Add Ptr KNearest::load and python binding

modules/ml/include/opencv2/ml.hpp

@@ -505,6 +505,14 @@ public:
     The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
      */
     CV_WRAP static Ptr<KNearest> create();
+    /** @brief Loads and creates a serialized knearest from a file
+     *
+     * Use KNearest::save to serialize and store an KNearest to disk.
+     * Load the KNearest from this file again, by calling this function with the path to the file.
+     *
+     * @param filepath path to serialized KNearest
+     */
+    CV_WRAP static Ptr<KNearest> load(const String& filepath);
 };
 
 /****************************************************************************************\

modules/ml/misc/python/test/test_knearest.py

+#!/usr/bin/env python
+import cv2 as cv
+
+from tests_common import NewOpenCVTests
+
+class knearest_test(NewOpenCVTests):
+    def test_load(self):
+        k_nearest = cv.ml.KNearest_load(self.find_file("ml/opencv_ml_knn.xml"))
+        self.assertFalse(k_nearest.empty())
+        self.assertTrue(k_nearest.isTrained())
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/ml/src/knearest.cpp

@@ -515,6 +515,17 @@ Ptr<KNearest> KNearest::create()
     return makePtr<KNearestImpl>();
 }
 
+Ptr<KNearest> KNearest::load(const String& filepath)
+{
+    FileStorage fs;
+    fs.open(filepath, FileStorage::READ);
+
+    Ptr<KNearest> knearest = makePtr<KNearestImpl>();
+
+    ((KNearestImpl*)knearest.get())->read(fs.getFirstTopLevelNode());
+    return knearest;
+}
+
 }
 }