JeVoisBase
1.21
JeVois Smart Embedded Machine Vision Toolkit Base Modules
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Identify objects using TensorFlow deep neural network. More...
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) |
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 ¶mdescriptor, T const &val) |
void | setParamValUnique (std::string const ¶mdescriptor, T const &val) |
std::vector< std::pair< std::string, T > > | getParamVal (std::string const ¶mdescriptor) const |
T | getParamValUnique (std::string const ¶mdescriptor) const |
std::vector< std::string > | setParamString (std::string const ¶mdescriptor, std::string const &val) |
void | setParamStringUnique (std::string const ¶mdescriptor, std::string const &val) |
std::vector< std::pair< std::string, std::string > > | getParamString (std::string const ¶mdescriptor) const |
std::string | getParamStringUnique (std::string const ¶mdescriptor) const |
void | freezeParam (std::string const ¶mdescriptor, 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 ¶mdescriptor, T const &val) |
void | setParamValUnique (std::string const ¶mdescriptor, T const &val) |
std::vector< std::pair< std::string, T > > | getParamVal (std::string const ¶mdescriptor) const |
T | getParamValUnique (std::string const ¶mdescriptor) const |
std::vector< std::string > | setParamString (std::string const ¶mdescriptor, std::string const &val) |
void | setParamStringUnique (std::string const ¶mdescriptor, std::string const &val) |
std::vector< std::pair< std::string, std::string > > | getParamString (std::string const ¶mdescriptor) const |
std::string | getParamStringUnique (std::string const ¶mdescriptor) const |
void | freezeParam (std::string const ¶mdescriptor, 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 () |
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).
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:
To easily select one of the available networks, see JEVOIS:/modules/JeVois/TensorFlowEasy/params.cfg on the microSD card of your JeVois camera.
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.
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:
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.
Definition at line 154 of file TensorFlowEasy.C.
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inline |
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inlinevirtual |
Virtual destructor for safe inheritance.
Definition at line 169 of file TensorFlowEasy.C.
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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.
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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().
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Parameter.
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protected |
Definition at line 319 of file TensorFlowEasy.C.
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protected |
Definition at line 318 of file TensorFlowEasy.C.
Referenced by process(), process(), and TensorFlowEasy().