[For Comments] Reworking ES objectconfig to make it more intuitive to add other models in future & concurrent execution limit

[pliablepixels]

I'll be adding tpu (#283) support on a google coral usb stick, but before I did, I wanted to make some fundamental changes to how the config is set up (objectconfig.ini) to make it more intuitive for the future. Obviously, these are breaking changes, so I wanted to share it, if anyone had an opinion. I've been making too many breaking changes in every release and wanted to reduce it with "one big breaking change" 🙄

• remove hog as a model for person detection - does anyone use it? Note that hog will still be available as a face training and detection algorithm.

• Rename zmes helpers to a proper pip package and upload it to pypi, and have mlapi use it. That way, I don't need to keep updating mlapi as it always falls behind

• I also want to put in a construct to limit the amount of parallel detections that are launched by the ES as it takes up memory and if you are on a GPU, it can segfault out. As of today, the ES launches a "fork" for each event, so obviously, I can limit the # of forks, and add a queue to manage pending detections. However, the problem is each "fork" lasts for the full duration of the event (till the alarm closes). So limiting forks may not be helpful. The other option is to keep a fork "waiting" till the # of detections reduces to the max rate. Any other ideas?

• Here is a sample config I have in mind

[general]
#This replaces models=yolo,face
detection_sequence=object,face,something_new

# "object" replaces "yolo" as yolo is really a model. We are detecting objects
# Also, I'm doing away with "tiny" and "full" configs. No reason for them - just switch weights
# and configs on the same attribute

[object]
object_config= # in the case of coral edge tpu, this is empty/ignored
object_weights=
object_labels= 

# object framework tells me which ML framework to use 
object_framework=opencv/edgetpu

# object processor tells me if I need to use GPU or CPU or TPU (if object_franework is tpu, 
# this is moot, but just for consistency
object_processor = gpu/cpu/tpu

# Each "detection_sequence" will have its own detect pattern and min confidence. 
# Different libraries have different algorithms that need different values
object_min_confidence=float
object_detect_pattern=regex

[face]
<existing stuff>
face_min_confidence=float
face_detection_pattern=regex

[something_new]
<when I add something new>

[matthewtgilbride]

A few humble observations:

• Maybe comment out all of the monitor-specific setting in the example objectconfig.ini file that you provide in the repo? I have run into issues before where, after having deleted and recreating a couple of monitors, I end up with a monitor whose identification matches one of the examples in that file. That can be tricky to debug.

• So, do I understand correctly that switching between tiny yolo and full yolo would just be a matter of switching the weights, so the example file might look something like this?

# use these weights for full yolo v3
object_config={{base_data_path}}/models/yolov3/yolov3.cfg
object_weights={{base_data_path}}/models/yolov3/yolov3.weights
object_labels={{base_data_path}}/models/yolov3/coco.names

# use these weights (and comment out the above) for tiny yolo v3
#object_config={{base_data_path}}/models/tinyyolov3/yolov3-tiny.cfg
#object_weights={{base_data_path}}/models/tinyyolov3/yolov3-tiny.weights
#object_labels={{base_data_path}}/models/tinyyolov3/yolov3-tiny.txt

As long as the sample is clear, I think that works.