Difference between revisions of "CUVI by Example"
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==Motion Detection== | |||
<p>CUVI library comes with all the image processing essentials that can be used to build countless applications. For example the '''Computer Vision''' module of CUVI can be used for motion and intrusion detection in a live video stream and tracking an object of interest throughout series of cameras installed in a premises. The processing pipeline for motion detection goes as follows:</p> | |||
*Read a frame from the camera stream | |||
*Select Strong Features in that Frame using CUVI | |||
*Read next frame | |||
*Track features of first frame in the second frame using CUVI | |||
*Set alarm if motion is detected | |||
<p>The CUVI functions used in this example are '''goodFeaturesToTrack()''' and '''trackFeatures()'''. For simplicity we have removed the I/O part on host side from the code</p> | |||
{| | {| | ||
|style="font-size:130%;"| | |style="font-size:130%;"| | ||
<syntaxhighlight lang=" | <syntaxhighlight lang="cpp"> | ||
#include <cuvi.h> | #include <cuvi.h> | ||
static const int width = 640; //Width of video frame | static const int width = 640; //Width of video frame | ||
static const int height = 480; //Height of video frame | static const int height = 480; //Height of video frame | ||
CuviFilter* f; // Declaring CUVI Filter | |||
//Parameters for feature selection | |||
static const int requestedFeatures = 150; //Number of features to look for | static const int requestedFeatures = 150; //Number of features to look for | ||
static const float featureQuality = 0.006f; //Quality of a feature | static const float featureQuality = 0.006f; //Quality of a feature | ||
static const int featureMinDistance = | static const int featureMinDistance = 15; //Minimum distance between 2 features | ||
static const int blockSize = 3; //block size for computing Eigen Matrix | |||
static const float k = -2.0f; //k for Harris Corner detector | static const float k = -2.0f; //k for Harris Corner detector | ||
static const int | |||
//Parameters for feature tracking | |||
static const int pyramidLevels = 3; //Level Of Scaling | |||
static const CuviSize trackingWindow = cuviSize(30,30); //Size of tracking window | static const CuviSize trackingWindow = cuviSize(30,30); //Size of tracking window | ||
static const float residue = 20.0f; //Absolute Difference Between Original Location Window & Tracked Location Window | static const float residue = 20.0f; //Absolute Difference Between Original Location Window & Tracked Location Window | ||
static const int iterations = 10; //Maximum number of iterations before a feature is found | static const int iterations = 10; //Maximum number of iterations before a feature is found | ||
//Pre-processing parameters | |||
static const bool smoothBeforeSelecting = false; //Smooth Image Before Feature Selection & Tracking | static const bool smoothBeforeSelecting = false; //Smooth Image Before Feature Selection & Tracking | ||
static const bool adjustImage = false; //Adjust Image Light Before Feature Selection | static const bool adjustImage = false; //Adjust Image Light Before Feature Selection | ||
//Post-processing parameters | |||
static const float movementThreshold = 0.33f; //Mark as motion if a feature moves 0.33 Pixels | |||
//Checks if the feature has moved from is original location. | |||
//It can be used in intrusion detection and the sensitivity can be set using 'threshold' parameter | |||
bool featureHasMoved(CuviPointValue2D point1, CuviPointValue2D point2, float threshold){ | |||
if(point2.val != 0.0f) return false; | |||
return ((fabsf(point1.x - point2.x)>threshold) || (fabsf(point1.y - point2.y)>threshold)); | |||
} | |||
void main() | void main() | ||
{ | { | ||
//Creating a smoothing 3x3 Gaussian Filter with Standard Deviation 0.7 | |||
cuviCreateFilter(&f,3,3); | |||
f->sigma = 0.7f; | |||
cuviCreateFilterSpecial(f,CUVI_FILTER_GAUSSIAN); | |||
//Image size | |||
CuviSize size = cuviSize(width,height); | |||
CuviImage* | //Buffer Images on GPU | ||
CuviImage* gimg2 = new CuviImage( | CuviImage* gFrame = new CuviImage(size,8,3); | ||
CuviImage* gimg1 = new CuviImage(size,8,1); | |||
CuviImage* gimg2 = new CuviImage(size,8,1); | |||
CuviROI roi = cuviROI(0,0, | //Region of Interest in the video frame | ||
CuviROI roi = cuviROI(0,0,width,height); | |||
CuviPointValue2D *features1, *features2; | CuviPointValue2D *features1, *features2; | ||
Line 53: | Line 75: | ||
do | do | ||
{ | { | ||
//Read Frame | //Read a Video Frame on host 'frame' and populate GPU image with it | ||
gFrame->upload(frame->imageData); | gFrame->upload(frame->imageData,frame->widthStep); | ||
cuvi:: | |||
//Converting to Gray Image for computations | |||
cuvi::colorOperations::RGB2Gray(gFrame,gimg1); | |||
// | //Do the same with next, adjacent frame | ||
gFrame->upload(frame->imageData); | gFrame->upload(frame->imageData); | ||
cuvi::colorConvert(gFrame,gimg2); | cuvi::colorConvert(gFrame,gimg2); | ||
Line 63: | Line 87: | ||
feature_count = RequestedFeatures; //Reset feature count to original | feature_count = RequestedFeatures; //Reset feature count to original | ||
if(AdjustImage) | //Use this option if the adjacent frames are lightening sensitive | ||
if(AdjustImage){ | |||
cuvi::adjust(gimg1); | cuvi::colorOperations::adjust(gimg1); | ||
cuvi::adjust(gimg2); | cuvi::colorOperations::adjust(gimg2); | ||
} | } | ||
if(SmoothBeforeSelecting) | //Use this option if the images contain fair amount of noise | ||
if(SmoothBeforeSelecting){ | |||
//Apply Gaussian Smoothing Filter On Both The Images | //Apply Gaussian Smoothing Filter On Both The Images | ||
cuvi:: | cuvi::imageFiltering::imageFilter(gimg1,roi,f); | ||
cuvi:: | cuvi::imageFiltering::imageFilter(gimg2,roi,f); | ||
} | } | ||
//Call | |||
cuvi::goodFeaturesToTrack(gimg1,roi,features1,&feature_count, | |||
//Defining feature selection criteria from parameters | |||
CuviFeaturesCriteria feature_criteria = cuviFeaturesCriteria(CUVI_FEATURES_HARRIS, featureQuality, featureMinDistance, blockSize, k); | |||
//Call any Feature Detector on first Frame( KLT | HARRIS | PETER ) | |||
cuvi::computerVision::goodFeaturesToTrack(gimg1,roi,features1,&feature_count,feature_criteria); | |||
//Defining tracking criteria from tracking parameters | |||
CuviTrackingCriteria tracking_criteria = cuviTrackingCriteria(pyramidLevels, trackingWindow, iterations, residue); | |||
//Track Features Using of Frame#1 onto Frame#2 using KLT Tracker | |||
cuvi::trackFeatures(gimg1,gimg2,features1,features2,feature_count,tracking_criteria ); | |||
//At this point you can indetify whether the selected features in frame one moved in frame two or not | |||
for(int i=0; i<feature_count; i++){ | |||
//True only if the feature has moved from its location | |||
if(FeatureHasMoved(features1[i],features2[i],MovementThreshold)) | |||
//You can also plot the tracked features on the screen | |||
} | |||
}while(video_Frames) | |||
//Freeing GPU Memory | |||
delete gFrame; | |||
delete gimg1; | |||
delete gimg2; | |||
} | } | ||
</syntaxhighlight> | |||
|} | |||
{{#ev:vimeo|38484537|500}} | |||
<p>Here's an exact same example applied on a video feed of a webcam</p> | |||
==Demosaic Example== | |||
CUVI demosaic, especially DFPD version is one of the most used and sought after feature of library. The sheer speed of debayering with CUVI linear debayer approach and the perfection in the resultant image in DFPD approach makes it the most demanded function of the library by camera manufacturers and video houses alike. In this example, we'll demonstrate how easy to use CUVI's own demosacing with just few lines of code. | |||
{| | |||
|style="font-size:130%;"| | |||
<syntaxhighlight lang="cpp"> | |||
#include <cuvi.h> | |||
CuviBayerSeq sensorAlignment = CuviBayerSeq::CUVI_BAYER_RGGB; | |||
// 8 bits data in an 8 bit container. Setting this is very important | |||
Cuvi32s containerBits = 8; | |||
Cuvi32s dataBits = 8; | |||
//Load and Upload image to GPU | |||
CuviImage input("D:/lighthouse_8bit_RGGB.tif", CUVI_LOAD_IMAGE_GRAYSCALE_KEEP_DEPTH); | |||
input.setDataBits(dataBits); | |||
//Create container for 3-channel output image | |||
CuviImage output(input.size(), containerBits, 3); | |||
//Perform Demosaic DFPD | |||
cuvi::colorOperations::DFPD(input, output, sensorAlignment); | |||
//Save resultant image to file | |||
cuvi::io::saveImage(output, "D:/lighthouse.tif"); | |||
</syntaxhighlight> | |||
|} | |} | ||
[[File:Lighthouse.jpg|700px]] |
Revision as of 16:44, 27 March 2018
Motion Detection
CUVI library comes with all the image processing essentials that can be used to build countless applications. For example the Computer Vision module of CUVI can be used for motion and intrusion detection in a live video stream and tracking an object of interest throughout series of cameras installed in a premises. The processing pipeline for motion detection goes as follows:
- Read a frame from the camera stream
- Select Strong Features in that Frame using CUVI
- Read next frame
- Track features of first frame in the second frame using CUVI
- Set alarm if motion is detected
The CUVI functions used in this example are goodFeaturesToTrack() and trackFeatures(). For simplicity we have removed the I/O part on host side from the code
|
Here's an exact same example applied on a video feed of a webcam
Demosaic Example
CUVI demosaic, especially DFPD version is one of the most used and sought after feature of library. The sheer speed of debayering with CUVI linear debayer approach and the perfection in the resultant image in DFPD approach makes it the most demanded function of the library by camera manufacturers and video houses alike. In this example, we'll demonstrate how easy to use CUVI's own demosacing with just few lines of code.
|