Metadata-Version: 2.1
Name: crowdcount
Version: 0.1.0
Summary: package for crowd counting
Home-page: https://github.com/FDU-VTS/crowd-count/
Author: Fudan-VTS
Author-email: sjchen18@fudan.edu.cn
License: UNKNOWN
Description: # Crowd Counting Package
        
        [![PyPi Version](https://img.shields.io/pypi/v/crowdcount.svg?style=flat-square)](https://pypi.org/project/crowdcount)
        [![GitHub stars](https://img.shields.io/github/stars/FDU-VTS/crowd-count.svg?style=flat-square&logo=github&label=Stars&logoColor=white)](https://github.com/FDU-VTS/crowd-count)
        [![PyPi downloads](https://img.shields.io/pypi/dm/crowdcount.svg?style=flat-square)](https://pypistats.org/packages/crowdcount)
        
        `crowdcount` is a library for crowd counting with `Pytorch` and supported by `Fudan-VTS Research`
        
        ## Source
         - `code`: [https://github.com/FDU-VTS/crowd-count](https://github.com/FDU-VTS/crowd-count)
         - `document`: [https://crowd-count.readthedocs.io/en/latest/](https://crowd-count.readthedocs.io/en/latest/)
        
        ## 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)
        
         - you can find more demos in [demo](https://github.com/FDU-VTS/crowd-count/blob/master/demo)
         
        ## 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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apple Public Source License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
