58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
|
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)
|
||
|
|
|