Project Overview

The defaults settings are written within the code in "detector_gui.py". Run the program after completing the pre-arrangements stated above. First you need to upload the image of a person, whom you want to detect. You must mention the name of the person while uploading. Then in the detector gui, you can select the video from youe local machine. Also specify the person name. Finaly, the detector button will start processing the video. It will take some time depending on the length of the video. At the end, the GUI will display whether the the person was detected in the video or not.

Result Image(s) (Regular TensorFlow)

You can find the output image(s) showing the person detections saved in a new created folder within the 'detections' folder.

Result Video

Output video is saved in the detection folder.

Getting Started

Conda (Recommended)

# Tensorflow CPU
conda env create -f conda-cpu.yml
conda activate yolov4-cpu

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov4-gpu

Pip

# TensorFlow CPU
pip install -r requirements.txt

# TensorFlow GPU
pip install -r requirements-gpu.txt

Nvidia Driver (For GPU, if you are not using Conda Environment and haven't set up CUDA yet)

Make sure to use CUDA Toolkit version 10.1 as it is the proper version for the TensorFlow version used in this repository. https://developer.nvidia.com/cuda-10.1-download-archive-update2

Downloading Official Pre-trained Weights

YOLOv4 comes pre-trained and able to detect 80 classes. For easy demo purposes we will use the pre-trained weights. Download pre-trained yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Copy and paste yolov4.weights from your downloads folder into the 'data' folder of this repository.

If you want to use yolov4-tiny.weights, a smaller model that is faster at running detections but less accurate, download file here: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights

Copy and paste your custom .weights file into the 'data' folder and copy and paste your custom .names into the 'data/classes/' folder.

The only change within the code you need to make in order for your custom model to work is on line 14 of 'core/config.py' file. Update the code to point at your custom .names file as seen below. (my custom .names file is called custom.names but yours might be named differently)

Note: If you are using the pre-trained yolov4 then make sure that line 14 remains coco.names.

Command Line Args Reference (I defined them manually in my code fir thi project)

save_model.py:
  --weights: path to weights file
    (default: './data/yolov4.weights')
  --output: path to output
    (default: './checkpoints/yolov4-416')
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'False')
  --input_size: define input size of export model
    (default: 416)
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)

detect_video.py:
  --video: path to input video (use 0 for webcam)
    (default: './data/video/video.mp4')
  --output: path to output video (remember to set right codec for given format. e.g. XVID for .avi)
    (default: None)
  --output_format: codec used in VideoWriter when saving video to file
    (default: 'XVID)
  --[no]tiny: yolov4 or yolov4-tiny
    (default: 'false')
  --weights: path to weights file
    (default: './checkpoints/yolov4-416')
  --framework: what framework to use (tf, trt, tflite)
    (default: tf)
  --model: yolov3 or yolov4
    (default: yolov4)
  --size: resize images to
    (default: 416)
  --iou: iou threshold
    (default: 0.45)
  --score: confidence threshold
    (default: 0.25)
  --count: count objects within video
    (default: False)
  --dont_show: dont show video output
    (default: False)
  --info: print info on detections
    (default: False)
  --crop: crop detections and save as new images
    (default: False)