JeVoisBase  1.11
JeVois Smart Embedded Machine Vision Toolkit Base Modules
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Yolo.H
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1 // ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
2 //
3 // JeVois Smart Embedded Machine Vision Toolkit - Copyright (C) 2017 by Laurent Itti, the University of Southern
4 // California (USC), and iLab at USC. See http://iLab.usc.edu and http://jevois.org for information about this project.
5 //
6 // This file is part of the JeVois Smart Embedded Machine Vision Toolkit. This program is free software; you can
7 // redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software
8 // Foundation, version 2. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
9 // without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
10 // License for more details. You should have received a copy of the GNU General Public License along with this program;
11 // if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
12 //
13 // Contact information: Laurent Itti - 3641 Watt Way, HNB-07A - Los Angeles, CA 90089-2520 - USA.
14 // Tel: +1 213 740 3527 - itti@pollux.usc.edu - http://iLab.usc.edu - http://jevois.org
15 // ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
16 /*! \file */
17 
18 #pragma once
19 
22 #include <atomic>
23 
24 #include <nnpack.h>
25 
26 extern "C" {
27 #include <darknet.h>
28 }
29 
30 namespace jevois { class StdModule; }
31 
32 namespace yolo
33 {
34  static jevois::ParameterCategory const ParamCateg("Darknet YOLO Options");
35 
36  //! Parameter \relates Yolo
37  JEVOIS_DECLARE_PARAMETER(dataroot, std::string, "Root path for data, config, and weight files. If empty, use "
38  "the module's path.",
39  JEVOIS_SHARE_PATH "/darknet/yolo", ParamCateg);
40 
41  //! Parameter \relates Yolo
42  JEVOIS_DECLARE_PARAMETER(datacfg, std::string, "Data configuration file (if relative, relative to dataroot)",
43  "cfg/coco.data", ParamCateg);
44 
45  //! Parameter \relates Yolo
46  JEVOIS_DECLARE_PARAMETER(cfgfile, std::string, "Network configuration file (if relative, relative to dataroot)",
47  "cfg/yolov3-tiny.cfg", ParamCateg);
48 
49  //! Parameter \relates Yolo
50  JEVOIS_DECLARE_PARAMETER(weightfile, std::string, "Network weights file (if relative, relative to dataroot)",
51  "weights/yolov3-tiny.weights", ParamCateg);
52 
53  //! Parameter \relates Yolo
54  JEVOIS_DECLARE_PARAMETER(namefile, std::string, "Category names file, or empty to fetch it from the network "
55  "config file (if relative, relative to dataroot)",
56  "", ParamCateg);
57 
58  //! Parameter \relates Yolo
59  JEVOIS_DECLARE_PARAMETER(nms, float, "Non-maximum suppression intersection-over-union threshold in percent",
60  45.0F, jevois::Range<float>(0.0F, 100.0F), ParamCateg);
61 
62  //! Parameter \relates Yolo
63  JEVOIS_DECLARE_PARAMETER(thresh, float, "Detection threshold in percent confidence",
64  24.0F, jevois::Range<float>(0.0F, 100.0F), ParamCateg);
65 
66  //! Parameter \relates Yolo
67  JEVOIS_DECLARE_PARAMETER(hierthresh, float, "Hierarchical detection threshold in percent confidence",
68  50.0F, jevois::Range<float>(0.0F, 100.0F), ParamCateg);
69 
70  //! Parameter \relates Yolo
71  JEVOIS_DECLARE_PARAMETER(threads, int, "Number of parallel computation threads",
72  6, jevois::Range<int>(1, 1024), ParamCateg);
73 }
74 
75 //! Detect multiple objects in scenes using the Darknet YOLO deep neural network
76 /*! Darknet is a popular neural network framework, and YOLO is a very interesting network that detects all objects in a
77  scene in one pass. This component detects all instances of any of the objects it knows about (determined by the
78  network structure, labels, dataset used for training, and weights obtained) in the image that is given to is.
79 
80  See https://pjreddie.com/darknet/yolo/
81 
82  Darknet is a great, bare-metal deep learning and deep neural network framework. It is great for embedded systems
83  like the small JeVois camera because it has a very small footprint and fewer dependencies than other deep neural
84  network frameworks like Tensorflow, MXNet, Theano, Keras, PyTorch, etc. In addition, the port of Darknet to JeVois
85  includes acceleration using the ARM NEON multimedia instructions through the popular NNPACK neural network
86  acceleration package.
87 
88  \ingroup Components */
89 class Yolo : public jevois::Component,
90  jevois::Parameter<yolo::dataroot, yolo::datacfg, yolo::cfgfile, yolo::weightfile, yolo::namefile,
91  yolo::nms, yolo::thresh, yolo::hierthresh, yolo::threads>
92 {
93  public:
94  //! Constructor
95  Yolo(std::string const & instance);
96 
97  //! Initialize, configure and load the network in a thread
98  /*! Any call to process() will simply throw until the network is loaded and ready */
99  void postInit() override;
100 
101  //! Virtual destructor for safe inheritance
102  virtual ~Yolo();
103 
104  //! Un-initialize and free resources
105  void postUninit() override;
106 
107  //! Processing function, results are stored internally in the underlying Darknet network object
108  /*! This version expects an OpenCV RGB byte image which will be converted to float RGB planar, and which may be
109  letterboxed if necessary to fit network input dims. Returns the prediction time (neural net forward pass) in
110  milliseconds. Throws std::logic_error if the network is still loading and not ready. */
111  float predict(cv::Mat const & cvimg);
112 
113  //! Processing function, results are stored internally in the underlying Darknet network object
114  /*! This version expects a Darknet image input, RGB float planar normalized to [0..1], with same dims as network
115  input dims. Returns the prediction time (neural net forward pass) in milliseconds. Throws std::logic_error if
116  the network is still loading and not ready. */
117  float predict(image & im);
118 
119  //! Compute the boxes
120  /*! You must have called predict() first for this to not violently crash. */
121  void computeBoxes(int inw, int inh);
122 
123  //! Draw the detections
124  /*! You must have called computeBoxes() first for this to not violently crash. */
125  void drawDetections(jevois::RawImage & outimg, int inw, int inh, int xoff, int yoff);
126 
127  //! Send serial messages about detections
128  /*! You must have called computeBoxes() first for this to not violently crash. The module given should be the owner
129  of this component, we will use it to actually send each serial message using some variant of
130  jevois::Module::sendSerial(). */
131  void sendSerial(jevois::StdModule * mod, int inw, int inh);
132 
133  //! Resize the network's input image dims
134  /*! This will prepare the network to receive inputs of the specified size. It is optional and will be called
135  automatically by predict() if the given image size does not match the current network input size. Note that this
136  only works with fully convolutional networks. Note that the number of channels cannot be changed at this
137  time. Throws std::logic_error if the network is still loading and not ready. */
138  void resizeInDims(int w, int h);
139 
140  //! Get input width, height, channels
141  /*! Throws std::logic_error if the network is still loading and not ready. */
142  void getInDims(int & w, int & h, int & c) const;
143 
144  // We leave these in the open in case one wants to access the probs, names, etc but just be careful with them
146  char ** names;
147  int nboxes;
148  detection * dets;
149  int classes;
150  int * map;
151 
152  protected:
153  std::future<void> itsReadyFut;
154  std::atomic<bool> itsReady;
155  };
Definition: Yolo.H:32
std::atomic< bool > itsReady
Definition: Yolo.H:154
detection * dets
Definition: Yolo.H:148
JEVOIS_DECLARE_PARAMETER(thresh1, double, "First threshold for hysteresis", 50.0, ParamCateg)
int nboxes
Definition: Yolo.H:147
char ** names
Definition: Yolo.H:146
int classes
Definition: Yolo.H:149
Network to load This meta parameter sets parameters cfgfile
Definition: Darknet.H:40
Network to load This meta parameter sets parameters datacfg
Definition: Darknet.H:40
Network to load This meta parameter sets parameters and namefile for the chosen network
Definition: Darknet.H:40
int * map
Definition: Yolo.H:150
std::future< void > itsReadyFut
Definition: Yolo.H:153
network * net
Definition: Yolo.H:145
Network to load This meta parameter sets parameters weightfile
Definition: Darknet.H:40
Detect multiple objects in scenes using the Darknet YOLO deep neural network.
Definition: Yolo.H:89
Network to load This meta parameter sets parameters dataroot
Definition: Darknet.H:40