A simple eda tool to check basic information of images under a directory(images are found recursively). This tool was made to quickly check info and prevent mistakes on reading, resizing, and normalizing images as inputs for neural networks. It can be used when first joining an image competition or training CNNs with images!
Notes:
- All images are converted to 3-channel(rgb) images. When images that have various channels are mixed, results can be misleading.
- uint8 and uint16 data types are supported. If different data types are mixed, error occurs.
pip install basic-image-edaprerequisites:
- opencv-python
- skimage.io
- numpy
- matplotlib
- tqdm
simple one line command!
basic-image-eda <data_dir>or
basic-image-eda <data_dir> --extensions png tiff --threads 12 --dimension_plot --channel_hist --nonzero --hw_division_factor 2.0
Options:
-e --extensions target image extensions.(default=['png', 'jpg', 'jpeg'])
-t --threads number of multiprocessing threads. if zero, automatically counted.(default=0)
-d --dimension_plot show dimension(height/width) scatter plot.(default=False)
-c --channel_hist show channelwise pixel value histogram. takes longer time.(default=False)
-n --nonzero calculate values only from non-zero pixels of the images.(default=False)
-f --hw_division_factor divide height,width of the images by this factor to make pixel value calculation faster.
Height, width information are not changed and will be printed correctly.(default=1.0)
-V --version show version.from basic_image_eda import BasicImageEDA
if __name__ == "__main__": # for multiprocessing
data_dir = "./data"
# below are default values.
extensions = ['png', 'jpg', 'jpeg']
threads = 0
dimension_plot = False
channel_hist = False
nonzero = False
hw_division_factor = 1.0
BasicImageEDA.explore(data_dir)
# or
BasicImageEDA.explore(data_dir, extensions, threads, dimension_plot, channel_hist, nonzero, hw_division_factor)Results on celeba dataset (test set)
|
|
found 19962 images.
Using 12 threads.
*--------------------------------------------------------------------------------------*
number of images | 19962
dtype | uint8
channels | [3]
extensions | ['jpg']
min height | 85
mean height | 591.8215108706543
max height | 5616
min width | 85
mean width | 490.2976655645727
max width | 5616
mean height/width ratio | 1.207065732587525
recommended input size | [592 488] (h x w, multiples of 8)
recommended input size | [592 496] (h x w, multiples of 16)
recommended input size | [576 480] (h x w, multiples of 32)
channel mean(0~1) | [0.49546506 0.42573904 0.39331011]
channel std(0~1) | [0.32161251 0.30237885 0.30192492]
*--------------------------------------------------------------------------------------*
download site: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
paper: S. Yang, P. Luo, C. C. Loy, and X. Tang, "From Facial Parts Responses to Face Detection: A Deep Learning Approach", in IEEE International Conference on Computer Vision (ICCV), 2015
Results on NIH Chest X-ray dataset (images_001.tar.gz)
|
|
found 4999 images.
Using 12 threads.
*--------------------------------------------------------------------------------------*
number of images | 4999
dtype | uint8
channels | [1, 4]
extensions | ['png']
min height | 1024
mean height | 1024.0
max height | 1024
min width | 1024
mean width | 1024.0
max width | 1024
mean height/width ratio | 1.0
recommended input size | [1024 1024] (h x w, multiples of 8)
recommended input size | [1024 1024] (h x w, multiples of 16)
recommended input size | [1024 1024] (h x w, multiples of 32)
channel mean(0~1) | [0.51725466 0.51725466 0.51725466]
channel std(0~1) | [0.25274113 0.25274113 0.25274113]
*--------------------------------------------------------------------------------------*
data provider: NIH Clinical Center
download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
paper: Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summers, ChestX-ray8:
Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of
Common Thorax Diseases, IEEE CVPR, pp. 3462-3471, 2017
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