize image values based on the provided bitmap
Creates the camera preview session
Pause handler
Initializes the view
Sets the size of the sensor

Get all kandi verified functions for this library.

TensorFlow_Android Key Features

No Key Features are available at this moment for TensorFlow_Android.

TensorFlow_Android Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlow_Android.

Community Discussions

Trending Discussions on TensorFlow_Android

Cannot compile bazel needed for tensorflow-android

QUESTION

Cannot compile bazel needed for tensorflow-android

Asked 2017-Jun-08 at 08:26

I'm trying to explore the tensorflow android demo. I want to adapt the app using android studio.

However when I simply follow the instructions I get compilation errors no matter what I do:

Trying to compile tensorflow using bazel and NDK r12b:

...

ANSWER

Answered 2017-Jun-08 at 08:26

Installing Bazel on Ubuntu can be done without compiling.

Follow all steps of the dedicated documentation: Install Bazel on Ubuntu

Source https://stackoverflow.com/questions/44345610

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlow_Android

You can download it from GitHub.
You can use TensorFlow_Android like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the TensorFlow_Android component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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