tl-YOLOv2

A tensorlayer implementation of YOLOv2

Environment

• python 3.6 or 3.5
• Anaconda 4.2.0
• tensorlayer 1.7.4 or 1.8.0
• tensorflow 1.4.1 or 1.4.0 or 1.8.0
• opencv-python 3.4.*
• (for GUI, tested on Win10) PyQt5

Cli-Usage

1. Clone the repo and cd into it: git clone https://github.com/dtennant/tl-YOLOv2.git && cd tl-YOLOv2/

2. Download the weights pretrained on COCO dataset from BaiduYun and unzip it into pretrained/

3. Run the following commend to detect objects in an image, you can change the input image if you want to:

python main.py --run_mode image --input_img data/before.jpg --output_img data/after.jpg --model_path pretrained/tl-yolov2.ckpt

For Video detection, run the following commend:

python main.py --run_mode video --input_video data/source.avi --output_video data/target.avi --model_path pretrained/tl-yolov2.ckpt

You can checkout the detailed commend-line options by type in python3 main.py -h, the output:

usage: main.py [-h] [--run_mode {video,image}] [--input_video INPUT_VIDEO]
               [--output_video OUTPUT_VIDEO] [--input_img INPUT_IMG]
               [--output_img OUTPUT_IMG] [--model_path MODEL_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --run_mode {video,image}
                        the mode will be video or image
  --input_video INPUT_VIDEO
                        the video to be processed, default: capture from the
                        Camera(if available)
  --output_video OUTPUT_VIDEO
                        the path to save the video, default: play on the
                        screen(not saving)
  --input_img INPUT_IMG
                        the image to be processed
  --output_img OUTPUT_IMG
                        the path to save the output img
  --model_path MODEL_PATH
                        the path to the model ckpt

4. Result:

Image Processing Result

Video Processing Result (if you are in China, maybe you can't see it):

GUI-Usage:

1. Clone the repo and cd into it: git clone https://github.com/dtennant/tl-YOLOv2.git && cd tl-YOLOv2/

2. Download the weights pretrained on COCO dataset from BaiduYun and unzip it into pretrained/

3. Run python gui.py, select the file you want to process, and start processing (support both video and image files)

TODO:

• Add MobileNet to achieve real-time Detection on CPUs (My Machine is i5-7200u, which takes 1 second to process 1 frame)
• Add Training phase, See this issue
• Change PassThroughLayer and MyConcatLayer into proper tensorlayer layers
• Refactoring gui.py (I am not very familiar with PyQt5)