Card Recognition
Computer Vision – EENG 512
Matthew Runas
Project Overview
• Identify hands of blackjack real time
• Allow for a player and a dealer to have
designated hands
– Determine initial value of hand
– Player and dealer can hit or stay
– Determine value of hand real time based on cards
present in camera view
– Display winner (and check for busts)
Blackjack Properties
• Uses card recognition for determining card
and hand values
• Each card in the video must be detected, then
properties pulled
– Card Value
– Card suit
– Card location
Goal
• Video feed will be split into two regions,
dealer and player
• For every card on the field, all properties are
extracted and card value and suit displayed
• Based on the two regions and current cards in
those regions, a second window will be open
displaying the current hands and winner
Assumptions
• All cards are from the same deck
• All cards are on the same surface
• Surface cards are on is of relatively high
contrast
• Lighting should be relatively similar on all
cards
Overall Algorithm
• Opens video feed
• For each frame
– Runs card detection over whole frame, accounting
for multiple cards (challenge)
– Runs card recognition over each card
– Checks which hand cards are a part of based on
location
– Outputs card properties to each hand
– Outputs hand properties to score window
Card Detection Algorithm
• Algorithm run on each frame
– Convert the frame to grey scale
– Perform a Gaussian blur on each frame
• Reduces noise on the frame, smoothing image
– Perform a Canny filter on each frame
• Detects edges based on changes in pixel values
• Works well when paired with Gaussian blur
– Contour each frame to finalize detection (using find
contours)
– Wrap a convex hull around each contour
– Export the wrapped convex hull to separate image
• Uses warp transform to go from one quadrilateral space to
another
Card Recognition Algorithm
• Algorithm run on each frame of each exported
card
– Maps a 52 card library to a metric space using a
gradient of pixel intensities
• Everything can be compared relatively with a definitive
distance (Hamming Distance)
– Look at frame intensities, then compare frame relative
intensities to library relative intensities
– To get intensities of video and card library:
• Down sample frame to an approximate 8x8
• Take the mean of all values in down sampled frame of the
card
• Threshold each bit to get a 0 or a 1
– Highest match will be the smallest Hamming distance
Limitations
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Card must be on a high contrast surface
Frame rate of the camera
Computational speed of the computer
Focal length must be similar to that of
reference library
• Overlapping or obstructed cards
Demonstration
https://www.youtube.com/watch?v=6eSwEcwo
eno
Possible Future Work
• Be able to adjust for overlapping or obstructed
cards
– Definitely possible, just based on time constraint
• Use less down sampling for comparing the
relative intensities
– Provides higher accuracy at the cost of more
computing power
• Combine with robotics to create a robotic
blackjack dealer
Questions?