OpenCV Introduction

Outline
●

Overview and practical issues.

●

A selection of OpenCV functionality:
–
–

Object classification and tracking

–

●

Image enhancement
Face detection and recognition

Conclusion and further resources.

Overview: License
●

BSD Licensed (free and open source)

●

May be used in commercial software.

●

No requirement to publish the source!

●

Must acknowledge OpenCV was used in the
documentation by including its copyright notice.
Note: There is a C#/.NET wrapper for OpenCV
called “Emgu CV” that may be commercially
licensed.

Overview: Patents

●

Note: A couple of algorithms (SIFT and SURF)
that are implemented are patented.
–

You can't accidentally use them because they are in
a separate module called “nonfree”.

Overview: Users

●

Stitching street-view images together,

●

Detecting intrusions in surveillance video in Israel

●

Detection of swimming pool drowning accidents in
Europe

Overview: Environment

Primary API
is C++

Leverages
ARM NEON

Overview: Installation
●

Ubuntu VM:
–

●

sudo apt-get install libopencv-dev

Windows:
–

Download latest version from http://opencv.org/
For Python:
●
●
●

Also install Python from http://www.python.org/
Install numpy module
Copy the “cv2” module from OpenCV to
C:Python27Libsite-packages

Overview: Hello World
Makefile
CC=g++
CFLAGS+=-std=c++0x `pkg-config
opencv --cflags`
LDFLAGS+=`pkg-config opencv
--libs`
PROG=hello
OBJS=$(PROG).o
.PHONY: all clean
$(PROG): $(OBJS)
$(CC) -o $(PROG).out $
(OBJS) $(LDFLAGS)

hello.cpp
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
int main()
{
cv::Mat image = cv::imread("lena.bmp");
if (image.empty())
{
std::cerr << "Could not load image";
return 1;
}

%.o: %.cpp
$(CC) -c $(CFLAGS) $<
all: $(PROG)
clean:
rm -f $(OBJS) $(PROG)

}

cv::namedWindow("Image");
cv::imshow("Image", image);
cv::waitKey();
return 0;

hello.cpp

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
int main()
{
if (image.empty())
{
return 1;
}

}

cv::waitKey();
return 0;

hello.cpp

#include
#include
#include
#include

<opencv2/core/core.hpp>
<opencv2/imgproc/imgproc.hpp>
<opencv2/highgui/highgui.hpp>
<iostream>

int main()
{
if (image.empty())
{
return 1;
}
cv::blur(image, image, cv::Size(10, 10));

}

cv::waitKey();
return 0;

Add a filter to blur
the image before
displaying it.

hello.cpp

#include
#include
#include
#include

<opencv2/core/core.hpp>
<opencv2/imgproc/imgproc.hpp>
<opencv2/highgui/highgui.hpp>
<iostream>

int main()
{
if (image.empty())
{
return 1;
}
cv::blur(image, image, cv::Size(10, 10));

}

cv::waitKey();
return 0;

Python: Display an image file
import cv2
image = cv2.imread("lena.bmp");
if image.empty():
print "Could not load image"
exit(1)
cv2.namedWindow("Image")
cv2.imshow("Image", image)
cv2.waitKey()

Similar structure
and naming as C++
version means
Python is good for
prototyping.

Video from IP camera w/ RTSP!
#include <opencv/cxcore.h>
#include <opencv/highgui.h>
int main(int argc, char* argv[])
{
cv::Ptr<CvCapture> capture = cvCaptureFromFile(
"rtsp://admin:admin@10.10.32.33/video");
cv::namedWindow("Frame");
for (;;)
{
cv::Mat frame = cvQueryFrame(capture);
cv::imshow("Frame", frame);
if (cv::waitKey(1) >= 0)
break;
}
return 0;
}

Network comm.,
RTSP protocol, etc.
is all handled for you
so all you have to do
is process each
frame as an image
(a cv::Mat object).

A Selection of Functionality
●

Image enhancement
–

●

Noise reduction, local contrast enhancement

Object classification and tracking
–
–

●

Track the paths that objects take in a scene
Differentiating between cars and trucks

–

Identify faces seen in images or video.

Image Enhancement
Many many algorithms. Here are a few:
●

●

●

Deconvolution – used to reduce focus blur or
motion blur where the motion is known.
Unsharp masking – increases sharpness and
local contrast (like WDR)
Histogram equalization – stretches contrast
and somewhat corrects for over- or underexposure.

Image Enhancement: Demo!
●

Deconvolution – Reducing motion blur below
where the motion is known.

●

Deconvolution – Can also be used for poor
camera focus, but the parameters of the blur
must be estimated in advance.

●

Deconvolution – Can also be used for poor
camera focus, but the parameters of the blur
must be estimated in advance.

Generated using OpenCV example:

/opencv/samples/python2/deconvolution.py

Image Enhancement
●

Histogram equalization: equalizeHist(img,

out)

Image Enhancement
●

Histogram equalization: equalizeHist(img,

Increases the
range of intensities
in an image, thereby
increasing contrast.

out)

Object detection and tracking
●

Foreground/background segmentation –
identify objects moving in a scene.
–

●

Histogram backprojection – identify objects by
their colour (even if they're not moving).
–

●

cv::BackgroundSubtractorMOG2

cv::calcBackProject()

Camshift tracking – track objects by their colour.
–

cv::CamShift

Face Detection and Recognition

●

Detection:
–
–

●

Haar cascade – detect faces by identifying
adjacent light and dark regions.
cv::CascadeClassifier

Recognition:
–

Eigenfaces classifier – for facial recognition

–

cv::FaceRecognizer

Face detection: C++
cv::CascadeClassifier profileFaceCascade;
profileFaceCascade.load("haarcascade_profileface.xml");
std::vector<cv::Rect> faceRects;
profileFaceCascade.detectMultiScale(image, faceRects);
cv::Mat foundFacesImage = image.clone();
for (std::vector<cv::Rect>::const_iterator rect =
faceRects.begin(); rect != faceRects.end(); ++ rect)
{
cv::rectangle(foundFacesImage, *rect,
cv::Scalar(0, 0, 255), 3);
}
cv::namedWindow("Faces");
cv::imshow("Faces", foundFacesImage);
cv::waitKey();

Face detection: C++
cv::CascadeClassifier profileFaceCascade;
profileFaceCascade.load("haarcascade_profileface.xml");
std::vector<cv::Rect> faceRects;
profileFaceCascade.detectMultiScale(image, faceRects); with
OpenCV comes

other classifier XML
cv::Mat foundFacesImage = image.clone();
files for detecting other
for (std::vector<cv::Rect>::const_iterator rect (e.g eyes,
things =
faceRects.begin(); rect != faceRects.end(); ++ rect)
glasses, profile faces)
{
}

cv::rectangle(foundFacesImage, *rect,
cv::Scalar(0, 0, 255), 3);

cv::namedWindow("Faces");
cv::imshow("Faces", foundFacesImage);
cv::waitKey();

Face detection
●

Can be defeated with makeup...

Face detection
●

... or with special glasses containing IR LEDs.

Conclusion
●

●
●

●

OpenCV is for image/video processing and
computer vision.
Free and open source (BSD licensed)
Cross-platform and actively developed (also
downloaded over 3 million times)!
This presentation covered just a few of the over
2,000 algorithms available in OpenCV.

More Information
●

Official Page: http://opencv.org

●

Tutorials: http://docs.opencv.org/doc/tutorials/tutorials.html

●

Books:

OpenCV Introduction

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