Can I get automatic Nail Image segmentation code?
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when input hand image is given,the output must contain segmented nail image![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1580346/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1580346/image.png)
4 个评论
Image Analyst
2024-1-2
Isn't this a duplicate post to https://www.mathworks.com/matlabcentral/answers/2065571-i-want-to-nails-semantic-segmentation-using-the-nail-image-from-fingers#comment_3014971?
Are you and Mithun the same person or just in the same class?
回答(1 个)
Ayush
2024-1-2
Hi @Yahya
I understand that you need an automatic Nail Image segmentation code which takes hand image as an input, to produce segmented nail image. Here is the conceptual code for that:
function segmented_nails = segment_nails(image_path)
% Read the image
hand_image = imread(image_path);
% Convert the image to YCbCr color space
YCbCr_img = rgb2ycbcr(hand_image);
Cb = YCbCr_img(:,:,2);
Cr = YCbCr_img(:,:,3);
% These thresholds are just starting points and may require fine-tuning
cb_min = 100; % Lower Cb threshold (adjust as needed)
cb_max = 140; % Upper Cb threshold (adjust as needed)
cr_min = 140; % Lower Cr threshold (adjust as needed)
cr_max = 175; % Upper Cr threshold (adjust as needed)
% Create a binary mask based on the adjusted thresholds
binary_mask = (Cb >= cb_min) & (Cb <= cb_max) & (Cr >= cr_min) & (Cr <= cr_max);
% Morphological operations to clean up the segmentation
binary_mask = imfill(binary_mask, 'holes');
binary_mask = bwareaopen(binary_mask, 50); % Remove small objects
binary_mask = imdilate(binary_mask, strel('disk', 5));
% Extract the segmented nails
segmented_nails = hand_image;
for i = 1:3
channel = segmented_nails(:,:,i);
channel(binary_mask == 0) = 0;
segmented_nails(:,:,i) = channel;
end
% Display the original and segmented images
subplot(1, 2, 1);
imshow(hand_image);
title('Original Image');
subplot(1, 2, 2);
imshow(segmented_nails);
title('Segmented Nails');
end
Note that even with adjustment, simple color-based thresholding is a heuristic approach and may not work perfectly in all cases. For more robust segmentation, you might consider training a machine learning model, such as a convolutional neural network (CNN), on a dataset of hand images with labeled nails.
Thanks,
Ayush
2 个评论
Ayush
2024-1-2
You may need your dataset for train such a model. However, I can help you with the conceptual code for creating and training a CNN model.
% Define the network input size and number of classes
inputSize = [256, 256, 3]; % Example input size (height, width, channels)
numClasses = 2; % Example number of classes (e.g., nail, background)
% Create the CNN for segmentation
% This function is defined in below code snippet
lgraph = createNailSegmentationCNN(inputSize, numClasses);
% visualize the network
analyzeNetwork(lgraph)
% Load your dataset (assuming imageDatastore and pixelLabelDatastore are prepared)
imageDir = 'path/to/images';
labelDir = 'path/to/labels';
% Create an imageDatastore for the images
imds = imageDatastore(imageDir);
% Create a pixelLabelDatastore for the labels
classNames = ["background", "nail"]; % Define class names as per your dataset
labelIDs = [0, 255]; % Define label IDs as per your dataset
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);
% Define training options
options = trainingOptions('sgdm', ...
'InitialLearnRate', 1e-3, ...
'MaxEpochs', 20, ...
'MiniBatchSize', 8, ...
'Shuffle', 'every-epoch', ...
'VerboseFrequency', 2, ...
'Plots', 'training-progress');
% Train the network
[net, trainInfo] = trainNetwork(imds, pxds, lgraph, options);
% After training, use the trained network to segment new images
newImage = imread('path/to/new/image.jpg');
C = semanticseg(newImage, net);
% Visualize the segmentation result
B = labeloverlay(newImage, C, 'Colormap', [0 1 0; 1 0 0], 'Transparency',0.4);
figure, imshow(B), title('Segmented Image');
Function to create the CNN:
function lgraph = createNailSegmentationCNN(inputSize, numClasses)
% Define the layers of the network
layers = [
imageInputLayer(inputSize, 'Name', 'input', 'Normalization', 'none')
convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_1')
reluLayer('Name', 'relu1_1')
convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_2')
reluLayer('Name', 'relu1_2')
maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool1')
convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_1')
reluLayer('Name', 'relu2_1')
convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_2')
reluLayer('Name', 'relu2_2')
maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool2')
% Add more layers as needed for your application
% Final layers for segmentation
convolution2dLayer(1, numClasses, 'Padding', 'same', 'Name', 'convFinal')
softmaxLayer('Name', 'softmax')
pixelClassificationLayer('Name', 'pixelClassification')
];
% Create a layer graph from the layer array
lgraph = layerGraph(layers);
end
Thanks,
Ayush
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