# Crowd Counting Package

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crowdcount is a library for crowd counting with Pytorch and supported by Fudan-VTS Research

## Source
## Install

  • pip install crowdcount –user –upgrade

## Introduction

#### Crowd counting task:

  • estimate the number of crowd

  • ![crowd counting demo](img/demo.png)

#### User guide:

  • models

    from crowdcount.models import * # crowd counting models includes csr_net, mcnn, resnet50, resnet101, unet, vgg

  • transforms

    import crowdcount.transforms as cc_transforms # transforms

  • data_loader

    from crowdcount.data.data_loader import * # includes ShanghaiTech, UCF_QNRF, UCF_CC_50, Fudan-ShanghaiTech temporarily

  • data_preprocess

    from crowdcount.data.data_preprocess import * # gaussian preprocess for datasets

  • utils

    from crowdcount.utils import * # includes loss functions, optimizers, tensorboard and save function

  • engine

    from crowdcount.engine import train # start to train train(*args, **kwargs)

  • More details in [document](https://crowd-count.readthedocs.io/en/latest/)

#### Demo

from crowdcount.engine import train from crowdcount.models import Res101 from crowdcount.data.data_loader import * from crowdcount.utils import * import crowdcount.transforms as cc_transforms import torchvision.transforms as transforms

# init model model = Res101() # init transforms img_transform = transforms.Compose([transforms.ToTensor(),

transforms.Normalize(mean=[0.452016860247, 0.447249650955, 0.431981861591],

std=[0.23242045939, 0.224925786257, 0.221840232611])

])

gt_transform = cc_transforms.LabelEnlarge() both_transform = cc_transforms.ComplexCompose([cc_transforms.TransposeFlip()]) # init dataset train_set = ShanghaiTechDataset(mode=”train”,

part=”b”, img_transform=img_transform, gt_transform=gt_transform, both_transform=both_transform, root=”/home/vts/chensongjian/CrowdCount/crowdcount/data/datasets/shtu_dataset_sigma_15”)

test_set = ShanghaiTechDataset(mode=”test”,

part=’b’, img_transform=img_transform, root=”/home/vts/chensongjian/CrowdCount/crowdcount/data/datasets/shtu_dataset_sigma_15”)

# init loss train_loss = AVGLoss() test_loss = EnlargeLoss(100) # init save function saver = Saver(path=”../exp/2019-12-22-main_sigma15_6”) # init tensorboard tb = TensorBoard(path=”../runs/2019-12-22-main_sigma15_6”) # start to train train(model, train_set, test_set, train_loss, test_loss, optim=”Adam”, saver=saver, cuda_num=[3], train_batch=2,

test_batch=2, learning_rate=1e-5, epoch_num=500, enlarge_num=100, tensorboard=tb)

## Experiments we will add the results soon

## Thanks for the supports from - [C-3-Framework](https://github.com/gjy3035/C-3-Framework) - [CSRNet](https://github.com/leeyeehoo/CSRNet-pytorch) - [Awesome-Crowd-Counting](https://github.com/gjy3035/Awesome-Crowd-Counting)

Package Reference