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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;
}
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