/video-detect-opencv-tensorflow-keras-imageai

A python local video && camera video demo, based on OpenCV+Tensorflow+Keras+ImageAI

Primary LanguagePython

Video Capture Demo (0.1)

Dependencies

To use ImageAI in your application developments, you must have installed the following dependencies before you install ImageAI :

  • Python 3.5.1 (and later versions)
  • Tensorflow 1.4.0 (and later versions) (Tensorflow 2.0 coming soon)
  • OpenCV
  • Keras 2.x
pip install -U tensorflow keras opencv-python

Installation

To install ImageAI, run the python installation instruction below in the command line:

pip3 install imageai --upgrade

resnet50_coco_best_v2

Please get the model from: https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5

References

  1. Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras https://github.com/titu1994/DenseNet
  2. Broad Institute of MIT and Harvard, Keras package for deep residual networks https://github.com/broadinstitute/keras-resnet
  3. Fizyr, Keras implementation of RetinaNet object detection https://github.com/fizyr/keras-retinanet
  4. Francois Chollet, Keras code and weights files for popular deeplearning models https://github.com/fchollet/deep-learning-models
  5. Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size https://arxiv.org/abs/1602.07360
  6. Kaiming H. et al, Deep Residual Learning for Image Recognition https://arxiv.org/abs/1512.03385
  7. Szegedy. et al, Rethinking the Inception Architecture for Computer Vision https://arxiv.org/abs/1512.00567
  8. Gao. et al, Densely Connected Convolutional Networks https://arxiv.org/abs/1608.06993
  9. Tsung-Yi. et al, Focal Loss for Dense Object Detection https://arxiv.org/abs/1708.02002
  10. O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge https://arxiv.org/abs/1409.0575
  11. TY Lin et al, Microsoft COCO: Common Objects in Context https://arxiv.org/abs/1405.0312
  12. Moses & John Olafenwa, A collection of images of identifiable professionals. https://github.com/OlafenwaMoses/IdenProf
  13. Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement. https://arxiv.org/abs/1804.02767
  14. Experiencor, Training and Detecting Objects with YOLO3 https://github.com/experiencor/keras-yolo3