{"id":230,"date":"2016-06-10T15:26:17","date_gmt":"2016-06-10T13:26:17","guid":{"rendered":"http:\/\/zaven.co\/blog\/?p=230"},"modified":"2025-04-08T19:55:21","modified_gmt":"2025-04-08T17:55:21","slug":"opencv-advanced-android-development-drawing-histogram","status":"publish","type":"post","link":"https:\/\/zaven.co\/blog\/opencv-advanced-android-development-drawing-histogram\/","title":{"rendered":"OpenCV in advanced Android development: drawing a histogram"},"content":{"rendered":"

In the last tutorial we\u2019ve covered OpenCV and Android Studio project integration<\/a>. This time we\u2019ll try to make actual use of this setup. A code sample that we\u2019ve used in a commercial Android application can be found on our GitHub page<\/a>.<\/p>\n

Extract the data from an image<\/h2>\n

An image histogram<\/em> is a chart that represents the distribution (frequency of occurrence) of pixels within an image.<\/p>\n

To calculate the input data, the best option is to use the Imgproc.calcHist<\/code> method, provided OpenCV<\/strong>. It will help us to create the histogram from a set of arrays<\/strong> (mostly image data).<\/p>\n

\"advanced

Image histogram showing distribution of Brightness and Red (source: en.wikipedia.org)<\/p><\/div>\n

\n\/\/ Create the required arrays and convert the bitmap (our image) \n\/\/ so that it fits the array.\nMat rgba = new Mat();\nUtils.bitmapToMat(bitmap, rgba);\n\n\/\/ Get the bitmap size.\nSize rgbaSize = rgba.size();\n<\/code><\/pre>\n
Now we are ready to configure our histograms<\/em>:<\/p>\n
\n\/\/ Set the amount of bars in the histogram.\nint histSize = 256;\nMatOfInt histogramSize = new MatOfInt(histSize);\n\n\/\/ Set the height of the histogram and width of the bar.\nint histogramHeight = (int) rgbaSize.height;\nint binWidth = 5;\n\n\/\/ Set the value range.\nMatOfFloat histogramRange = new MatOfFloat(0f, 256f);\n\n\/\/ Create two separate lists: one for colors and one for channels (these will be used as separate datasets).\nScalar[] colorsRgb = new Scalar[]{new Scalar(200, 0, 0, 255), new Scalar(0, 200, 0, 255), new Scalar(0, 0, 200, 255)};\nMatOfInt[] channels = new MatOfInt[]{new MatOfInt(0), new MatOfInt(1), new MatOfInt(2)};\n\n\/\/ Create an array to be saved in the histogram and a second array, on which the histogram chart will be drawn.\nMat[] histograms = new Mat[]{new Mat(), new Mat(), new Mat()};\nMat histMatBitmap = new Mat(rgbaSize, rgba.type());\n\n<\/code><\/pre>\n
Separating channels<\/h2>\n
Now we must calculate the histogram for each channel<\/strong>, standardize the datasets and then draw it.<\/p>\n
\nfor (int i = 0; i < channels.length; i++) {\n    Imgproc.calcHist(Collections.singletonList(rgba), channels[i], new Mat(), histograms[i], histogramSize, histogramRange);\n    Core.normalize(histograms[i], histograms[i], histogramHeight, 0, Core.NORM_INF);\n    for (int j = 0; j < histSize; j++) {\n        Point p1 = new Point(binWidth * (j - 1), histogramHeight - Math.round(histograms[i].get(j - 1, 0)[0]));\n        Point p2 = new Point(binWidth * j, histogramHeight - Math.round(histograms[i].get(j, 0)[0]));\n        Imgproc.line(histMatBitmap, p1, p2, colorsRgb[i], 2, 8, 0);\n    }\n}\n<\/code><\/pre>\n
Using the Imgproc.calcHist<\/code> method, calculate the histogram for each channel. The following parameters can be used:<\/p>\n