predict_test_66_7_percent.py

import glob
import numpy as np
import os
import pickle
import sys
#sys.path.append('/data4/xgboost/python-package')
from xgboost import XGBClassifier
import xgboost as xgb
import matplotlib.pyplot as plt

facials = ['anger','contentment','disgust','happy','sadness','surprise']
result = {}

pairsImage = pickle.load(open('testPairs.pkl','rb'))


import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image_folder", required=True,
	help="path to image folder")
ap.add_argument("-s", "--save",  required=True,
	help="save pair of images to file")
args = vars(ap.parse_args())


def majority(pred):
	pred = pred.mean()
	if pred > 0.5: return 1
	else:	return 0
	'''
	return pred.mean(axis=0).argmax()
	count = [0,0]
	for i in range(len(pred)):
	   count[pred[i]] =count[pred[i]] + 1
	if count[0] > count[1]:
		return 0
	else:
		return 1

	'''
def showFaces(s1,s2,path):
	import cv2
	
	img = cv2.imread(path + s1 + '/0001.jpg')
	fig = plt.figure()
	fig.add_subplot(1,2,1)
	plt.imshow(img)
	fig.add_subplot(1,2,2)
	plt.imshow(cv2.imread(path + s2 + '/0001.jpg'))
	plt.show()	


for facial in facials:
	#model = pickle.load(open(facial + '/model.dat','rb'))
	model = xgb.Booster()
	model.load_model(facial + '/model2.dat')
	subjects = os.listdir(facial + '/' + args["image_folder"])
	
	sub = []
	while(len(subjects) > 0):
		s1 = subjects[0]
		for i in range(1,len(subjects)):
			if subjects[i] == pairsImage[s1]:
				break
		s2 = pairsImage[s1]
		subjects.pop(i)
		subjects.pop(0)
		name1 = '' + s1 + '.mp4'
		filenames = os.listdir(facial + '/' + args["image_folder"]+ '/' + s1)
		X = []
		for filename in filenames:
			X.append(np.load(facial + '/' + args["image_folder"]+ '/' + s1 + '/' + filename).flatten().tolist())
		X = np.array(X)
		pred_f = model.predict(xgb.DMatrix(X))
		pred1 = pred_f.mean()

		name2 = '' + s2 + '.mp4'
		filenames = os.listdir(facial + '/' + args["image_folder"]+ '/' + s2)
		X = []
		for filename in filenames:
			X.append(np.load(facial + '/' + args["image_folder"]+ '/' + s2 + '/' + filename).flatten().tolist())
		X = np.array(X)
		pred_f = model.predict(xgb.DMatrix(X))
		pred2 = pred_f.mean()
		if pred2> pred1:
			result[name2] = 'fake'
			result[name1] = 'true'
		else:
			result[name2] = 'true'
			result[name1] = 'fake'
		#showFaces(s1,s2,'/server/dataset/Real_Fake/data/testImage/')


		
	'''
				
	for subject in subjects:
		X = []
		pred_files = []
		name = '' + subject + '.mp4'
		filenames = os.listdir(facial + '/dataTest/' + subject)
		for filename in filenames:
			X.append(np.load(facial + '/dataTest/'+ subject + '/' + filename).flatten().tolist())
		X = np.array(X)
		if len(X) <=0:	continue
		
		pred_f = model.predict(xgb.DMatrix(X))
		pred = majority(pred_f)
		if pred == 0: result[name] = 'true'
		else:	result[name] = 'fake'
	'''
pickle.dump(result, open(args["save"],"wb"))