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![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).](https://image.slidesharecdn.com/opencvintro-131206011641-phpapp02/85/OpenCV-Introduction-16-320.jpg)
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OpenCV Introduction
- 1. (Open Source ComputerVision)
- 2. Outline ● Overview and practicalissues. ● A selection of OpenCV functionality: – – Object classification and tracking – ● Image enhancement Face detection and recognition Conclusion and further resources.
- 3.
- 4. 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.
- 5. Overview: Patents ● Note: Acouple 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”.
- 6. Overview: Users ● Stitching street-viewimages together, ● Detecting intrusions in surveillance video in Israel ● Detection of swimming pool drowning accidents in Europe
- 7.
- 8. Overview: Environment Primary API isC++ Leverages ARM NEON
- 9. Overview: Installation ● Ubuntu VM: – ● sudoapt-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
- 10. 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) $(PROG) } cv::namedWindow("Image"); cv::imshow("Image", image); cv::waitKey(); return 0;
- 11. Overview: Hello World Makefile CC=g++ CFLAGS+=-std=c++0x`pkg-config opencv --cflags` LDFLAGS+=`pkg-config opencv --libs` PROG=hello OBJS=$(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;
- 12. Overview: Hello World 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; } } cv::namedWindow("Image"); cv::imshow("Image", image); cv::waitKey(); return 0;
- 13. Overview: Hello World hello.cpp #include #include #include #include <opencv2/core/core.hpp> <opencv2/imgproc/imgproc.hpp> <opencv2/highgui/highgui.hpp> <iostream> intmain() { cv::Mat image = cv::imread("lena.bmp"); if (image.empty()) { std::cerr << "Could not load image"; return 1; } cv::blur(image, image, cv::Size(10, 10)); } cv::namedWindow("Image"); cv::imshow("Image", image); cv::waitKey(); return 0; Add a filter to blur the image before displaying it.
- 14. Overview: Hello World hello.cpp #include #include #include #include <opencv2/core/core.hpp> <opencv2/imgproc/imgproc.hpp> <opencv2/highgui/highgui.hpp> <iostream> intmain() { cv::Mat image = cv::imread("lena.bmp"); if (image.empty()) { std::cerr << "Could not load image"; return 1; } cv::blur(image, image, cv::Size(10, 10)); } cv::namedWindow("Image"); cv::imshow("Image", image); cv::waitKey(); return 0;
- 15. Python: Display animage 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.
- 16. Video from IPcamera 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).
- 17. A Selection ofFunctionality ● 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.
- 18. Image Enhancement Many manyalgorithms. 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.
- 19. Image Enhancement: Demo! ● Deconvolution– Reducing motion blur below where the motion is known.
- 20. Image Enhancement: Demo! ● Deconvolution– Can also be used for poor camera focus, but the parameters of the blur must be estimated in advance.
- 21. 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
- 22. Image Enhancement ● Histogram equalization:equalizeHist(img, out)
- 23. Image Enhancement ● Histogram equalization:equalizeHist(img, Increases the range of intensities in an image, thereby increasing contrast. out)
- 24. Object detection andtracking ● 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
- 25. Face Detection andRecognition
- 26. Face detection andrecognition ● Detection: – – ● Haar cascade – detect faces by identifying adjacent light and dark regions. cv::CascadeClassifier Recognition: – Eigenfaces classifier – for facial recognition – cv::FaceRecognizer
- 27. Face detection: C++ cv::CascadeClassifierprofileFaceCascade; 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();
- 28. Face detection: C++ cv::CascadeClassifierprofileFaceCascade; 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();
- 29. Face detection ● Can bedefeated with makeup...
- 30. Face detection ● ... orwith special glasses containing IR LEDs.
- 31. Conclusion ● ● ● ● OpenCV is forimage/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.
- 32. More Information ● Official Page:http://opencv.org ● Tutorials: http://docs.opencv.org/doc/tutorials/tutorials.html ● Books: