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| | | ## Sept 14th, 2018 |
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| | | -------------------- |
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| | | Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU. |
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| | | Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU. |
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| | | ## Sept 15th, 2018 |
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| | | -------------------- |
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| | | I tried to do an alternate approach - instead of making model identify cards as annonymous, train the model for EVERY single card. As you may imagine, this isn't sustainable for 10000+ different cards that exists in MTG, but I thought it would be reasonable for classifying 10 different cards. |
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| | | Result? Suprisingly effective. |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_2.jpg" width="360"><img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_4.png" width="360"> |
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| | | They're of course slightly worse than annonymous detection and impractical for any large number of cardbase, but it was an interesting approach. |
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| | | ------------------ |
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| | | I've made a quick openCV algorithm to extract cards from the image, and it works decently well: |
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| | | <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_5.jpg" width="360"> |
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| | | At the moment, it's fairly limited - the entire card must be shown without obstruction nor cropping, otherwise it won't detect at all. |
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| | | Unfortunately, there is very little use case for my trained network in this algorithm. It's just using contour detection and perceptual hashing to match the card. |
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