A little set of tools to batch download images and weed through, delete and classify them into groups for building deep learning image datasets.
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:
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.
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
Once downloaded use fcc to quickly inspect the loaded files and rate or classify them. You can also mark them for deletion.
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).