These last 3 weeks I have been working in two different type of approaches to improve the behaviour of the application. On the one hand, I built a first prototype of the application with a buffer with delay. This buffer allows to show all the detections and segmentations in the GUI with a delay given by the length of the buffer in each moment but this way all frames injected to the Net are the last frames (this did not happen before). The buffer working at the moment has some little bugs that need to be fixed. Furthermore, another proposal was to do a double buffer technique but it is not implemented yet.
In the other hand, I had access to a GPU in a Docker container (thanks to Francisco Rivas) working with CUDA where all the necessary packages were installed (Tensorflow, Keras, …) and I have launched the program without errors. The performance was not measured yet because some features need to be installed in the Docker container. The program will capture the video here from a recorded video instead of the webcam because there is not a real camera in the hardware used (using the cameraserver).
Some issues were fixed to allow the program to download the COCO trained weights of the model of the Mask R-CNN in the case they were not already downloaded.