Compute Maximum Average HSV of Images with MapReduce

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This example shows how to useImageDatastoreandmapreduce找到图像以最大的色调,饱和度和brightness values in an image collection.

Prepare Data

Create a datastore using the images intoolbox/matlab/demosandtoolbox/matlab/imagesci. The selected images have the extensions.jpg,.tifand.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 theFileExtensionsName-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 usereadimagewithin a for-loop, processing the images one at a time. For an example of this method, seeRead and Analyze Image Files.

This example usesmapreduceto accomplish the same task, however, themapreducemethod 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

Themapreducefunction 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'.

functionhueSaturationValueMapper(data, info, intermKVStore)if~ismatrix(data) hsv = rgb2hsv(data);% Extract Hue valuesh = hsv(:,:,1);% Extract Saturation valuess = hsv(:,:,2);% Extract Brightness valuesv = hsv(:,:,3);% Find average of HSV valuesavgH = mean(h(:)); avgS = mean(s(:)); avgV = mean(v(:));% Add intermediate key-value pairsadd(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));endend

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.
mapreduceonly calls this reduce function 3 times, since the map function only adds three unique keys.
The reduce function usesaddto 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.

functionhueSaturationValueReducer(key, intermValIter, outKVSTore) maxAvg = 0; maxImageFilename ='';% Loop over values for each keywhilehasnext(intermValIter) value = getnext(intermValIter);% Compare values to determine maximumifvalue.Avg > maxAvg maxAvg = value.Avg; maxImageFilename = value.Filename;endend% Add final key-value pairadd(outKVSTore, ['Maximum 'key], maxImageFilename);end

Run MapReduce

Usemapreduceto 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%

mapreducereturns a datastore,maxHSV, with files in the current folder.

Read and display the final result from the output datastore,maxHSV. Usefindandstrcmpto find the file index from theFiles财产。

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

Figure contains an axes object. The axes object with title Maximum Average Hue contains an object of type image.

Figure contains an axes object. The axes object with title Maximum Average Saturation contains an object of type image.

Figure contains an axes object. The axes object with title Maximum Average Brightness contains an object of type image.

Local Functions

Listed here are the map and reduce functions thatmapreduceapplies to the data.

functionhueSaturationValueMapper(data, info, intermKVStore)if~ismatrix(data) hsv = rgb2hsv(data);% Extract Hue valuesh = hsv(:,:,1);% Extract Saturation valuess = hsv(:,:,2);% Extract Brightness valuesv = hsv(:,:,3);% Find average of HSV valuesavgH = mean(h(:)); avgS = mean(s(:)); avgV = mean(v(:));% Add intermediate key-value pairsadd(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));endend%------------------------------------------------------------------------------------------functionhueSaturationValueReducer(key, intermValIter, outKVSTore) maxAvg = 0; maxImageFilename ='';% Loop over values for each keywhilehasnext(intermValIter) value = getnext(intermValIter);% Compare values to determine maximumifvalue.Avg > maxAvg maxAvg = value.Avg; maxImageFilename = value.Filename;endend% Add final key-value pairadd(outKVSTore, ['Maximum 'key], maxImageFilename);end%------------------------------------------------------------------------------------------