Read and Analyze Image Files
This example shows how to create a datastore for a collection of images, read the image files, and find the images with the maximum average hue, saturation, and brightness (HSV). For a similar example on image processing using the mapreduce
function, see Compute Maximum Average HSV of Images with MapReduce.
Create a datastore containing images with .jpg
and .tif
extensions.
images = {'cloudCombined.jpg','landOcean.jpg','ngc6543a.jpg','street1.jpg',... 'street2.jpg','corn.tif'}; ds = imageDatastore(images);
Initialize the maximum average HSV values and the corresponding image data.
maxAvgH = 0; maxAvgS = 0; maxAvgV = 0; dataH = 0; dataS = 0; dataV = 0;
For each image in the collection, read the image file and calculate the average HSV values across all of the image pixels. If an average value is larger than that of a previous image, then record it as the new maximum (maxAvgH
, maxAvgS
, or maxAvgV
) and record the corresponding image data (dataH
, dataS
, or dataV
).
for i = 1:length(ds.Files) data = readimage(ds,i); % Read the ith image if ~ismatrix(data) % Only process 3-dimensional color data hsv = rgb2hsv(data); % Compute the HSV values from the RGB data h = hsv(:,:,1); % Extract the HSV values s = hsv(:,:,2); v = hsv(:,:,3); avgH = mean(h(:)); % Find the average HSV values across the image avgS = mean(s(:)); avgV = mean(v(:)); if avgH > maxAvgH % Check for new maximum average hue maxAvgH = avgH; dataH = data; end if avgS > maxAvgS % Check for new maximum average saturation maxAvgS = avgS; dataS = data; end if avgV > maxAvgV % Check for new maximum average brightness maxAvgV = avgV; dataV = data; end end end
View the images with the largest average hue, saturation, and brightness.
imshow(dataH,'InitialMagnification','fit'); title('Maximum Average Hue')
figure imshow(dataS,'InitialMagnification','fit'); title('Maximum Average Saturation');
figure imshow(dataV,'InitialMagnification','fit'); title('Maximum Average Brightness');
See Also
imageDatastore
| tall
| mapreduce