def saveimage(f_image, image):
""" Save image"""
# check that folder of image file exists; else, make it
io.mkdir(io.parent_dir(f_image))
cv2.imwrite(f_image, image)
from __future__ import print_function
import pandas as pd
import numpy as np
import urllib.request
import fiwtools.utils.io as io
import os
f_csv = "/Users/josephrobinson/Dropbox/Families_In_The_Wild/Database/FIW_PIDs.csv"
dir_out = "/Users/josephrobinson/Dropbox/Families_In_The_Wild/Database/ImagesDB/"
df = pd.read_csv(f_csv)
fids = df.FID.unique()
for fid in fids:
ids = np.where(df.FID == fid)[0]
dout = dir_out + fid + "/"
io.mkdir(dout)
for id in range(len(ids)):
pid = df.PID[ids[id]]
url = df.URL[ids[id]]
fout = dout + pid + ".jpg"
print(fout)
path = urllib.request.urlretrieve(url)
print(path)
os.rename(path[0], fout)
# print('2', path)
header[0]: arr_all_face[:, 0],
header[1]: arr_all_face[:, 1],
header[2]: arr_all_face[:, 2]
})
# arr_pairs = np.array(all_faces)
#
# print('No. Face Pairs is {}.'.format(arr_pairs.shape[0]))
# return pd.DataFrame({"p1": arr_pairs[:, 0], "p2": arr_pairs[:, 1], "p3": arr_pairs[:, 2]})
logger = log.setup_custom_logger(__name__,
f_log='tri-info.log',
level=log.INFO)
out_bin = io.sys_home() + "/Dropbox/Families_In_The_Wild/Database/tripairs//"
mkdir(out_bin)
dir_fids = io.sys_home() + "/Dropbox/Families_In_The_Wild/Database/FIDs_New/"
do_save = False
logger.info("Parsing Tri-Subject Pairs:\n\t{}\n\t{}\n".format(
out_bin, dir_fids))
fmd, fms = fiw.tri_subjects(dir_data=dir_fids)
logger.info("{}".format(out_bin, dir_fids))
# pair_set.write_pairs(out_bin + "sibs-pairs.csv")
# df_all_faces.to_csv(out_bin + 'sibs-faces.csv', index=False)
if do_save:
fiw.write_list_tri_pairs(out_bin + "fmd-pairs.csv", fmd)
fiw.write_list_tri_pairs(out_bin + "fms-pairs.csv", fms)
print(len(fmd))
for index in range(0, 5):
help='Run on cpu or gpu.')
parser.add_argument('-gpu', '--gpu_id', default=0)
parser.add_argument('--dims',
default=200,
help="Dimension to reduce features (for --pca)")
parser.add_argument('--overwrite',
action='store_true',
help="Overwrite existing files.")
args = parser.parse_args()
dout = os.path.join(args.output, args.layer) + "/"
logger.info("Output Directory: {}\nInput Image Directory: {}\n".format(
args.output, args.input))
io.mkdir(dout)
my_net = cw.CaffeWrapper(model_def=args.model_def,
gpu_id=args.gpu_id,
mode=args.mode,
model_weights=args.weights,
do_init=True)
dirs_fid, fids = fiwdb.load_fids(args.input)
ifiles = glob.glob(args.input + "*/MID*/*.jpg")
ofiles = [
dout + str(f).replace(args.input, "").replace(".jpg", ".csv")
for f in ifiles
]
# layers = args.layers
for ifile in ifiles:
ofile = dout + str(ifile).replace(args.input, "").replace(
import os
import fiwtools.utils.log as log
import src.frameworks.pycaffe.net_wrapper as cw
import src.frameworks.pycaffe.tools as caffe_tools
logger = log.setup_custom_logger(__name__, f_log='kinwildW-feat-extractor.log', level=log.INFO)
# if __name__ == "__main__":
model_def = '/model/face_deploy.prototxt'
weights = '/model/face_train_test_iter_1600.caffemodel'
input = '/data/KinFaceW-I/images/'
output = '/data/KinFaceW-I/features/fine-tuned/'
io.mkdir(output)
mode = 'cpu'
dims = 200
layer = 'fc5'
types = ['father-dau', 'father-son', 'mother-dau', 'mother-son']
gpu_id = 0
overwrite = False
layers = ['fc5', 'conv5_5', 'conv5_4']
for l in layers:
dout = output + "/" + l + "/"
io.mkdir(dout)
logger.info("Output Directory: {}\nInput Image Directory: {}\n".format(output, input))
io.mkdir(dout)
import glob
import sklearn.metrics.pairwise as pw
from sklearn.metrics import roc_curve, auc
import fiwtools.utils.io as io
import fiwtools.data.kinwild as kinwild
import sklearn.preprocessing as skpreprocess
from sklearn.decomposition import TruncatedSVD
features = ['fc5'] # ''conv5_2', 'conv5_3', 'pool5', 'fc6', 'fc7']
sub_dirs = ['father-dau'] #, 'father-son', 'mother-dau', 'mother-son']
dir_root = '/media/jrobby/Seagate Backup Plus Drive1/DATA/Kinship/KinFaceW-II/'
dir_features = dir_root + '/features/fine-tuned/'
dir_results = dir_features + 'results_spca/'
io.mkdir(dir_results)
dir_perms = dir_root + 'perm/'
dir_lists = dir_root + 'meta_data/'
do_pca = True
k = 200
# load experimental settings for 5-fold verification
f_lists = glob.glob(dir_lists + "*.csv")
pair_types = [io.file_base(f) for f in f_lists]
dir_feats = [dir_features + p + "/" for p in sub_dirs]
fold_list = [1, 2, 3, 4, 5]
for ids in fold_list:
folds, labels, pairs1, pairs2 = kinwild.read_pair_list(f_lists[ids])
import fiwtools.utils.io as io
# from src.common.utilities import *
import src.database.kinwild as kinwild
import sklearn.preprocessing as skpreprocess
import pandas as pd
layers = ['conv5_2', 'conv5_3', 'pool5', 'fc6', 'fc7']
layers = ['res5a']
# lid = 4
# sub_dirs = ['father-dau', 'father-son', 'mother-dau', 'mother-son']
dir_root = io.sys_home() + '/Dropbox/Families_In_The_Wild/Database/journal_data/'
dir_features = '/media/jrobby/Seagate Backup Plus Drive1/FIW_dataset/FIW_Extended/features/vgg_face/resnet/'
dir_results = io.sys_home() + '/Dropbox/Families_In_The_Wild/Database/journal_results/verification/res_net/'
io.mkdir(dir_results)
dir_pairs = dir_root + "Pairs/folds_5splits/"
# load experimental settings for 5-fold verification
f_lists = glob.glob(dir_pairs + "*.csv")
pair_types = [io.file_base(f).replace('-folds', '') for f in f_lists]
# dir_feats = [dir_features + p + "/" for p in pair_types]
import os
for i in range(0, 11):
df_list = pd.read_csv(f_lists[i])
pair_type = io.file_base(f_lists[i]).replace('-folds', '')
labels = np.array(df_list.label)
folds = np.array(df_list.fold)
for layer in layers: