JeVoisBase  1.22
JeVois Smart Embedded Machine Vision Toolkit Base Modules
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Public Member Functions | Protected Attributes | Related Symbols | List of all members
TensorFlowEasy Class Reference
JeVoisBase machine vision modules

Identify objects using TensorFlow deep neural network. More...

Inheritance diagram for TensorFlowEasy:
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Collaboration diagram for TensorFlowEasy:
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Public Member Functions

 TensorFlowEasy (std::string const &instance)
 Constructor.
 
virtual ~TensorFlowEasy ()
 Virtual destructor for safe inheritance.
 
virtual void process (jevois::InputFrame &&inframe) override
 Processing function, no video output.
 
virtual void process (jevois::InputFrame &&inframe, jevois::OutputFrame &&outframe) override
 Processing function with video output to USB.
 
- Public Member Functions inherited from jevois::StdModule
 StdModule (std::string const &instance)
 
virtual ~StdModule ()
 
void sendSerialImg1Dx (unsigned int camw, float x, float size=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialStd1Dx (float x, float size=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialImg1Dy (unsigned int camh, float y, float size=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialStd1Dy (float y, float size=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialImg2D (unsigned int camw, unsigned int camh, float x, float y, float w=0.0F, float h=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialStd2D (float x, float y, float w=0.0F, float h=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialContour2D (unsigned int camw, unsigned int camh, std::vector< cv::Point_< T > > points, std::string const &id="", std::string const &extra="")
 
void sendSerialStd3D (float x, float y, float z, float w=0.0F, float h=0.0F, float d=0.0F, float q1=0.0F, float q2=0.0F, float q3=0.0f, float q4=0.0F, std::string const &id="", std::string const &extra="")
 
void sendSerialStd3D (std::vector< cv::Point3f > points, std::string const &id="", std::string const &extra="")
 
void sendSerialObjReco (std::vector< ObjReco > const &res)
 
void sendSerialObjDetImg2D (unsigned int camw, unsigned int camh, float x, float y, float w, float h, std::vector< ObjReco > const &res)
 
void sendSerialObjDetImg2D (unsigned int camw, unsigned int camh, ObjDetect const &det)
 
void sendSerialObjDetImg2D (unsigned int camw, unsigned int camh, ObjDetectOBB const &det)
 
 JEVOIS_DEFINE_ENUM_CLASS (SerStyle,(Terse)(Normal)(Detail)(Fine))
 
 JEVOIS_DECLARE_PARAMETER (serstyle, SerStyle, "Style for standardized serial messages as defined in " "http://jevois.org/doc/UserSerialStyle.html", SerStyle::Terse, SerStyle_Values, ParamCateg)
 
 JEVOIS_DECLARE_PARAMETER (serprec, unsigned int, "Number of decimal points in standardized serial messages as " "defined in http://jevois.org/doc/UserSerialStyle.html", 0U, jevois::Range< unsigned int >(0U, 10U), ParamCateg)
 
 JEVOIS_DEFINE_ENUM_CLASS (SerStamp,(None)(Frame)(Time)(FrameTime)(FrameDateTime))
 
 JEVOIS_DECLARE_PARAMETER (serstamp, SerStamp, "Prepend standardized serial messages with a frame number, " "time, frame+time, or frame+date+time. See details in " "http://jevois.org/doc/UserSerialStyle.html", SerStamp::None, SerStamp_Values, ParamCateg)
 
 JEVOIS_DEFINE_ENUM_CLASS (SerMark,(None)(Start)(Stop)(Both))
 
 JEVOIS_DECLARE_PARAMETER (sermark, SerMark, "Send serial message to mark the beginning (MARK START) of the " "processing of a video frame from the camera sensor, the end (MARK STOP), or both. " "Useful, among others, if one needs to know when no results were sent over serial " "on a given frame. Combine with parameter serstamp if you need to know the frame number.", SerMark::None, SerMark_Values, ParamCateg)
 
- Public Member Functions inherited from jevois::Module
 Module (std::string const &instance)
 
virtual ~Module ()
 
virtual void process (InputFrame &&inframe, GUIhelper &helper)
 
virtual void sendSerial (std::string const &str)
 
virtual void parseSerial (std::string const &str, std::shared_ptr< UserInterface > s)
 
virtual void supportedCommands (std::ostream &os)
 
- Public Member Functions inherited from jevois::Component
 Component (std::string const &instance)
 
virtual ~Component ()
 
std::shared_ptr< Comp > addSubComponent (std::string const &instance, Args &&...args)
 
void removeSubComponent (std::shared_ptr< Comp > &component)
 
void removeSubComponent (std::string const &instance, bool warnIfNotFound=true)
 
std::shared_ptr< Comp > getSubComponent (std::string const &instance) const
 
bool isTopLevel () const
 
bool initialized () const
 
std::string const & className () const
 
std::string const & instanceName () const
 
std::vector< std::string > setParamVal (std::string const &paramdescriptor, T const &val)
 
void setParamValUnique (std::string const &paramdescriptor, T const &val)
 
std::vector< std::pair< std::string, T > > getParamVal (std::string const &paramdescriptor) const
 
T getParamValUnique (std::string const &paramdescriptor) const
 
std::vector< std::string > setParamString (std::string const &paramdescriptor, std::string const &val)
 
void setParamStringUnique (std::string const &paramdescriptor, std::string const &val)
 
std::vector< std::pair< std::string, std::string > > getParamString (std::string const &paramdescriptor) const
 
std::string getParamStringUnique (std::string const &paramdescriptor) const
 
void freezeParam (std::string const &paramdescriptor, bool doit)
 
void freezeAllParams (bool doit)
 
std::string descriptor () const
 
void setParamsFromFile (std::string const &filename)
 
std::istream & setParamsFromStream (std::istream &is, std::string const &absfile)
 
virtual void paramInfo (std::shared_ptr< UserInterface > s, std::map< std::string, std::string > &categs, bool skipFrozen, std::string const &cname="", std::string const &pfx="")
 
void foreachParam (std::function< void(std::string const &compname, ParameterBase *p)> func, std::string const &cname="")
 
std::shared_ptr< DynamicParameter< T > > addDynamicParameter (std::string const &name, std::string const &description, T const &defaultValue, ParameterCategory const &category)
 
std::shared_ptr< DynamicParameter< T > > addDynamicParameter (std::string const &name, std::string const &description, T const &defaultValue, ValidValuesSpec< T > const &validValuesSpec, ParameterCategory const &category)
 
void setDynamicParameterCallback (std::string const &name, std::function< void(T const &)> cb, bool callnow=true)
 
void removeDynamicParameter (std::string const &name, bool throw_if_not_found=true)
 
void setPath (std::string const &path)
 
std::filesystem::path absolutePath (std::filesystem::path const &path="")
 
std::shared_ptr< Comp > addSubComponent (std::string const &instance, Args &&...args)
 
void removeSubComponent (std::shared_ptr< Comp > &component)
 
void removeSubComponent (std::string const &instance, bool warnIfNotFound=true)
 
std::shared_ptr< Comp > getSubComponent (std::string const &instance) const
 
bool isTopLevel () const
 
bool initialized () const
 
std::string const & className () const
 
std::string const & instanceName () const
 
std::vector< std::string > setParamVal (std::string const &paramdescriptor, T const &val)
 
void setParamValUnique (std::string const &paramdescriptor, T const &val)
 
std::vector< std::pair< std::string, T > > getParamVal (std::string const &paramdescriptor) const
 
T getParamValUnique (std::string const &paramdescriptor) const
 
std::vector< std::string > setParamString (std::string const &paramdescriptor, std::string const &val)
 
void setParamStringUnique (std::string const &paramdescriptor, std::string const &val)
 
std::vector< std::pair< std::string, std::string > > getParamString (std::string const &paramdescriptor) const
 
std::string getParamStringUnique (std::string const &paramdescriptor) const
 
void freezeParam (std::string const &paramdescriptor, bool doit)
 
void freezeAllParams (bool doit)
 
std::string descriptor () const
 
void setParamsFromFile (std::string const &filename)
 
std::istream & setParamsFromStream (std::istream &is, std::string const &absfile)
 
virtual void paramInfo (std::shared_ptr< UserInterface > s, std::map< std::string, std::string > &categs, bool skipFrozen, std::string const &cname="", std::string const &pfx="")
 
void foreachParam (std::function< void(std::string const &compname, ParameterBase *p)> func, std::string const &cname="")
 
std::shared_ptr< DynamicParameter< T > > addDynamicParameter (std::string const &name, std::string const &description, T const &defaultValue, ParameterCategory const &category)
 
std::shared_ptr< DynamicParameter< T > > addDynamicParameter (std::string const &name, std::string const &description, T const &defaultValue, ValidValuesSpec< T > const &validValuesSpec, ParameterCategory const &category)
 
void setDynamicParameterCallback (std::string const &name, std::function< void(T const &)> cb, bool callnow=true)
 
void removeDynamicParameter (std::string const &name, bool throw_if_not_found=true)
 
void setPath (std::string const &path)
 
std::filesystem::path absolutePath (std::filesystem::path const &path="")
 
- Public Member Functions inherited from jevois::ParameterRegistry
virtual ~ParameterRegistry ()
 

Protected Attributes

std::shared_ptr< TensorFlow > itsTensorFlow
 
std::vector< jevois::ObjReco > itsResults
 

Related Symbols

(Note that these are not member symbols.)

 JEVOIS_DECLARE_PARAMETER (foa, cv::Size, "Width and height (in pixels) of the fixed, central focus of attention. " "This is the size of the central image crop that is taken in each frame and fed to the " "deep neural network. If the foa size does not fit within the camera input frame size, " "it will be shrunk to fit. To avoid spending CPU resources on rescaling the selected " "image region, it is best to use here the size that the deep network expects as input.", cv::Size(128, 128), ParamCateg)
 Parameter.
 

Additional Inherited Members

- Protected Member Functions inherited from jevois::StdModule
void sendSerialMarkStart ()
 
void sendSerialMarkStop ()
 
std::string getStamp () const
 
- Protected Member Functions inherited from jevois::Component
virtual void preInit ()
 
virtual void postInit ()
 
virtual void preUninit ()
 
virtual void postUninit ()
 
virtual void preInit ()
 
virtual void postInit ()
 
virtual void preUninit ()
 
virtual void postUninit ()
 
- Protected Member Functions inherited from jevois::ParameterRegistry
void addParameter (ParameterBase *const param)
 
void removeParameter (ParameterBase *const param)
 
void callbackInitCall ()
 

Detailed Description

Identify objects using TensorFlow deep neural network.

TensorFlow is a popular neural network framework. This module identifies the object in a square region in the center of the camera field of view using a deep convolutional neural network.

The deep network analyzes the image by filtering it using many different filter kernels, and several stacked passes (network layers). This essentially amounts to detecting the presence of both simple and complex parts of known objects in the image (e.g., from detecting edges in lower layers of the network to detecting car wheels or even whole cars in higher layers). The last layer of the network is reduced to a vector with one entry per known kind of object (object class). This module returns the class names of the top scoring candidates in the output vector, if any have scored above a minimum confidence threshold. When nothing is recognized with sufficiently high confidence, there is no output.

This module runs a TensorFlow network and shows the top-scoring results. In this module, we run the deep network on every video frame, so framerate will vary depending on network complexity (see below). Point your camera towards some interesting object, make the object fit within the grey box shown in the video (which will be fed to the neural network), keep it stable, and TensorFlow will tell you what it thinks this object is.

Note that by default this module runs different flavors of MobileNets trained on the ImageNet dataset. There are 1000 different kinds of objects (object classes) that these networks can recognize (too long to list here). The input layer of these networks is 299x299, 224x224, 192x192, 160x160, or 128x128 pixels by default, depending on the network used. The networks provided on the JeVois microSD image have been trained on large clusters of GPUs, using 1.2 million training images from the ImageNet dataset.

For more information about MobileNets, see https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md

For more information about the ImageNet dataset used for training, see http://www.image-net.org/challenges/LSVRC/2012/

Sometimes this module will make mistakes! The performance of mobilenets is about 40% to 70% correct (mean average precision) on the test set, depending on network size (bigger networks are more accurate but slower).

Neural network size and speed

This module takes a central image region of size given by the foa parameter. If necessary, this image region is then rescaled to match the deep network's expected input size. The network input size varies depending on which network is used; for example, mobilenet_v1_0.25_128_quant expects 128x128 input images, while mobilenet_v1_1.0_224 expects 224x224. Note that there is a CPU cost to rescaling, so, for best performance, you should match the foa size to the network's input size.

For example:

  • mobilenet_v1_0.25_128_quant (network size 128x128), runs at about 8ms/prediction (125 frames/s).
  • mobilenet_v1_0.5_128_quant (network size 128x128), runs at about 18ms/prediction (55 frames/s).
  • mobilenet_v1_0.25_224_quant (network size 224x224), runs at about 24ms/prediction (41 frames/s).
  • mobilenet_v1_1.0_224_quant (network size 224x224), runs at about 139ms/prediction (7 frames/s).

To easily select one of the available networks, see JEVOIS:/modules/JeVois/TensorFlowEasy/params.cfg on the microSD card of your JeVois camera.

Serial messages

When detections are found with confidence scores above thresh, a message containing up to top category:score pairs will be sent per video frame. Exact message format depends on the current serstyle setting and is described in Standardized serial messages formatting. For example, when serstyle is Detail, this module sends:

DO category:score category:score ... category:score

where category is a category name (from namefile) and score is the confidence score from 0.0 to 100.0 that this category was recognized. The pairs are in order of decreasing score.

See Standardized serial messages formatting for more on standardized serial messages, and Helper functions to convert coordinates from camera resolution to standardized for more info on standardized coordinates.

More networks

Search the web for models in TFLITE format and for TensorFlow 1.x series. For example, see https://tfhub.dev/s?module-type=image-classification

To add a new model to your microSD card:

  • create a directory for it under JEVOIS:/share/tensorflow
  • put your .tflite in there as model.tflite
  • put a list of labels as a plain text file, one label per line, in your directory as labels.txt
  • edit params.cfg for this module (best done in JeVois Inventor) to add a new entry for your network, and to comment out the default entry.

Using your own network

For a step-by-step tutorial, see Training custom TensorFlow networks for JeVois.

This module supports RGB or grayscale inputs, byte or float32. You should create and train your network using fast GPUs, and then follow the instruction here to convert your trained network to TFLite format:

https://www.tensorflow.org/lite/

Then you just need to create a directory under JEVOIS:/share/tensorflow/ with the name of your network, and, in there, two files, labels.txt with the category labels, and model.tflite with your model converted to TensorFlow Lite (flatbuffer format). Finally, edit JEVOIS:/modules/JeVois/TensorFlowEasy/params.cfg to select your new network when the module is launched.

Author
Laurent Itti
Display Name:
TensorFlow Easy
Videomapping:
NONE 0 0 0.0 YUYV 320 240 60.0 JeVois TensorFlowEasy
Videomapping:
YUYV 320 308 30.0 YUYV 320 240 30.0 JeVois TensorFlowEasy
Videomapping:
YUYV 640 548 30.0 YUYV 640 480 30.0 JeVois TensorFlowEasy
Videomapping:
YUYV 1280 1092 7.0 YUYV 1280 1024 7.0 JeVois TensorFlowEasy
Email:
itti@usc.edu
Address:
University of Southern California, HNB-07A, 3641 Watt Way, Los Angeles, CA 90089-2520, USA
Copyright
Copyright (C) 2018 by Laurent Itti, iLab and the University of Southern California
Main URL:
http://jevois.org
Support URL:
http://jevois.org/doc
Other URL:
http://iLab.usc.edu
License:
GPL v3
Distribution:
Unrestricted
Restrictions:
None

Definition at line 154 of file TensorFlowEasy.C.

Constructor & Destructor Documentation

◆ TensorFlowEasy()

TensorFlowEasy::TensorFlowEasy ( std::string const &  instance)
inline

Constructor.

Definition at line 161 of file TensorFlowEasy.C.

References itsTensorFlow.

◆ ~TensorFlowEasy()

virtual TensorFlowEasy::~TensorFlowEasy ( )
inlinevirtual

Virtual destructor for safe inheritance.

Definition at line 169 of file TensorFlowEasy.C.

Member Function Documentation

◆ process() [1/2]

virtual void TensorFlowEasy::process ( jevois::InputFrame &&  inframe)
inlineoverridevirtual

Processing function, no video output.

Reimplemented from jevois::Module.

Definition at line 175 of file TensorFlowEasy.C.

References jevois::rawimage::cvImage(), h, jevois::RawImage::height, itsResults, itsTensorFlow, LINFO, jevois::rescaleCv(), jevois::StdModule::sendSerialObjReco(), and jevois::RawImage::width.

◆ process() [2/2]

virtual void TensorFlowEasy::process ( jevois::InputFrame &&  inframe,
jevois::OutputFrame &&  outframe 
)
inlineoverridevirtual

Processing function with video output to USB.

Reimplemented from jevois::Module.

Definition at line 221 of file TensorFlowEasy.C.

References jevois::async(), jevois::yuyv::Black, jevois::rawimage::cvImage(), jevois::rawimage::drawFilledRect(), jevois::rawimage::drawRect(), h, jevois::RawImage::height, itsResults, itsTensorFlow, jevois::yuyv::MedGrey, jevois::rawimage::paste(), jevois::RawImage::require(), jevois::rescaleCv(), jevois::StdModule::sendSerialObjReco(), jevois::sformat(), jevois::Timer::start(), jevois::Timer::stop(), jevois::yuyv::White, jevois::RawImage::width, and jevois::rawimage::writeText().

Friends And Related Symbol Documentation

◆ JEVOIS_DECLARE_PARAMETER()

JEVOIS_DECLARE_PARAMETER ( foa  ,
cv::Size  ,
"Width and height (in pixels) of the  fixed,
central focus of attention. " "This is the size of the central image crop that is taken in each frame and fed to the " "deep neural network. If the foa size does not fit within the camera input frame  size,
" "it will be shrunk to fit. To avoid spending CPU resources on rescaling the selected " "image  region,
it is best to use here the size that the deep network expects as input."  ,
cv::Size(128, 128)  ,
ParamCateg   
)
related

Parameter.

Member Data Documentation

◆ itsResults

std::vector<jevois::ObjReco> TensorFlowEasy::itsResults
protected

Definition at line 319 of file TensorFlowEasy.C.

Referenced by process(), and process().

◆ itsTensorFlow

std::shared_ptr<TensorFlow> TensorFlowEasy::itsTensorFlow
protected

Definition at line 318 of file TensorFlowEasy.C.

Referenced by process(), process(), and TensorFlowEasy().

The documentation for this class was generated from the following file: