* Add cmake option to use external libjpeg on Mac OS
* Add adjust_threshold to python object detector
* Add cmake option to use external libjpeg on Mac OS
* Add adjust_threshold to python object detector
* Revert "Add cmake option to use external libjpeg on Mac OS"
This reverts commit 01f7fd13ea.
* Update detector example to set adjust_threshold
Added the missing documentation for the correlation tracker.
Didn't realise that the tracker returned the side lobe ratio,
so fixed the API to return it properly.
This aids an API for the correlation based tracker that Davis
recently added to Dlib. I've made sure to allow overrides for
passing normal rectangles rather than drectangles (which isn't
currently supported in the Dlib C++ API). This is mostly
because I imagine people might initialize the tracking using
something like a bounding box from a detector (e.g.
load_frontal_face_detector).
The correlation tracker deals with the drectangle
(double rectangle) class which currently isn't wrapped. Therefore,
I add the drectangle class and refactor rectangles into their
own file. I also added a load of methods on rectangle that might
be useful such as intersection/contains/area etc.
A little bit hacky, but should be fine. Supports both fhog
detectors and the "cached" simple_object_detector. Also, maintains
the upscale parameter for testing
This is the biggest change so far. Now, there are two different
classes of interface. One where you pass ONLY file paths,
and one where you pass ONLY Python objects.
The file paths are maintained to keep a matching interface with
the C++ examples of dlib. So shape predicition and object
detection can be trained using the dlib XML file paths and then
serialize the detectors to disk.
Shape prediction and object detection can also be trained using
numpy arrays and in-memory objects. In this case, the predictor
and detector objects are returned from the training functions.
To facilitate serializing these objects, they now have a 'save'
method.
Tetsing follows a similar pattern, in that it can take either XML
files are or in-memory objects. I also added back the concept of
upsampling during testing to make amends for removing the
simple_object_detector_py struct.
Also, removed the saving of the upsample which I missed from
before (since I'm not using the struct now). I understand why
the upsample was being saved, but I don't necessarily agree it
is particularly useful as you should really be upsampling on
a case by case basis at test time.
I also cleaned up a bunch of code. I'm not sure why the
simple_object_detector was keeping track of the upsample amount,
since it can't even be passed as an argument to the constructor.
Therefore, I removed the simple_object_detector_py and the second
declaration of the hog object detector. I also changed the
view code to optionally take keyword args of color and added
a single view of a rectangle.
Finally, I added viewing of the shape parts.
Can either be a list of full_object_detections or a single
full_object_detection. I couldn't get the vector type to work
for full_object_detection due to a template error.
This includes the full_object_detection, a new struct in the same
vein as the simple_object_detector_training_options and of
course, the shape predictor classes themselves.
All of training, fitting and testing are wrapped.
