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diff --git a/ml/dlib/examples/random_cropper_ex.cpp b/ml/dlib/examples/random_cropper_ex.cpp
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+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    When you are training a convolutional neural network using the loss_mmod loss
+    layer, you need to generate a bunch of identically sized training images.  The
+    random_cropper is a convenient tool to help you crop out a bunch of
+    identically sized images from a training dataset.
+
+    This example shows you what it does exactly and talks about some of its options.
+*/
+
+
+#include <iostream>
+#include <dlib/data_io.h>
+#include <dlib/gui_widgets.h>
+#include <dlib/image_transforms.h>
+
+using namespace std;
+using namespace dlib;
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc != 2)
+    {
+        cout << "Give an image dataset XML file to run this program." << endl;
+        cout << "For example, if you are running from the examples folder then run this program by typing" << endl;
+        cout << "   ./random_cropper_ex faces/training.xml" << endl;
+        cout << endl;
+        return 0;
+    }
+
+    // First lets load a dataset
+    std::vector<matrix<rgb_pixel>> images;
+    std::vector<std::vector<mmod_rect>> boxes;
+    load_image_dataset(images, boxes, argv[1]);
+
+    // Here we make our random_cropper.  It has a number of options. 
+    random_cropper cropper;
+    // We can tell it how big we want the cropped images to be.
+    cropper.set_chip_dims(400,400);
+    // Also, when doing cropping, it will map the object annotations from the
+    // dataset to the cropped image as well as perform random scale jittering.
+    // You can tell it how much scale jittering you would like by saying "please
+    // make the objects in the crops have a min and max size of such and such".
+    // You do that by calling these two functions.  Here we are saying we want the
+    // objects in our crops to be no more than 0.8*400 pixels in height and width.
+    cropper.set_max_object_size(0.8);
+    // And also that they shouldn't be too small. Specifically, each object's smallest
+    // dimension (i.e. height or width) should be at least 60 pixels and at least one of
+    // the dimensions must be at least 80 pixels.  So the smallest objects the cropper will
+    // output will be either 80x60 or 60x80.
+    cropper.set_min_object_size(80,60);
+    // The cropper can also randomly mirror and rotate crops, which we ask it to
+    // perform as well.
+    cropper.set_randomly_flip(true);
+    cropper.set_max_rotation_degrees(50);
+    // This fraction of crops are from random parts of images, rather than being centered
+    // on some object.
+    cropper.set_background_crops_fraction(0.2);
+
+    // Now ask the cropper to generate a bunch of crops.  The output is stored in
+    // crops and crop_boxes.
+    std::vector<matrix<rgb_pixel>> crops;
+    std::vector<std::vector<mmod_rect>> crop_boxes;
+    // Make 1000 crops.
+    cropper(1000, images, boxes, crops, crop_boxes);
+
+    // Finally, lets look at the results
+    image_window win;
+    for (size_t i = 0; i < crops.size(); ++i)
+    {
+        win.clear_overlay();
+        win.set_image(crops[i]);
+        for (auto b : crop_boxes[i])
+        {
+            // Note that mmod_rect has an ignore field.  If an object was labeled
+            // ignore in boxes then it will still be labeled as ignore in
+            // crop_boxes.  Moreover, objects that are not well contained within
+            // the crop are also set to ignore.
+            if (b.ignore)
+                win.add_overlay(b.rect, rgb_pixel(255,255,0)); // draw ignored boxes as orange 
+            else
+                win.add_overlay(b.rect, rgb_pixel(255,0,0));   // draw other boxes as red
+        }
+        cout << "Hit enter to view the next random crop.";
+        cin.get();
+    }
+
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+
+
+
+
+