add part of opencv

Lib/opencv/sources/samples/java/sbt/src/main/scala/Main.scala

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+/*
+ * The main runner for the Java demos.
+ * Demos whose name begins with "Scala" are written in the Scala language,
+ * demonstrating the generic nature of the interface.
+ * The other demos are in Java.
+ * Currently, all demos are run, sequentially.
+ *
+ * You're invited to submit your own examples, in any JVM language of
+ * your choosing so long as you can get them to build.
+ */
+
+import org.opencv.core.Core
+
+object Main extends App {
+  // We must load the native library before using any OpenCV functions.
+  // You must load this library _exactly once_ per Java invocation.
+  // If you load it more than once, you will get a java.lang.UnsatisfiedLinkError.
+  System.loadLibrary(Core.NATIVE_LIBRARY_NAME)
+
+  ScalaCorrespondenceMatchingDemo.run()
+  ScalaDetectFaceDemo.run()
+  new DetectFaceDemo().run()
+}

Lib/opencv/sources/samples/java/sbt/src/main/scala/ScalaCorrespondenceMatchingDemo.scala

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+import org.opencv.imgcodecs.Imgcodecs
+import org.opencv.features2d.DescriptorExtractor
+import org.opencv.features2d.Features2d
+import org.opencv.core.MatOfKeyPoint
+import org.opencv.core.Mat
+import org.opencv.features2d.FeatureDetector
+import org.opencv.features2d.DescriptorMatcher
+import org.opencv.core.MatOfDMatch
+import reflect._
+
+/*
+ * Finds corresponding points between a pair of images using local descriptors.
+ * The correspondences are visualized in the image "scalaCorrespondences.png",
+ * which is written to disk.
+ */
+object ScalaCorrespondenceMatchingDemo {
+  def run() {
+    println(s"\nRunning ${classTag[this.type].toString.replace("$", "")}")
+
+    // Detects keypoints and extracts descriptors in a given image of type Mat.
+    def detectAndExtract(mat: Mat) = {
+      // A special container class for KeyPoint.
+      val keyPoints = new MatOfKeyPoint
+      // We're using the ORB detector.
+      val detector = FeatureDetector.create(FeatureDetector.ORB)
+      detector.detect(mat, keyPoints)
+
+      println(s"There were ${keyPoints.toArray.size} KeyPoints detected")
+
+      // Let's just use the best KeyPoints.
+      val sorted = keyPoints.toArray.sortBy(_.response).reverse.take(50)
+      // There isn't a constructor that takes Array[KeyPoint], so we unpack
+      // the array and use the constructor that can take any number of
+      // arguments.
+      val bestKeyPoints: MatOfKeyPoint = new MatOfKeyPoint(sorted: _*)
+
+      // We're using the ORB descriptor.
+      val extractor = DescriptorExtractor.create(DescriptorExtractor.ORB)
+      val descriptors = new Mat
+      extractor.compute(mat, bestKeyPoints, descriptors)
+
+      println(s"${descriptors.rows} descriptors were extracted, each with dimension ${descriptors.cols}")
+
+      (bestKeyPoints, descriptors)
+    }
+
+    // Load the images from the |resources| directory.
+    val leftImage = Imgcodecs.imread(getClass.getResource("/img1.png").getPath)
+    val rightImage = Imgcodecs.imread(getClass.getResource("/img2.png").getPath)
+
+    // Detect KeyPoints and extract descriptors.
+    val (leftKeyPoints, leftDescriptors) = detectAndExtract(leftImage)
+    val (rightKeyPoints, rightDescriptors) = detectAndExtract(rightImage)
+
+    // Match the descriptors.
+    val matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE)
+    // A special container class for DMatch.
+    val dmatches = new MatOfDMatch
+    // The backticks are because "match" is a keyword in Scala.
+    matcher.`match`(leftDescriptors, rightDescriptors, dmatches)
+
+    // Visualize the matches and save the visualization.
+    val correspondenceImage = new Mat
+    Features2d.drawMatches(leftImage, leftKeyPoints, rightImage, rightKeyPoints, dmatches, correspondenceImage)
+    val filename = "scalaCorrespondences.png"
+    println(s"Writing ${filename}")
+    assert(Imgcodecs.imwrite(filename, correspondenceImage))
+  }
+}

Lib/opencv/sources/samples/java/sbt/src/main/scala/ScalaDetectFaceDemo.scala

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+import org.opencv.core.Core
+import org.opencv.core.MatOfRect
+import org.opencv.core.Point
+import org.opencv.core.Scalar
+import org.opencv.imgcodecs.Imgcodecs
+import org.opencv.imgproc.Imgproc
+import org.opencv.objdetect.CascadeClassifier
+import reflect._
+
+/*
+ * Detects faces in an image, draws boxes around them, and writes the results
+ * to "scalaFaceDetection.png".
+ */
+object ScalaDetectFaceDemo {
+  def run() {
+    println(s"\nRunning ${classTag[this.type].toString.replace("$", "")}")
+
+    // Create a face detector from the cascade file in the resources directory.
+    val faceDetector = new CascadeClassifier(getClass.getResource("/lbpcascade_frontalface.xml").getPath)
+    val image = Imgcodecs.imread(getClass.getResource("/AverageMaleFace.jpg").getPath)
+
+    // Detect faces in the image.
+    // MatOfRect is a special container class for Rect.
+    val faceDetections = new MatOfRect
+    faceDetector.detectMultiScale(image, faceDetections)
+
+    println(s"Detected ${faceDetections.toArray.size} faces")
+
+    // Draw a bounding box around each face.
+    for (rect <- faceDetections.toArray) {
+      Imgproc.rectangle(
+        image,
+        new Point(rect.x, rect.y),
+        new Point(rect.x + rect.width,
+          rect.y + rect.height),
+        new Scalar(0, 255, 0))
+    }
+
+    // Save the visualized detection.
+    val filename = "scalaFaceDetection.png"
+    println(s"Writing ${filename}")
+    assert(Imgcodecs.imwrite(filename, image))
+  }
+}