xhost +"local:docker@" sudo nvidia-docker run -it --entrypoint "/bin/bash" --env DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix yolo3-opencv-cuda- - - - diff --git a/docker/start_container.sh b/docker/start_container.sh new file mode 100644 index 0000000..df9bca8 --- /dev/null +++ b/docker/start_container.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +xhost +"local:docker@" +sudo nvidia-docker run -it --entrypoint "/bin/bash" --env DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix yolo3-opencv-cuda diff --git a/docker/test output.png b/docker/test output.png deleted file mode 100644 index fa4bc0b..0000000 Binary files a/docker/test output.png and /dev/null differ diff --git a/java/Dataset/annotations/aimage152.xml b/java/Dataset/annotations/aimage152.xml deleted file mode 100644 index 604fcc1..0000000 --- a/java/Dataset/annotations/aimage152.xml +++ /dev/null @@ -1,26 +0,0 @@ -
| Name | Its Function |
| Dataset | Folder contains the images and annotation files. |
| YoloTrainer.java | Training Yolo with our custom dataset. |
| RubixDetector.java | Real-time rubik's cube detector, it reads a stream of frames from the webcam the then detects the rubik's cube in each one. |
| YoloModel.java | Loading the trained model by the class YoloTrainer and make the detection given an image. |
| NonMaxSuppression.java | Implementation of the non-maximum suppression algorithm to cope the problem of detecting the same object multiple times by the yolo algorithm. |
| model.data | Pre-trained model to detect rubik's cube, can be downloaded from here. |
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