Skip to content
Little tools to download and then weed through images, delete and classify them into groups for building deep learning image datasets (based on crawler and tkinter)
Branch: master
Clone or download
H4dr1en and cwerner Closes #15 #17 #18 #19 (#16)
* Closes #15

* Closes #17

* Closes #18

* Closes #19

* Added remaining number of classes while downloading
Latest commit 50ea881 Mar 27, 2019

README.md

fastclass

A little set of tools to batch download images and weed through, delete and classify them into groups for building deep learning image datasets.

Installation

pip install git+https://github.com/cwerner/fastclass.git#egg=fastclass

The installer will also place the executables fcc and fcd in your $PATH.

The package currently contains the follwing tools:

Download images

Use fcd to crawl search engines (Google, Bing, Baidu) and pull all images for a defined set of queries. In addition, files are renamed, scaled and checked for duplicates.

You provide queries and terms that should be excluded when naming the category folders. There is an example (guitars.csv) provided in the repository.

Usage

Call the script from the commandline. If you omit any input parameters it will show you the help page.

Usage: fcd [OPTIONS] INFILE

Options:
  -c, --crawler [ALL|GOOGLE|BING|BAIDU]
                                  selection of crawler (multiple invocations
                                  supported)  [default: ALL] (Note: BAIDU is not included in ALL option)
  -k, --keep                      keep original results of crawlers  [default:
                                  False]
  -m, --maxnum                    maximum number of images per crawler [default: 1000]
  -s, --size INTEGER              image size for rescaling  [default: 299]
  -o, --outpath TEXT              name of output directory  [default: dataset]
  -h, --help                      Show this message and exit.

  ::: FastClass fcd :::

  ...an easy way to crawl the net for images when building a dataset for
  deep learning.

  Example: fcd -c GOOGLE -c BING -s 224 example/guitars.csv

Clean image sets

Once downloaded use fcc to quickly inspect the loaded files and rate or classify them. You can also mark them for deletion.

Usage

Call the script from the commandline. If you omit any input parameters it will show you the help page.

Usage: fcc [OPTIONS] INFOLDER [OUTFOLDER]

  FastClass fcc

Options:
  --nocopy TEXT  disable filecopy for cleaned image set  [default: False]
  -h, --help     Show this message and exit.

  ::: FastClass fcc ::: ...a fast way to cleanup/ sort your images when
  building a dataset for deep learning.

  Note: In the application use the following keys: <1>, <2>, ... <9> for
  class assignments or quality ratings <space> assigns <1> <d> to mark a
  deletion <x> to terminate the app/ write output

  Use the buttons to navigate back and forth without changing the
  classification. The current classification of an image is given in the
  title bar (X indicated a mark for deletion). The counter in the titlebar
  gives number of classified images vs the total number in the input folder.

  In the output csv file 1,2 depcit class assignments/ ratings,  -1
  indicates files marked for deletion (if not excluded with -d).
You can’t perform that action at this time.