From 504ece5b00f192d5c1b343fd06ce1648f9139180 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Mon, 17 Sep 2018 03:06:19 +0000
Subject: [PATCH] Code cleaning & training new YOLO model

---
 README.md |   12 +++++++++++-
 1 files changed, 11 insertions(+), 1 deletions(-)

diff --git a/README.md b/README.md
index e10253f..0d00b55 100644
--- a/README.md
+++ b/README.md
@@ -91,4 +91,14 @@
 
 <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">
 
-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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+They're of course slightly worse than annonymous detection and impractical for any large number of cardbase, but it was an interesting approach.
+
+------------------
+
+I've made a quick openCV algorithm to extract cards from the image, and it works decently well:
+
+<img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_5.jpg" width="360">
+
+At the moment, it's fairly limited - the entire card must be shown without obstruction nor cropping, otherwise it won't detect at all.
+
+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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