Crowd Counting with MCNNs - MindSpark Hackathon 2018

Crowd counting on the ShanghaiTech dataset, using multi-column convolutional neural networks.

This is an unofficial implementation of CVPR 2016 paper "Single Image Crowd Counting via Multi Column Convolutional Neural Network".

Note: Predictions can be made. The work on heatmap generation in under progress.

Installation

• Install Tensorflow and Keras
  
• Install OpenCV
  
• Clone this repository (in case you don't want train the model and want to use the pre-trained one).
  

Data Setup

Download ShanghaiTech Dataset from:

• Dropbox: https://www.dropbox.com/s/fipgjqxl7uj8hd5/ShanghaiTech.zip?dl=0
  

• Baidu Disk: http://pan.baidu.com/s/1nuAYslz
  
  Create a folder: ROOT/data/original/shanghaitech/
  
  Here ROOT is the folder conatining all files, it's the main folder. (Don't create a folder called ROOT!)
  

• Save "part_A" under ROOT/data/original/shanghaitech/
  

• Save "part_B" under ROOT/data/original/shanghaitech/
  

• Got to ROOT/data_preparation/
  

• There, run create_gt_test_set_shtech.m in matlab/octave to create ground truth files for test data.
  

• Then go to ROOT/data_preparation/ again.
  

• run create_training_set_shtech.m in matlab to create training and validataion set along with ground truth files.
  
  

• Save the ground truth csv files in ROOT/data/
  That completes data-setup!
  

Test

• Follow steps 1, 2, 3, 4 and 5 from Data-Setup.
  
• Download pre-trained model files:
  [Shanghai Tech A] : https://www.dropbox.com/s/8bxwvr4cj4bh5d8/mcnn_shtechA_660.h5?dl=0
  [Shanghai Tech B] : https://www.dropbox.com/s/kqqkl0exfshsw8v/mcnn_shtechB_110.h5?dl=0
  
  
• Save the model files under: ROOT/final_models
  
• Finally run: test.py
  
  

Train

• Not recommended unless you have a great processor, or a GPU, because training takes a lot of time. Load the pre-trained model and test it on the test set instead.
  

• For tensorflow:
  run from prompt: python3 train.py A(or B)
  Model is saved to modelA/ or modelB/.
  

• For keras: run: python3 keras_train.py B
  model is saved to keras_modelB/