Plot YOLOv5 instance segmentation predictions as masks

I'm detecting tennis courts to then pull corner coordinates from. YOLOv5 instance segmentation provides a rough polygon in a txt file as prediction. How do you plot this YOLO polygon label?

Example of txt file from predictions:

0 0.289062 0.24375 0.2875 0.245312 0.2875 0.248437 0.285937 0.25 0.285937 0.253125 0.284375 0.254687 0.282813 0.254687 0.282813 0.25625 0.28125 0.257812 0.28125 0.265625 0.279687 0.267188 0.279687 0.26875 0.276563 0.271875 0.276563 0.273438 0.275 0.275 0.275 0.282813 0.273438 0.284375 0.273438 0.285937 0.271875 0.2875 0.270312 0.2875 0.26875 0.289062 0.26875 0.301562 0.2625 0.307813 0.2625 0.315625 0.260938 0.317187 0.260938 0.31875 0.257812 0.321875 0.257812 0.323438 0.25625 0.325 0.25625 0.334375 0.253125 0.3375 0.251563 0.3375 0.25 0.339063 0.25 0.348437 0.248437 0.35 0.248437 0.351562 0.245312 0.354688 0.245312 0.35625 0.24375 0.357812 0.24375 0.365625 0.2375 0.371875 0.2375 0.378125 0.234375 0.38125 0.232812 0.38125 0.23125 0.382812 0.23125 0.390625 0.229687 0.392188 0.229687 0.395312 0.226562 0.398438 0.226562 0.4 0.225 0.401563 0.225 0.4125 0.223438 0.414062 0.223438 0.415625 0.21875 0.420312 0.21875 0.429688 0.217187 0.43125 0.217187 0.432813 0.214062 0.435937 0.214062 0.4375 0.2125 0.439063 0.2125 0.446875 0.209375 0.45 0.207813 0.45 0.20625 0.451562 0.20625 0.459375 0.204688 0.460938 0.204688 0.4625 0.201562 0.465625 0.201562 0.467187 0.2 0.46875 0.2 0.476562 0.19375 0.482812 0.19375 0.490625 0.192188 0.492188 0.192188 0.49375 0.189063 0.496875 0.189063 0.498437 0.1875 0.5 0.1875 0.507812 0.18125 0.514063 0.18125 0.521875 0.179688 0.523438 0.179688 0.526563 0.175 0.53125 0.175 0.539062 0.16875 0.545313 0.16875 0.55625 0.167187 0.557813 0.167187 0.559375 0.1625 0.564062 0.1625 0.571875 0.159375 0.575 0.157813 0.575 0.157813 0.576563 0.15625 0.578125 0.15625 0.5875 0.154687 0.589063 0.154687 0.590625 0.15 0.595312 0.15 0.603125 0.14375 0.609375 0.14375 0.61875 0.142188 0.620313 0.142188 0.621875 0.1375 0.626562 0.1375 0.634375 0.13125 0.640625 0.13125 0.651563 0.125 0.657812 0.125 0.665625 0.11875 0.671875 0.11875 0.68125 0.117188 0.682813 0.117188 0.684375 0.1125 0.689062 0.1125 0.696875 0.10625 0.703125 0.10625 0.710938 0.104687 0.7125 0.104687 0.714063 0.101562 0.717188 0.101562 0.71875 0.1 0.720312 0.1 0.728125 0.0953125 0.732813 0.0953125 0.735937 0.09375 0.7375 0.09375 0.74375 0.0921875 0.745313 0.0921875 0.746875 0.0875 0.751562 0.0875 0.759375 0.0828125 0.764063 0.0828125 0.765625 0.08125 0.767187 0.08125 0.771875 0.0796875 0.773438 0.0796875 0.775 0.0765625 0.778125 0.0765625 0.779688 0.075 0.78125 0.075 0.7875 0.0765625 0.789062 0.0765625 0.790625 0.078125 0.790625 0.0796875 0.792188 0.10625 0.792188 0.107813 0.790625 0.121875 0.790625 0.123438 0.789062 0.139062 0.789062 0.140625 0.7875 0.192188 0.7875 0.19375 0.785937 0.198437 0.785937 0.2 0.7875 0.384375 0.7875 0.385938 0.785937 0.432813 0.785937 0.434375 0.7875 0.440625 0.7875 0.442187 0.785937 0.679688 0.785937 0.68125 0.7875 0.81875 0.7875 0.820312 0.789062 0.832812 0.789062 0.834375 0.790625 0.86875 0.790625 0.870313 0.792188 0.921875 0.792188 0.923437 0.790625 0.923437 0.778125 0.917188 0.771875 0.917188 0.764063 0.915625 0.7625 0.915625 0.760938 0.9125 0.757812 0.9125 0.75625 0.910937 0.754687 0.910937 0.746875 0.909375 0.745313 0.909375 0.74375 0.907812 0.74375 0.904688 0.740625 0.904688 0.729688 0.903125 0.728125 0.903125 0.726562 0.898438 0.721875 0.898438 0.714063 0.896875 0.7125 0.896875 0.710938 0.89375 0.707812 0.89375 0.70625 0.892187 0.704687 0.892187 0.696875 0.890625 0.695312 0.890625 0.69375 0.889063 0.69375 0.885938 0.690625 0.885938 0.682813 0.884375 0.68125 0.884375 0.679688 0.88125 0.676562 0.88125 0.675 0.879687 0.673437 0.879687 0.664062 0.873438 0.657812 0.873438 0.646875 0.867188 0.640625 0.867188 0.632812 0.8625 0.628125 0.8625 0.626562 0.860937 0.625 0.860937 0.614062 0.859375 0.6125 0.857813 0.6125 0.854688 0.609375 0.854688 0.598437 0.853125 0.596875 0.853125 0.595312 0.848437 0.590625 0.848437 0.582812 0.84375 0.578125 0.84375 0.576563 0.842188 0.575 0.842188 0.564062 0.840625 0.5625 0.839063 0.5625 0.835938 0.559375 0.835938 0.55 0.834375 0.548437 0.834375 0.545313 0.832812 0.545313 0.83125 0.54375 0.83125 0.542188 0.829687 0.540625 0.829687 0.532812 0.825 0.528125 0.825 0.526563 0.823438 0.525 0.823438 0.514063 0.821875 0.5125 0.820312 0.5125 0.817187 0.509375 0.817187 0.5 0.815625 0.498437 0.815625 0.495313 0.810938 0.490625 0.810938 0.482812 0.80625 0.478125 0.80625 0.476562 0.804688 0.475 0.804688 0.464063 0.803125 0.4625 0.801562 0.4625 0.798437 0.459375 0.798437 0.446875 0.796875 0.445312 0.796875 0.44375 0.795313 0.44375 0.792188 0.440625 0.792188 0.432813 0.7875 0.428125 0.7875 0.426562 0.785937 0.425 0.785937 0.415625 0.784375 0.414062 0.784375 0.4125 0.782812 0.4125 0.779688 0.409375 0.779688 0.395312 0.775 0.390625 0.775 0.389062 0.773438 0.3875 0.773438 0.38125 0.771875 0.379687 0.771875 0.378125 0.76875 0.375 0.76875 0.373437 0.767187 0.371875 0.767187 0.364062 0.760938 0.357812 0.760938 0.345313 0.759375 0.34375 0.757812 0.34375 0.754687 0.340625 0.754687 0.33125 0.753125 0.329688 0.753125 0.326562 0.751562 0.326562 0.75 0.325 0.75 0.323438 0.748438 0.321875 0.748438 0.314063 0.746875 0.3125 0.746875 0.310937 0.74375 0.307813 0.74375 0.30625 0.742188 0.304688 0.742188 0.295312 0.735937 0.289062 0.735937 0.278125 0.734375 0.276563 0.734375 0.275 0.732813 0.275 0.729688 0.271875 0.729688 0.259375 0.723437 0.253125 0.723437 0.245312 0.721875 0.24375

The format given by Yolo in your txt file is such as:

[category_idx x1 y1 x2 y2 ... xn yn]

You have to make the coordinate absolute by multiplying the x by the image width, and the y by the image height.

[x1 y1 ... xn yn] is your polygon.

Then just use a "Polygon to Mask" algorithm. You can find an algorithm in the comments of this issue : https://github.com/scikit-image/scikit-image/issues/1103

You also have similar questions on the forum :
https://stackoverflow.com/questions/42176846/python-turn-polygon-into-mask-array

I recommend this to make it with opencv : https://stackoverflow.com/questions/67708224/shapely-polygon-to-binary-mask

You can also search on Google.

About the label, you have to associate the categoy_idx with the actual category name.

Have fun