Detector features:
New handling of image uploads. As there will be an upload throttle mechanism in the learning loop, we had to make sure that the outbox will not grow infinitely. There are now two queues in the outbox, a priority queue and a normal queue. Regular images to upload (e.g. selected by the detection step) are added to the normal queue which is cut off at 1000 images. when using the upload functions (sio or rest) it is possible to declare an image as priority. The images in this queue will be uploaded first and never thrown away.
Detector breaking change:
The abstract method evaluate which needs to be implemented by detectors now provides the raw jpg image bytes instead of a semi-converted np.array. This means if the detector uses cv2, reading the file can be done with cv_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR). Alternatively PIL could be used as well.