Learning-loop-node

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.

Detector fixes:

- Handle scenario where the download of model files would time out (e.g. due to a slow internet connection).
- Increase timeout time
- Properly handle timeout error and fix check if model is existing

Detector Nodes

- Fix problems with model updates

Detector fixes:

- Make sure the detector does not end up in an endless retry loop if initialization of the model fails

Detector config:
- Set sio data limit to 20 MB

53 - Fixes a major bug in loop communication where the uploaded files in a batch upload all had
the same name. This caused the learning loop to import the same file up
to 20 times while losing up to 19 uploaded images.