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‎Tutorial48_reference_based_image_quality.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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"""
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Older versions of scikit-image: older than 0.16
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https://scikit-image.org/docs/dev/api/skimage.measure.html
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Newer scikit-image: 0.17 or later
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https://scikit-image.org/docs/stable/api/skimage.metrics.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, metrics
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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/noisy_images/sandstone.tif", 1)
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img = cv2.imread("images/noisy_images/sandstone_25sigma_noisy.tif", 1)
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################################################################
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#skimage tools
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#Mean square error
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#Older skimage versions, older than 0.17: skimage.measure.compare_mse
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#skimage.metrics.mean_squared_error
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mse_skimg = metrics.mean_squared_error(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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#Older versions of skimage: skimage.measure.compare_psnr
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#skimage.metrics.peak_signal_noise_ratio
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psnr_skimg = metrics.peak_signal_noise_ratio(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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#Older versions of skimage: skimage.measure.compare_nrmse
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rmse_skimg = metrics.normalized_root_mse(ref_img, img)
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print("RMSE: based on scikit-image = ", rmse_skimg)
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from skimage.metrics import structural_similarity as ssim
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ssim_skimg = ssim(ref_img, img,
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data_range = img.max() - img.min(),
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multichannel = True)
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print("SSIM: based on scikit-image = ", ssim_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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‎Tutorial49_no_reference_image_quality_sharpness.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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"""
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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/blurred_images/sandstone.tif", 1)
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img2 = cv2.imread("images/blurred_images/sandstone_2sigma_blur.tif", 1)
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img3 = cv2.imread("images/blurred_images/sandstone_3sigma_blur.tif", 1)
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img4 = cv2.imread("images/blurred_images/sandstone_5sigma_blur.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)

‎tutorial04_appreciating the simplicity of python.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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"""
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Introductory python tutorials for image processing
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Tutorial 3: Appreciating the simplicity of Python code
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"""
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from skimage import io, filters
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from matplotlib import pyplot as plt
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img = io.imread('images/Osteosarcoma_01_8bit_salt_pepper_cropped.tif')
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gaussian_img = filters.gaussian(img, sigma=1)
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plt.imshow(gaussian_img)

‎tutorial09_top5_libraries.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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"""
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@author: Sreenivas Bhattiprolu
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"""
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import skimage
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from skimage import io
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img1 = io.imread('images/Osteosarcoma_01.tif')
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import cv2
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img2 = cv2.imread('images/Osteosarcoma_01.tif')
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import numpy as np
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a=np.ones((5,5))
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import pandas as pd
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df = pd.read_csv('images/image_measurements.csv')
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print(df.head())
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from matplotlib import pyplot as plt
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plt.imshow(img1)

‎tutorial10_first_lines_of_code.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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"""
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This is my first program
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"""
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#Here goes my first line
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print("Hello World")
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a=2
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b=10.
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c=2*a
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d=2*b
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e=b/a
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f=b**2
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g=-32.5
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print(abs(g))
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print(round(g))
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h=7%2 #remainder
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print(h)

‎tutorial11_operators_basic_math.py‎

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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
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#This tutorial explains operators and basic math
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a = 2 #assign a variable
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a == 2 #logical operator
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b=5
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a<b
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#Why does this matter?
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if a<b:
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print("Wow, coding is easy!")
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else:
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print('I hope I can code one day')
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#Simple math equation: E=mc^2
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#External input:
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m=input("Please enter mass: ")
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m=int(m)
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#m = 10 #kg
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c = 300000000 #m/s
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E = m*(c**2)
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print("Energy=", E)
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print("Energy = ", '{:.2E}'.format(E))

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