Blur Detection Haar Wavelet opencv-python

Blur Detection of Digital Images using Haar Wavelet Transform. This is the implementation of this paper

Prerequisites

• Python3
• opencv-python
• PyWavelets

How to use

Make sure python3 and pip is installed. Then, install cv2 and PyWavelets.

# install opencv-python
pip install cv2
# install PyWavelets
pip install PyWavelets

To run the python script with the sample images uploaded to this repo.

python blur_wavelet.py -i images/blur

The output will be

images/blur/Original_391.jpg, Per: 0.00011, blur extent: 41.142, is blur: True
images/blur/Original_426.jpg, Per: 0.00000, blur extent: 81.158, is blur: True
images/blur/Original_292.jpg, Per: 0.00000, blur extent: 50.951, is blur: True
images/blur/Original_244.jpg, Per: 0.00000, blur extent: 299.500, is blur: True
images/blur/Original_297.jpg, Per: 0.00282, blur extent: 10.708, is blur: False
images/blur/Original_254.jpg, Per: 0.00017, blur extent: 1445.000, is blur: True
images/blur/Original_6.jpg, Per: 0.00000, blur extent: 9.936, is blur: True
images/blur/Original_5.jpg, Per: 0.00032, blur extent: 12.265, is blur: True
images/blur/Original_359.jpg, Per: 0.00000, blur extent: 121.937, is blur: True
images/blur/Original_217.jpg, Per: 0.00016, blur extent: 28.323, is blur: True

Configure the edge threshold

The paper defines two parameters in order to configure the algorithm. The first is threshold. It is used to select if a pixel of Haar transform image is considered as Edge Point. Default value is 35. If you select a smaller threshold, it is more likely an image to be classified as blur.

The default threshold is 35. You can define it by adding the parameter in the bash call. In the following call to the script we select 25 as threshold.

python blur_wavelet.py -i images/noblur --threshold 25

The output will be

images/noblur/DSCN0593.JPG, Per: 0.00486, blur extent: 1.878, is blur: False
images/noblur/DSCN6481.JPG, Per: 0.00108, blur extent: 3.424, is blur: False
images/noblur/DSC02100.JPG, Per: 0.00181, blur extent: 0.974, is blur: False
images/noblur/DSC00700.JPG, Per: 0.00782, blur extent: 1.117, is blur: False
images/noblur/DSC05345.JPG, Per: 0.00424, blur extent: 0.741, is blur: False
images/noblur/DSC01910.JPG, Per: 0.00694, blur extent: 1.269, is blur: False
images/noblur/IMG_0066.JPG, Per: 0.00250, blur extent: 4.715, is blur: False
images/noblur/IMG_0487.JPG, Per: 0.01155, blur extent: 1.795, is blur: False
images/noblur/DSC05405.JPG, Per: 0.01503, blur extent: 0.645, is blur: False
images/noblur/DSCN0375.JPG, Per: 0.00356, blur extent: 2.422, is blur: False

Configure the decision threshold

In the paper is called MinZero. If Per is smaller than MinZero the image is classified as blur. The default value is 0.001 . In order to configure the MinZero threshold, run the script with the flag -d MIN_ZERO

python blur_wavelet.py -i images/noblur -d 0.005

The output will be

images/noblur/DSCN0593.JPG, Per: 0.00459, blur extent: 2.029, is blur: True
images/noblur/DSCN6481.JPG, Per: 0.00068, blur extent: 5.012, is blur: True
images/noblur/DSC02100.JPG, Per: 0.00135, blur extent: 1.786, is blur: True
images/noblur/DSC00700.JPG, Per: 0.00782, blur extent: 1.405, is blur: False
images/noblur/DSC05345.JPG, Per: 0.00343, blur extent: 0.929, is blur: True
images/noblur/DSC01910.JPG, Per: 0.00647, blur extent: 1.697, is blur: False
images/noblur/IMG_0066.JPG, Per: 0.00205, blur extent: 5.373, is blur: True
images/noblur/IMG_0487.JPG, Per: 0.01045, blur extent: 2.182, is blur: False
images/noblur/DSC05405.JPG, Per: 0.01310, blur extent: 0.775, is blur: False
images/noblur/DSCN0375.JPG, Per: 0.00296, blur extent: 2.865, is blur: True

The prediction fails in this case.

Save output as .JSON

In order to save the output as .JSON, run the script with the flag -s SAVE_PATH.json . Example: save .json output in the project directory as output.json:

python blur_wavelet.py -i images/blur -s output.json

Checking the .json file:

[
    {
        "blur extent": 41.14235294117647,
        "input_path": "images/blur/Original_391.jpg",
        "is blur": true,
        "per": 0.0001104911330865698
    },
    {
        "blur extent": 81.15789473684211,
        "input_path": "images/blur/Original_426.jpg",
        "is blur": true,
        "per": 0.0
    }
]

Acknowlegements

Dataset

The sample images have been taken from this blur image dataset

E. Mavridaki, V. Mezaris, "No-Reference blur assessment in natural images using Fourier transform and spatial pyramids", Proc. IEEE International Conference on Image Processing (ICIP 2014), Paris, France, October 2014.

The dataset consists of undistorted, naturally-blurred and artificially-blurred images for image quality assessment purposes. Download the dataset from here: http://mklab.iti.gr/files/imageblur/CERTH_ImageBlurDataset.zip

Paper

This algorithm is based on this paper

Tong, Hanghang & Li, Mingjing & Zhang, Hongjiang & Zhang, Changshui. (2004). Blur detection for digital images using wavelet transform. IEEE International Conference on Multimedia and EXPO. 1. 17 - 20 Vol.1. 10.1109/ICME.2004.1394114.

License

(c) 2019 Pedro Rodenas. MIT License