From b0bb218b2a4b937e48b36dd33e82fe757af53e9d Mon Sep 17 00:00:00 2001 From: jha11aditya Date: Tue, 2 Mar 2021 23:29:59 +0530 Subject: [PATCH] predictor --- predictor.py | 57 ++++++++++++++++++++++++++++++++++++++++ subject01.happy.png | Bin 0 -> 23271 bytes subject07.surprised.png | Bin 0 -> 17292 bytes subject09.glasses.png | Bin 0 -> 24358 bytes subject11.surprised.png | Bin 0 -> 22232 bytes subject14.leftlight.png | Bin 0 -> 28494 bytes svm_model.sav | Bin 0 -> 4639719 bytes trainer.py | 24 ++++++++++------- 8 files changed, 72 insertions(+), 9 deletions(-) create mode 100644 predictor.py create mode 100644 subject01.happy.png create mode 100644 subject07.surprised.png create mode 100644 subject09.glasses.png create mode 100644 subject11.surprised.png create mode 100644 subject14.leftlight.png create mode 100644 svm_model.sav diff --git a/predictor.py b/predictor.py new file mode 100644 index 00000000..2dc3c807 --- /dev/null +++ b/predictor.py @@ -0,0 +1,57 @@ +import os +import sys +import pandas as pd +import numpy as np +from skimage.io import imread, imshow, imsave +from skimage.transform import resize +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn import svm +from sklearn import metrics +import pickle +from skimage import img_as_ubyte + +tempdir = "predictor_temp" + +if len(sys.argv) < 3: + print("no input image") + exit(0) + +if len(sys.argv) < 2: + print("no input model") + exit(0) + +svm_model = pickle.load(open(sys.argv[1], 'rb')) + +os.system('mkdir -p ' + tempdir) +os.system("python3 face_cutter.py " + sys.argv[2] + " " + tempdir ) +img_files = [name for name in os.listdir(tempdir) if not os.path.isdir(os.path.join(tempdir, name)) ] +print(img_files) +cutf = tempdir +"/"+img_files[0] +os.system("python3 hogger.py " + cutf + " " + tempdir ) +os.system("rm " + cutf) +img_files = [name for name in os.listdir(tempdir) if not os.path.isdir(os.path.join(tempdir, name)) ] +hogf = tempdir + "/" + img_files[0] + +imgdat = imread(hogf, as_gray=True) +os.system("rm -rf " + tempdir) +imgdat = resize(imgdat, (64,64)) + +imgdat = img_as_ubyte(imgdat) + + +flat_imgdat = np.array( imgdat ).flatten() +print(flat_imgdat.shape) +X = np.array(flat_imgdat) +X = X.reshape(-1,1) +X = StandardScaler().fit_transform(X) +print(X.shape,X) +# pca = PCA(n_components=128) +# pcaofX = pca.fit_transform(X) + + +res = svm_model.predict(X.T) + +print("Prediction:",res) + diff --git a/subject01.happy.png b/subject01.happy.png new file mode 100644 index 0000000000000000000000000000000000000000..f4d98009c5b81e2156134462315f6f965595b87e GIT binary patch literal 23271 zcmV)XK&`)tP)004R>004l5008;`004mK004C`008P>0026e000+ooVrmw00002 zVoOIv0063uBQgL000(qQO+^RI1P2Ku9OKQu)c^ol?@2^KRCwC#{YkH7S(YY-eQT}V zoOADe?^)yKeMCk^W@M+zN{UQnktz~JN~nTRoQQy+fdmZ%%?M33C-e(6&_ICxfJPcn zf&h{wqR0XPilri}No7_x5gCn#cjji#?A_*^z4vM~xMt?&9`3s5X 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