Expose libvips' median filter operation (#1161)

lib/constructor.js

@@ -153,6 +153,7 @@ const Sharp = function (input, options) {
     background: [0, 0, 0, 255],
     flatten: false,
     negate: false,
+    medianSize: 0,
     blurSigma: 0,
     sharpenSigma: 0,
     sharpenFlat: 1,

lib/operation.js

@@ -156,6 +156,26 @@ function sharpen (sigma, flat, jagged) {
   return this;
 }
 
+/**
+ * Apply median filter using vips_rank( in, out, m, m, m * m / 2 );
+ * when used witout parameters the defaul window is 3x3
+ * @param {Number} [size] square mask size: size x size
+ * @returns {Sharp}
+ * @throws {Error} Invalid parameters
+ */
+function median (size) {
+  if (!is.defined(size)) {
+    // No arguments: default to 3x3
+    this.options.medianSize = 3;
+  } else if (is.integer(size) && is.inRange(size, 1, 1000)) {
+    // Numeric argument: specific sigma
+    this.options.medianSize = size;
+  } else {
+    throw new Error('Invalid median size ' + size);
+  }
+  return this;
+}
+
 /**
  * Blur the image.
  * When used without parameters, performs a fast, mild blur of the output image.
@@ -444,6 +464,7 @@ module.exports = function (Sharp) {
     flip,
     flop,
     sharpen,
+    median,
     blur,
     extend,
     flatten,

src/pipeline.cc

@@ -359,6 +359,8 @@ class PipelineWorker : public Nan::AsyncWorker {
       bool const shouldBlur = baton->blurSigma != 0.0;
       bool const shouldConv = baton->convKernelWidth * baton->convKernelHeight > 0;
       bool const shouldSharpen = baton->sharpenSigma != 0.0;
+      bool const shouldApplyMedian = baton->medianSize > 0;
+
       bool const shouldPremultiplyAlpha = HasAlpha(image) &&
         (shouldResize || shouldBlur || shouldConv || shouldSharpen || shouldOverlayWithAlpha);
 
@@ -544,7 +546,10 @@ class PipelineWorker : public Nan::AsyncWorker {
         image = image.embed(baton->extendLeft, baton->extendTop, baton->width, baton->height,
           VImage::option()->set("extend", VIPS_EXTEND_BACKGROUND)->set("background", background));
       }
-
+      // Median - must happen before blurring, due to the utility of blurring after thresholding
+      if (shouldApplyMedian) {
+        image = image.median(baton->medianSize);
+      }
       // Threshold - must happen before blurring, due to the utility of blurring after thresholding
       if (baton->threshold != 0) {
         image = sharp::Threshold(image, baton->threshold, baton->thresholdGrayscale);
@@ -1194,6 +1199,7 @@ NAN_METHOD(pipeline) {
   baton->flatten = AttrTo<bool>(options, "flatten");
   baton->negate = AttrTo<bool>(options, "negate");
   baton->blurSigma = AttrTo<double>(options, "blurSigma");
+  baton->medianSize = AttrTo<uint32_t>(options, "medianSize");
   baton->sharpenSigma = AttrTo<double>(options, "sharpenSigma");
   baton->sharpenFlat = AttrTo<double>(options, "sharpenFlat");
   baton->sharpenJagged = AttrTo<double>(options, "sharpenJagged");

src/pipeline.h

@@ -73,6 +73,7 @@ struct PipelineBaton {
   bool flatten;
   bool negate;
   double blurSigma;
+  int medianSize;
   double sharpenSigma;
   double sharpenFlat;
   double sharpenJagged;
@@ -157,6 +158,7 @@ struct PipelineBaton {
     flatten(false),
     negate(false),
     blurSigma(0.0),
+    medianSize(0),
     sharpenSigma(0.0),
     sharpenFlat(1.0),
     sharpenJagged(2.0),

test/fixtures/index.js

@@ -69,6 +69,8 @@ module.exports = {
   inputJpgOverlayLayer2: getPath('alpha-layer-2-ink.jpg'),
   inputJpgTruncated: getPath('truncated.jpg'), // head -c 10000 2569067123_aca715a2ee_o.jpg > truncated.jpg
   inputJpgCenteredImage: getPath('centered_image.jpeg'),
+  inputJpgRandom: getPath('random.jpg'), // convert -size 200x200 xc:   +noise Random   random.jpg
+  inputJpgThRandom: getPath('thRandom.jpg'), // convert random.jpg  -channel G -threshold 5% -separate +channel -negate thRandom.jpg
 
   inputPng: getPath('50020484-00001.png'), // http://c.searspartsdirect.com/lis_png/PLDM/50020484-00001.png
   inputPngWithTransparency: getPath('blackbug.png'), // public domain

test/unit/median.js

@@ -0,0 +1,72 @@
+'use strict';
+
+const assert = require('assert');
+
+const sharp = require('../../');
+const fixtures = require('../fixtures');
+
+describe('Median filter', function () {
+  it('1x1 window', function (done) {
+    sharp(fixtures.inputJpgThRandom)
+      .median(1)
+      .toBuffer(function (err, data, info) {
+        if (err) throw err;
+        assert.strictEqual('jpeg', info.format);
+        assert.strictEqual(200, info.width);
+        assert.strictEqual(200, info.height);
+        fixtures.assertSimilar(fixtures.expected('median_1.jpg'), data, done);
+      });
+  });
+
+  it('3x3 window', function (done) {
+    sharp(fixtures.inputJpgThRandom)
+      .median(3)
+      .toBuffer(function (err, data, info) {
+        if (err) throw err;
+        assert.strictEqual('jpeg', info.format);
+        assert.strictEqual(200, info.width);
+        assert.strictEqual(200, info.height);
+        fixtures.assertSimilar(fixtures.expected('median_3.jpg'), data, done);
+      });
+  });
+  it('5x5 window', function (done) {
+    sharp(fixtures.inputJpgThRandom)
+      .median(5)
+      .toBuffer(function (err, data, info) {
+        if (err) throw err;
+        assert.strictEqual('jpeg', info.format);
+        assert.strictEqual(200, info.width);
+        assert.strictEqual(200, info.height);
+        fixtures.assertSimilar(fixtures.expected('median_5.jpg'), data, done);
+      });
+  });
+
+  it('color image', function (done) {
+    sharp(fixtures.inputJpgRandom)
+      .median(5)
+      .toBuffer(function (err, data, info) {
+        if (err) throw err;
+        assert.strictEqual('jpeg', info.format);
+        assert.strictEqual(200, info.width);
+        assert.strictEqual(200, info.height);
+        fixtures.assertSimilar(fixtures.expected('median_color.jpg'), data, done);
+      });
+  });
+
+  it('no windows size', function (done) {
+    sharp(fixtures.inputJpgThRandom)
+      .median()
+      .toBuffer(function (err, data, info) {
+        if (err) throw err;
+        assert.strictEqual('jpeg', info.format);
+        assert.strictEqual(200, info.width);
+        assert.strictEqual(200, info.height);
+        fixtures.assertSimilar(fixtures.expected('median_3.jpg'), data, done);
+      });
+  });
+  it('invalid radius', function () {
+    assert.throws(function () {
+      sharp(fixtures.inputJpg).median(0.1);
+    });
+  });
+});