Skip to main content

package for crowd counting

Project description

Crowd Counting Package

PyPi Version GitHub stars PyPi downloads

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

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

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)
  • you can find more demos in demo

Experiments

we will add the results soon

Thanks for the supports from

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

crowdcount-0.1.1.tar.gz (16.7 kB view hashes)

Uploaded source

Built Distribution

crowdcount-0.1.1-py3-none-any.whl (30.8 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page