From afea89157d3ba0e941fce0a35649cbc1b828dcdb Mon Sep 17 00:00:00 2001 From: mayank Date: Sun, 21 Oct 2018 13:09:05 +0530 Subject: [PATCH] added --- sr.py | 344 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 344 insertions(+) create mode 100644 sr.py diff --git a/sr.py b/sr.py new file mode 100644 index 0000000..d98a1bb --- /dev/null +++ b/sr.py @@ -0,0 +1,344 @@ +import numpy as nmp +import cv2 +import scipy.io as scp +import random +import pickle +from sklearn import svm +from scipy.stats import pearsonr +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPRegressor +from sklearn.linear_model import LogisticRegression +from sklearn import preprocessing +# from scipy.stats import pearsonr +from scipy.stats import pearsonr +orgs=[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, + 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, + 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, + 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, + 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, + 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, + 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, + 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, + 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, + 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, + 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 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48.84324745, 26.13791358, + 0. , 62.4787039 , 37.59887641, 19.96662226, 0. , + 32.69469884, 0. , 41.92234749, 0. , 0. , + 25.34100954, 50.83957042, 66.46194644, 61.41371717, 69.29851566, + 33.27987228, 70.19193144, 53.84629978, 23.2929013 , 0. , + 0. , 35.02681669, 62.90035885, 35.77898515, 69.87055816, + 47.53287131, 54.86002883, 64.16748928, 67.00586811, 68.78335868, + 67.97348673, 50.54857713, 70.9612236 , 26.67330249, 0. , + 31.35996618, 52.45539931, 60.60039324, 0. , 49.04623821, + 45.83052554, 0. , 21.24474003, 24.26413611, 61.17839966, + 32.09558699, 21.7373924 , 37.97355042, 0. , 74.71223055, + 0. , 0. , 34.49728357, 21.3314339 , 0. , + 72.81703406, 0. , 0. , 53.34011511, 25.03062048, + 0. , 25.98402652, 38.46900883, 72.09944236, 31.49196891, + 48.34116742, 25.09633166, 46.58432245, 0. , 30.00013498, + 70.25982518, 0. , 63.01532451, 0. , 25.29582718, + 50.9670401 , 42.60054761, 0. , 38.2549339 , 0. , + 39.49439172, 0. , 46.73519188, 52.00419635, 0. , + 24.27640881, 21.88262166, 24.35211476, 49.72531306, 22.28656663, + 50.87767309, 72.58409007, 47.47269421, 42.01934142, 0. , + 28.18884257, 0. , 25.90239364, 26.10206391, 63.79745841, + 49.72862816, 0. , 0. , 0. , 42.50096822, + 73.25236622, 53.45609932, 0. , 49.46765177, 33.47316109, + 24.62483882, 63.27089048, 0. , 27.44722192, 43.71826643, + 0. , 31.55956871, 42.16153848, 0. , 69.31988838, + 38.49937917, 54.48232334, 51.98430098, 0. , 0. , + 21.78729842, 44.62558143, 45.0968447 , 57.47745564, 0. , + 51.92884613, 0. , 24.89895707, 38.06964087, 21.41886534, + 0. , 55.90459914, 0. , 59.44196572, 67.19668334, + 61.18215742, 0. , 55.77643512, 0. , 53.41030486, + 0. , 25.54790809, 48.0727456 , 34.31882592, 36.80369125, + 0. , 50.1281044 , 50.2223036 , 55.98522773, 48.30468097, + 31.38706429, 69.37405447, 30.06590643, 23.04535862, 69.62940065, + 34.747364 , 66.28179453, 52.61692321, 69.5016062 , 0. , + 69.9783258 , 42.46037967, 22.27981853, 0. , 27.20195442, + 0. , 36.14531005, 0. , 0. , 0. , + 65.3353856 , 40.20353483, 56.81506839, 61.33228069, 21.30512298, + 55.88732005, 45.89200996, 23.93855287, 21.07955184, 59.30217261, + 73.02643348, 63.86475437, 42.82439106, 0. , 25.59001806, + 0. , 0. , 47.21025838, 0. , 34.16120589, + 22.45713134, 21.27322193, 64.00907199, 22.76639531, 62.22323173, + 25.477352 , 24.25551854, 59.08349135, 61.67638379, 60.7673798 , + 56.98843896, 37.02130195, 0. , 0. , 68.0538022 , + 51.60071919, 60.76165786, 55.67451331, 42.18605088, 0. , + 79.6320014 , 0. , 0. , 0. , 29.32258918, + 60.3114101 , 0. , 56.44735042, 0. , 19.53742657, + 22.42779752, 47.8425074 , 52.55754453, 22.5499129 , 51.8433268 , + 0. , 0. , 53.81866417, 0. , 74.25212012, + 28.30780106, 43.68712908, 0. , 43.01621753, 0. , + 63.22821648, 23.81928622, 0. , 42.52118608, 48.86232627, + 0. , 70.9584544 , 68.8158273 , 63.77800814, 31.94008076, + 54.04967816, 28.32827895, 60.57822645, 0. , 47.79653011, + 45.67871474, 43.26832514, 69.45204261, 52.68258889, 0. , + 41.94637326, 58.66222272, 39.51151909, 0. , 26.53064136, + 0. , 42.18685408, 41.433843 , 17.90242446, 26.8204324 , + 42.49899725, 50.91458217, 25.77050586, 66.06308109, 40.20037532, + 21.91503733, 56.16757692, 0. , 34.36752099, 0. , + 52.8906603 , 19.56748372, 31.07407134, 27.80481229, 44.56841487, + 23.47304698, 54.7009997 , 0. , 56.02031619, 69.18511019, + 80.88636722, 65.92309096, 49.05615491, 0. , 39.12654755, + 51.01535769, 63.64900698, 48.6708014 , 0. , 0. , + 44.31871653, 48.20131912, 73.58912301, 52.72380858, 59.76883361, + 29.62778181, 0. , 22.21876535, 23.35933215, 33.17487453, + 48.44187432, 75.27899604, 26.45959905, 29.5567772 , 45.39754889, + 65.70767925, 42.87448315, 21.61691919, 37.1873187 , 56.20055377, + 0. , 46.98621621, 44.80579447, 0. , 0. , + 0. , 0. , 25.29696961, 28.52528489, 0. , + 34.14049053, 23.20315085, 38.88609401, 0. , 0. , + 60.08253117, 0. , 60.0695372 , 59.24963652, 41.49997147, + 52.84704265, 22.43611397, 59.07206937, 22.64523472, 0. , + 51.03826641, 21.36863737, 0. , 21.34582622, 61.19507964, + 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46.68926806, 45.65957935, 30.69813595, + 19.94454652, 67.05636897, 59.82513779, 38.6747713 , 53.13733445, + 30.95353068, 33.81920938, 66.33219374, 23.87507059, 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 60.2039623 , 58.27823033, + 56.94935356, 28.70906308, 23.99436678, 52.17093178, 46.11334734, + 64.48012752, 52.84306696, 19.88077777, 58.15876473, 43.11096126, + 22.6085245 , 27.85283357, 22.74784333, 54.41368683, 47.84995396, + 63.25450438, 40.68903263, 29.21077713, 61.71215264, 52.53970356, + 43.5227752 , 47.88482938, 24.33354975, 55.14997768, 22.72195309, + 25.56142235, 25.76215199, 20.65754723, 74.02538179, 46.69234531, + 35.594522 , 22.55429071, 44.68442987, 48.05937946, 71.74686317, + 34.17122364, 36.02501785, 25.92493301, 44.06401187, 50.09550072, + 20.65866991, 28.42655184, 18.38475281, 65.14447651, 73.67472823, + 49.4094247 , 24.16302779, 24.81676818, 65.45383852, 75.75589095, + 44.84198669, 22.90728765, 25.74324918, 45.10698347, 60.28957268, + 71.87112529, 21.93614318, 24.7848016 , 60.18761196, 59.03672673, + 28.23275064, 22.34725068, 67.2364356 , 50.6184479 , 58.85341168, + 56.51507879, 68.30919109, 21.16647786, 73.00077914, 29.5508494 , + 37.71485968, 22.88254039, 22.80177818, 73.01922494, 43.34393378, + 42.88348282, 49.28064464, 20.37669296, 44.67319601, 40.44999366, + 30.55163058, 27.49648527, 47.99701824, 69.00738835, 60.05538598, + 50.70893478, 34.45272742, 42.48963528, 48.62980021, 63.51936094, + 38.63163726, 43.35766049, 50.29223218, 71.89723737, 57.61656847, + 28.56515376, 51.33216625, 53.8549311 , 51.59891269, 38.38777059, + 55.53380232, 21.98994243, 23.92044941, 53.82623745, 49.57856814, + 48.51290495, 34.27540696, 21.86108278, 47.86847842, 56.93055184, + 47.92923241, 35.9191565 , 33.23060383, 74.84023977, 61.57439856, + 51.5098204 , 21.24550719, 66.50936891, 51.20181454, 55.75483745, + 40.29495291, 25.11356489, 21.0137419 , 58.4941291 , 28.61033537, + 28.61970866, 21.63835686, 21.859096 , 76.9708069 , 75.00164168, + 25.03934265, 26.33453159, 38.99377495, 54.17803636, 44.6232087 , + 32.19273642, 20.51356186, 21.87702387, 57.7862583 , 46.97249402, + 36.79926614, 48.94357165, 36.06660549, 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , + 0. , 0. ] +DMOS.pop(808) +orgs.pop(808) +def histo(a): + row, col = a.shape # img is a grayscale image + y = nmp.zeros((256)) + + for i in range(0,row): + for j in range(0,col): + v = a[i][j] + y[v] = y[v] + 1 + # x = nmp.arange(0,256) + print("Calculating...") + return y +mat = scp.loadmat('refnames_all.mat') +data = nmp.array(mat.get('refnames_all')) +JP2K=[] +JPEG=[] +WN=[] +GBLUR=[] +FF=[] +for i in range (0,227): + c = data[0][i][0] + a=((cv2.imread( str('./refimgs/'+c) , 0 ) )) + b = (cv2.imread( str('./jp2k/img'+str(i+1)+ '.bmp') , 0 )) + JP2K.append(histo(a)-histo(b)); + #print(len(Features)) +jp2k_x_train,jp2k_x_test,jp2k_y_train,jp2k_y_test=train_test_split(JP2K,DMOS[0:227],test_size=0.2) +for i in range (227,460): + c = data[0][i][0] + a=((cv2.imread( str('./refimgs/'+c) , 0 ) )) + b = (cv2.imread( str('./jpeg/img'+str(i+1-227)+ '.bmp') , 0 )) + JPEG.append(histo(a)-histo(b)) + #print(len(Features)) +jpeg_x_train,jpeg_x_test,jpeg_y_train,jpeg_y_test=train_test_split(JPEG,DMOS[227:460],test_size=0.2) +for i in range (460,634): + c = data[0][i][0] + a=((cv2.imread( str('./refimgs/'+c) , 0 ) )) + b = (cv2.imread( str('./wn/img'+str(i+1-460)+ '.bmp') , 0 )) + WN.append(histo(a)-histo(b)) + #print(len(Features)) +wn_x_train,wn_x_test,wn_y_train,wn_y_test=train_test_split(WN,DMOS[460:634],test_size=0.2) +for i in range (634,808): + c = data[0][i][0] + a=((cv2.imread( str('./refimgs/'+c) , 0 ) )) + b = (cv2.imread( str('./gblur/img'+str(i+1-634)+ '.bmp') , 0 )) + GBLUR.append(histo(a)-histo(b)) + #print(len(Features)) +gblur_x_train,gblur_x_test,gblur_y_train,gblur_y_test=train_test_split(GBLUR,DMOS[634:808],test_size=0.2) +for i in range (809,982): + c = data[0][i][0] + a=((cv2.imread( str('./refimgs/'+c) , 0 ) )) + b = (cv2.imread( str('./fastfading/img'+str(i+1-808)+ '.bmp') , 0 )) + FF.append(histo(a)-histo(b)) + #print(len(Features)) +ff_x_train,ff_x_test,ff_y_train,ff_y_test=train_test_split(FF,DMOS[808:981],test_size=0.2) +#ff_y_train= nmp.pad(ff_y_train, (0,1), 'constant') +#print(len(gblur_y_train)) +total_X_train=ff_x_train+gblur_x_train+wn_x_train+jpeg_x_train+jp2k_x_train +total_X_test=ff_x_test+gblur_x_test+wn_x_test+jpeg_x_test+jp2k_x_test +total_Y_test=ff_y_test+gblur_y_test+wn_y_test+jpeg_y_test+jp2k_y_test +total_Y_train=ff_y_train+gblur_y_train+wn_y_train+jpeg_y_train+jp2k_y_train + + +# nn = MLPRegressor(hidden_layer_sizes=(981,1),max_iter=1000) +# nn.fit(total_X_train,total_Y_train) + +# logreg = LogisticRegression(C= 1000, random_state=0,solver = 'lbfgs', +# multi_class='multinomial',max_iter=1000) +# lab = preprocessing.LabelEncoder() +# logreg.fit(total_X_train,lab.fit_transform(total_Y_train)) +# print("ho gya fit") +# answ = lab.inverse_transform(logreg.predict(total_X_test)) +nn = MLPRegressor(hidden_layer_sizes=(1000,1),activation='logistic',max_iter=1000,solver='lbfgs') +nn.fit(total_X_train,total_Y_train) +answ = nn.predict(total_X_test) + +# cc=svm.SVR() +# sw = cc.fit(finalX_train,FinalY_train) + +# answ = nn.predict(total_X_test) + +print (answ) +print(total_Y_test) + +print("pearson") +# print(pearsonr(answ,total_Y_test)) +vb = nmp.corrcoef(answ,total_Y_test) +# print(vb)nn = MLPRegressor(hidden_layer_sizes=(227,1),max_iter=500) +# nn.fit(finalX_train,FinalY_train)