dlib - imglab

./imglab -c mydataset.xml /tmp/images

./imglab mydataset.xml

    Next, let's assume you have a folder of images called /tmp/images.  These images 
    should contain examples of the objects you want to learn to detect.  You will 
    use the imglab tool to label these objects.  Do this by typing the following
        ./imglab -c mydataset.xml /tmp/images
    This will create a file called mydataset.xml which simply lists the images in 
    /tmp/images.  To annotate them run
        ./imglab mydataset.xml
    A window will appear showing all the images.  You can use the up and down arrow 
    keys to cycle though the images and the mouse to label objects.  In particular, 
    holding the shift key, left clicking, and dragging the mouse will allow you to 
    draw boxes around the objects you wish to detect.  So next, label all the objects 
    with boxes.  Note that it is important to label all the objects since any object 
    not labeled is implicitly assumed to be not an object we should detect.  If there
    are objects you are not sure about you should draw a box around them, then double
    click the box and press i.  This will cross out the box and mark it as "ignore".
    The training code in dlib will then simply ignore detections matching that box.
    

    Once you finish labeling objects go to the file menu, click save, and then close 
    the program. This will save the object boxes back to mydataset.xml.  You can verify 
    this by opening the tool again with
        ./imglab mydataset.xml
    and observing that the boxes are present.

    Returning to the present example program, we can compile it using cmake just as we 
    did with the imglab tool.  Once compiled, we can issue the command 
        ./train_object_detector -tv mydataset.xml
    which will train an object detection model based on our labeled data.  The model 
    will be saved to the file object_detector.svm.  Once this has finished we can use 
    the object detector to locate objects in new images with a command like
        ./train_object_detector some_image.png
    This command will display some_image.png in a window and any detected objects will
    be indicated by a red box.

    Finally, to make running this example easy dlib includes some training data in the
    examples/faces folder.  Therefore, you can test this program out by running the
    following sequence of commands:
      ./train_object_detector -tv examples/faces/training.xml -u1 --flip
      ./train_object_detector --test examples/faces/testing.xml -u1
      ./train_object_detector examples/faces/*.jpg -u1
    That will make a face detector that performs perfectly on the test images listed in
    testing.xml and then it will show you the detections on all the images.