文件列表:

.idea (0, 2020-08-02)
.idea\cyw_zed_yolo_3d.iml (505, 2020-08-02)
.idea\encodings.xml (202, 2020-08-02)
.idea\inspectionProfiles (0, 2020-08-02)
.idea\inspectionProfiles\profiles_settings.xml (174, 2020-08-02)
.idea\misc.xml (191, 2020-08-02)
.idea\modules.xml (282, 2020-08-02)
.idea\vcs.xml (512, 2020-08-02)
AudioDemo (0, 2020-08-02)
AudioDemo\box1.wav (34044, 2020-08-02)
AudioDemo\box2.wav (25044, 2020-08-02)
AudioDemo\demo.py (2751, 2020-08-02)
AudioDemo\hello.wav (32044, 2020-08-02)
AudioDemo\water.wav (29044, 2020-08-02)
AudioDemo\water2.wav (21044, 2020-08-02)
AudioDemo\water3.wav (21044, 2020-08-02)
AudioOperator.py (4309, 2020-08-02)
CYWMain.py (2151, 2020-08-02)
CYWMain.spec (867, 2020-08-02)
CYWMainWindow.py (17853, 2020-08-02)
CYWMain_backup20200208.py (2102, 2020-08-02)
CYW_RemoteControl.py (46142, 2020-08-02)
CYW_RemoteControl.pyc (31236, 2020-08-02)
CYW_RemoteControl2_2.py (52939, 2020-08-02)
CYW_RemoteControl2_2_backup.py (50192, 2020-08-02)
CYW_RemoteControl2_3.py (54108, 2020-08-02)
CYW_RemoteControl_MATLAB.py (51861, 2020-08-02)
CYW_RemoteControl_backup.py (7661, 2020-08-02)
CYW_RemoteControl_backup2.py (19693, 2020-08-02)
CYW_RemoteControl_backup3.py (23755, 2020-08-02)
DFYW5.py (16875, 2020-08-02)
DenseFusion (0, 2020-08-02)
DenseFusion\LICENSE (1067, 2020-08-02)
DenseFusion\YCB_Video_toolbox (0, 2020-08-02)
DenseFusion\YCB_Video_toolbox\LICENSE (1065, 2020-08-02)
... ...

# Introduction ## Demo Video https://drive.google.com/file/d/1hkisMa2LTrxH8xd4LbyMy1s35OdmSRwm/view?usp=sharing ## Architecture [](https://github.com/yiwc/OdysseyAlpha/blob/master/#) [](https://github.com/yiwc/OdysseyAlpha/blob/master/#) # Install and Usage ## Environment ubuntu 16.04 conda dependencies: see environment.yml cuda: 9.2 ## Download Weights Manually https://drive.google.com/file/d/1yD8FOPCnh3q-j7pekfab4pfkV8t3dN2x/view?usp=sharing put weights in the root ## Run python CYWMain.py\ or\ python3 CYWMain.py\ ## Reference ###1. Stereolabs ZED - YOLO 3D in Python This package lets you use YOLO the deep learning object detector using the ZED stereo camera in Python 3. The left image will be used to display the detected objects alongside the distance of each, using the ZED Depth. >###### Prerequisites >- Windows 7 ***bits or later, Ubuntu 16.04 >- [ZED SDK](https://github.com/yiwc/OdysseyAlpha/blob/master/https://www.stereolabs.com/developers/) and its dependencies ([CUDA](https://github.com/yiwc/OdysseyAlpha/blob/master/https://developer.nvidia.com/cuda-downloads)) >- [ZED Python 3 wrapper](https://github.com/yiwc/OdysseyAlpha/blob/master/https://github.com/stereolabs/zed-python) >###### Setup ZED Python >Download and install the [ZED Python wrapper](https://github.com/yiwc/OdysseyAlpha/blob/master/https://github.com/stereolabs/zed-python) following the instructions, to make sure everything works you sould try a [sample](https://github.com/yiwc/OdysseyAlpha/blob/master/https://github.com/stereolabs/zed-python/tree/master/examples). >###### Setup Darknet >We will use a fork of darknet from @AlexeyAB : https://github.com/AlexeyAB/darknet >- It is already present in the folder libdarknet >- Simply call make in the folder cd libdarknet make -j4 >- For more information regarding the compilation instructions, check the darknet Readme [here](https://github.com/yiwc/OdysseyAlpha/blob/master/../libdarknet/README.md) >###### Setup the application >- Download the model file, for instance Yolov3 tiny wget https://pjreddie.com/media/files/yolov3-tiny.weights >###### Run the application >To launch the ZED with YOLO simply run the script : python3 darknet_zed.py >The input parameters can be changed using the command line : python3 darknet_zed.py -c -w -m -t -s >For instance : python3 darknet_zed.py -c cfg/yolov3.cfg -w yolov3.weights -m cfg/coco.data -t 0.5 >To display the help : python3 darknet_zed.py -h

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