MATLAB Examples

Compute Maximum Average HSV of Images with MapReduce

This example shows how to use ImageDatastore and mapreduce to find images with maximum hue, saturation and brightness values in an image collection.

Prepare Data

Create a datastore using the images in toolbox/matlab/demos and toolbox/matlab/imagesci. The selected images have the extensions .jpg, .tif and .png.

```demoFolder = fullfile(matlabroot, 'toolbox', 'matlab', 'demos'); imsciFolder = fullfile(matlabroot, 'toolbox', 'matlab', 'imagesci'); ```

Create a datastore using the folder paths, and filter which images are included in the datastore using the FileExtensions Name-Value pair.

```ds = imageDatastore({demoFolder, imsciFolder}, ... 'FileExtensions', {'.jpg', '.tif', '.png'}); ```

Find Average Maximum HSV from All Images

One way to find the maximum average hue, saturation, and brightness values in the collection of images is to use readimage within a for-loop, processing the images one at a time. For an example of this method, see Read and Analyze Image Files.

This example uses mapreduce to accomplish the same task, however, the mapreduce method is highly scalable to larger collections of images. While the for-loop method is reasonable for small collections of images, it does not scale well to a large collection of images.

Scale to MapReduce

• The mapreduce function requires a map function and a reduce function as inputs.
• The map function receives chunks of data and outputs intermediate results.
• The reduce function reads the intermediate results and produces a final result.

Map function

• In this example, the map function stores the image data and the average HSV values as intermediate values.
• The intermediate values are associated with 3 keys, 'Average Hue', 'Average Saturation' and 'Average Brightness'.
```function hueSaturationValueMapper(data, info, intermKVStore)
% Map function for the Hue Saturation Value MapReduce example.

% Copyright 1984-2015 The MathWorks, Inc.
if ~ismatrix(data)
hsv = rgb2hsv(data);

% Extract Hue values
h = hsv(:,:,1);

% Extract Saturation values
s = hsv(:,:,2);

% Extract Brightness values
v = hsv(:,:,3);

% Find average of HSV values
avgH = mean(h(:));
avgS = mean(s(:));
avgV = mean(v(:));

% Add intermediate key-value pairs
add(intermKVStore, 'Average Hue', struct('Filename', info.Filename, 'Avg', avgH));
add(intermKVStore, 'Average Saturation', struct('Filename', info.Filename, 'Avg', avgS));
add(intermKVStore, 'Average Brightness', struct('Filename', info.Filename, 'Avg', avgV));
end
end

```

Reduce function

• The reduce function receives a list of the image file names along with the respective average HSV values and finds the overall maximum values of average hue, saturation and brightness values.
• mapreduce only calls this reduce function 3 times, since the map function only adds three unique keys.
• The reduce function uses add to add a final key-value pair to the output. For example, 'Maximum Average Hue' is the key and the respective file name is the value.
```function hueSaturationValueReducer(key, intermValIter, outKVSTore)
% Reduce function for the Hue Saturation Value MapReduce example.

% Copyright 1984-2015 The MathWorks, Inc.

maxAvg = 0;
maxImageFilename = '';

% Loop over values for each key
while hasnext(intermValIter)
value = getnext(intermValIter);

% Compare values to determine maximum
if value.Avg > maxAvg
maxAvg = value.Avg;
maxImageFilename = value.Filename;
end

end

% Add final key-value pair
add(outKVSTore, ['Maximum ' key], maxImageFilename);
end

```

Run MapReduce

Use mapreduce to apply the map and reduce functions to the datastore, ds.

```maxHSV = mapreduce(ds, @hueSaturationValueMapper, @hueSaturationValueReducer); ```
```******************************** * MAPREDUCE PROGRESS * ******************************** Map 0% Reduce 0% Map 12% Reduce 0% Map 25% Reduce 0% Map 37% Reduce 0% Map 50% Reduce 0% Map 62% Reduce 0% Map 75% Reduce 0% Map 87% Reduce 0% Map 100% Reduce 0% Map 100% Reduce 33% Map 100% Reduce 67% Map 100% Reduce 100% ```

mapreduce returns a datastore, maxHSV, with files in the current folder.

Read and display the final result from the output datastore, maxHSV. Use find and strcmp to find the file index from the Files property.

```tbl = readall(maxHSV); for i = 1:height(tbl) figure; idx = find(strcmp(ds.Files, tbl.Value{i})); imshow(readimage(ds, idx), 'InitialMagnification', 'fit'); title(tbl.Key{i}); end ```