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Crowd estimator using MCNN, implemented with Keras for Chulalongkorn University ICE project.

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tann9949/vCanteen-crowd-estimator-keras

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vCanteen-crowd-estimator

An unofficial implementation of CVPR2016 paper Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

My source for the files train_preprocessing.m, get_density_map_gaussian.m and weight.h5 are from uestcchicken. This is the link to his github about the implementation of this paper too.

I wholeheartly thank him for his contribution. Without him(or her) this project wouldn't be complete.

We use Keras as an implementation ONLY

Installation

  1. Install Keras, Tensorflow.
pip3 install keras
pip3 install tensorflow
  1. Install Jupyter.
pip3 install jupyter
  1. Clone this repository.
git clone https://github.com/tann9949/vCanteen-crowd-estimator.git

To launch it on your camera

  1. In vCanteen.py, line 141, delete argument videopath.
  2. Run this command on your terminal/command prompt
python3 vcanteen.py

To launch it on your video file

  1. Add your video to icanteen_video directory.
  2. In vCanteen.py, change the videopath variable (line 140) as your video.
  3. Run this command on your terminal/command prompt
python3 vcanteen.py

Predicting headcount with your images

  1. Launch jupyter notebook and open Crowd Count MCNN_icanteen.ipynb.
  2. Change the img_path of every cell to be the PATH to your images.
  3. Change the name of the loaded image (see the line with cv2.imread).
  4. Enjoy estimating the crowd.

Label your own crowd dataset

  1. Launch image_preprocessor/Head_Labeler.m with Matlab.
  2. Change num_images, img_path and img_name to match with your dataset.
  3. Run Head_Labeler.m
  4. Mark the head on your images by clicking on the head (one point per head is enough).
  5. To exit, close the figure.

Note for labeling with getpts

  1. To delete the latest label, press backspace.
  2. To finish labeling, press return.

Other note

It is recommended to read the paper before try using this code to guarantee an understanding of the topics. Prerequisites include:

  • Neural network.
  • Convolutional Neural Network.
  • Keras.
  • Python Programming.

Authors

  • Chompakorn Chaksangchaichot (5931229821)
  • Peeramit Masana (5931316721)
  • Akekamon Boonsith (5931393021)