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)