Intro to OpenCV

(Open Source Computer Vision)

Outline Overview and practical issues.

A selection of OpenCV functionality: Image enhancement Object classification and tracking Face detection and recognition

Conclusion and further resources.

Overview: Capabilities

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

Overview: Environment Primary APIis C++

LeveragesARM 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:\Python27\Lib\site-packages

Overview: Hello WorldMakefileCC=g++CFLAGS+=-std=c++0x `pkg-config opencv --cflags`LDFLAGS+=`pkg-config opencv --libs`

PROG=helloOBJS=$(PROG).o

.PHONY: all clean$(PROG): $(OBJS)

$(CC) -o $(PROG).out $(OBJS) $(LDFLAGS)

%.o: %.cpp$(CC) -c $(CFLAGS) $= 0)break;

}return 0;

}

Network comm.,RTSP protocol, etc.is all handled for youso all you have to do

is process eachframe 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

Face detection and recognition 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 under-exposure.

Image Enhancement: Demo! Deconvolution Reducing motion blur below

where the motion is known.

Image Enhancement: Demo! Deconvolution Can also be used for poor

camera focus, but the parameters of the blur must be estimated in advance.

Image Enhancement: Demo! 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, out)

Increases therange of intensities

in an image, therebyincreasing contrast.

Object detection and tracking Foreground/background segmentation

identify objects moving in a scene. cv::BackgroundSubtractorMOG2

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

Camshift tracking track objects by their colour. cv::CamShift

Face Detection and Recognition

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 faceRects;profileFaceCascade.detectMultiScale(image, faceRects);

cv::Mat foundFacesImage = image.clone();for (std::vector::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 faceRects;profileFaceCascade.detectMultiScale(image, faceRects);

cv::Mat foundFacesImage = image.clone();for (std::vector::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();

OpenCV comes withother classifier XML

files for detecting otherthings (e.g eyes,

glasses, profile faces)

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:

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Intro to OpenCV