carlosjimenezmwb / seq_nms_yolo

This project combines YOLOv2 and seq-nms to realise real time video detection. Main contribution: creation of easy to follow insctructions.

Seq_nms_YOLO

Membres: Yunyun SUN, Yutong YAN, Sixiang XU, Heng ZHANG

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Introduction

This project combines YOLOv2(reference) and seq-nms(reference) to realise real time video detection.

[Tutorial in Ubuntu]

Steps

1. Open a terminal.
2. Create a virtual environment with python 2.7:
   • conda create --name EnvExample python=2.7
   • conda activate EnvExample
3. Clone the repository:
   • git clone https://github.com/carlosjimenezmwb/seq_nms_yolo.git
4. Make the project:
   • cd seq_nms_yolo
   • make
5. Download the yolo.weights and tiny-yolo.weights:
   • wget https://pjreddie.com/media/files/yolo.weights
   • wget https://pjreddie.com/media/files/yolov2-tiny.weights
6. Make sure you have the following libraries installed (with indicated versions):
   • cv2: pip install opencv-python==4.2.0.32
   • matplotlib: pip install matplotlib
   • scipy: pip install scipy
   • pillow: conda install -c anaconda pillow
   • tensorflow: conda install tensorflow=1.15
   • tf_object_detection: conda install -c conda-forge tf_object_detection
7. Copy a video file to the video (/seq_nms_yolo/video) folder, for example, 'input.mp4';
8. Go to the directory /seq_nms_yolo/video and run video2img.py and get_pkllist.py:
   • python video2img.py -i input.mp4
   • python get_pkllist.py
9. Export the paths:
   • export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
   • export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64
   • export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-10.1/lib64
10. Return to root folder and run yolo_seqnms.py to generate output images in video/output:
    • python yolo_seqnms.py
11. If you want to reconstruct a video from these output images, you can go to the video folder and run img2video.py:
    • python img2video.py -i output

And you will see detection results in video/output

Reference

This project copies lots of code from darknet , Seq-NMS and models.

Main reference: https://github.com/melodiepupu/seq_nms_yolo. The purpose of this project was to create easy-to-understand instructions to facilitate accessibility.