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‎123-reference_based_image_quality.py

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#!/usr/bin/env python
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__author__ = "Sreenivas Bhattiprolu"
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__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
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# https://youtu.be/jZ97DtM3YMQ
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"""
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@author: Sreenivas Bhattiprolu
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https://scikit-image.org/docs/dev/api/skimage.measure.html
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https://pypi.org/project/sewar/
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https://sewar.readthedocs.io/en/latest/_modules/sewar/full_ref.html#ergas
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"""
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import cv2
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import numpy as np
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from sewar import full_ref
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from skimage import measure
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#Reference and image to be compared must be of the same size
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ref_img = cv2.imread("images/BSE.jpg", 1)
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img = cv2.imread("images/BSE_25sigma_noisy.jpg", 1)
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################################################################
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#skimage tools
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#Mean square error
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mse_skimg = measure.compare_mse(ref_img, img)
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print("MSE: based on scikit-image = ", mse_skimg)
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#Same as PSNR available in sewar
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psnr_skimg = measure.compare_psnr(ref_img, img, data_range=None)
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print("PSNR: based on scikit-image = ", psnr_skimg)
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#Normalized root mean squared error
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rmse_skimg = measure.compare_nrmse(ref_img, img)
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print("RMSE: based on scikit-image = ", rmse_skimg)
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###############################################################
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#ERGAS Global relative error
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"""calculates global relative error
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GT: first (original) input image.
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P: second (deformed) input image.
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r: ratio of high resolution to low resolution (default=4).
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ws: sliding window size (default = 8).
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:returns: float -- ergas value.
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"""
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ergas_img = full_ref.ergas(ref_img, img, r=4, ws=8)
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print("EGRAS: global relative error = ", ergas_img)
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####################################################################
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#Multiscale structural similarity index
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"""calculates multi-scale structural similarity index (ms-ssim).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param weights: weights for each scale (default = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).
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:param ws: sliding window size (default = 11).
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:param K1: First constant for SSIM (default = 0.01).
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:param K2: Second constant for SSIM (default = 0.03).
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:param MAX: Maximum value of datarange (if None, MAX is calculated using image dtype).
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:returns: float -- ms-ssim value.
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"""
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msssim_img=full_ref.msssim(ref_img, img, weights=[0.0448, 0.2856, 0.3001, 0.2363, 0.1333], ws=11, K1=0.01, K2=0.03, MAX=None)
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print("MSSSIM: multi-scale structural similarity index = ", msssim_img)
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##############################################################################
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#PSNR
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"""calculates peak signal-to-noise ratio (psnr).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param MAX: maximum value of datarange (if None, MAX is calculated using image dtype).
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:returns: float -- psnr value in dB.
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"""
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psnr_img=full_ref.psnr(ref_img, img, MAX=None)
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print("PSNR: peak signal-to-noise ratio = ", psnr_img)
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##########################################################################
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#PSNRB: Calculates PSNR with Blocking Effect Factor for a given pair of images (PSNR-B)
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"""Calculates PSNR with Blocking Effect Factor for a given pair of images (PSNR-B)
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:param GT: first (original) input image in YCbCr format or Grayscale.
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:param P: second (corrected) input image in YCbCr format or Grayscale..
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:return: float -- psnr_b.
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"""
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#psnrb_img = full_ref.psnrb(ref_img, img)
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#print("PSNRB: peak signal-to-noise ratio with blocking effect = ", psnrb_img)
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#######################################################################
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#relative average spectral error (rase)
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"""calculates relative average spectral error (rase).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param ws: sliding window size (default = 8).
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:returns: float -- rase value.
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"""
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RASE_img = full_ref.rase(ref_img, img, ws=8)
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#print("RASE: relative average spectral error = ", RASE_img)
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######################################################################
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#RMSE
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"""calculates root mean squared error (rmse).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:returns: float -- rmse value.
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"""
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rmse_img = full_ref.rmse(ref_img, img)
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print("RMSE: root mean squared error = ", rmse_img)
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######################################################################
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#root mean squared error (rmse) using sliding window
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"""calculates root mean squared error (rmse) using sliding window.
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param ws: sliding window size (default = 8).
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:returns: tuple -- rmse value,rmse map.
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"""
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rmse_sw_img = full_ref.rmse_sw(ref_img, img, ws=8)
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#print("RMSE_SW: root mean squared error with sliding window = ", rmse_sw_img)
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#########################################################################
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#calculates spectral angle mapper (sam).
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"""calculates spectral angle mapper (sam).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:returns: float -- sam value.
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"""
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ref_sam_img = full_ref.sam(ref_img, img)
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print("REF_SAM: spectral angle mapper = ", ref_sam_img)
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######################################################################
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#Spatial correlation coefficient
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full_ref.scc(ref_img, img, win=[[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], ws=8)
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#Structural similarity index
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"""calculates structural similarity index (ssim).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param ws: sliding window size (default = 8).
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:param K1: First constant for SSIM (default = 0.01).
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:param K2: Second constant for SSIM (default = 0.03).
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:param MAX: Maximum value of datarange (if None, MAX is calculated using image dtype).
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:returns: tuple -- ssim value, cs value.
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"""
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ssim_img = full_ref.ssim(ref_img, img, ws=11, K1=0.01, K2=0.03, MAX=None, fltr_specs=None, mode='valid')
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print("SSIM: structural similarity index = ", ssim_img)
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##############################################################################
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#Universal image quality index
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"""calculates universal image quality index (uqi).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param ws: sliding window size (default = 8).
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:returns: float -- uqi value.
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"""
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UQI_img = full_ref.uqi(ref_img, img, ws=8)
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print("UQI: universal image quality index = ", UQI_img)
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##############################################################################
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#Pixel Based Visual Information Fidelity (vif-p)
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"""calculates Pixel Based Visual Information Fidelity (vif-p).
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:param GT: first (original) input image.
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:param P: second (deformed) input image.
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:param sigma_nsq: variance of the visual noise (default = 2)
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:returns: float -- vif-p value.
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"""
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VIFP_img = full_ref.vifp(ref_img, img, sigma_nsq=2)
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print("VIFP: Pixel Based Visual Information Fidelity = ", VIFP_img)
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‎124-evaluate_sharpness_of_image.py

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#!/usr/bin/env python
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__author__ = "Sreenivas Bhattiprolu"
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__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
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# https://youtu.be/VHIM2FKGLzc
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"""
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@author: Sreenivas Bhattiprolu
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Sharpness Estimation for Document and Scene Images
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by Jayant Kumar , Francine Chen , David Doermann
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http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=33CD0038A0D2D24AE2C4F1A30B6EF1A4?doi=10.1.1.359.7002&rep=rep1&type=pdf
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https://github.com/umang-singhal/pydom
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pip install git+https://github.com/umang-singhal/pydom.git
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#Use difference of differences in grayscale values
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of a median-filtered image as an indicator of edge sharpness
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"""
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from dom import DOM
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import cv2
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#img = cv2.imread("images/image_quality_estimation/02_2sigma_blurred.tif", 1)
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img1 = cv2.imread("images/image_quality_estimation/02.tif", 1)
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img2 = cv2.imread("images/image_quality_estimation/02_2sigma_blurred.tif", 1)
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img3 = cv2.imread("images/image_quality_estimation/02_3sigma_blurred.tif", 1)
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img4 = cv2.imread("images/image_quality_estimation/02_5sigma_blurred.tif", 1)
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# initialize DOM
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iqa = DOM()
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#Calculate scores
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score1 = iqa.get_sharpness(img1)
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score2 = iqa.get_sharpness(img2)
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score3 = iqa.get_sharpness(img3)
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score4 = iqa.get_sharpness(img4)
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print("Sharpness for reference image:", score1)
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print("Sharpness for 2 sigma blurred image:", score2)
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print("Sharpness for 3 sigma blurred image:", score3)
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print("Sharpness for 5 sigma blurred image:", score4)

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