I am working on dress feature identification using opencv. As a first step, I need to segment t-shirt by removing face and hands from the image. Any suggestion is appreciated.
OpenCV is a pre-built, open-source CPU-only library (package) that is widely used for computer vision, machine learning, and image processing applications.
Edge-Based Segmentation Edge-based segmentation is one of the most popular implementations of segmentation in image processing. It focuses on identifying the edges of different objects in an image.
Image segmentation is a method in which a digital image is broken down into various subgroups called Image segments which helps in reducing the complexity of the image to make further processing or analysis of the image simpler. Segmentation in easy words is assigning labels to pixels.
I suggest the following approach:
code:
%stage 1: skin detection - Adrian Rosebrock solution
im = imread(<path to input image>);
hsb = rgb2hsv(im)*255;
skinMask = hsb(:,:,1) > 0 & hsb(:,:,1) < 20;
skinMask = skinMask & (hsb(:,:,2) > 48 & hsb(:,:,2) < 255);
skinMask = skinMask & (hsb(:,:,3) > 80 & hsb(:,:,3) < 255);
skinMask = imclose(skinMask,strel('disk',6));
%stage 2: calculate top, left and right centroid from the different connected
%components of the skin
stats = regionprops(skinMask,'centroid');
topCentroid = stats(1).Centroid;
rightCentroid = stats(1).Centroid;
leftCentroid = stats(1).Centroid;
for x = 1 : length(stats)
centroid = stats(x).Centroid;
if topCentroid(2)>centroid(2)
topCentroid = centroid;
elseif centroid(1)<leftCentroid(1)
leftCentroid = centroid;
elseif centroid(1)>rightCentroid(1)
rightCentroid = centroid;
end
end
%first seed - the average of the most left and right centroids.
centralSeed = int16((rightCentroid+leftCentroid)/2);
%second seed - a pixel which is right below the face centroid.
faceSeed = int16(topCentroid);
faceSeed(2) = faceSeed(2)+40;
%stage 3: std filter
varIm = stdfilt(rgb2gray(im));
%stage 4 - region growing on varIm using faceSeed and centralSeed
res1=regiongrowing(varIm,centralSeed(2),centralSeed(1),8);
res2=regiongrowing(varIm,faceSeed(2),faceSeed(1),8);
res = res1|res2;
%noise reduction
res = imclose(res,strel('disk',3));
res = imopen(res,strel('disk',2));
result after stage 1(skin detection):
final result:
Comments:
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