void ImagesDemos( Mat& image1, Mat& image2, Mat& logo_image, Mat& people_image )
{
Timestamper* timer = new Timestamper();
// Basic colour image access (demonstration using invert)
Mat output_image;
InvertColour( image1, output_image );
Mat output1 = JoinImagesHorizontally(image1,"Original Image",output_image,"Inverted Image",4);
imshow("Basic Image Processing", output1);
char c = cvWaitKey();
cvDestroyAllWindows();
// Sampling & Quantisation (Grey scale)
Mat image1_gray, smaller_image, resized_image, two_bit_image;
cvtColor(image1, image1_gray, CV_BGR2GRAY);
resize(image1_gray, smaller_image, Size( image1.cols/2, image1.rows/2 ));
resize(smaller_image, resized_image, image1.size() );
two_bit_image = image1_gray.clone();
ChangeQuantisationGrey( two_bit_image, 2 );
Mat image1_gray_display, smaller_image_display, resized_image_display, two_bit_image_display;
cvtColor(image1_gray, image1_gray_display, CV_GRAY2BGR);
cvtColor(smaller_image, smaller_image_display, CV_GRAY2BGR);
cvtColor(resized_image, resized_image_display, CV_GRAY2BGR);
cvtColor(two_bit_image, two_bit_image_display, CV_GRAY2BGR);
output1 = JoinImagesHorizontally(two_bit_image_display,"Quantisation 8->2 bits",image1_gray_display,"Original Greyscale Image",4);
Mat output2 = JoinImagesHorizontally(output1,"",smaller_image_display,"Half sized image",4);
Mat output3 = JoinImagesHorizontally(output2,"",resized_image_display,"Resized image",4);
// Sampling & Quantisation
Mat quantised_frame;
quantised_frame = image1.clone();
resize(image1, smaller_image, Size( image1.cols/2, image1.rows/2 ));
resize(smaller_image, resized_image, image1.size(), 0.0, 0.0, INTER_NEAREST );
changeQuantisation(quantised_frame, 2);
output1 = JoinImagesHorizontally(quantised_frame,"Quantisation 8->2 bits",image1,"Original Colour Image",4);
output2 = JoinImagesHorizontally(output1,"",smaller_image,"Half sized image",4);
Mat output4 = JoinImagesHorizontally(output2,"",resized_image,"Resized image",4);
Mat output5 = JoinImagesVertically(output3,"",output4,"",4);
imshow("Sampling & Quantisation", output5);
c = cvWaitKey();
cvDestroyAllWindows();
// Colour channels.
resize(image2, smaller_image, Size( image2.cols/2, image2.rows/2 ));
vector<Mat> input_planes(3);
split(smaller_image,input_planes);
Mat channel1_display, channel2_display, channel3_display;
cvtColor(input_planes[2], channel1_display, CV_GRAY2BGR);
cvtColor(input_planes[1], channel2_display, CV_GRAY2BGR);
cvtColor(input_planes[0], channel3_display, CV_GRAY2BGR);
output1 = JoinImagesHorizontally(channel1_display,"Red",channel2_display,"Green",4);
output2 = JoinImagesHorizontally(output1,"",channel3_display,"Blue",4);
Mat yuv_image;
cvtColor(smaller_image, yuv_image, CV_BGR2YUV);
split(yuv_image,input_planes);
cvtColor(input_planes[0], channel1_display, CV_GRAY2BGR);
cvtColor(input_planes[1], channel2_display, CV_GRAY2BGR);
cvtColor(input_planes[2], channel3_display, CV_GRAY2BGR);
output1 = JoinImagesHorizontally(channel1_display,"Y",channel2_display,"U",4);
output3 = JoinImagesHorizontally(output1,"",channel3_display,"V",4);
output4 = JoinImagesVertically(output2,"",output3,"",4);
Mat hls_image;
cvtColor(smaller_image, hls_image, CV_BGR2HLS);
vector<Mat> hls_planes(3);
split(hls_image,hls_planes);
Mat& hue_image = hls_planes[0];
cvtColor(hls_planes[0], channel1_display, CV_GRAY2BGR);
cvtColor(hls_planes[1], channel2_display, CV_GRAY2BGR);
cvtColor(hls_planes[2], channel3_display, CV_GRAY2BGR);
output1 = JoinImagesHorizontally(channel1_display,"Hue",channel2_display,"Luminance",4);
output2 = JoinImagesHorizontally(output1,"",channel3_display,"Saturation",4);
output3 = JoinImagesVertically(output4,"",output2,"",4);
Mat lab_image;
cvtColor(smaller_image, lab_image, CV_BGR2Lab);
vector<Mat> lab_planes(3);
split(lab_image,lab_planes);
cvtColor(lab_planes[0], channel1_display, CV_GRAY2BGR);
cvtColor(lab_planes[1], channel2_display, CV_GRAY2BGR);
cvtColor(lab_planes[2], channel3_display, CV_GRAY2BGR);
output1 = JoinImagesHorizontally(channel1_display,"Luminance",channel2_display,"A",4);
output2 = JoinImagesHorizontally(output1,"",channel3_display,"B",4);
output4 = JoinImagesVertically(output3,"",output2,"",4);
output3 = JoinImagesHorizontally(smaller_image,"",output4,"",4);
imshow("Colour Models - RGB, YUV, HLS, Lab", output3);
c = cvWaitKey();
cvDestroyAllWindows();
Mat hls_people_image, hls_skin_image, skin_image, redeye_image;
cvtColor(people_image, hls_people_image, CV_BGR2HLS);
SelectSkin( hls_people_image, hls_skin_image );
SelectRedEyePixels( people_image, redeye_image );
cvtColor(hls_skin_image, skin_image, CV_HLS2BGR);
output1 = JoinImagesHorizontally(people_image,"Original Image",skin_image,"Possible skin pixels",4);
output2 = JoinImagesHorizontally(output1,"",redeye_image,"Possible Red-Eye pixels",4);
imshow("Skin & Redeye detection", output2);
c = cvWaitKey();
cvDestroyAllWindows();
// Noise & Smoothing
resize(image1, smaller_image, Size( image1.cols*3/4, image1.rows*3/4 ));
Mat noise_test = smaller_image.clone();
addGaussianNoise(noise_test, 0.0, 20.0);
Mat noise_test1 = noise_test.clone();
Mat noise_test2 = noise_test.clone();
Mat noise_test3 = noise_test.clone();
blur(noise_test1,noise_test1,Size(5,5));
GaussianBlur(noise_test2,noise_test2,Size(5,5),1.5);
medianBlur(noise_test3,noise_test3,5);
output1 = JoinImagesHorizontally(noise_test,"Gaussian Noise (0, 20)",noise_test1,"Local Average",4);
output2 = JoinImagesHorizontally(output1,"",noise_test2,"Gaussian filtered",4);
output3 = JoinImagesHorizontally(output2,"",noise_test3,"Median filtered",4);
noise_test = smaller_image.clone();
addSaltAndPepperNoise(noise_test, 5.0);
noise_test1 = noise_test.clone();
noise_test2 = noise_test.clone();
noise_test3 = noise_test.clone();
blur(noise_test1,noise_test1,Size(5,5));
GaussianBlur(noise_test2,noise_test2,Size(5,5),1.5);
medianBlur(noise_test3,noise_test3,5);
output1 = JoinImagesHorizontally(noise_test,"Salt and Pepper Noise (5%)",noise_test1,"Local Average",4);
output2 = JoinImagesHorizontally(output1,"",noise_test2,"Gaussian filtered",4);
output4 = JoinImagesHorizontally(output2,"",noise_test3,"Median filtered",4);
output5 = JoinImagesVertically(output3,"",output4,"",4);
output1 = JoinImagesHorizontally(smaller_image,"Original Image",output5,"",4);
imshow("Noise and Smoothing", output1);
c = cvWaitKey();
cvDestroyAllWindows();
// Regions of Interest and weighted image addition.
Mat watermarked_image = image1.clone();
double scale = (((double)logo_image.cols)/((double)image1.cols)) > (((double)logo_image.rows)/((double)image1.rows)) ?
0.5/(((double)logo_image.cols)/((double)image1.cols)) : 0.5/(((double)logo_image.rows)/((double)image1.rows));
int new_logo_size = image1.cols < image1.rows ? image1.cols/8 : image1.rows/8;
resize(logo_image,logo_image,Size(((int) (((double) logo_image.cols)*scale)),((int) (((double) logo_image.rows)*scale))));
Mat imageROI;
imageROI = watermarked_image(cv::Rect((image1.cols-logo_image.cols)/2,(image1.rows-logo_image.rows)/2,logo_image.cols,logo_image.rows));
addWeighted(imageROI,1.0,logo_image,0.1,0.0,imageROI);
output1 = JoinImagesHorizontally(image1,"Original Image",logo_image,"Watermark",4);
output2 = JoinImagesHorizontally(output1,"",watermarked_image,"Watermarked Image",4);
imshow("Watermarking (Demo of Image ROIs & weighted addition)", output2);
c = cvWaitKey();
cvDestroyAllWindows();
}
void RecognitionDemos( Mat& full_image, Mat& template1, Mat& template2, Mat& template1locations, Mat& template2locations, VideoCapture& bicycle_video, Mat& bicycle_background, Mat& bicycle_model, VideoCapture& people_video, CascadeClassifier& cascade, Mat& numbers, Mat& good_orings, Mat& bad_orings, Mat& unknown_orings )
{
Timestamper* timer = new Timestamper();
// Principal Components Analysis
PCASimpleExample();
char ch = cvWaitKey();
cvDestroyAllWindows();
PCAFaceRecognition();
ch = cvWaitKey();
cvDestroyAllWindows();
// Statistical Pattern Recognition
Mat gray_numbers,binary_numbers;
cvtColor(numbers, gray_numbers, CV_BGR2GRAY);
threshold(gray_numbers,binary_numbers,128,255,THRESH_BINARY_INV);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(binary_numbers,contours,hierarchy,CV_RETR_TREE,CV_CHAIN_APPROX_NONE);
Mat contours_image = Mat::zeros(binary_numbers.size(), CV_8UC3);
contours_image = Scalar(255,255,255);
// Do some processing on all contours (objects and holes!)
vector<RotatedRect> min_bounding_rectangle(contours.size());
vector<vector<Point>> hulls(contours.size());
vector<vector<int>> hull_indices(contours.size());
vector<vector<Vec4i>> convexity_defects(contours.size());
vector<Moments> contour_moments(contours.size());
for (int contour_number=0; (contour_number<(int)contours.size()); contour_number++)
{
if (contours[contour_number].size() > 10)
{
min_bounding_rectangle[contour_number] = minAreaRect(contours[contour_number]);
convexHull(contours[contour_number], hulls[contour_number]);
convexHull(contours[contour_number], hull_indices[contour_number]);
convexityDefects( contours[contour_number], hull_indices[contour_number], convexity_defects[contour_number]);
contour_moments[contour_number] = moments( contours[contour_number] );
}
}
for (int contour_number=0; (contour_number>=0); contour_number=hierarchy[contour_number][0])
{
if (contours[contour_number].size() > 10)
{
Scalar colour( rand()&0x7F, rand()&0x7F, rand()&0x7F );
drawContours( contours_image, contours, contour_number, colour, CV_FILLED, 8, hierarchy );
char output[500];
double area = contourArea(contours[contour_number])+contours[contour_number].size()/2+1;
// Process any holes (removing the area from the are of the enclosing contour)
for (int hole_number=hierarchy[contour_number][2]; (hole_number>=0); hole_number=hierarchy[hole_number][0])
{
area -= (contourArea(contours[hole_number])-contours[hole_number].size()/2+1);
Scalar colour( rand()&0x7F, rand()&0x7F, rand()&0x7F );
drawContours( contours_image, contours, hole_number, colour, CV_FILLED, 8, hierarchy );
sprintf(output,"Area=%.0f", contourArea(contours[hole_number])-contours[hole_number].size()/2+1);
Point location( contours[hole_number][0].x +20, contours[hole_number][0].y +5 );
putText( contours_image, output, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
}
// Draw the minimum bounding rectangle
Point2f bounding_rect_points[4];
min_bounding_rectangle[contour_number].points(bounding_rect_points);
line( contours_image, bounding_rect_points[0], bounding_rect_points[1], Scalar(0, 0, 127));
line( contours_image, bounding_rect_points[1], bounding_rect_points[2], Scalar(0, 0, 127));
line( contours_image, bounding_rect_points[2], bounding_rect_points[3], Scalar(0, 0, 127));
line( contours_image, bounding_rect_points[3], bounding_rect_points[0], Scalar(0, 0, 127));
float bounding_rectangle_area = min_bounding_rectangle[contour_number].size.area();
// Draw the convex hull
drawContours( contours_image, hulls, contour_number, Scalar(127,0,127) );
// Highlight any convexities
int largest_convexity_depth=0;
for (int convexity_index=0; convexity_index < (int)convexity_defects[contour_number].size(); convexity_index++)
{
if (convexity_defects[contour_number][convexity_index][3] > largest_convexity_depth)
largest_convexity_depth = convexity_defects[contour_number][convexity_index][3];
if (convexity_defects[contour_number][convexity_index][3] > 256*2)
{
line( contours_image, contours[contour_number][convexity_defects[contour_number][convexity_index][0]], contours[contour_number][convexity_defects[contour_number][convexity_index][2]], Scalar(0,0, 255));
line( contours_image, contours[contour_number][convexity_defects[contour_number][convexity_index][1]], contours[contour_number][convexity_defects[contour_number][convexity_index][2]], Scalar(0,0, 255));
}
}
double hu_moments[7];
HuMoments( contour_moments[contour_number], hu_moments );
sprintf(output,"Perimeter=%d, Area=%.0f, BArea=%.0f, CArea=%.0f", contours[contour_number].size(),area,min_bounding_rectangle[contour_number].size.area(),contourArea(hulls[contour_number]));
Point location( contours[contour_number][0].x, contours[contour_number][0].y-3 );
putText( contours_image, output, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf(output,"HuMoments = %.2f, %.2f, %.2f", hu_moments[0],hu_moments[1],hu_moments[2]);
Point location2( contours[contour_number][0].x+100, contours[contour_number][0].y-3+15 );
putText( contours_image, output, location2, FONT_HERSHEY_SIMPLEX, 0.4, colour );
}
}
imshow("Shape Statistics", contours_image );
char c = cvWaitKey();
cvDestroyAllWindows();
// Support Vector Machine
imshow("Good - original",good_orings);
imshow("Defective - original",bad_orings);
imshow("Unknown - original",unknown_orings);
SupportVectorMachineDemo(good_orings,"Good",bad_orings,"Defective",unknown_orings);
c = cvWaitKey();
cvDestroyAllWindows();
// Template Matching
Mat display_image, correlation_image;
full_image.copyTo( display_image );
double min_correlation, max_correlation;
Mat matched_template_map;
int result_columns = full_image.cols - template1.cols + 1;
int result_rows = full_image.rows - template1.rows + 1;
correlation_image.create( result_columns, result_rows, CV_32FC1 );
timer->reset();
double before_tick_count = static_cast<double>(getTickCount());
matchTemplate( full_image, template1, correlation_image, CV_TM_CCORR_NORMED );
double after_tick_count = static_cast<double>(getTickCount());
double duration_in_ms = 1000.0*(after_tick_count-before_tick_count)/getTickFrequency();
minMaxLoc( correlation_image, &min_correlation, &max_correlation );
FindLocalMaxima( correlation_image, matched_template_map, max_correlation*0.99 );
timer->recordTime("Template Matching (1)");
Mat matched_template_display1;
cvtColor(matched_template_map, matched_template_display1, CV_GRAY2BGR);
Mat correlation_window1 = convert_32bit_image_for_display( correlation_image, 0.0 );
DrawMatchingTemplateRectangles( display_image, matched_template_map, template1, Scalar(0,0,255) );
double precision, recall, accuracy, specificity, f1;
Mat template1locations_gray;
cvtColor(template1locations, template1locations_gray, CV_BGR2GRAY);
CompareRecognitionResults( matched_template_map, template1locations_gray, precision, recall, accuracy, specificity, f1 );
char results[400];
Scalar colour( 255, 255, 255);
sprintf( results, "precision=%.2f", precision);
Point location( 7, 213 );
putText( display_image, "Results (1)", location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "recall=%.2f", recall);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "accuracy=%.2f", accuracy);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "specificity=%.2f", specificity);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "f1=%.2f", f1);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
result_columns = full_image.cols - template2.cols + 1;
result_rows = full_image.rows - template2.rows + 1;
correlation_image.create( result_columns, result_rows, CV_32FC1 );
timer->ignoreTimeSinceLastRecorded();
matchTemplate( full_image, template2, correlation_image, CV_TM_CCORR_NORMED );
minMaxLoc( correlation_image, &min_correlation, &max_correlation );
FindLocalMaxima( correlation_image, matched_template_map, max_correlation*0.99 );
timer->recordTime("Template Matching (2)");
Mat matched_template_display2;
cvtColor(matched_template_map, matched_template_display2, CV_GRAY2BGR);
Mat correlation_window2 = convert_32bit_image_for_display( correlation_image, 0.0 );
DrawMatchingTemplateRectangles( display_image, matched_template_map, template2, Scalar(0,0,255) );
timer->putTimes(display_image);
Mat template2locations_gray;
cvtColor(template2locations, template2locations_gray, CV_BGR2GRAY);
CompareRecognitionResults( matched_template_map, template2locations_gray, precision, recall, accuracy, specificity, f1 );
sprintf( results, "precision=%.2f", precision);
location.x = 123;
location.y = 213;
putText( display_image, "Results (2)", location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "recall=%.2f", recall);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "accuracy=%.2f", accuracy);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "specificity=%.2f", specificity);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
sprintf( results, "f1=%.2f", f1);
location.y += 13;
putText( display_image, results, location, FONT_HERSHEY_SIMPLEX, 0.4, colour );
Mat correlation_display1, correlation_display2;
cvtColor(correlation_window1, correlation_display1, CV_GRAY2BGR);
cvtColor(correlation_window2, correlation_display2, CV_GRAY2BGR);
Mat output1 = JoinImagesVertically(template1,"Template (1)",correlation_display1,"Correlation (1)",4);
Mat output2 = JoinImagesVertically(output1,"",matched_template_display1,"Local maxima (1)",4);
Mat output3 = JoinImagesVertically(template2,"Template (2)",correlation_display2,"Correlation (2)",4);
Mat output4 = JoinImagesVertically(output3,"",matched_template_display2,"Local maxima (2)",4);
Mat output5 = JoinImagesHorizontally( full_image, "Original Image", output2, "", 4 );
Mat output6 = JoinImagesHorizontally( output5, "", output4, "", 4 );
Mat output7 = JoinImagesHorizontally( output6, "", display_image, "", 4 );
imshow( "Template matching result", output7 );
c = cvWaitKey();
cvDestroyAllWindows();
// Chamfer Matching
Mat model_gray,model_edges,model_edges2;
cvtColor(bicycle_model, model_gray, CV_BGR2GRAY);
threshold(model_gray,model_edges,127,255,THRESH_BINARY);
Mat current_frame;
bicycle_video.set(CV_CAP_PROP_POS_FRAMES,400); // Just in case the video has already been used.
bicycle_video >> current_frame;
bicycle_background = current_frame.clone();
bicycle_video.set(CV_CAP_PROP_POS_FRAMES,500);
timer->reset();
int count = 0;
while (!current_frame.empty() && (count < 8))
{
Mat result_image = current_frame.clone();
count++;
Mat difference_frame, difference_gray, current_edges;
absdiff(current_frame,bicycle_background,difference_frame);
cvtColor(difference_frame, difference_gray, CV_BGR2GRAY);
Canny(difference_frame, current_edges, 100, 200, 3);
vector<vector<Point> > results;
vector<float> costs;
threshold(model_gray,model_edges,127,255,THRESH_BINARY);
Mat matching_image, chamfer_image, local_minima;
timer->ignoreTimeSinceLastRecorded();
threshold(current_edges,current_edges,127,255,THRESH_BINARY_INV);
distanceTransform( current_edges, chamfer_image, CV_DIST_L2 , 3);
timer->recordTime("Chamfer Image");
ChamferMatching( chamfer_image, model_edges, matching_image );
timer->recordTime("Matching");
FindLocalMinima( matching_image, local_minima, 500.0 );
timer->recordTime("Find Minima");
DrawMatchingTemplateRectangles( result_image, local_minima, model_edges, Scalar( 255, 0, 0 ) );
Mat chamfer_display_image = convert_32bit_image_for_display( chamfer_image );
Mat matching_display_image = convert_32bit_image_for_display( matching_image );
//timer->putTimes(result_image);
Mat current_edges_display, local_minima_display, model_edges_display, colour_matching_display_image, colour_chamfer_display_image;
cvtColor(current_edges, current_edges_display, CV_GRAY2BGR);
cvtColor(local_minima, local_minima_display, CV_GRAY2BGR);
cvtColor(model_edges, model_edges_display, CV_GRAY2BGR);
cvtColor(matching_display_image, colour_matching_display_image, CV_GRAY2BGR);
cvtColor(chamfer_display_image, colour_chamfer_display_image, CV_GRAY2BGR);
Mat output1 = JoinImagesVertically(current_frame,"Video Input",current_edges_display,"Edges from difference", 4);
Mat output2 = JoinImagesVertically(output1,"",model_edges_display,"Model", 4);
Mat output3 = JoinImagesVertically(bicycle_background,"Static Background",colour_chamfer_display_image,"Chamfer image", 4);
Mat output4 = JoinImagesVertically(output3,"",colour_matching_display_image,"Degree of fit", 4);
Mat output5 = JoinImagesVertically(difference_frame,"Difference",result_image,"Result", 4);
Mat output6 = JoinImagesVertically(output5,"",local_minima_display,"Local minima", 4);
Mat output7 = JoinImagesHorizontally( output2, "", output4, "", 4 );
Mat output8 = JoinImagesHorizontally( output7, "", output6, "", 4 );
imshow("Chamfer matching", output8);
c = waitKey(1000); // This makes the image appear on screen
bicycle_video >> current_frame;
}
c = cvWaitKey();
cvDestroyAllWindows();
// Cascade of Haar classifiers (most often shown for face detection).
VideoCapture camera;
camera.open(1);
camera.set(CV_CAP_PROP_FRAME_WIDTH, 320);
camera.set(CV_CAP_PROP_FRAME_HEIGHT, 240);
if( camera.isOpened() )
{
timer->reset();
Mat current_frame;
do {
camera >> current_frame;
if( current_frame.empty() )
break;
vector<Rect> faces;
timer->ignoreTimeSinceLastRecorded();
Mat gray;
cvtColor( current_frame, gray, CV_BGR2GRAY );
equalizeHist( gray, gray );
cascade.detectMultiScale( gray, faces, 1.1, 2, CV_HAAR_SCALE_IMAGE, Size(30, 30) );
timer->recordTime("Haar Classifier");
for( int count = 0; count < (int)faces.size(); count++ )
rectangle(current_frame, faces[count], cv::Scalar(255,0,0), 2);
//timer->putTimes(current_frame);
imshow( "Cascade of Haar Classifiers", current_frame );
c = waitKey(10); // This makes the image appear on screen
} while (c == -1);
}
void MeanShiftDemo( VideoCapture& video, Rect& starting_position, int starting_frame_number, int end_frame)
{
bool half_size = true;
video.set(CV_CAP_PROP_POS_FRAMES,starting_frame_number);
Mat current_frame, hls_image;
std::vector<cv::Mat> hls_planes(3);
video >> current_frame;
Rect current_position(starting_position);
if (half_size)
{
resize(current_frame, current_frame, Size( current_frame.cols/2, current_frame.rows/2 ));
current_position.height /= 2;
current_position.width /= 2;
current_position.x /= 2;
current_position.y /= 2;
}
cvtColor(current_frame, hls_image, CV_BGR2HLS);
split(hls_image,hls_planes);
int chosen_channel = 0; // Hue channel
Mat image1ROI = hls_planes[chosen_channel](current_position);
float channel_range[2] = { 0.0, 255.0 };
int channel_numbers[1] = { 0 };
int number_bins[1] = { 32 };
MatND histogram[1];
const float* channel_ranges = channel_range;
calcHist(&(image1ROI), 1, channel_numbers, Mat(), histogram[0], 1 , number_bins, &channel_ranges);
normalize(histogram[0],histogram[0],1.0);
rectangle(current_frame,current_position,Scalar(0,255,0),2);
Mat starting_frame = current_frame.clone();
int frame_number = starting_frame_number;
while (!current_frame.empty() && (frame_number < end_frame))
{
// Calculate back projection
Mat back_projection_probabilities;
calcBackProject(&(hls_planes[chosen_channel]),1,channel_numbers,*histogram,back_projection_probabilities,&channel_ranges,255.0);
// Remove low saturation points from consideration
Mat saturation_mask;
inRange( hls_image, Scalar(0,10,50,0),Scalar(180,256,256,0), saturation_mask );
bitwise_and( back_projection_probabilities, back_projection_probabilities,back_projection_probabilities, saturation_mask );
// Mean shift
TermCriteria criteria(cv::TermCriteria::MAX_ITER,5,0.01);
meanShift(back_projection_probabilities,current_position,criteria);
// Output to screen
rectangle(current_frame,current_position,Scalar(0,255,0),2);
Mat chosen_channel_image, back_projection_image;
cvtColor(hls_planes[chosen_channel], chosen_channel_image, CV_GRAY2BGR);
cvtColor(back_projection_probabilities, back_projection_image, CV_GRAY2BGR);
Mat row1_output = JoinImagesHorizontally( starting_frame, "Starting position", chosen_channel_image, "Chosen channel (Hue)", 4 );
Mat row2_output = JoinImagesHorizontally( back_projection_image, "Back projection", current_frame, "Current position", 4 );
Mat mean_shift_output = JoinImagesVertically(row1_output,"",row2_output,"", 4);
imshow("Mean Shift Tracking", mean_shift_output );
// Advance to next frame
video >> current_frame;
if (half_size)
resize(current_frame, current_frame, Size( current_frame.cols/2, current_frame.rows/2 ));
cvtColor(current_frame, hls_image, CV_BGR2HLS);
split(hls_image,hls_planes);
frame_number++;
cvWaitKey(1000);
}
char c = cvWaitKey();
cvDestroyAllWindows();
}
void PCAFaceRecognition() {
#define NUMBER_OF_FACES 10
#define NUMBER_OF_IMAGES_PER_FACE 3
vector<Mat> known_face_images;
vector<int> known_labels;
vector<Mat> unknown_face_images;
vector<int> unknown_labels;
// Load greyscale face images (which are from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html)
char file_name[40];
Mat original_images,row_so_far,image_so_far,temp_image1;
int face_number = 1;
for (; face_number<=NUMBER_OF_FACES; face_number++)
{
for (int image_number = 1; image_number<=NUMBER_OF_IMAGES_PER_FACE; image_number++)
{
sprintf(file_name,"Media/att_faces/s%d/%d.pgm",face_number,image_number);
Mat current_image = imread(file_name,0);
if (image_number>1)
{
known_face_images.push_back(current_image);
known_labels.push_back(face_number);
}
else
{
// Keep the last image of each face as a test case.
unknown_face_images.push_back(current_image);
unknown_labels.push_back(face_number);
}
cvtColor(current_image, current_image, CV_GRAY2BGR);
if (image_number == 2)
{
if (face_number%10 == 1)
{
if (face_number > 1)
if (face_number == 11)
original_images = row_so_far.clone();
else original_images = JoinImagesVertically( original_images, "", row_so_far, "", 1 );
row_so_far = current_image.clone();
}
else
{
char image_number_string[10],previous_image_number_string[10];
sprintf(previous_image_number_string,"%d",face_number-1);
sprintf(image_number_string,"%d",face_number);
row_so_far = JoinImagesHorizontally( row_so_far, (face_number%10==2)?previous_image_number_string:"", current_image, image_number_string, 1 );
}
}
}
}
if (face_number <= 11)
original_images = row_so_far.clone();
else original_images = JoinImagesVertically( original_images, "", row_so_far, "", 1 );
imshow("Known face images", original_images);
imwrite("pca_unknown_faces.bmp",original_images);
Ptr<FaceRecognizer> face_recogniser = createEigenFaceRecognizer();
face_recogniser->train(known_face_images, known_labels);
char previous_face_number_string[100]="";
char face_number_string[100]="";
int correct_count = 0;
for (face_number = 0; face_number < (int)unknown_face_images.size(); face_number++)
{
int predicted_face_number = 0;
double recognition_confidence = 0.0;
face_recogniser->predict(unknown_face_images[face_number],predicted_face_number,recognition_confidence);
if (unknown_labels[face_number]==predicted_face_number)
correct_count++;
strcpy(previous_face_number_string,face_number_string);
cvtColor(unknown_face_images[face_number], temp_image1, CV_GRAY2BGR);
sprintf(face_number_string,"%d (%.0f)",predicted_face_number,recognition_confidence);
Point location(2,15);
putText( temp_image1, face_number_string, location, FONT_HERSHEY_SIMPLEX, 0.4, unknown_labels[face_number]==predicted_face_number?Scalar( 0,255,0 ):Scalar( 0,0,255 ) );
if (face_number%10 == 0)
{
if (face_number > 10)
image_so_far = JoinImagesVertically( image_so_far, "", row_so_far, "", 1 );
else image_so_far = row_so_far.clone();
row_so_far = temp_image1.clone();
}
else
{
row_so_far = JoinImagesHorizontally( row_so_far, "", temp_image1, "", 1 );
}
}
if (face_number > 10)
image_so_far = JoinImagesVertically( image_so_far, "", row_so_far, "", 1 );
else image_so_far = row_so_far.clone();
char output[300];
sprintf(output,"OVERALL Recognised %d/%d (with %d training image%s of %d subjects)",correct_count,unknown_face_images.size(),NUMBER_OF_IMAGES_PER_FACE-1,(NUMBER_OF_IMAGES_PER_FACE-1==1)?"":"s",NUMBER_OF_FACES);
Point location(10,image_so_far.rows-10);
putText( image_so_far, output, location, FONT_HERSHEY_SIMPLEX, 0.4, Scalar( 255,0,0 ) );
imshow("Recognised faces using PCA (Eigenfaces)", image_so_far);
}
void VideoDemos( VideoCapture& surveillance_video, int starting_frame, bool clean_binary_images )
{
Mat previous_gray_frame, optical_flow, optical_flow_display;
Mat current_frame, thresholded_image, closed_image, first_frame;
Mat current_frame_gray, running_average_background;
Mat temp_running_average_background, running_average_difference;
Mat running_average_foreground_mask, running_average_foreground_image;
Mat selective_running_average_background;
Mat temp_selective_running_average_background, selective_running_average_difference;
Mat selective_running_average_foreground_mask, selective_running_average_background_mask, selective_running_average_foreground_image;
double running_average_learning_rate = 0.01;
surveillance_video.set(CV_CAP_PROP_POS_FRAMES,starting_frame);
surveillance_video >> current_frame;
first_frame = current_frame.clone();
cvtColor(current_frame, current_frame_gray, CV_BGR2GRAY);
current_frame.convertTo(running_average_background, CV_32F);
selective_running_average_background = running_average_background.clone();
int rad = running_average_background.depth();
MedianBackground median_background( current_frame, (float) 1.005, 1 );
Mat median_background_image, median_foreground_image;
int codec = static_cast<int>(surveillance_video.get(CV_CAP_PROP_FOURCC));
// V3.0.0 update on next line. OLD CODE was BackgroundSubtractorMOG2 gmm; //(50,16,true);
Ptr<BackgroundSubtractorMOG2> gmm = createBackgroundSubtractorMOG2();
Mat foreground_mask, foreground_image = Mat::zeros(current_frame.size(), CV_8UC3);
double frame_rate = surveillance_video.get(CV_CAP_PROP_FPS);
double time_between_frames = 1000.0/frame_rate;
Timestamper* timer = new Timestamper();
int frame_count = 0;
while ((!current_frame.empty()) && (frame_count++ < 1000))//1800))
{
double duration = static_cast<double>(getTickCount());
vector<Mat> input_planes(3);
split(current_frame,input_planes);
cvtColor(current_frame, current_frame_gray, CV_BGR2GRAY);
if (frame_count%2 == 0) // Skip every second frame so the flow is greater.
{
if ( previous_gray_frame.data )
{
Mat lucas_kanade_flow;
timer->ignoreTimeSinceLastRecorded();
LucasKanadeOpticalFlow(previous_gray_frame, current_frame_gray, lucas_kanade_flow);
timer->recordTime("Lucas Kanade Optical Flow");
calcOpticalFlowFarneback(previous_gray_frame, current_frame_gray, optical_flow, 0.5, 3, 15, 3, 5, 1.2, 0);
cvtColor(previous_gray_frame, optical_flow_display, CV_GRAY2BGR);
drawOpticalFlow(optical_flow, optical_flow_display, 8, Scalar(0, 255, 0), Scalar(0, 0, 255));
timer->recordTime("Farneback Optical Flow");
char frame_str[100];
sprintf( frame_str, "Frame = %d", frame_count);
Mat temp_output = JoinImagesHorizontally( current_frame, frame_str, optical_flow_display, "Farneback Optical Flow", 4 );
Mat optical_flow_output = JoinImagesHorizontally( temp_output, "", lucas_kanade_flow, "Lucas Kanade Optical Flow", 4 );
imshow("Optical Flow", optical_flow_output );
}
std::swap(previous_gray_frame, current_frame_gray);
}
// Static background image
Mat difference_frame, binary_difference;
Mat structuring_element(3,3,CV_8U,Scalar(1));
timer->ignoreTimeSinceLastRecorded();
absdiff(current_frame,first_frame,difference_frame);
cvtColor(difference_frame, thresholded_image, CV_BGR2GRAY);
threshold(thresholded_image,thresholded_image,30,255,THRESH_BINARY);
if (clean_binary_images)
{
morphologyEx(thresholded_image,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,binary_difference,MORPH_OPEN,structuring_element);
current_frame.copyTo(binary_difference, thresholded_image);
}
else
{
binary_difference.setTo(Scalar(0,0,0));
current_frame.copyTo(binary_difference, thresholded_image);
}
timer->recordTime("Static difference");
// Running Average (three channel version)
vector<Mat> running_average_planes(3);
split(running_average_background,running_average_planes);
accumulateWeighted(input_planes[0], running_average_planes[0], running_average_learning_rate);
accumulateWeighted(input_planes[1], running_average_planes[1], running_average_learning_rate);
accumulateWeighted(input_planes[2], running_average_planes[2], running_average_learning_rate);
merge(running_average_planes,running_average_background);
running_average_background.convertTo(temp_running_average_background,CV_8U);
absdiff(temp_running_average_background,current_frame,running_average_difference);
split(running_average_difference,running_average_planes);
// Determine foreground points as any point with a difference of more than 30 on any one channel:
threshold(running_average_difference,running_average_foreground_mask,30,255,THRESH_BINARY);
split(running_average_foreground_mask,running_average_planes);
bitwise_or( running_average_planes[0], running_average_planes[1], running_average_foreground_mask );
bitwise_or( running_average_planes[2], running_average_foreground_mask, running_average_foreground_mask );
if (clean_binary_images)
{
morphologyEx(running_average_foreground_mask,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,running_average_foreground_mask,MORPH_OPEN,structuring_element);
}
running_average_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(running_average_foreground_image, running_average_foreground_mask);
timer->recordTime("Running Average");
// Running Average with selective update
vector<Mat> selective_running_average_planes(3);
// Find Foreground mask
selective_running_average_background.convertTo(temp_selective_running_average_background,CV_8U);
absdiff(temp_selective_running_average_background,current_frame,selective_running_average_difference);
split(selective_running_average_difference,selective_running_average_planes);
// Determine foreground points as any point with an average difference of more than 30 over all channels:
Mat temp_sum = (selective_running_average_planes[0]/3 + selective_running_average_planes[1]/3 + selective_running_average_planes[2]/3);
threshold(temp_sum,selective_running_average_foreground_mask,30,255,THRESH_BINARY_INV);
// Update background
split(selective_running_average_background,selective_running_average_planes);
accumulateWeighted(input_planes[0], selective_running_average_planes[0], running_average_learning_rate,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[1], selective_running_average_planes[1], running_average_learning_rate,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[2], selective_running_average_planes[2], running_average_learning_rate,selective_running_average_foreground_mask);
invertImage(selective_running_average_foreground_mask,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[0], selective_running_average_planes[0], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[1], selective_running_average_planes[1], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[2], selective_running_average_planes[2], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
merge(selective_running_average_planes,selective_running_average_background);
if (clean_binary_images)
{
morphologyEx(selective_running_average_foreground_mask,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,selective_running_average_foreground_mask,MORPH_OPEN,structuring_element);
}
selective_running_average_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(selective_running_average_foreground_image, selective_running_average_foreground_mask);
timer->recordTime("Selective Running Average");
// Median background
timer->ignoreTimeSinceLastRecorded();
median_background.UpdateBackground( current_frame );
timer->recordTime("Median");
median_background_image = median_background.GetBackgroundImage();
Mat median_difference;
absdiff(median_background_image,current_frame,median_difference);
cvtColor(median_difference, median_difference, CV_BGR2GRAY);
threshold(median_difference,median_difference,30,255,THRESH_BINARY);
median_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(median_foreground_image, median_difference);
// Update the Gaussian Mixture Model
// V3.0.0 update on next line. OLD CODE was gmm(current_frame, foreground_mask);
gmm->apply(current_frame, foreground_mask);
// Clean the resultant binary (moving pixel) mask using an opening.
threshold(foreground_mask,thresholded_image,150,255,THRESH_BINARY);
Mat moving_incl_shadows, shadow_points;
threshold(foreground_mask,moving_incl_shadows,50,255,THRESH_BINARY);
absdiff( thresholded_image, moving_incl_shadows, shadow_points );
Mat cleaned_foreground_mask;
if (clean_binary_images)
{
morphologyEx(thresholded_image,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,cleaned_foreground_mask,MORPH_OPEN,structuring_element);
}
else cleaned_foreground_mask = thresholded_image.clone();
foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(foreground_image, cleaned_foreground_mask);
timer->recordTime("Gaussian Mixture Model");
// Create an average background image (just for information)
Mat mean_background_image;
timer->ignoreTimeSinceLastRecorded();
// V3.0.0 update on next line. OLD CODE was gmm.getBackgroundImage(mean_background_image);
gmm->getBackgroundImage(mean_background_image);
duration = static_cast<double>(getTickCount())-duration;
duration /= getTickFrequency()/1000.0;
int delay = (time_between_frames>duration) ? ((int) (time_between_frames-duration)) : 1;
char c = cvWaitKey(delay);
char frame_str[100];
sprintf( frame_str, "Frame = %d", frame_count);
Mat temp_static_output = JoinImagesHorizontally( current_frame, frame_str, first_frame, "Static Background", 4 );
Mat static_output = JoinImagesHorizontally( temp_static_output, "", binary_difference, "Foreground", 4 );
imshow("Static Background Model", static_output );
Mat temp_running_output = JoinImagesHorizontally( current_frame, frame_str, temp_running_average_background, "Running Average Background", 4 );
Mat running_output = JoinImagesHorizontally( temp_running_output, "", running_average_foreground_image, "Foreground", 4 );
imshow("Running Average Background Model", running_output );
Mat temp_selective_output = JoinImagesHorizontally( current_frame, frame_str, temp_selective_running_average_background, "Selective Running Average Background", 4 );
Mat selective_output = JoinImagesHorizontally( temp_selective_output, "", selective_running_average_foreground_image, "Foreground", 4 );
imshow("Selective Running Average Background Model", selective_output );
Mat temp_median_output = JoinImagesHorizontally( current_frame, frame_str, median_background_image, "Median Background", 4 );
Mat median_output = JoinImagesHorizontally( temp_median_output, "", median_foreground_image, "Foreground", 4 );
imshow("Median Background Model", median_output );
Mat temp_gaussian_output = JoinImagesHorizontally( current_frame, frame_str, mean_background_image, "GMM Background", 4 );
Mat gaussian_output = JoinImagesHorizontally( temp_gaussian_output, "", foreground_image, "Foreground", 4 );
imshow("Gaussian Mixture Model", gaussian_output );
timer->putTimes( current_frame );
imshow( "Computation Times", current_frame );
surveillance_video >> current_frame;
}
cvDestroyAllWindows();
}
