TensorFlowAndroidDemo | TensorFlow android demo 车道线车辆人脸动作骨架识别检测抽烟打电话闭眼睁眼 | Machine Learning library

by yuxitong Java Version: Current License: No License

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kandi X-RAY | TensorFlowAndroidDemo Summary

TensorFlowAndroidDemo is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. TensorFlowAndroidDemo has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Denis Tome, Chris Russell, Lourdes Agapito提出的Convolutional 3D Pose Estimation from a Single Image论文. 目前本Demo模型能识别出抽烟打电话闭眼睁眼. TensorFlowImageClassifier2 为车道检测之后不规则绘制（因时间仓促还没有进行绘图优化）识别道路的测试方法请自行百度寻找图片或者视频都可以. TensorFlowImageClassifier3 是用来识别人体骨架的这个模型是有特定输入和特定输出的需要经过3层转换才能使用接下来准备上线道路障碍物识别... 最新版骨架识别目前支持区分各个身体部位具体情况请看注释. Camera2BasicFragment4 这是一个用检测来识别车道和前车里面增加了点逻辑来判断是否是车道偏离或者前车过近具体做法是如果检测出线则判断斜率k = (y2-y1)/(x2-x1)然后设定一个固定斜率来判断是否是车道偏离如果是检测出前面的车辆中心点在横屏8分之2到8分之6的范围内则判断中心点居上距离大于一定范围则算前车过近或者如果车的高度大于一定级别则算前车过近. 另外：有人私下问我本项目在他们的手机上跑起来卡顿严重这是算力的问题，目前tensorFlow在移动设备上貌似不支持GPU，而CPU的浮点运算速度比较慢导致的推荐使用华为P10 或者骁龙845 635之类的U来跑跑看一般P10的话 1能一秒4帧 2能1秒8帧 3能一秒1帧 4能一秒6帧左右当然以上数据仅供产考. 计算 Vx = Dx / k Vy = Dy / k. 连线的过程中每个线均分10个点分出2个数组分别是X数组 Y数组每个数组都是10个数字均分公式如下. 计算每个连线的分数： CocoPairs 与 CocoPairsNetwork 一一对应每一组对应的CocoPairs连线方式可以在CocoPairsNetwork里取出2层（如：1和2之间连线可以在12层和13层里分别取出10个均等值）并把均等值存到2个新的长度为10的组里 Px[10] 和 Py[10].

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Quality

Security

License

Reuse

Support

TensorFlowAndroidDemo has a low active ecosystem.

It has 690 star(s) with 201 fork(s). There are 30 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 17 have been closed. On average issues are closed in 60 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlowAndroidDemo is current.

Quality

TensorFlowAndroidDemo has no bugs reported.

Security

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

License

TensorFlowAndroidDemo 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

TensorFlowAndroidDemo 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.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Processes the input image
Change image to RGB
Internal function
Callback method
Invoked when the camera is created
Rounds to round
To blur a bitmap
Creates a new camera preview session
Convert a bitmap to a round rectangle
Open import statas bar
Sets a click method on a button
Convert a bitmap to base64 string
Add a bitmap to a bitmap
Check if a bitmap is simple color image
Encode html
Convert a bitmap to a new bitmap
Get all xml
Uses a bitmap to recognize the image
Returns a list of recognize images based on the provided bitmap
Classify image
Get all contact info

Get all kandi verified functions for this library.

TensorFlowAndroidDemo Key Features

No Key Features are available at this moment for TensorFlowAndroidDemo.

TensorFlowAndroidDemo Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlowAndroidDemo.

Community Discussions

Trending Discussions on TensorFlowAndroidDemo

Use my own images with tensorflow?

QUESTION

Use my own images with tensorflow?

Asked 2017-Apr-21 at 13:35

I am working with this demo of tensorflow : https://github.com/miyosuda/TensorFlowAndroidDemo (Android Studio project without Bazel)

Can I use tensorflow with my own images ? How ? Is there any example to set a new dataset?

...

ANSWER

Answered 2017-Apr-21 at 13:35

1) You have to setup your own tensorflow environment first, using virtualenv (or docker or anaconda) first ( refer https://www.tensorflow.org/install/ )

2) And then, clone tensorflow source repo : https://github.com/tensorflow/tensorflow

3) After that, you should build several files for training/testing your own images and creating/optimizing graph files such as retrain, optimize_for_inference, quantization, etc.

4) Put your own images in appropriate classified names in the training folder

5) start training

Refer this page for how to (re)train images and put them onto android demo:

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#0 (This tutorial assumes you're using docker, but virtualenv and/or anaconda is almost same in how to do it)

and this Peter Warden's blog: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ for mobile implementation (it's based on ios example but can be applied to android also)

Source https://stackoverflow.com/questions/43491047

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

Vulnerabilities

No vulnerabilities reported

Install TensorFlowAndroidDemo

You can download it from GitHub.
You can use TensorFlowAndroidDemo 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 TensorFlowAndroidDemo 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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