convert_to_records_multiCPU.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import math
# use multi CPU
import multiprocessing
import os
import time
from datetime import datetime

import cv2
import dlib
import numpy as np
import pandas as pd
import tensorflow as tf
from imutils.face_utils import FaceAligner
from scipy.io import loadmat
from sklearn.model_selection import train_test_split

FLAGS = None


def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))


def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def convert_to(data_set, name, i, base_path, dataset_name):
    """Converts a dataset to tfrecords."""
    file_name = data_set.file_name
    genders = data_set.gender
    ages = data_set.age
    face_score = data_set.score
    second_face_score = data_set.second_score
    num_examples = data_set.shape[0]
    if dataset_name == "imdb":
        image_base_dir = os.path.join(base_path, "imdb_crop")
    elif dataset_name == "wiki":
        image_base_dir = os.path.join(base_path, "wiki_crop")
    else:
        raise NameError

    # initialize dlib's face detector (HOG-based) and then create
    # the facial landmark predictor and the face aligner
    shape_predictor = 'shape_predictor_68_face_landmarks.dat'
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(shape_predictor)
    fa = FaceAligner(predictor, desiredFaceWidth=160)

    error = 0
    total = 0
    # if images.shape[0] != num_examples:
    #     raise ValueError('Images size %d does not match label size %d.' %
    #                      (images.shape[0], num_examples))
    # rows = images.shape[1]
    # cols = images.shape[2]
    # depth = images.shape[3]
    tfrecords_path = os.path.join(base_path, name)
    if not os.path.exists(tfrecords_path):
        os.mkdir(tfrecords_path)
    filename = os.path.join(tfrecords_path, name + '-%.3d' % i + '.tfrecords')
    print('Writing', filename)
    with tf.python_io.TFRecordWriter(filename) as writer:
        for index in range(num_examples):
            if face_score[index] < 0.75:
                continue
            # if (~np.isnan(second_face_score[index])) and second_face_score[index] > 0.0:
            #     continue
            if ~(0 <= ages[index] <= 100):
                continue

            if np.isnan(genders[index]):
                continue

            try:
                # image_raw = io.imread(os.path.join(base_dir,file_names[index])).tostring()
                # image_raw = open(os.path.join(base_dir,str(file_name[index][0]))).read()

                # load the input image, resize it, and convert it to grayscale
                image = cv2.imread(os.path.join(image_base_dir, str(file_name[index][0])), cv2.IMREAD_COLOR)
                # image = imutils.resize(image, width=256)
                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                rects = detector(gray, 2)
                if len(rects) != 1:
                    continue
                else:
                    image_raw = fa.align(image, gray, rects[0])
                    image_raw = image_raw.tostring()
            except IOError:  # some files seem not exist in face_data dir
                error = error + 1
                pass
            # image_raw = images[index].tostring()
            example = tf.train.Example(features=tf.train.Features(feature={
                # 'height': _int64_feature(rows),
                # 'width': _int64_feature(cols),
                # 'depth': _int64_feature(depth),
                'age': _int64_feature(int(ages[index])),
                'gender': _int64_feature(int(genders[index])),
                'image_raw': _bytes_feature(image_raw),
                'file_name': _bytes_feature(str(file_name[index][0]))}))
            writer.write(example.SerializeToString())
            total = total + 1
    print("There are ", error, " missing pictures")
    print("Found", total, "valid faces")


def get_meta(mat_path, db):
    if len(db)==2:
        meta = loadmat(mat_path[0])
        full_path = meta[db[0]][0, 0]["full_path"][0]
        dob = meta[db[0]][0, 0]["dob"][0]  # Matlab serial date number
        gender = meta[db[0]][0, 0]["gender"][0]
        photo_taken = meta[db[0]][0, 0]["photo_taken"][0]  # year
        face_score = meta[db[0]][0, 0]["face_score"][0]
        second_face_score = meta[db[0]][0, 0]["second_face_score"][0]
        age = [calc_age(photo_taken[i], dob[i]) for i in range(len(dob))]
        data = {"file_name": full_path, "gender": gender, "age": age, "score": face_score,
                "second_score": second_face_score}
        dataset1 = pd.DataFrame(data)

        meta = loadmat(mat_path[1])
        full_path = meta[db[1]][0, 0]["full_path"][0]
        dob = meta[db[1]][0, 0]["dob"][0]  # Matlab serial date number
        gender = meta[db[1]][0, 0]["gender"][0]
        photo_taken = meta[db[1]][0, 0]["photo_taken"][0]  # year
        face_score = meta[db[1]][0, 0]["face_score"][0]
        second_face_score = meta[db[1]][0, 0]["second_face_score"][0]
        age = [calc_age(photo_taken[i], dob[i]) for i in range(len(dob))]
        data = {"file_name": full_path, "gender": gender, "age": age, "score": face_score,
                "second_score": second_face_score}
        dataset2 = pd.DataFrame(data)
        dataset = pd.concat([dataset1,dataset2],axis=0)
    else:
        meta = loadmat(mat_path)
        full_path = meta[db][0, 0]["full_path"][0]
        dob = meta[db][0, 0]["dob"][0]  # Matlab serial date number
        gender = meta[db][0, 0]["gender"][0]
        photo_taken = meta[db][0, 0]["photo_taken"][0]  # year
        face_score = meta[db][0, 0]["face_score"][0]
        second_face_score = meta[db][0, 0]["second_face_score"][0]
        age = [calc_age(photo_taken[i], dob[i]) for i in range(len(dob))]
        data = {"file_name": full_path, "gender": gender, "age": age, "score": face_score,
                "second_score": second_face_score}
        dataset = pd.DataFrame(data)
    return dataset


def calc_age(taken, dob):
    birth = datetime.fromordinal(max(int(dob) - 366, 1))

    # assume the photo was taken in the middle of the year
    if birth.month < 7:
        return taken - birth.year
    else:
        return taken - birth.year - 1


def main(db_path, db_name, test_size, cpu_cores, tfrecords_bath_dir):
    start_time = time.time()
    # Get the data.
    # data_sets = pd.read_csv("gender_age_train.txt", header=None, sep=" ")
    # data_sets.columns = ["file_name", "gender", "age"]
    data_sets = get_meta(db_path, db_name)
    # data_sets = data_sets[data_sets.age >= 0]
    # data_sets = data_sets[data_sets.age <= 100]

    train_sets, test_sets = train_test_split(data_sets, test_size=test_size, random_state=2017)
    train_sets.reset_index(drop=True, inplace=True)
    test_sets.reset_index(drop=True, inplace=True)
    train_nums = train_sets.shape[0]
    test_nums = test_sets.shape[0]
    train_idx = np.linspace(0, train_nums, cpu_cores + 1, dtype=np.int)
    test_idx = np.linspace(0, test_nums, math.ceil(cpu_cores / 4.0) + 1, dtype=np.int)
    # multi cpu
    pool = multiprocessing.Pool(processes=cpu_cores)
    for p in range(cpu_cores):
        pool.apply_async(convert_to,
                         (train_sets[train_idx[p]:train_idx[p + 1] - 1].copy().reset_index(drop=True), 'train', p,
                          tfrecords_bath_dir, db_name,))
    for p in range(cpu_cores // 4):
        pool.apply_async(convert_to,
                         (test_sets[test_idx[p]:test_idx[p + 1] - 1].copy().reset_index(drop=True), 'test', p,
                          tfrecords_bath_dir, db_name,))
    pool.close()
    pool.join()
    duration = time.time() - start_time
    print("Running %.3f sec All done!" % duration)
    # data_sets = mnist.read_data_sets(FLAGS.directory,
    #                                  dtype=tf.uint8,
    #                                  reshape=False,
    #                                  validation_size=FLAGS.validation_size)

    # Convert to Examples and write the result to TFRecords.
    # convert_to(train_sets, 'train-')
    # convert_to(test_sets,'test-')
    # convert_to(data_sets.validation, 'validation')
    # convert_to(data_sets.test, 'test')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--imdb_db", type=str, default="./data/imdb_crop/imdb.mat")
    parser.add_argument("--wiki_db", type=str, default="./data/wiki_crop/wiki.mat")
    parser.add_argument("--imdb", action="store_true", default=False, help="Set this flag if use imdb datasets")
    parser.add_argument("--wiki", action="store_true", default=False, help="Set this flag if use wiki datasets")
    parser.add_argument("--base_path", type=str, default="./data", help="Base path of datasets and tfrecords")
    parser.add_argument("--nworks", default=8, type=int, help="Use n cores to create tfrecords at a time")
    parser.add_argument("--test_size", type=float, default=0.01, help="How many items as testset")
    args = parser.parse_args()
    if (args.imdb and args.wiki):
        print("Using imdb and wiki datasets")
        main(db_path=[args.imdb_db,args.wiki_db], db_name=["imdb","wiki"], test_size=args.test_size, cpu_cores=args.nworks,
             tfrecords_bath_dir=args.base_path)
    elif args.imdb:
        print("Using imdb dataset")
        main(db_path=args.imdb_db, db_name="imdb", test_size=args.test_size, cpu_cores=args.nworks,
             tfrecords_bath_dir=args.base_path)
    elif args.wiki:
        print("Using wiki dataset")
        main(db_path=args.wiki_db, db_name="wiki", test_size=args.test_size, cpu_cores=args.nworks,
             tfrecords_bath_dir=args.base_path)
    else:
        raise NameError("You should choose one of --imdb or --wiki when running this script.")