The main goal of the project is to count people on the streets. So all parameters are adjusted for the task.
- Clone repository.
- Download converted weights of yolo.h5 model file with tf-1.4.0/ Put them into model_data folder.
- Install requirements.
- Specify path to input fileRun model with cmd :
python demo.py --videofile="path/to/your/videofile/" --out_root_dir="path/to/outptu/dir/"
The code is compatible with Python 3. The following dependencies are needed to run the tracker:
NumPy
sklean
OpenCV
Pillow
Keras
Additionally, feature generation requires TensorFlow-1.4.0.
Be careful that the code ignores everything but person. Change class if you want run for other instance:
[A3/yolo3/yolo.py]:
if predicted_class != 'person':
continue
You can use any Detector you like to replace Keras_version YOLO to get bboxes , for it is to slow !
Model file model_data/mars-small128.pb need by deep_sort had convert to tensorflow-1.4.0
This work mainly based on https://github.com/Qidian213/deep_sort_yolov3. Thanks a lot guy.