a neural network that counts the percentage of the bluer image in matlab

To create a neural network that counts the percentage of blue in an image, we can use MATLAB's deep learning toolbox. The following steps outline the general process:

1. Load the image into MATLAB and convert it to grayscale. We can use the imread() and rgb2gray() functions for this.

main.m
rgbImage = imread('path/to/image.png');
grayImage = rgb2gray(rgbImage);
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2. Threshold the grayscale image to create a binary mask that highlights the blue areas of the image. We can use the imbinarize() function for this, which takes a grayscale image and returns a binary image by thresholding it based on a specified threshold value.

main.m
thresholdValue = 0.5; % adjust this value to control the sensitivity of the thresholding
binaryImage = imbinarize(grayImage, thresholdValue);
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3. Reshape the binary image into a vector and normalize it between 0 and 1. This vector will be the input to our neural network.

main.m
inputVector = reshape(binaryImage, [], 1); % reshape the matrix into a vector
inputVector = inputVector / max(inputVector); % normalize the vector
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4. Define and train the neural network. We can use a simple feedforward neural network with one input layer, one hidden layer, and one output layer. The input layer will have as many neurons as there are pixels in the image, the hidden layer can have any number of neurons, and the output layer will have one neuron that outputs the percentage of blue in the image. We can use backpropagation to train the network.

main.m
net = feedforwardnet([nHiddens]); % create a feedforward neural network with nHiddens hidden neurons
net = train(net, inputVector, [percentBlues]); % train the network on the input vector and target output (percentage of blue in the image)
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5. Test the network on new images. We can repeat steps 1-3 for new images and use the trained neural network to predict the percentage of blue in each image.

main.m
newRgbImage = imread('path/to/new_image.png');
newGrayImage = rgb2gray(newRgbImage);
newBinaryImage = imbinarize(newGrayImage, thresholdValue);
newInputVector = reshape(newBinaryImage, [], 1) / max(newInputVector);
predictedPercentBlue = net(newInputVector);
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