forked from 170010011/fr
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
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import os
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import sys
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import pandas as pd
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import numpy as np
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from skimage.io import imread, imshow, imsave
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from skimage.transform import resize
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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from sklearn.model_selection import train_test_split
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from sklearn import svm
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from sklearn import metrics
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import pickle
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from skimage import img_as_ubyte
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tempdir = "predictor_temp"
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if len(sys.argv) < 3:
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print("no input image")
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exit(0)
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if len(sys.argv) < 2:
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print("no input model")
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exit(0)
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svm_model = pickle.load(open(sys.argv[1], 'rb'))
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os.system('mkdir -p ' + tempdir)
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os.system("python3 face_cutter.py " + sys.argv[2] + " " + tempdir )
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img_files = [name for name in os.listdir(tempdir) if not os.path.isdir(os.path.join(tempdir, name)) ]
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print(img_files)
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cutf = tempdir +"/"+img_files[0]
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os.system("python3 hogger.py " + cutf + " " + tempdir )
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os.system("rm " + cutf)
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img_files = [name for name in os.listdir(tempdir) if not os.path.isdir(os.path.join(tempdir, name)) ]
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hogf = tempdir + "/" + img_files[0]
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imgdat = imread(hogf, as_gray=True)
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os.system("rm -rf " + tempdir)
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imgdat = resize(imgdat, (64,64))
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imgdat = img_as_ubyte(imgdat)
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flat_imgdat = np.array( imgdat ).flatten()
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print(flat_imgdat.shape)
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X = np.array(flat_imgdat)
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X = X.reshape(-1,1)
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X = StandardScaler().fit_transform(X)
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print(X.shape,X)
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# pca = PCA(n_components=128)
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# pcaofX = pca.fit_transform(X)
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res = svm_model.predict(X.T)
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print("Prediction:",res)
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