The goal is to detect if an image (or group of images) is blurry or has low contrast and store the result in a json file
This repository is using nbdev, a library that allows fully developing a library in Jupyter Notebooks, putting all your code, tests and documentation in one place. That is: you now have a true literate programming environment, as envisioned by Donald Knuth back in 1983!
To extract python functions from jupyter please run "make all" in the root folder.
# Example usage (natively):
$ make all
$ python batch.py -i parts_vehicles/ -s results.json -f
$ python batch.py -i few_images/ -s results_small.json -f# Example usage (in docker):
$ sudo service docker restart
$ jupyter-repo2docker https://github.com/AndreasMerentitis/detect-contrast-blurriness# To test the installation (natively, verified in python 3.6):
$ pytest -vAfter this step copy the link that will be produced to your web browser and run the example from there
- https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/
- https://github.com/WillBrennan/BlurDetection2
- http://scikit-image.org/docs/dev/api/skimage.exposure.html
{
"input_dir": "few_images/",
"results": [
{
"blurry": true,
"clearness_score": 1.249282523178281,
"contrast_score": 0.1591137254901961,
"input_path": "few_images/96201_0.png",
"low_contrast": true
},
{
"blurry": true,
"clearness_score": 0.5864822106149936,
"contrast_score": 0.22583392156862747,
"input_path": "few_images/54575_0.png",
"low_contrast": false
},
{
"blurry": true,
"clearness_score": 2.089412541253312,
"contrast_score": 0.41052254901960783,
"input_path": "few_images/5346_0.png",
"low_contrast": false
},
{
"blurry": false,
"clearness_score": 82.57983014623107,
"contrast_score": 0.4271205882352942,
"input_path": "few_images/37088_0.png",
"low_contrast": false
},
{
"blurry": false,
"clearness_score": 10.196974843081705,
"contrast_score": 0.35345693921568677,
"input_path": "few_images/29017_1.png",
"low_contrast": false
},
{
"blurry": true,
"clearness_score": 0.8789627063002503,
"contrast_score": 0.27411588235294115,
"input_path": "few_images/32947_0.png",
"low_contrast": false
},
{
"blurry_ratio": 0.6666666666666666,
"low_contrast_ratio": 0.16666666666666666
}
],
"threshold": 5.0
}The same files in which the json output (shown above) is calculated are presented here. It is clear that we have three blurry and two sharp images, as indicated also in the json file.
