insightface/recognition/_evaluation_/megaface/remove_noises.py

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

import os
import datetime
import time
import shutil
import sys
import numpy as np
import argparse
import struct
import cv2
import mxnet as mx
from mxnet import ndarray as nd

feature_dim = 512
feature_ext = 1


def load_bin(path, fill=0.0):
    with open(path, 'rb') as f:
        bb = f.read(4 * 4)
        #print(len(bb))
        v = struct.unpack('4i', bb)
        #print(v[0])
        bb = f.read(v[0] * 4)
        v = struct.unpack("%df" % (v[0]), bb)
        feature = np.full((feature_dim + feature_ext, ),
                          fill,
                          dtype=np.float32)
        feature[0:feature_dim] = v
        #feature = np.array( v, dtype=np.float32)
    #print(feature.shape)
    #print(np.linalg.norm(feature))
    return feature


def write_bin(path, feature):
    feature = list(feature)
    with open(path, 'wb') as f:
        f.write(struct.pack('4i', len(feature), 1, 4, 5))
        f.write(struct.pack("%df" % len(feature), *feature))


def main(args):

    fs_noise_map = {}
    for line in open(args.facescrub_noises, 'r'):
        if line.startswith('#'):
            continue
        line = line.strip()
        fname = line.split('.')[0]
        p = fname.rfind('_')
        fname = fname[0:p]
        fs_noise_map[line] = fname

    print(len(fs_noise_map))

    i = 0
    fname2center = {}
    noises = []
    for line in open(args.facescrub_lst, 'r'):
        if i % 1000 == 0:
            print("reading fs", i)
        i += 1
        image_path = line.strip()
        _path = image_path.split('/')
        a, b = _path[-2], _path[-1]
        feature_path = os.path.join(args.feature_dir_input, 'facescrub', a,
                                    "%s_%s.bin" % (b, args.algo))
        feature_dir_out = os.path.join(args.feature_dir_out, 'facescrub', a)
        if not os.path.exists(feature_dir_out):
            os.makedirs(feature_dir_out)
        feature_path_out = os.path.join(feature_dir_out,
                                        "%s_%s.bin" % (b, args.algo))
        #print(b)
        if not b in fs_noise_map:
            #shutil.copyfile(feature_path, feature_path_out)
            feature = load_bin(feature_path)
            write_bin(feature_path_out, feature)
            if not a in fname2center:
                fname2center[a] = np.zeros((feature_dim + feature_ext, ),
                                           dtype=np.float32)
            fname2center[a] += feature
        else:
            #print('n', b)
            noises.append((a, b))
    print(len(noises))

    for k in noises:
        a, b = k
        assert a in fname2center
        center = fname2center[a]
        g = np.zeros((feature_dim + feature_ext, ), dtype=np.float32)
        g2 = np.random.uniform(-0.001, 0.001, (feature_dim, ))
        g[0:feature_dim] = g2
        f = center + g
        _norm = np.linalg.norm(f)
        f /= _norm
        feature_path_out = os.path.join(args.feature_dir_out, 'facescrub', a,
                                        "%s_%s.bin" % (b, args.algo))
        write_bin(feature_path_out, f)

    mf_noise_map = {}
    for line in open(args.megaface_noises, 'r'):
        if line.startswith('#'):
            continue
        line = line.strip()
        _vec = line.split("\t")
        if len(_vec) > 1:
            line = _vec[1]
        mf_noise_map[line] = 1

    print(len(mf_noise_map))

    i = 0
    nrof_noises = 0
    for line in open(args.megaface_lst, 'r'):
        if i % 1000 == 0:
            print("reading mf", i)
        i += 1
        image_path = line.strip()
        _path = image_path.split('/')
        a1, a2, b = _path[-3], _path[-2], _path[-1]
        feature_path = os.path.join(args.feature_dir_input, 'megaface', a1, a2,
                                    "%s_%s.bin" % (b, args.algo))
        feature_dir_out = os.path.join(args.feature_dir_out, 'megaface', a1,
                                       a2)
        if not os.path.exists(feature_dir_out):
            os.makedirs(feature_dir_out)
        feature_path_out = os.path.join(feature_dir_out,
                                        "%s_%s.bin" % (b, args.algo))
        bb = '/'.join([a1, a2, b])
        #print(b)
        if not bb in mf_noise_map:
            feature = load_bin(feature_path)
            write_bin(feature_path_out, feature)
            #shutil.copyfile(feature_path, feature_path_out)
        else:
            feature = load_bin(feature_path, 100.0)
            write_bin(feature_path_out, feature)
            #g = np.random.uniform(-0.001, 0.001, (feature_dim,))
            #print('n', bb)
            #write_bin(feature_path_out, g)
            nrof_noises += 1
    print(nrof_noises)


def parse_arguments(argv):
    parser = argparse.ArgumentParser()

    parser.add_argument('--facescrub-noises',
                        type=str,
                        help='',
                        default='./data/facescrub_noises.txt')
    parser.add_argument('--megaface-noises',
                        type=str,
                        help='',
                        default='./data/megaface_noises.txt')
    parser.add_argument('--algo', type=str, help='', default='insightface')
    parser.add_argument('--facescrub-lst',
                        type=str,
                        help='',
                        default='./data/facescrub_lst')
    parser.add_argument('--megaface-lst',
                        type=str,
                        help='',
                        default='./data/megaface_lst')
    parser.add_argument('--feature-dir-input',
                        type=str,
                        help='',
                        default='./feature_out')
    parser.add_argument('--feature-dir-out',
                        type=str,
                        help='',
                        default='./feature_out_clean')
    return parser.parse_args(argv)


if __name__ == '__main__':
    main(parse_arguments(sys.argv[1:]))