def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = "./{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
np.savez(output_path,image=np.array(out_imgs), gender=np.array(out_genders), age=np.array(out_ages), img_size=img_size)
def plot_age_dist():
mat_path = r"D:\wiki_crop\wiki.mat"
db = "wiki"
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(mat_path, db)
ok_idx = filter_unusual(full_path, gender, face_score, second_face_score, age)
plot.histgram_demo(age[ok_idx])
def main():
args=get_args()
rootpath=args.rootpath
outfile=args.outfile
metafile =args.metafile
min_score = args.minscore
full_path, dob, gender, photo_taken, face_score, second_face_score, age=utils.get_meta(os.path.join(rootpath,'%s.mat'%metafile),metafile)
total = 0
label = []
print("%d images " % len(face_score))
for i in range(len(face_score)):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
fname=str(full_path[i][0])
label.append([fname, age[i], gender[i]])
total +=1
with open(os.path.join(rootpath,outfile),'w') as f:
for line in label:
f.write(line[0] + '\t' + str(line[1]) + '\t' + str(line[2]) + '\n')
print("filter data")
print("total: %d image" %(total))
print('Done!!!')
def main():
args = get_args()
parent_folder = args.input + "/"
db = args.db
newdb_path = parent_folder + "new_database/"
mat_path = parent_folder + "{}.mat".format(db)
#create folder for new dataset
if not os.path.exists(newdb_path):
os.makedirs(newdb_path)
#create a folder for every age
if args.folders:
for i in range(101):
new_folder_path = newdb_path + ('%03d' % i)
if not os.path.exists(new_folder_path):
os.makedirs(new_folder_path)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
for i in tqdm(range(len(full_path))):
if face_score[i] < 1.0:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
image = face_recognition.load_image_file(parent_folder +
full_path[i][0])
face_locations = face_recognition.face_locations(image)
if len(face_locations) == 0:
continue
face_locations = face_locations[0]
img = image[face_locations[0]:face_locations[2],
face_locations[3]:face_locations[1], :]
resized_image = cv2.resize(img, (224, 224))
name_of_file = full_path[i][0].split('/')[-1].split('.')[0]
new_name = name_of_file + "A" + str(age[i]) + ".jpg"
if args.folders:
cv2.imwrite(newdb_path + ('%03d' % age[i]) + "/" + new_name,
resized_image)
else:
cv2.imwrite(newdb_path + new_name, resized_image)
def main():
args = get_args()
output_path = args.output
db = args.db
max_count = args.max_count
img_size = args.img_size
min_score = args.min_score
max_num_per_file = args.max_num_per_file
train_ratio = args.train_ratio
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
length = len(face_score)
# make out filepath
outpath_prefix = output_path.split('/')[:-1]
outpath_prefix = path_concat(outpath_prefix)
filename = output_path.split('/')[-1].split('.')[0]
# filter bad images and get passed indexes
indexes = get_passed(length, min_score, age, face_score, gender)
# shuffle indexes
random.shuffle(indexes)
effective_length = len(indexes)
train_length = int(max_count *
train_ratio) if max_count is not None else int(
effective_length * train_ratio)
print('train_length=%s' % train_length)
# spilit meta data
train_idxs, val_idxs = split_meta_indexes(indexes,
train_length,
max_count=max_count)
# get meta data from filtered indexes
train_metas = make_meta(train_idxs, full_path, age, gender)
val_metas = make_meta(val_idxs, full_path, age, gender)
# split to mini batchs
mini_list = minibatch_gen(train_metas, max_num_per_file)
# process and save train data
Parallel(n_jobs=-1, verbose=5)([
delayed(process)('train', root_path, img_size, outpath_prefix,
filename, i, mini) for i, mini in enumerate(mini_list)
])
# process and save validation data
mini_list = minibatch_gen(val_metas, max_num_per_file)
Parallel(n_jobs=-1, verbose=5)([
delayed(process)('val', root_path, img_size, outpath_prefix, filename,
i, mini) for i, mini in enumerate(mini_list)
])
def eval(env, num):
meta = utils.get_meta(env)
sender = utils.sender
pool = ThreadPoolExecutor(max_workers=1000)
logging.info('request num: {}'.format(num))
lam = (60 * 1000.0) / num
samples = np.random.poisson(lam, num)
for s in samples:
pool.submit(sender, meta)
time.sleep(s/1000.0)
def main_process(data_path, db, limit=None):
mat_path = os.path.join(data_path, db + "_crop", db + ".mat")
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
ok_idx = filter_unusual(full_path, gender, face_score, second_face_score,
age)
meta_file = os.path.join(data_path, db + ".txt")
with open(meta_file, 'w') as f:
for i in trange(len(ok_idx)):
if limit and i >= int(limit):
break
f.write("%s_crop/%s %d\n" % (db, full_path[i][0], age[i]))
return meta_file
def main():
args = get_args()
output_path = args.output
db = args.db
max_count = args.max_count
img_size = args.img_size
min_score = args.min_score
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
length = len(face_score)
for i in tqdm(range(length)):
if face_score[i] < min_score:
continue
#if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
# continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]), 1)
img = cv2.resize(img, (img_size, img_size))
img = img[..., ::-1]
img = np.around(np.transpose(img, (2, 0, 1)) / 255.0, decimals=12)
#img = img
out_imgs.append(img)
if max_count is not None and len(out_imgs) >= max_count:
break
output = {
"image": np.array(out_imgs),
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
scipy.io.savemat(output_path, output)
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = "data/wiki_crop/".format(db)
mat_path = root_path + "wiki.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
out_faceScore = []
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_faceScore.append(int(face_score[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
output = {
"image": np.array(out_imgs),
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"fscore": np.array(out_faceScore)
}
scipy.io.savemat(output_path, output)
with open('mycsvfilet.csv', 'w') as f: # Just use 'w' mode in 3.x
w = csv.DictWriter(f, output.keys())
w.writeheader()
w.writerow(output)
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
fr = args.train_fraction
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
number_of_train = int(fr * len(out_imgs))
train_images = out_imgs[0:number_of_train]
labels_train_gender = out_genders[0:number_of_train]
labels_train_age = out_ages[0:number_of_train]
valid_images = out_imgs[number_of_train:]
labels_valid_gender = out_genders[number_of_train:]
labels_valid_age = out_ages[number_of_train:]
tfrecord_train = 'train_' + str(img_size) + '.tfrecords'
tfrecord_valid = 'valid_' + str(img_size) + '.tfrecords'
create_tfrecord(tfrecord_train, train_images, labels_train_age,
labels_train_gender)
create_tfrecord(tfrecord_valid, valid_images, labels_valid_age,
labels_valid_gender)
def main():
logging.debug("Alaa --> start creating dataBase ...")
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
sample_num = len(face_score)
out_imgs = np.empty((sample_num, img_size, img_size, 3), dtype=np.uint8)
valid_sample_num = 0
for i in tqdm(range(sample_num)):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs[valid_sample_num] = cv2.resize(img, (img_size, img_size))
valid_sample_num += 1
output = {
"image": out_imgs[:valid_sample_num],
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
scipy.io.savemat(output_path, output)
logging.debug("Alaa --> succes to create dataBase and save it ...")
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = "/data/chercheurs/agarwals/DEX/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
output = {
"image": np.array(out_imgs),
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
scipy.io.savemat(output_path, output)
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = args.input
mat_path = "{}/{}.mat".format(root_path, db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
for i in tqdm(range(len(face_score))):
if args.max_samples and i >= args.max_samples:
print 'Reached maximum samples {}'.format(args.max_samples)
break
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
output = {"image": np.array(out_imgs), "gender": np.array(out_genders), "age": np.array(out_ages),
"db": db, "img_size": img_size, "min_score": min_score}
scipy.io.savemat(output_path, output)
def eval(env):
meta = utils.get_meta(env)
sender = utils.sender
pool = ThreadPoolExecutor(max_workers=10000)
# nums = np.arange(100, 1001, 100)
# nums = np.append(nums, np.arange(1000, 99, -100))
nums = []
with open('{}/workload/test_2h.csv'.format(folder), 'r') as f:
reader = csv.DictReader(f)
nums = [ int(row['tweets']) for row in reader ]
print(sum(nums))
for i in range(len(nums)):
num = nums[i]
logging.info('request num: {}'.format(num))
lam = (60 * 1000.0) / num
samples = np.random.poisson(lam, num)
for s in samples:
pool.submit(sender, meta)
time.sleep(s/1000.0)
def _load_db(self, db_dir, db):
root_path = db_dir
mat_path = str(root_path) + "/{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
for i in tqdm(range(len(face_score))):
if face_score[i] < self.minscore:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
# if Path(root_path.name + str(full_path[i][0])).is_file():
self.image_path_and_age_gender.append(
[str(root_path+ "/"+str(full_path[i][0])), age[i], gender[i]])
def useful_image_generate(crop_name, db_name):
imdb_mat_path = crop_name + os.sep + db_name + '.mat'
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
imdb_mat_path, db_name)
useful_counter = 0
images_full_path = []
images_ages = []
images_genders = []
for i in range(len(face_score)):
if face_score[i] < 1.30:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
if not os.path.exists(crop_name + os.sep + str(full_path[i][0])):
continue
useful_counter += 1
images_full_path.append(crop_name + os.sep + str(full_path[i][0]))
images_ages.append(str(age[i]))
images_genders.append(str(int(gender[i])))
with open(crop_name + os.sep + db_name + '.csv', 'w') as f:
for i in range(useful_counter):
f.write(images_full_path[i] + ',' \
+ images_ages[i] + ',' \
+ images_genders[i] + '\n')
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, age = get_meta(mat_path, db)
out_ages = []
out_imgs = []
for i in tqdm(range(len(age))):
if ~(0 <= age[i] <= 100):
continue
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]))
out_imgs.append(cv2.resize(img, (img_size, img_size)))
output = {"image": np.array(out_imgs), "age": np.array(out_ages),
"db": db, "img_size": img_size}
scipy.io.savemat(output_path, output)
import matplotlib.pyplot as plt
import numpy as np
import cv2
from utils import get_meta
db = "wiki"
# db = "imdb"
mat_path = "data/{}_crop/{}.mat".format(db, db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age\
= get_meta(mat_path, db)
print("#images: {}".format(len(face_score)))
print("#images with inf scores: {}".format(np.isinf(face_score).sum()))
hist = plt.hist(face_score[face_score > 0],
bins=np.arange(0, 8, 0.2),
color='b')
plt.xlabel("face score")
print("#images with second face scores: {}".format(
(~np.isnan(second_face_score)).sum()))
hist = plt.hist(second_face_score[~np.isnan(second_face_score)],
bins=np.arange(0, 8, 0.2),
color='b')
plt.xlabel("second face score")
def main():
args = get_args()
db = args.db
min_score = args.min_score
validation = args.validation_split
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_fullpath = []
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
out_fullpath.append(full_path[i][0])
print(type(out_genders))
data_num = len(out_ages)
indexes = np.arange(data_num)
np.random.shuffle(indexes)
out_fullpath = np.array(out_fullpath)[indexes]
out_genders = np.array(out_genders)[indexes]
out_ages = np.array(out_ages)[indexes]
train_num = int(data_num*(1-validation))
genders_train = out_genders[:train_num]
genders_test = out_genders[train_num:]
age_train = out_ages[:train_num]
age_test = out_ages[train_num:]
fullpath_train = out_fullpath[:train_num]
fullpath_test = out_fullpath[train_num:]
out_name = [name.split("/")[1] for name in out_fullpath]
name_train=out_name[:train_num]
name_test=out_name[train_num:]
train_dir = Path(__file__).resolve().parent.joinpath(root_path,"train")
train_dir.mkdir(parents=True, exist_ok=True)
train_mat = {"full_path": np.array(name_train), "age": np.array(age_train),
"gender": np.array(genders_train), "db": db, "min_score": min_score}
print(train_dir)
for i in tqdm(range(len(fullpath_train))):
copy2(root_path+fullpath_train[i], train_dir)
scipy.io.savemat(str(train_dir), train_mat)
test_dir = Path(__file__).resolve().parent.joinpath(root_path,"test")
test_dir.mkdir(parents=True, exist_ok=True)
test_mat = {"full_path": np.array(name_test), "age": np.array(age_test),
"gender": np.array(genders_test), "db": db, "min_score": min_score}
scipy.io.savemat(str(test_dir), test_mat)
for i in tqdm(range(len(fullpath_test))):
copy2(root_path+fullpath_test[i], test_dir)
def main():
datadir = '/data/evs/VCTK'
version = 'VCTK-Corpus-0.92' # 'VCTK-Corpus-0.92' or 'VCTK-sie'
sr = '48k' # '22k' or '48k'
# version = 'VCTK-sie' # 'VCTK-Corpus-0.92' or 'VCTK-sie'
# sr = '22k' # '22k' or '48k'
# speakers that the original authors selected (another source)
spk_file = os.path.join(datadir, version, 'speaker-selected.txt')
sids_sel = ''.join(open(spk_file).readlines()).split('\n')
# # runtime mode
# args = parse_args()
# interactive mode (comment out before running the script)
args = argparse.ArgumentParser()
# args.acoustic_dir = '{}/{}/wav{}_silence_trimmed'.format(datadir, version, sr[:2])
args.acoustic_dir = '{}/{}/spec'.format(datadir, version)
args.text_dir = '{}/{}/text'.format(datadir, version)
args.list_dir = '{}/{}/list'.format(datadir, version)
args.metafile = '{}/{}/speaker-info.txt'.format(datadir, version)
args.delimiter = '|'
args.seed = 0
args.ordered = True
args.gender = 'both'
args.accents_excluded = ['Indian']
args.mic = 'dual'
# print out arguments
print('acoustic dir: {}'.format(args.acoustic_dir))
print('text dir: {}'.format(args.text_dir))
print('list dir: {}'.format(args.list_dir))
print('meta file: {}'.format(args.metafile))
print('delimiter: {}'.format(args.delimiter))
print('list random seed: {}'.format(args.seed))
print('sort list by duration: {}'.format(args.ordered))
print('gender: {}'.format(args.gender))
print('accents excluded: {}'.format(args.accents_excluded))
print('microphone: {}'.format(args.mic))
# get speaker info (list of dicts) from meta file
speakers = get_meta(args.metafile)
# accent-to-speaker distribution from speaker info
accent2nspkr = get_accent2nspk(speakers)
speakers = sel_speaker(speakers,
gender=args.gender,
accents_excluded=args.accents_excluded)
nspeakers = len(speakers)
print('{} speakers selected by gender ({}) and accents (no {})'.format(
nspeakers, args.gender, ' '.join(args.accents_excluded)))
# get speaker IDs for those being selected
sids = sorted(([spk['id'] for spk in speakers]))
sids_intersected = sorted([s for s in sids if s in sids_sel])
assert len(sids_intersected)==len(sids_sel), \
'selected speakers not found in speaker pool!'
del sids_intersected
# get spaekers IDs for those not being selected
sids_unsel = [spk['id'] for spk in speakers if spk['id'] not in sids_sel]
for spk in speakers:
if spk['id'] in sids_unsel:
print(spk)
ext = '.spec.npy' # '.spec.npy' or '-feats_norm.npy'
# ext = '-feats_norm.npy' # '.spec.npy' or '-feats_norm.npy'
listname = 'audio-txt-nframe-nphone_no-indian.txt'
# listname = 'wgannorm-txt-nframe-nphone_no-indian.txt'
listpath = os.path.join(args.list_dir, listname)
if os.path.isfile(listpath):
flists_flat = read_flist(listpath)
else:
# get file list per speaker
flists = []
for i, sid in enumerate(sids):
print('processing speaker {} ({}/{}) ...'.format(
sid, i, nspeakers))
flist = []
feats = sorted(
glob.glob(
os.path.join(args.acoustic_dir, sid, '*{}'.format(ext))))
# filter by microphone
if args.mic != 'dual':
feats = [
f for f in feats if 'mic{}{}'.format(args.mic, ext) in f
]
for j, feat in enumerate(feats):
feat_no_ext = os.path.join(
os.path.dirname(feat),
os.path.basename(feat).split('.')[0])
uid = os.path.basename(feat).split('.')[0].split('_')[1]
txtfile = '{}_{}.phones'.format(sid, uid)
txtpath = os.path.join(args.text_dir, sid, txtfile)
if os.path.isfile(txtpath):
phone_str = open(txtpath).readline().strip()
nphones = len(phone_str.split())
log_spectrogram = np.load(feat)
nframes = log_spectrogram.shape[0]
flist.append([feat_no_ext, phone_str, nframes, nphones])
# append file list for current speaker
flists.append(flist)
# flatten 2-layer file list (flists) to 1-layer
flists_flat = [item for sublist in flists for item in sublist]
# write file list
write_flist(flists_flat, args.delimiter, listpath)
# find sid-uid with both mics
if args.mic == 'dual':
flists_dual, flists_mono = exclude_mono(flists_flat)
flists_all = flists_dual[:]
else:
flists_all = flists_flat[:]
# # find common utterance ids (potentially to choose valid, test set from)
# uid2text = get_uid2text(flists_all)
# uid2ntexts = get_uid2ntexts(uid2text)
# nuids_uniq_text = len([1 for (uid, ntexts) in uid2ntexts.items() if ntexts==1])
# msg = 'uids with single text / # of uids: {} / {}'
# print(msg.format(nuids_uniq_text, len(uid2ntexts)))
#
# # find the dictionary of {sid:ntexts}
# sid2nuids = find_sid2nuids(flists_all)
#
# # select sid with enough utterances
# nuids_cutoff = 600 # select sids with at least 600 utterances
# nuids_sorted = sorted(sid2nuids.values(), reverse=True)
# sids_selected = [sid for sid in sid2nuids.keys() if sid2nuids[sid]>=nuids_cutoff]
# get 2-layer file lists (file list per sid)
flists = get_2dflist(flists_all)
# randomly select 10/20 utterances for validation/testing per speaker
flists_train, flists_valid, flists_test = [], [], []
for i, sid in enumerate(sids_sel):
flists_sid = flists[sid]
uids_sid = get_uids(flists_sid)
# randomize utterance ids with fixed random seed
random.seed(args.seed)
random.shuffle(uids_sid)
uids_valid = uids_sid[:10]
uids_test = uids_sid[10:30]
uids_train = uids_sid[30:]
flist_train = sel_flist(uids_train, flists_sid)
flist_valid = sel_flist(uids_valid, flists_sid)
flist_test = sel_flist(uids_test, flists_sid)
flists_train += flist_train
flists_valid += flist_valid
flists_test += flist_test
# write out split file list
# listname = 'wgannorm-txt-nframe-nphone_no-indian_train.txt'
listname = 'audio-txt-nframe-nphone_no-indian_train.txt'
write_flist(flists_train, args.delimiter,
os.path.join(args.list_dir, listname))
# listname = 'wgannorm-txt-nframe-nphone_no-indian_valid.txt'
listname = 'audio-txt-nframe-nphone_no-indian_valid.txt'
write_flist(flists_valid, args.delimiter,
os.path.join(args.list_dir, listname))
# listname = 'wgannorm-txt-nframe-nphone_no-indian_test.txt'
listname = 'audio-txt-nframe-nphone_no-indian_test.txt'
write_flist(flists_test, args.delimiter,
os.path.join(args.list_dir, listname))
def show_imgs(img_paths):
img_ids = np.random.choice(len(img_paths), img_num, replace=False)
for i, img_id in enumerate(img_ids):
print(img_id)
plt.subplot(rows, cols, i + 1)
img = cv2.imread(path_root + str(img_paths[img_id]))
plt.title(str(prediction(img)))
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()
mat_path = str(path_root + 'wiki.mat')
full_path, dob, gender, photo_taken, face_score, second_face_score, age\
= get_meta(mat_path, 'wiki')
img_paths = []
for i in range(len(face_score)):
if face_score[i] >= 1.0 and np.isnan(second_face_score[i]):
img_paths.append(full_path[i][0])
print("#images with scores >= than 1.0 and no second face: {}".format(
len(img_paths)))
show_imgs(img_paths)
from utils import get_meta
import cv2
import os
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
"imdb_crop/imdb.mat", "imdb")
# preprocess for gender classification
def preprocess_for_gen():
print str(full_path.shape[0]) + " images found"
countf = 0
countm = 0
countnan = 0
for i in range(full_path.shape[0]):
head, tail = os.path.split(full_path[i][0])
if gender[i] == 0.0 and countf <= 5000:
newtail = "FF/" + tail
_path = "imdb_crop/" + str(full_path[i][0])
t = cv2.imread(_path)
tru = cv2.imwrite(newtail, t)
if tru:
countf += 1
elif gender[i] == 1.0 and countm <= 5000:
newtail = "MM/" + tail
_path = "imdb_crop/" + str(full_path[i][0])
def __init__(self, source_path, cache_folder, destination, use_in_feeds,
translations, default_lang, blog_url, messages):
"""Initialize post.
The base path is the .txt post file. From it we calculate
the meta file, as well as any translations available, and
the .html fragment file path.
`compile_html` is a function that knows how to compile this Post to
html.
"""
self.prev_post = None
self.next_post = None
self.blog_url = blog_url
self.is_draft = False
self.source_path = source_path # posts/blah.txt
self.post_name = os.path.splitext(source_path)[0] # posts/blah
# cache/posts/blah.html
self.base_path = os.path.join(cache_folder, self.post_name + ".html")
self.metadata_path = self.post_name + ".meta" # posts/blah.meta
self.folder = destination
self.translations = translations
self.default_lang = default_lang
self.messages = messages
if os.path.isfile(self.metadata_path):
with codecs.open(self.metadata_path, "r", "utf8") as meta_file:
meta_data = meta_file.readlines()
while len(meta_data) < 6:
meta_data.append("")
(default_title, default_pagename, self.date, self.tags,
self.link, default_description) = \
[x.strip() for x in meta_data][:6]
else:
(default_title, default_pagename, self.date, self.tags,
self.link, default_description) = \
utils.get_meta(self.source_path)
if not default_title or not default_pagename or not self.date:
raise OSError("You must set a title and slug and date!")
self.date = utils.to_datetime(self.date)
self.tags = [x.strip() for x in self.tags.split(',')]
self.tags = filter(None, self.tags)
# While draft comes from the tags, it's not really a tag
self.use_in_feeds = use_in_feeds and "draft" not in self.tags
self.is_draft = 'draft' in self.tags
self.tags = [t for t in self.tags if t != 'draft']
self.pagenames = {}
self.titles = {}
self.descriptions = {}
# Load internationalized titles
# TODO: this has gotten much too complicated. Rethink.
for lang in translations:
if lang == default_lang:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
self.descriptions[lang] = default_description
else:
metadata_path = self.metadata_path + "." + lang
source_path = self.source_path + "." + lang
try:
if os.path.isfile(metadata_path):
with codecs.open(
metadata_path, "r", "utf8") as meta_file:
meta_data = [x.strip() for x in
meta_file.readlines()]
while len(meta_data) < 6:
meta_data.append("")
self.titles[lang] = meta_data[0] or default_title
self.pagenames[lang] = meta_data[1] or\
default_pagename
self.descriptions[lang] = meta_data[5] or\
default_description
else:
ttitle, ppagename, tmp1, tmp2, tmp3, ddescription = \
utils.get_meta(source_path)
self.titles[lang] = ttitle or default_title
self.pagenames[lang] = ppagename or default_pagename
self.descriptions[lang] = ddescription or\
default_description
except:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
self.descriptions[lang] = default_description
Age -> idade
dob-> data de nasci(n é necessário para o pre-process)
secondfacescore-> imagens em q há mais de um rosto detectado(descartar)
'''
db = "imdb"
root_path = "{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
min_score = 1.00 #Minimo de Confiança do detector de faces
img_size = 64 #Resolução minima
out_genders = []
out_ages = []
out_imgs = []
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
def main():
args = get_args()
output_path = args.output
db = args.db
# mat_path = args.input
max_count = args.max_count
img_size = args.img_size
min_score = args.min_score
max_num_per_file = args.max_num_per_file
train_ratio = args.train_ratio
root_path = "data/{}_crop/".format(db)
mat_path = root_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
out_imgs = []
length = len(face_score)
# max_num_per_file = num_per_file
file_count = 0
outpath_prefix = output_path.split('/')[:-1]
outpath_prefix = path_concat(outpath_prefix)
filename = output_path.split('/')[-1].split('.')[0]
ext = output_path.split('/')[-1].split('.')[1]
total_count = 0
indexes = get_passed(length, min_score, age, face_score, gender)
random.shuffle(indexes)
effective_length = len(indexes)
train_length = int(max_count *
train_ratio) if max_count is not None else int(
effective_length * train_ratio)
print('train_length=%s' % train_length)
for i in tqdm(indexes):
#print('total_count=%s' % total_count)
# if face_score[i] < min_score:
# print('face score bad')
# continue
# if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
# continue
# if ~(0 <= age[i] <= 100):
# print('age bad')
# continue
# if np.isnan(gender[i]):
# print('gender bad')
# continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img = cv2.imread(root_path + str(full_path[i][0]), 1)
img = cv2.resize(img, (img_size, img_size))
img = img[..., ::-1]
img = np.around(np.transpose(img, (2, 0, 1)) / 255.0, decimals=12)
out_imgs.append(img)
total_count += 1
if max_count is not None and total_count >= max_count:
break
if (len(out_imgs) % max_num_per_file == 0 and len(out_imgs) > 0) or \
total_count == train_length:
write_mat(out_imgs, out_genders, out_ages, db, img_size, min_score,
total_count, train_length, outpath_prefix, filename,
file_count, ext)
file_count += 1
if total_count == train_length:
print('train range end')
file_count = 0
out_imgs = []
out_genders = []
out_ages = []
output = {}
write_mat(out_imgs, out_genders, out_ages, db, img_size, min_score,
total_count, train_length, outpath_prefix, filename, file_count,
ext)
def main():
args = get_args()
output_path = args.train_out
valout_path = args.val_out
db = args.db
img_size = args.img_size
min_score = args.min_score
root_path = "/data/{}_crop/".format(db)
mat_path = "./data/{}_crop/".format(db)
mat_path = mat_path + "{}.mat".format(db)
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
#out_imgs = []
img_path = []
val_genders = []
val_ages = []
val_path = []
f_count = 0
m_count = 0
for i in tqdm(range(len(face_score))):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
if i < (len(face_score) - 10000):
out_genders.append(int(gender[i]))
if int(gender[i]) == 0:
f_count += 1
elif int(gender[i]) == 1:
m_count += 1
out_ages.append(age[i])
#img = cv2.imread(root_path + str(full_path[i][0]))
img_path.append(root_path + str(full_path[i][0]))
#print(full_path[i][0])
#out_imgs.append(cv2.resize(img, (img_size, img_size)))
else:
val_genders.append(int(gender[i]))
val_ages.append(age[i])
val_path.append(root_path + str(full_path[i][0]))
output = {
"image_path": np.array(img_path),
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
valout = {
"image_path": np.array(val_path),
"gender": np.array(val_genders),
"age": np.array(val_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
#scipy.io.savemat(output_path, output)
#scipy.io.savemat(valout_path, valout)
print("the femal num ", f_count)
print("the male num ", m_count)
def __init__(self, source_path, destination, use_in_feeds,
translations, default_lang, blog_url, messages):
"""Initialize post.
The base path is the .txt post file. From it we calculate
the meta file, as well as any translations available, and
the .html fragment file path.
`compile_html` is a function that knows how to compile this Post to
html.
"""
self.prev_post = None
self.next_post = None
self.blog_url = blog_url
self.is_draft = False
self.source_path = source_path # posts/blah.txt
self.post_name = os.path.splitext(source_path)[0] # posts/blah
# cache/posts/blah.html
self.base_path = os.path.join('cache', self.post_name + ".html")
self.metadata_path = self.post_name + ".meta" # posts/blah.meta
self.folder = destination
self.translations = translations
self.default_lang = default_lang
self.messages = messages
if os.path.isfile(self.metadata_path):
with codecs.open(self.metadata_path, "r", "utf8") as meta_file:
meta_data = meta_file.readlines()
while len(meta_data) < 6:
meta_data.append("")
(default_title, default_pagename, self.date, self.tags,
self.link, default_description) = \
[x.strip() for x in meta_data][:6]
else:
(default_title, default_pagename, self.date, self.tags,
self.link, default_description) = \
utils.get_meta(self.source_path)
if not default_title or not default_pagename or not self.date:
raise OSError("You must set a title and slug and date!")
self.date = utils.to_datetime(self.date)
self.tags = [x.strip() for x in self.tags.split(',')]
self.tags = filter(None, self.tags)
# While draft comes from the tags, it's not really a tag
self.use_in_feeds = use_in_feeds and "draft" not in self.tags
self.is_draft = 'draft' in self.tags
self.tags = [t for t in self.tags if t != 'draft']
self.pagenames = {}
self.titles = {}
self.descriptions = {}
# Load internationalized titles
# TODO: this has gotten much too complicated. Rethink.
for lang in translations:
if lang == default_lang:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
self.descriptions[lang] = default_description
else:
metadata_path = self.metadata_path + "." + lang
source_path = self.source_path + "." + lang
try:
if os.path.isfile(metadata_path):
with codecs.open(
metadata_path, "r", "utf8") as meta_file:
meta_data = [x.strip() for x in
meta_file.readlines()]
while len(meta_data) < 6:
meta_data.append("")
self.titles[lang] = meta_data[0] or default_title
self.pagenames[lang] = meta_data[1] or\
default_pagename
self.descriptions[lang] = meta_data[5] or\
default_description
else:
ttitle, ppagename, tmp1, tmp2, tmp3, ddescription = \
utils.get_meta(source_path)
self.titles[lang] = ttitle or default_title
self.pagenames[lang] = ppagename or default_pagename
self.descriptions[lang] = ddescription or\
default_description
except:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
self.descriptions[lang] = default_description
def __init__(self, source_path, destination, use_in_feeds, translations, default_lang, blog_url, compile_html):
"""Initialize post.
The base path is the .txt post file. From it we calculate
the meta file, as well as any translations available, and
the .html fragment file path.
`compile_html` is a function that knows how to compile this Post to
html.
"""
self.prev_post = None
self.next_post = None
self.use_in_feeds = use_in_feeds
self.blog_url = blog_url
self.source_path = source_path # posts/blah.txt
self.post_name = os.path.splitext(source_path)[0] # posts/blah
self.base_path = os.path.join("cache", self.post_name + ".html") # cache/posts/blah.html
self.metadata_path = self.post_name + ".meta" # posts/blah.meta
self.folder = destination
self.translations = translations
self.default_lang = default_lang
if os.path.isfile(self.metadata_path):
with codecs.open(self.metadata_path, "r", "utf8") as meta_file:
meta_data = meta_file.readlines()
while len(meta_data) < 5:
meta_data.append("")
default_title, default_pagename, self.date, self.tags, self.link = [x.strip() for x in meta_data][:5]
else:
default_title, default_pagename, self.date, self.tags, self.link = utils.get_meta(self.source_path)
if not default_title or not default_pagename or not self.date:
raise OSError, "You must set a title and slug and date!"
self.date = utils.to_datetime(self.date)
self.tags = [x.strip() for x in self.tags.split(",")]
self.tags = filter(None, self.tags)
self.compile_html = compile_html
self.pagenames = {}
self.titles = {}
# Load internationalized titles
for lang in translations:
if lang == default_lang:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
else:
metadata_path = self.metadata_path + "." + lang
source_path = self.source_path + "." + lang
try:
if os.path.isfile(metadata_path):
with codecs.open(metadata_path, "r", "utf8") as meta_file:
meta_data = [x.strip() for x in meta_file.readlines()]
while len(meta_data) < 2:
meta_data.append("")
self.titles[lang] = meta_data[0] or default_title
self.pagenames[lang] = meta_data[1] or default_pagename
else:
ttitle, ppagename, tmp1, tmp2, tmp3 = utils.get_meta(source_path)
self.titles[lang] = ttitle or default_title
self.pagenames[lang] = ppagename or default_pagename
except:
self.titles[lang] = default_title
self.pagenames[lang] = default_pagename
def main():
args = get_args()
output_path = args.output
db = args.db
img_size = args.img_size
min_score = args.min_score
if args.db_path:
root_path = args.db_path
else:
root_path = "data/{}_crop/".format(db)
mat_path = os.path.join(root_path, "{}.mat".format(db))
full_path, dob, gender, photo_taken, face_score, second_face_score, age = get_meta(
mat_path, db)
out_genders = []
out_ages = []
sample_num = len(face_score)
out_imgs = np.empty((sample_num, img_size, img_size, 3), dtype=np.uint8)
valid_sample_num = 0
print(f"root_path = {root_path}")
for i in tqdm(range(sample_num)):
if face_score[i] < min_score:
continue
if (~np.isnan(second_face_score[i])) and second_face_score[i] > 0.0:
continue
if ~(0 <= age[i] <= 100):
continue
if np.isnan(gender[i]):
continue
out_genders.append(int(gender[i]))
out_ages.append(age[i])
img_path = os.path.join(root_path, str(full_path[i][0]))
# print(f"Read {img_path}")
img = cv2.imread(img_path)
out_imgs[valid_sample_num] = cv2.resize(img, (img_size, img_size))
valid_sample_num += 1
print(f"Saving {len(out_imgs)} items")
if args.h5:
base, ext = os.path.splitext(output_path)
output_path = base + '.h5'
h5 = h5py.File(output_path, mode='w')
h5.create_dataset('image', data=out_imgs[:valid_sample_num])
h5.create_dataset('gender', data=np.array(out_genders))
h5.create_dataset('age', data=np.array(out_ages))
h5.attrs['db'] = db
h5.attrs['img_size'] = img_size
h5.attrs['min_score'] = min_score
h5.close()
print(f"Data has been written to {output_path}.")
else:
output = {
"image": out_imgs[:valid_sample_num],
"gender": np.array(out_genders),
"age": np.array(out_ages),
"db": db,
"img_size": img_size,
"min_score": min_score
}
scipy.io.savemat(output_path, output, do_compression=True)