dlib C++ Library - random_cropper_ex.cpp

// 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;
}