Files
insightface/recognition/partial_fc/unpack_glint360k.py
2020-11-13 16:21:09 +08:00

59 lines
1.9 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import cv2
import mxnet as mx
def main(args):
include_datasets = args.include.split(',')
rec_list = []
for ds in include_datasets:
path_imgrec = os.path.join(ds, 'train.rec')
path_imgidx = os.path.join(ds, 'train.idx')
imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type
rec_list.append(imgrec)
if not os.path.exists(args.output):
os.makedirs(args.output)
#
imgid = 0
for ds_id in range(len(rec_list)):
imgrec = rec_list[ds_id]
s = imgrec.read_idx(0)
header, _ = mx.recordio.unpack(s)
assert header.flag > 0
seq_identity = range(int(header.label[0]), int(header.label[1]))
for identity in seq_identity:
s = imgrec.read_idx(identity)
header, _ = mx.recordio.unpack(s)
for _idx in range(int(header.label[0]), int(header.label[1])):
s = imgrec.read_idx(_idx)
_header, _img = mx.recordio.unpack(s)
label = int(_header.label[0])
class_path = os.path.join(args.output, "id_%d" % label)
if not os.path.exists(class_path):
os.makedirs(class_path)
image_path = os.path.join(class_path, "%d_%d.jpg" % (label, imgid))
with open(image_path, "wb") as ff:
ff.write(_img)
imgid += 1
if imgid % 10000 == 0:
print(imgid)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='do dataset merge')
# general
parser.add_argument('--include', default='', type=str, help='')
parser.add_argument('--output', default='', type=str, help='')
args = parser.parse_args()
main(args)