Deep Learning with OpenCV

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2017-09-03

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Deep Learning with OpenCV

Jack Mo

INTRODUCTIONS

OpenCV3.3 has come along with better support for deep-learning. The DNN (Deep-learning Neuron Network) module has been moved from opencv_contrib repository to main repository and therefor became a standard part of OpenCV. Current release supports the backend and model IO of Caffe, TensorFlow, Torch and PyTorch. You can pick any of them to build your deep-learning powered computer vision applications, of course, with much less work to do.

This blog trys to get your hands ready for Deep Learning with OpenCV, if you are a beginner like me.

ENVIRONMENT SETUP

1. Recompile Protobuf As a Static Lib

1) Remove libgtk-3-dev, use libgtk-2.0-dev instead, because libgtk-3-dev depends on libmirprotobuf-dev. As we need to recompile protobuf, any existing older version of protobuf may cause conflicts.

sudo apt-get remove libgtk-3-dev
sudo apt-get install libgtk2.0-dev

 

2) Remove libprotobuf-dev, libmirprotobuf-dev

sudo apt-get remove libprotobuf-dev libmirprotobuf-dev
sudo rm -rf /usr/include/google/protobuf
sudo rm -rf /usr/lib/libprotobuf*
sudo rm -rf /usr/local/include/google/protobuf
sudo rm -rf /usr/local/lib/libprotobuf*

 

3) Install protobuf build dependencies

sudo apt-get install autoconf automake libtool curl make g++ unzip

 

4) Download protobuf sources and generate configuration

git clone https://github.com/google/protobuf.git
cd protobuf
./autogen.sh

 

5) Configure protobuf build profile

gedit configure

Make some changes from line 2659 to 2664 (line numbers depend on your source version):

Original:

if test "x${ac_cv_env_CFLAGS_set}" = "x"; then :
   CFLAGS=""
fi
if test "x${ac_cv_env_CXXFLAGS_set}" = "x"; then :
   CXXFLAGS=""
fi

 

Modified:

if test "x${ac_cv_env_CFLAGS_set}" = "x"; then :
   CFLAGS="-fPIC"
fi
if test "x${ac_cv_env_CXXFLAGS_set}" = "x"; then :
   CXXFLAGS="-fPIC"
fi

 

6) Make & Install

make -j`nproc`
sudo make install

 

7) Test

a) Check protobuf compiler version:

protoc --version

 

You should get this:

protoc --version

b) Check protobuf static lib installation:

ls /usr/local/lib | grep libprotobuf

 

You should get this:

libprotobuf

2. Caffe Installation

1) Disable OpenCV and check your own python include path (with numpy)

USE_OPENCV := 0
PYTHON_INCLUDE := /usr/include/python2.7 \
   /usr/local/lib/python2.7/dist-packages/numpy/core/include

 

2) For customizing, follow the official installation guide: http://caffe.berkeleyvision.org/installation.html

3) If errors occurs during the installation, refer to this page: http://blog.csdn.net/hongye000000/article/details/51043913

3. OpenCV3.3 Installation

1) Download sources:

2) For more detailed information, please refer to: http://www.linuxfromscratch.org/blfs/view/svn/general/opencv.html

3) In opencv-3.3.0/modules/python/python2/CmakeLists.txt, add one line:

if(NOT PYTHON2_INCLUDE_PATH OR NOT PYTHON2_NUMPY_INCLUDE_DIRS)
   ocv_module_disable(python2)
endif()

set(the_description "The python2 bindings")
set(MODULE_NAME python2)
# Buildbot requires Python 2 to be in root lib dir
set(MODULE_INSTALL_SUBDIR "")

set(PYTHON PYTHON2)

include(../common.cmake)

# line added. Don’t diretly copy and paste, use your own caffe path
include_directories(/home/bj/caffe/distribute/include)

unset(MODULE_NAME)
unset(MODULE_INSTALL_SUBDIR)

 

4) CMake options

cmake  -DCMAKE_BUILD_TYPE=Release -
   DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib/modules> -
   DCaffe_INCLUDE_DIR=~/caffe/distribute/include -
   DCaffe_LIB_DIR=~/caffe/distribute/lib -
   DENABLE_PRECOMPILED_HEADERS=OFF -DBUILD_PERF_TESTS=OFF -
   DBUILD_TESTS=OFF -DENABLE_CXX11=ON -DBUILD_opencv_python2=ON

 

5) Make & Install

First, clean old opencv header files and libs:

sudo apt-get remove libopencv-dev
sudo rm /usr/local/include/opencv* -rf
sudo rm /usr/local/lib/libopencv*

 

In your cmake build directory:

make -j4
sudo make install

RUN EXAMPLE

a) For Python user:

# please refer to: http://www.pyimagesearch.com/2017/08/21/deep-learning-with-opencv/
# for detail

import numpy as np
import argparse as ap
import time
import cv2

# parse command line arguments
arp = ap.ArgumentParser()
arp.add_argument("-i", "--image", required=False,
	help="path to input image")
arp.add_argument("-p", "--prototxt", required=True,
	help="path to Caffe 'deploy' prototxt file")
arp.add_argument("-m", "--model", required=True,
	help="path to Caffe pretrained model")
arp.add_argument("-l", "--labels", required=True,
	help="path to ImageNet labels (i.e., syn-sets)")
args = vars(arp.parse_args())

# Use live demo if image is not given
cap = cv2.VideoCapture()
if (args["image"] is None) :
   cap.open(0)
else :
   cap.open(args["image"])

if (cap.isOpened() == False) :
   print("ERROR: Fail to capture")
   exit(0)

# set opencv video capture properties
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 224)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 224)

# load labels
rows = open(args["labels"]).read().strip().split("\n")
classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]

# load model
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# loop 
while True:
   
   # read new frame
   ret, image = cap.read()
   if (image is None) : break
   
   # create blob from image
   blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))
   
   # assign net input
   net.setInput(blob)
   
   # time before forwarding
   start = time.time()
   
   # forward
   preds = net.forward()
   
   # time after forwarding
   end = time.time()
   
   # show classification time cost
   print("[INFO] classification took {:.5} seconds".format(end - start))

   # find the top 3 scored classes
   idxs = np.argsort(preds[0])[::-1][:3]
   for (i, idx) in enumerate(idxs):
      # mark the top one
	   if i == 0:
		   text = "Label: {}, {:.2f}".format(classes[idx], preds[0][idx])
		   cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
			   0.7, (0, 0, 255), 2)
	   # for each class in top 3, print the info
	   print("[INFO] {}. label: {}, probability: {:.5}".format(i + 1,
		   classes[idx], preds[0][idx]))
   
   # show image
   cv2.imshow("cvdnn_py", image)
   cv2.waitKey(1)

# before exiting
cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()

# end of file

 

Command line:

cvdnn py cmd

Result:

cvdnn py result

b) For C++ user:

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include 
#include 
#include 
#include 

// command line help
void help()
{
   std::cout << "Usage: cvdnn    " << std::endl;
}

// load labels from file
bool load_labels(const std::string &path, std::vector &labels)
{
   std::ifstream label_stream(path.c_str());

   if (!label_stream)
	   return false;

   while (!label_stream.eof())
   {
      std::string line;
      std::getline(label_stream, line);
      size_t label_beg_idx = line.find(" ") + 1;
      size_t label_end_idx = line.find(",", label_beg_idx + 1);
      if (label_end_idx == std::string::npos)
         label_end_idx = line.length();
      size_t label_len = label_end_idx - label_beg_idx;
      std::string label = line.substr(label_beg_idx, label_len);
      labels.push_back(label);
   }

   label_stream.close();

   return true;
}

int main(int argc, char **argv)
{
   if (argc != 5)
   {
	   help();
	   return -1;
   }

   // std string for each argument
   std::string image_path(argv[1]);
   std::string proto_path(argv[2]);
   std::string model_path(argv[3]);
   std::string label_path(argv[4]);

   // load labels from file
   std::vector labels;
   if (!load_labels(label_path, labels))
   {
      std::cout << "ERROR: cannot open label file: " << label_path << std::endl;
      return -1;
   }

   // read image
   cv::Mat image = cv::imread(image_path);
   if (image.empty())
   {
      std::cout << "ERROR: cannot open image file: " << image_path << std::endl;
      return -1;
   }

   // create blob from image
   cv::Mat blob = cv::dnn::blobFromImage(image, 1, cv::Size(224, 224), cv::Scalar(104, 117, 123));

   // read net from caffe
   cv::dnn::Net net = cv::dnn::readNetFromCaffe(proto_path, model_path);

   // set net input
   net.setInput(blob);

   // time tick before forwarding
   double t = cv::getTickCount();

   // forward
   cv::Mat probs = net.forward();

   // calculate time cost
   t = (cv::getTickCount() - t) / cv::getTickFrequency();

   // show forward time cost
   std::cout << "[INFO] classification took " << (int)t << " seconds" << std::endl;

   // debug
   //std::cout << "[INFO] probs.rows: " << probs.rows << ", probs.cols: " << probs.cols << std::endl;

   // sort result
   cv::Mat idxs;
   cv::sortIdx(probs, idxs, cv::SORT_EVERY_ROW + cv::SORT_DESCENDING);

   // debug
   //std::cout << "[INFO] idxs.rows: " << idxs.rows << ", idxs.cols: " << idxs.cols << std::endl;

   // show the top 3 scored classes
   for (int i = 0; i < 3; i++)
   {
      int idx = idxs.at(0, i);
      std::string label = labels[idx];
      float prob = probs.at(0, idx);

      std::stringstream ss;
      ss << label << ": " << prob;
      std::string text = ss.str();

      std::cout << "[INFO] " << (i + 1) << ". " << text << std::endl;

      if (i == 0)
         cv::putText(image, text, cv::Size(5, 25), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 0, 255), 2);
   }

   cv::imshow("cvdnn_cpp", image);
   cv::waitKey(0);

   cv::destroyAllWindows();

   return 0;
}

// end of file

 

Command line:

cvdnn cpp cmd

Result:

cvdnn cpp result

COMPLEMENT

The example code and dataset above were mostly borrowed from Deep Learning with OpenCV Tutorials By Adrian Rosebrock and restored in github.

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