    // 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 between 0.2*400 and 0.8*400 pixels in height.
        cropper.set_min_object_height(0.2);
        cropper.set_max_object_height(0.8);
        // 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;
    }