MATLAB: Pixeling only detected face

blurringface detectionfacedetectionImage Processing Toolboxpixelation

% Read image
A = imread('lena512c.bmp');
%Get FaceDetector object
FaceDetector = vision.CascadeObjectDetector();
%Use FaceDetector
BBOX = step(FaceDetector, A);
%Annotation of faces
B = insertObjectAnnotation(A,'rectangle', BBOX, 'Face');
imshow(B),title('Detected Faces');
%Display number of faces
n = size (BBOX,1);
str_n = num2str(n);
str = strcat ('Number of detected faces are = ',str_n);
disp(str);

How can i pixelate just face which was detected,not whole image please?

Detector Works fine.

Best Answer

See my demo below. It creates masked images, both by pixelation and blurring.

Color demo image and m-file script are attached.

It's well commented so I think it's self explanatory.

% Locate the face in a color image using the vision.CascadeObjectDetector of the Computer Vision Toolbox
% and mask it in 2 ways: by blurring and pixelation.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 18;
fprintf('Beginning to run %s.m ...\n', mfilename);
% Read image from the drive.
rgbImage = imread('lena.tif');
imwrite(rgbImage, 'LenaColor.png');
[rows, columns, numberOfColorChannels] = size(rgbImage)
subplot(2, 3, 1);
imshow(rgbImage);
title('Original Image', 'FontSize', fontSize);
% Get FaceDetector object.  Requires the Computer Vision Toolbox.
FaceDetector = vision.CascadeObjectDetector();
% Use FaceDetector
BBOX = step(FaceDetector, rgbImage)
% Annotation of faces by putting boxes over them.
B = insertObjectAnnotation(rgbImage, 'rectangle', BBOX, 'Face');
subplot(2, 3, 2);
imshow(B);
title('Detected Face', 'FontSize', fontSize);
% Display box over it.


hold on;
rectangle('Position', BBOX, 'EdgeColor', 'b', 'LineWidth', 2);
% Display the number of detected faces.
n = size (BBOX,1);
fprintf('Number of detected faces = %d.\n', n);
%------------------------------------------------------------------------------------------------------------

% OPTION 1: Create an image where the image is pixelated within the face box area:
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
blockSize = [16, 16]; % 16 pixel by 16 pixel window that jumps along in steps of 16.
% Block process the image to replace every pixel in the
% 16 pixel by 16 pixel block by the mean of the pixels in the block.
% The image is 512 pixels across which will give 512/16 = 32 blocks.
% The image is 480 pixels tall which will give 480/16 = 30 blocks.
outputMagnificationRatio1 = 1;
meanFilterFunction1 = @(theBlockStructure) mean2(theBlockStructure.data(:));
% Next process the image and each 64x64 block is an array of 64 x 64 pixels.
% So it will be the same size as the original image.
% Now,here we actually to the actual filtering.
blockyImageR = blockproc(redChannel, blockSize, meanFilterFunction1);
blockyImageG = blockproc(greenChannel, blockSize, meanFilterFunction1);
blockyImageB = blockproc(blueChannel, blockSize, meanFilterFunction1);
% Recombine separate color channels into a single, true color RGB image.
rgbBlockyImage = cat(3, blockyImageR, blockyImageG, blockyImageB);
% rgbBlockyImage is a double image (because we took the mean) and would display as white unless we cast to uint8, so let's do that.
rgbBlockyImage = cast(rgbBlockyImage, 'like', rgbImage);
[blockRows, blockColumns, numberOfColorChannels2] = size(rgbBlockyImage)
% Display the block mean image.
subplot(2, 3, 3);
imshow(rgbBlockyImage, []);
axis('on', 'image');
caption = sprintf('Block mean image with block size = %d\nOutput image size = %d rows by %d columns', ...
	blockSize(1), blockRows, blockColumns);
title(caption, 'FontSize', fontSize);
% Create a pixelated image
% Resize the image to be the same size as the original.

rgbPixelatedImage = imresize(rgbBlockyImage, [rows, columns], 'nearest'); % Use 'nearest' to pixelate.
% Display the blurry image.

subplot(2, 3, 4);
imshow(rgbPixelatedImage);
axis('on', 'image');
title('Resized, Pixelated Image', 'FontSize', fontSize);
% Display box over it.
hold on;
rectangle('Position', BBOX, 'EdgeColor', 'b', 'LineWidth', 2);
% Replace the pixels in the original image with the pixelated image.
row1 = BBOX(2);
row2 = BBOX(2) + BBOX(4);
col1 = BBOX(1);
col2 = BBOX(1) + BBOX(3);
rgbMaskedImage = rgbImage; % Initialize with a copy of the original image.

% Replace only within the box.

rgbMaskedImage(row1:row2, col1:col2, :) = rgbPixelatedImage(row1:row2, col1:col2, :);
% Display the masked, pixelated image.

subplot(2, 3, 5);
imshow(rgbMaskedImage, []);
axis('on', 'image');
title('Final Masked, Pixelated Image', 'FontSize', fontSize);
% Maximize the figure window.
g = gcf;
g.WindowState = 'maximized';
g.Name = 'Masked, Pixelated Image';
%------------------------------------------------------------------------------------------------------------
% OPTION 2: Create an image where the image is blurred within the face box area:
% Create a blurry image
% Resize the image to be the same size as the original.
windowSize = 41;
kernel = ones(windowSize) / windowSize ^ 2;
rgbBlurredImage = imfilter(rgbImage, kernel); % Requires the Image Processing Toolbox.
% Display the blurry image.
hFig2 = figure;
subplot(1, 2, 1);
imshow(rgbBlurredImage);
axis('on', 'image');
title('Resized, Blurred Image', 'FontSize', fontSize);
% Display box over it.
hold on;
rectangle('Position', BBOX, 'EdgeColor', 'b', 'LineWidth', 2, 'LineWidth', 2);
% Replace the pixels in the original image with the blurred image.
row1 = BBOX(2);
row2 = BBOX(2) + BBOX(4);
col1 = BBOX(1);
col2 = BBOX(1) + BBOX(3);
rgbMaskedImage = rgbImage; % Initialize with a copy of the original image.
% Replace only within the box.
rgbMaskedImage(row1:row2, col1:col2, :) = rgbBlurredImage(row1:row2, col1:col2, :);
% Display the masked, pixelated image.
subplot(1, 2, 2);
imshow(rgbMaskedImage, []);
axis('on', 'image');
title('Final Masked, Blurred Image', 'FontSize', fontSize);
% Enlarge the figure window.
hFig2.Units = 'normalized';
hFig2.Position = [0.3, 0.3, 0.4, 0.4];
hFig2.Name = 'Masked, Blurred Image'

Related Solutions

MATLAB: Crop dark area and display the face only

Convert to grayscale and then threshold and find the bounding box

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
% Read in the color image.
folder = pwd;
baseFileName = 'm2.bmp';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
  % Didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
rgbImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(rgbImage);
% Display the original color image.
subplot(2, 3, 1);
imshow(rgbImage, []);
axis on;
title('Original Color Image', 'FontSize', fontSize);
drawnow;
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Histogram the red channel, but there is a huge spike at 255 so let's not count those.
subplot(2, 3, 2);
grayImage = rgb2gray(rgbImage);
imshow(grayImage, []);
title('Gray Scale version', 'FontSize', fontSize);
subplot(2, 3, 3);
histogram(grayImage(grayImage<255))
grid on;
title('Histogram of Gray Scale Image', 'FontSize', fontSize);
someThresholdValue = 150;
binaryImage = grayImage > someThresholdValue;
% Take largest blob
binaryImage = bwareafilt(binaryImage, 1);
subplot(2, 3, 4);
imshow(binaryImage);
grid on;
title('Binary Image', 'FontSize', fontSize);
% Label the binary image.
labeledImage = bwlabel(binaryImage);
props = regionprops(labeledImage, 'BoundingBox');
bbox = props.BoundingBox
croppedImageRGB = imcrop(rgbImage, bbox);
subplot(2, 3, 5);
imshow(croppedImageRGB);
grid on;
title('Cropped RGB Image', 'FontSize', fontSize);
% Gray scale version of cropped image
croppedImageGray = imcrop(grayImage, bbox);
subplot(2, 3, 6);
imshow(croppedImageGray);
grid on;
title('Cropped Gray Scale Image', 'FontSize', fontSize);

MATLAB: Binary Image – Black region detection

Mohsina, try this:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format short g;
format compact;
fontSize = 25;
%===============================================================================

% Get the name of the image the user wants to use.
baseFileName = 'image1.png';
% Get the full filename, with path prepended.
folder = []; % Determine where demo folder is (works with all versions).
fullFileName = fullfile(folder, baseFileName);
%===============================================================================
% Read in a demo image.
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage)
if numberOfColorChannels > 1
  % It's not really gray scale like we expected - it's color.
  % Use weighted sum of ALL channels to create a gray scale image.
  grayImage = rgb2gray(grayImage);
  % ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
  % which in a typical snapshot will be the least noisy channel.
  % grayImage = grayImage(:, :, 2); % Take green channel.
end
% Display the image.



subplot(2, 2, 1);
imshow(grayImage, []);
axis on;
axis image;
caption = sprintf('Original Gray Scale Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0, 1, 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
drawnow;
% Create binary image of the black blobs from grayscale image.
binaryImage = grayImage == 0;
% Get rid of the background.
binaryImage = imclearborder(binaryImage);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Label the blobs
[labeledImage, numBlobs] = bwlabel(binaryImage);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Let's assign each blob a different color to visually show the user the distinct blobs.
coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random color labels
% coloredLabels is an RGB image.  We could have applied a colormap instead (but only with R2014b and later)
subplot(2, 2, 3);
imshow(coloredLabels);
axis on;
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
caption = sprintf('Pseudo colored labels, from label2rgb().');
title(caption, 'FontSize', fontSize);
drawnow;
% Make measurements of bounding box.
props = regionprops(labeledImage, 'BoundingBox');
% Display the image.
subplot(2, 2, 4);
imshow(binaryImage, []);
axis on;
axis image;
title('Binary Image with Boxes', 'FontSize', fontSize, 'Interpreter', 'None');
% Display the bounding boxes around them.
hold on;
for k = 1 : numBlobs
  thisBoundingBox = props(k).BoundingBox
  rectangle('Position', thisBoundingBox, ...
    'EdgeColor', 'c', 'LineWidth', 2);
end

Best Answer

Related Solutions

MATLAB: Crop dark area and display the face only

MATLAB: Binary Image – Black region detection

Related Question