add data preparation

datapreparation.py

@@ -0,0 +1,188 @@
+# Copyright 2015, Gil Levi and Tal Hassner 
+#
+# The SOFTWARE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use.
+# It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally
+# be caused through its use.
+#
+# The purpose of this repository is to assist readers in reproducing our results on age and gender classification for facial images as
+# described in the following work:
+#
+# Gil Levi and Tal Hassner, Age and Gender Classification Using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of
+# Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015
+#
+# Project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
+# ==============================================================================
+# MIT License
+#
+# Modifications copyright (c) 2018 Image & Vision Computing Lab, Institute of Information Science, Academia Sinica
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# ==============================================================================
+import os
+import random
+import sys
+import argparse
+
+age_list=['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']
+gender_list=['m','f']
+
+def main(args):
+
+	# creat output dir
+	if not os.path.exists(args.outfilesdir):
+		os.mkdir(args.outfilesdir)
+
+	for cur_test_fold_ind in range(5):
+
+		# make output dirs
+		cur_fold_out_foldername='test_fold_is_{0}'.format(cur_test_fold_ind)
+		cur_fold_out_foldername=os.path.join(args.outfilesdir,cur_fold_out_foldername)
+		if not os.path.exists(cur_fold_out_foldername):
+			os.mkdir(cur_fold_out_foldername)
+
+		# read raw data set
+		cur_test_fold_filename = 'fold_{0}_data.txt'.format(cur_test_fold_ind)
+		cur_test_fold_filename = os.path.join(args.rawfoldsdir, cur_test_fold_filename)
+		with open(cur_test_fold_filename) as f:
+			def_lines=f.readlines()
+
+		def_lines.pop(0)
+		# for test files 
+		full_test_list = []
+		for def_line in def_lines:
+
+			def_dic={}
+			subject_dir   = def_line.split('\t')[0]
+			image_subject = def_line.split('\t')[2]
+			image_name='landmark_aligned_face.{0}.{1}'.format(image_subject,def_line.split('\t')[1])
+
+			image_age = def_line.split('\t')[3]
+			if image_age=='(25 23)':
+				 image_age='(25 32)'
+
+			image_gender = def_line.split('\t')[4]
+
+			def_dic['subject_dir']  = subject_dir
+			def_dic['image_name']   = image_name
+			def_dic['image_subject']= image_subject
+			def_dic['image_age']    = image_age
+			def_dic['image_gender'] = image_gender
+
+			full_test_list.append(def_dic)
+
+		images_num = len(full_test_list)
+		indices=random.sample(set(range(0,images_num)), images_num)
+
+		agegender_test_txt_filename=os.path.join(cur_fold_out_foldername, 'agegender_test.txt')
+		if os.path.exists(agegender_test_txt_filename):
+			os.remove(agegender_test_txt_filename)
+
+		agegender_test_txt_file = open(agegender_test_txt_filename,'w+')
+		for ind in indices:
+			subject_dir  = full_test_list[ind]['subject_dir']
+			image_name   = full_test_list[ind]['image_name']
+			image_age    = full_test_list[ind]['image_age']
+			image_gender = full_test_list[ind]['image_gender']
+			image_subject= full_test_list[ind]['image_subject']
+
+			if image_age in age_list and image_gender in gender_list:
+				image_age_index=age_list.index(image_age)
+				image_gender_index=gender_list.index(image_gender)
+				s='{0}/{1} {2} {3}\n'.format(subject_dir,image_name,image_age_index,image_gender_index)
+				agegender_test_txt_file.write(s)
+
+		agegender_test_txt_file.close()
+
+		# for train, val files
+		full_train_list = []
+		train_folds_indices=list(set(range(5)) - set([cur_test_fold_ind]))
+		for train_fold_ind in train_folds_indices:
+			# read raw data
+			cur_train_fold_filename='fold_{0}_data.txt'.format(train_fold_ind)
+			cur_train_fold_filename=os.path.join(args.rawfoldsdir,cur_train_fold_filename)
+			with open(cur_train_fold_filename) as f:
+				def_lines = f.readlines()
+
+			def_lines.pop(0)
+			for def_line in def_lines:
+
+				def_dic={}
+				subject_dir  =def_line.split('\t')[0]
+				image_subject=def_line.split('\t')[2]
+				image_name='landmark_aligned_face.{0}.{1}'.format(image_subject,def_line.split('\t')[1])
+
+				image_age=def_line.split('\t')[3]
+				if image_age == '(25 23)':
+					image_age='(25 32)'
+
+				image_gender=def_line.split('\t')[4]
+
+				def_dic['subject_dir']  =subject_dir
+				def_dic['image_name']   =image_name
+				def_dic['image_subject']=image_subject
+				def_dic['image_age']    =image_age
+				def_dic['image_gender'] =image_gender
+
+				full_train_list.append(def_dic)
+
+		images_num=len(full_train_list)
+		indices=random.sample(set(range(0,images_num)), images_num)
+
+		val_indices=indices[:images_num/10]
+		train_indices=indices[(images_num/10) + 1:]
+		train_subset_indices=indices[(images_num/10) + 1: 2* (images_num/10)]
+
+		cases=['val','train','train_subset']
+		for case,indices in zip(cases,[val_indices,train_indices,train_subset_indices]):
+
+			agegender_txt_filename=os.path.join(cur_fold_out_foldername,'agegender_{0}.txt'.format(case))
+			if os.path.exists(agegender_txt_filename):
+				os.remove(agegender_txt_filename)
+
+			agegender_txt_file=open(agegender_txt_filename, 'w+')
+			for ind in indices:
+				subject_dir=full_train_list[ind]['subject_dir']
+				image_name=full_train_list[ind]['image_name']
+				image_age=full_train_list[ind]['image_age']
+				image_gender=full_train_list[ind]['image_gender']
+				image_subject=full_train_list[ind]['image_subject']
+
+				if image_age in age_list and image_gender in gender_list:
+					image_age_index=age_list.index(image_age)
+					image_gender_index=gender_list.index(image_gender)
+					s='{0}/{1} {2} {3}\n'.format(subject_dir,image_name,image_age_index,image_gender_index)
+					agegender_txt_file.write(s)
+
+			agegender_txt_file.close()
+
+
+def parse_arguments(argv):
+	parser = argparse.ArgumentParser()
+
+	parser.add_argument('--inputdir', type=str, default='./adiencedb/aligned',
+		help='directory of adience dataset')
+	parser.add_argument('--rawfoldsdir', type=str, default='./DataPreparation/FiveFolds/original_txt_files',
+		help='directory of raw folds')
+	parser.add_argument('--outfilesdir', type=str, default='./DataPreparation/FiveFolds/train_val_test_per_fold_agegender',
+		help='directory stored the output files separate from raw data')
+
+	return parser.parse_args(argv)
+
+if __name__ == '__main__':
+	main(parse_arguments(sys.argv[1:]))