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prepare_wider_data.py
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prepare_wider_data.py
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#-*- coding:utf-8 -*-
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
import os
from data.config import cfg
import cv2
WIDER_ROOT = os.path.join(cfg.HOME, 'WIDER')
train_list_file = os.path.join(WIDER_ROOT, 'wider_face_split',
'wider_face_train_bbx_gt.txt')
val_list_file = os.path.join(WIDER_ROOT, 'wider_face_split',
'wider_face_val_bbx_gt.txt')
WIDER_TRAIN = os.path.join(WIDER_ROOT, 'WIDER_train', 'images')
WIDER_VAL = os.path.join(WIDER_ROOT, 'WIDER_val', 'images')
def parse_wider_file(root, file):
with open(file, 'r') as fr:
lines = fr.readlines()
face_count = []
img_paths = []
face_loc = []
img_faces = []
count = 0
flag = False
for k, line in enumerate(lines):
line = line.strip().strip('\n')
if count > 0:
line = line.split(' ')
count -= 1
loc = [int(line[0]), int(line[1]), int(line[2]), int(line[3])]
face_loc += [loc]
if flag:
face_count += [int(line)]
flag = False
count = int(line)
if 'jpg' in line:
img_paths += [os.path.join(root, line)]
flag = True
total_face = 0
for k in face_count:
face_ = []
for x in xrange(total_face, total_face + k):
face_.append(face_loc[x])
img_faces += [face_]
total_face += k
return img_paths, img_faces
def wider_data_file():
img_paths, bbox = parse_wider_file(WIDER_TRAIN, train_list_file)
fw = open(cfg.FACE.TRAIN_FILE, 'w')
for index in xrange(len(img_paths)):
path = img_paths[index]
boxes = bbox[index]
fw.write(path)
fw.write(' {}'.format(len(boxes)))
for box in boxes:
data = ' {} {} {} {} {}'.format(box[0], box[1], box[2], box[3], 1)
fw.write(data)
fw.write('\n')
fw.close()
img_paths, bbox = parse_wider_file(WIDER_VAL, val_list_file)
fw = open(cfg.FACE.VAL_FILE, 'w')
for index in xrange(len(img_paths)):
path = img_paths[index]
boxes = bbox[index]
fw.write(path)
fw.write(' {}'.format(len(boxes)))
for box in boxes:
data = ' {} {} {} {} {}'.format(box[0], box[1], box[2], box[3], 1)
fw.write(data)
fw.write('\n')
fw.close()
if __name__ == '__main__':
wider_data_file()