Week 3

Completing week2 work and to separate the cards from the background and create the bounding boxes for the corner of the cards, the jupyter notebook by https://github.com/geaxgx/playing-card-detection was used.

The function extract_card returns the cropped version of the card against a transparent background. It also automatically rotates the card if it is in the .wrong orientation

pre processing:

after processing: 

For each video we only extract the image every 5 frames due to the similarities between every two consecutive frames.

To get the bounding box location for the top left and bottom right card number/suit pair, the function “find_hull” is used to find the convex hull in the corner of the card. This is done so that we did not need to manually draw in the location for all 52 cards.

The DTD dataset (https://www.robots.ox.ac.uk/~vgg/data/dtd/) was used to simulate backgrounds of various textures for our dataset.

Different types of image augmentation techniques such as rotation and enlarging were used to create data of varying permutations. The cards were also used to create “fanned” version where they overlap each other but the corners can be seen. This simulates what would normally be seen when playing cards games such as poker.

 

 

 

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