Image Quality Assessment

This project aims to determine if an image is blurred or not with a blurriness score along with brightness & contrast score. This project is specifically customized for automotive industry.

Techinical quality assessment : Low level degradations such as noise, blur, compression artifacts
Aesthetic quality assessment: semantic level characteristics such as emotions and beauty in images

You can play around here: https://share.streamlit.io/nikhilgunti/image-blur-detection/main/streamlit.py

Data: 20k clear images and 100k blurred images(generated synthetically using input clear images through Simple, Box and Gaussian blur functions)

Label: Label '0' as Not Blur and Label '1' as Blur

Scores: Blurriness, Brightness, Contrast

Features: Maximum and Variance values of Laplacian & Sobel filters

Methodology:

1. Read the input image
2. Convert the RGB image to grayscale mode
3. Compute focus measure using different operators like Laplacian, Sobel, etc.
4. If the computed value is less than the threshold, then the image is labeled as Blur

Model: Of all the trained classification models, I chose Random Forest classifer based on F1 score metric(imbalanced data)

Deployment: I have deployed the application in Streamlit Cloud. You can either select an image to test from the list or upload one of your own

Data prepartion : blur_preprocess.ipynb
Model inference : inference.py
Streamlit application : streamlit.py

img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#Extracting Laplacian filter and Sobel filter values
lapvar, lapmax = varMaxLaplacian(gray)
sobvar, sobmax = varMaxSobel(gray)

#Label and score prediction based on pre-trained random forest model
class_label = ["Blur" if model.predict([[lapmax, lapvar, sobmax, sobvar]])==1 else "Clear"]
blur_score = model.predict_proba([[lapmax, lapvar, sobmax, sobvar]])

#Label and Score formatting
class_label = ''.join(class_label)
blur_score = str(round(blur_score[0][1],2))