Hello all,
I want to detect forehead of the face.I have detect the face and now I want to detect only forehead of the face or other parts of the face. What should I?
MATLAB: Detect forehead of face after face detecting
detect facedigital image processingforeheadimage processing
Related Solutions
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'
Yes, it is possible. You would have to train your own banana detector model using the trainCascadeObjectDetector function. This will produce an XML file, which you can then use with vision.CascadeObjectDetector. See the Train a Cascade Object Detector tutorial.
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