From 36078f9903a14c1f52ebd3de9b67bfacfa1b0c44 Mon Sep 17 00:00:00 2001
From: Lovell Fuller <github@lovell.info>
Date: Sat, 6 May 2017 14:46:28 +0100
Subject: [PATCH] Switch to the libvips crop strategy implementations

---
 docs/changelog.md                             |   3 +
 src/operations.cc                             | 112 ------------------
 src/operations.h                              |  16 ---
 src/pipeline.cc                               |  20 ++--
 .../expected/crop-strategy-attention.jpg      | Bin 0 -> 4514 bytes
 .../expected/crop-strategy-entropy.jpg        | Bin 0 -> 6004 bytes
 test/fixtures/expected/crop-strategy.jpg      | Bin 8715 -> 0 bytes
 test/unit/crop.js                             |  20 +---
 8 files changed, 15 insertions(+), 156 deletions(-)
 create mode 100644 test/fixtures/expected/crop-strategy-attention.jpg
 create mode 100644 test/fixtures/expected/crop-strategy-entropy.jpg
 delete mode 100644 test/fixtures/expected/crop-strategy.jpg

diff --git a/docs/changelog.md b/docs/changelog.md
index 48446e03..7644b1a9 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -22,6 +22,9 @@ Requires libvips v8.5.2.
   [#607](https://github.com/lovell/sharp/issues/607)
   [@puzrin](https://github.com/puzrin)
 
+* Switch to the libvips implementation of "attention" and "entropy" crop strategies.
+  [#727](https://github.com/lovell/sharp/issues/727)
+
 * Improve performance and accuracy of nearest neighbour integral upsampling.
   [#752](https://github.com/lovell/sharp/issues/752)
   [@MrIbby](https://github.com/MrIbby)
diff --git a/src/operations.cc b/src/operations.cc
index 1cfdb02b..274ec911 100644
--- a/src/operations.cc
+++ b/src/operations.cc
@@ -267,118 +267,6 @@ namespace sharp {
     }
   }
 
-  /*
-    Calculate the Shannon entropy
-  */
-  double EntropyStrategy::operator()(VImage image) {
-    return image.hist_find().hist_entropy();
-  }
-
-  /*
-    Calculate the intensity of edges, skin tone and saturation
-  */
-  double AttentionStrategy::operator()(VImage image) {
-    // Flatten RGBA onto a mid-grey background
-    if (image.bands() == 4 && HasAlpha(image)) {
-      double const midgrey = sharp::Is16Bit(image.interpretation()) ? 32768.0 : 128.0;
-      std::vector<double> background { midgrey, midgrey, midgrey };
-      image = image.flatten(VImage::option()->set("background", background));
-    }
-    // Convert to LAB colourspace
-    VImage lab = image.colourspace(VIPS_INTERPRETATION_LAB);
-    VImage l = lab[0];
-    VImage a = lab[1];
-    VImage b = lab[2];
-    // Edge detect luminosity with the Sobel operator
-    VImage sobel = vips::VImage::new_matrixv(3, 3,
-      -1.0, 0.0, 1.0,
-      -2.0, 0.0, 2.0,
-      -1.0, 0.0, 1.0);
-    VImage edges = l.conv(sobel).abs() + l.conv(sobel.rot90()).abs();
-    // Skin tone chroma thresholds trained with http://humanae.tumblr.com/
-    VImage skin = (a >= 3) & (a <= 22) & (b >= 4) & (b <= 31);
-    // Chroma >~50% saturation
-    VImage lch = lab.colourspace(VIPS_INTERPRETATION_LCH);
-    VImage c = lch[1];
-    VImage saturation = c > 60;
-    // Find maximum in combined saliency mask
-    VImage mask = edges + skin + saturation;
-    return mask.max();
-  }
-
-  /*
-    Calculate crop area based on image entropy
-  */
-  std::tuple<int, int> Crop(
-    VImage image, int const outWidth, int const outHeight, std::function<double(VImage)> strategy
-  ) {
-    int left = 0;
-    int top = 0;
-    int const inWidth = image.width();
-    int const inHeight = image.height();
-    if (inWidth > outWidth) {
-      // Reduce width by repeated removing slices from edge with lowest score
-      int width = inWidth;
-      double leftScore = 0.0;
-      double rightScore = 0.0;
-      // Max width of each slice
-      int const maxSliceWidth = static_cast<int>(ceil((inWidth - outWidth) / 8.0));
-      while (width > outWidth) {
-        // Width of current slice
-        int const slice = std::min(width - outWidth, maxSliceWidth);
-        if (leftScore == 0.0) {
-          // Update score of left slice
-          leftScore = strategy(image.extract_area(left, 0, slice, inHeight));
-        }
-        if (rightScore == 0.0) {
-          // Update score of right slice
-          rightScore = strategy(image.extract_area(width - slice - 1, 0, slice, inHeight));
-        }
-        // Keep slice with highest score
-        if (leftScore >= rightScore) {
-          // Discard right slice
-          rightScore = 0.0;
-        } else {
-          // Discard left slice
-          leftScore = 0.0;
-          left = left + slice;
-        }
-        width = width - slice;
-      }
-    }
-    if (inHeight > outHeight) {
-      // Reduce height by repeated removing slices from edge with lowest score
-      int height = inHeight;
-      double topScore = 0.0;
-      double bottomScore = 0.0;
-      // Max height of each slice
-      int const maxSliceHeight = static_cast<int>(ceil((inHeight - outHeight) / 8.0));
-      while (height > outHeight) {
-        // Height of current slice
-        int const slice = std::min(height - outHeight, maxSliceHeight);
-        if (topScore == 0.0) {
-          // Update score of top slice
-          topScore = strategy(image.extract_area(0, top, inWidth, slice));
-        }
-        if (bottomScore == 0.0) {
-          // Update score of bottom slice
-          bottomScore = strategy(image.extract_area(0, height - slice - 1, inWidth, slice));
-        }
-        // Keep slice with highest score
-        if (topScore >= bottomScore) {
-          // Discard bottom slice
-          bottomScore = 0.0;
-        } else {
-          // Discard top slice
-          topScore = 0.0;
-          top = top + slice;
-        }
-        height = height - slice;
-      }
-    }
-    return std::make_tuple(left, top);
-  }
-
   /*
     Insert a tile cache to prevent over-computation of any previous operations in the pipeline
   */
diff --git a/src/operations.h b/src/operations.h
index 66836e77..529bca44 100644
--- a/src/operations.h
+++ b/src/operations.h
@@ -72,22 +72,6 @@ namespace sharp {
    */
   VImage Sharpen(VImage image, double const sigma, double const flat, double const jagged);
 
-  /*
-    Crop strategy functors
-  */
-  struct EntropyStrategy {
-    double operator()(VImage image);
-  };
-  struct AttentionStrategy {
-    double operator()(VImage image);
-  };
-
-  /*
-    Calculate crop area based on given strategy (Entropy, Attention)
-  */
-  std::tuple<int, int> Crop(
-    VImage image, int const outWidth, int const outHeight, std::function<double(VImage)> strategy);
-
   /*
     Insert a tile cache to prevent over-computation of any previous operations in the pipeline
   */
diff --git a/src/pipeline.cc b/src/pipeline.cc
index f31540cb..47c50772 100644
--- a/src/pipeline.cc
+++ b/src/pipeline.cc
@@ -509,24 +509,20 @@ class PipelineWorker : public Nan::AsyncWorker {
             ->set("background", background));
         } else if (baton->canvas != Canvas::IGNORE_ASPECT) {
           // Crop/max/min
-          int left;
-          int top;
           if (baton->crop < 9) {
             // Gravity-based crop
+            int left;
+            int top;
             std::tie(left, top) = sharp::CalculateCrop(
               image.width(), image.height(), baton->width, baton->height, baton->crop);
-          } else if (baton->crop == 16) {
-            // Entropy-based crop
-            std::tie(left, top) = sharp::Crop(image, baton->width, baton->height, sharp::EntropyStrategy());
+            int width = std::min(image.width(), baton->width);
+            int height = std::min(image.height(), baton->height);
+            image = image.extract_area(left, top, width, height);
           } else {
-            // Attention-based crop
-            std::tie(left, top) = sharp::Crop(image, baton->width, baton->height, sharp::AttentionStrategy());
+            // Attention-based or Entropy-based crop
+            image = image.smartcrop(baton->width, baton->height, VImage::option()
+              ->set("interesting", baton->crop == 16 ? VIPS_INTERESTING_ENTROPY : VIPS_INTERESTING_ATTENTION));
           }
-          int width = std::min(image.width(), baton->width);
-          int height = std::min(image.height(), baton->height);
-          image = image.extract_area(left, top, width, height);
-          baton->cropCalcLeft = left;
-          baton->cropCalcTop = top;
         }
       }
 
diff --git a/test/fixtures/expected/crop-strategy-attention.jpg b/test/fixtures/expected/crop-strategy-attention.jpg
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diff --git a/test/fixtures/expected/crop-strategy-entropy.jpg b/test/fixtures/expected/crop-strategy-entropy.jpg
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diff --git a/test/unit/crop.js b/test/unit/crop.js
index b8087a3a..9a5a2759 100644
--- a/test/unit/crop.js
+++ b/test/unit/crop.js
@@ -161,7 +161,7 @@ describe('Crop', function () {
 
   describe('Entropy-based strategy', function () {
     it('JPEG', function (done) {
-      sharp(fixtures.inputJpgWithCmykProfile)
+      sharp(fixtures.inputJpg)
         .resize(80, 320)
         .crop(sharp.strategy.entropy)
         .toBuffer(function (err, data, info) {
@@ -170,9 +170,7 @@ describe('Crop', function () {
           assert.strictEqual(3, info.channels);
           assert.strictEqual(80, info.width);
           assert.strictEqual(320, info.height);
-          assert.strictEqual(250, info.cropCalcLeft);
-          assert.strictEqual(0, info.cropCalcTop);
-          fixtures.assertSimilar(fixtures.expected('crop-strategy.jpg'), data, done);
+          fixtures.assertSimilar(fixtures.expected('crop-strategy-entropy.jpg'), data, done);
         });
     });
 
@@ -186,8 +184,6 @@ describe('Crop', function () {
           assert.strictEqual(4, info.channels);
           assert.strictEqual(320, info.width);
           assert.strictEqual(80, info.height);
-          assert.strictEqual(0, info.cropCalcLeft);
-          assert.strictEqual(80, info.cropCalcTop);
           fixtures.assertSimilar(fixtures.expected('crop-strategy.png'), data, done);
         });
     });
@@ -202,8 +198,6 @@ describe('Crop', function () {
           assert.strictEqual(4, info.channels);
           assert.strictEqual(320, info.width);
           assert.strictEqual(80, info.height);
-          assert.strictEqual(0, info.cropCalcLeft);
-          assert.strictEqual(80, info.cropCalcTop);
           fixtures.assertSimilar(fixtures.expected('crop-strategy.png'), data, done);
         });
     });
@@ -211,7 +205,7 @@ describe('Crop', function () {
 
   describe('Attention strategy', function () {
     it('JPEG', function (done) {
-      sharp(fixtures.inputJpgWithCmykProfile)
+      sharp(fixtures.inputJpg)
         .resize(80, 320)
         .crop(sharp.strategy.attention)
         .toBuffer(function (err, data, info) {
@@ -220,9 +214,7 @@ describe('Crop', function () {
           assert.strictEqual(3, info.channels);
           assert.strictEqual(80, info.width);
           assert.strictEqual(320, info.height);
-          assert.strictEqual(250, info.cropCalcLeft);
-          assert.strictEqual(0, info.cropCalcTop);
-          fixtures.assertSimilar(fixtures.expected('crop-strategy.jpg'), data, done);
+          fixtures.assertSimilar(fixtures.expected('crop-strategy-attention.jpg'), data, done);
         });
     });
 
@@ -236,8 +228,6 @@ describe('Crop', function () {
           assert.strictEqual(4, info.channels);
           assert.strictEqual(320, info.width);
           assert.strictEqual(80, info.height);
-          assert.strictEqual(0, info.cropCalcLeft);
-          assert.strictEqual(80, info.cropCalcTop);
           fixtures.assertSimilar(fixtures.expected('crop-strategy.png'), data, done);
         });
     });
@@ -252,8 +242,6 @@ describe('Crop', function () {
           assert.strictEqual(4, info.channels);
           assert.strictEqual(320, info.width);
           assert.strictEqual(80, info.height);
-          assert.strictEqual(0, info.cropCalcLeft);
-          assert.strictEqual(80, info.cropCalcTop);
           fixtures.assertSimilar(fixtures.expected('crop-strategy.png'), data, done);
         });
     });