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 several images in the current folder. Select .jpg
images only using the FileExtensions
Name-Value pair.
ds = imageDatastore(pwd,'FileExtensions','.jpg');
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 blocks 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) 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) 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 20% Reduce 0% Map 40% Reduce 0% Map 60% Reduce 0% Map 80% 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
See Also
mapreduce
| imageDatastore
| tall