This implements testing of the trained model and evaluates its performance on different experiments and models. This repository will not deal with any training currently.

Pysot path has already been added to the bashrc file. In case, you face issues like

ModuleNotFoundError: No module named 'pysot'

do

export PYTHONPATH=export PYTHONPATH=/home/ubuntu/object_tracking/pysot/:$PYTHONPATH

So, testing will run the model against a set of images, generally grouped together in separate folders based on its labels/class. This set of images also have its groundtruth ( labels, bounding box coordinates) known. We set a desired IOU threshold(eg:0.85) and when the tracker fails to track the object of interest, meaning goes below the specified IOU, we reset the tracker using the known groundtruth data for that frame

Note: We also set burning-rate- which tells the model how many frames to skip, before it starts to re-track again after a failure. This is essential as sometimes, there is occlusion for a few frames and a good re-initization might be essential.

All associated groundtruth is in the ebs-volume saurabh-OT-umich --> evaluation_data

• Each folder name is organized according to the Dataset, for example, VOT2019

  | -- VOT_datasets
    | -- VOT2019
        | -- agility
            | -- camera_motion.tag
            | -- color
                | -- 00000001.jpg
                | -- 00000002.jpg
                | -- ......
                | -- 00000100.jpg
            | -- groundtruth.txt
            | -- illum_change.tag
            | -- occlusion.tag
            | -- size_change.tag
        | -- girl
            | -- ...
            | -- color
                | -- .....
            | -- ...
        | -- ...
        | -- ...
        | -- VOT2019.json
  

  Note: the .tag files only make sense when using VOT20XX datasets. For custom models, we will ignore those. For example, umich/Sauron

  | -- Sauron-umich
    | -- Sauron
        | --  Cystotome
            | -- frame001671.png
            | -- framexxxx.png
            | -- ....
        | -- keratome
            | -- ...
        | -- ...
        | -- Sauron.json
  

  Note: The groudtruth data( bounding box coordinates) is converted from topo-chico to VOT-like dataset and placed in Sauron.json .

• ssh into the instance OT-eval-tests
• source activate pytorch_p36 <-- this enables pytorch and the mandatory cuda environment
• mount the ebs volume if it is not already mounted to ec-2 instance. check with lsblk and then sudo mount /dev/xvdf[check this location] /mountdir
• cd /home/ubuntu/object-tracking and run

python pysot/tools/test.py 
--dataset VOT2019 --config pysot/experiments/siamrpn_r50_l234_dwxcorr_8gpu/config.yaml 
--snapshot /mountdir/14_videos_Nov_2020/results/generic_model_snapshot/model.pth 
--model_name SiamRPNpp --experiment_name generic --results_path /mountdir/evaluation_results 
--dataset_directory /mountdir/evaluation_data/VOT_datasets

• For running a custom trained model, like umich, you can do the following:

python pysot/tools/test.py 
--dataset Sauron --config pysot/experiments/siamrpn_r50_l234_dwxcorr_8gpu/config.yaml 
--snapshot /mountdir/14_videos_Nov_2020/results/snapshot/checkpoint_e9.pth --model_name SiamRPNpp 
--experiment_name checkpoint_e9_ec2 --results_path /mountdir/evaluation_results 
--dataset_directory /mountdir/evaluation_data/Sauron-umich

So both the above scripts will test your models against the test data and generate text files in the results directory according to its class/labels in this location: /mountdir/evaluation_results/Sauron[should match daatset name] or /mountdir/evaluation_results/VOT2019[should match dataset name]

The next step is to evaluate and get the metrics of how our model performed. At this time, we are only using VOT metrics like Accuracy, Precision, Lost Number and EAO( Expected Average Overlap). Read this for their detailed description Generally, Lost number should be as low as possible, while Accuracy, Precision and EAO should be high

• All test results are written here /home/ubuntu/evaluation_results

  | -- VOT2019
    | -- SiamRPNpp[or your architecture name]
        | -- generic [or your experiment name]
            | -- agility
                | -- agility.txt
            | -- girl
                | --girl.txt
            | -- ..
                | -- ..
  

• So what is in this .txt files? Let's look at one snippet

1
643.3181,515.2716,210.0846,565.4160
647.2288,517.6163,213.7098,568.8172
653.1119,510.3336,215.2389,572.5180
652.6388,510.9387,217.8031,575.4478
661.2344,512.7603,219.1808,575.4930
658.1307,526.9829,219.1412,568.9101
656.1899,539.7589,216.6842,557.0591
655.7529,546.2583,215.3035,545.8845
654.4932,552.3325,211.3256,530.9781
2
1
643.6036,672.2226,195.3636,417.2767
635.6833,678.4359,193.6209,412.7940
641.2213,705.8779,186.5106,399.3938
2
1

Here, 1 indicates, we initialize the tracker, the coordinates indicate that we are successfully tracking the object for that many frames, in above case, 9 frames, with IOU of atleast 0.85 and 2 indicates, we lost the tracker. Then we again initialized it, so on and so forth.

• From /home/ubuntu/object-tracking do:

python pysot/tools/eval.py 
--tracker_path /mountdir/evaluation_results --dataset Sauron 
--tracker_prefix SiamRPNpp 
--groundtruth_path /mountdir/evaluation_data/Sauron-umich

The comparison results will look something like this:

eval ar: 100%|████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 11.20it/s]
eval eao: 100%|███████████████████████████████████████████████████████| 2/2 [00:00<00:00,  6.07it/s]


    |     Tracker Name     | Accuracy | Robustness | Lost Number |  EAO  |
    | SiamRPNpp_chk_e9_ec2 |  0.919   |   11.506   |    81.0     | 0.079 |
    |  SiamRPNpp_generic   |  0.897   |   12.784   |    90.0     | 0.066 |

Here- its safe to assume that SiamRPNpp_chk_e9_ec2 is performing better than SiamRPNpp_generic.