本篇不在于复现SSD论文算法结果，主要介绍 算法细节。
1.数据准备
下载VOC2007和2012合并的数据集（需要科学上网）
数据集地址
VGG16预训练model地址
下载后的文件如下：
Annotations：合并后的xml文件，共21503项;
JPEGImages：合并后的图像文件，共21503项，与xml文件一一对应。
ImageSets：在该文件夹下的main/中，制作MXNet的.lst文件和.rec文件。
(base) yuyang@oceanshadow:~/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-jectDetection/tools$ python create_list.py --set test --save-path ../data/VOC
devkit/VOC/ImageSets/Main --dataset-path ../data/VOCdevkit/VOC
(base) yuyang@oceanshadow:~/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-jectDetection/tools$ python create_list.py --set trainval --save-path ../data
/VOCdevkit/VOC/ImageSets/Main --dataset-path ../data/VOCdevkit/VOC --shuffle True
(base) yuyang@oceanshadow:~/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-jectDetection/tools$ conda activate mxnet
(mxnet) yuyang@oceanshadow:~/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2bjectDetection/tools$ python im2rec.py ../data/VOCdevkit/VOC/ImageSets/Main/te
st.lst ../data/VOCdevkit --no-shuffle --pack-label
Creating .rec file from /home/yuyang/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-objectDetection/data/VOCdevkit/VOC/ImageSets/Main/test.lst in /home/yuyang/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-objectDetection/data/VOCdevkit/VOC/ImageSets/Main
multiprocessing not available, fall back to single threaded encoding
time: 0.02251458168029785 count: 0
time: 3.2134971618652344 count: 1000
time: 3.0257110595703125 count: 2000
time: 3.4036951065063477 count: 3000
time: 3.0267889499664307 count: 4000
(mxnet) yuyang@oceanshadow:~/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2bjectDetection/tools$ python im2rec.py ../data/VOCdevkit/VOC/ImageSets/Main/tr
ainval.lst ../data/VOCdevkit --pack-label
Creating .rec file from /home/yuyang/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-objectDetection/data/VOCdevkit/VOC/ImageSets/Main/trainval.lst in /home/yuyang/下载/MXNet-Deep-Learning-in-Action-master/demo9/9.2-objectDetection/data/VOCdevkit/VOC/ImageSets/Main
multiprocessing not available, fall back to single threaded encoding
time: 0.0036492347717285156 count: 0
time: 3.3942601680755615 count: 1000
time: 3.395172119140625 count: 2000
time: 3.3868696689605713 count: 3000
time: 3.387535810470581 count: 4000
time: 3.3465471267700195 count: 5000
time: 3.422719955444336 count: 6000
time: 3.7703046798706055 count: 7000
time: 4.644562482833862 count: 8000
time: 3.329374313354492 count: 9000
time: 3.3966259956359863 count: 10000
time: 3.372823476791382 count: 11000
time: 3.3226966857910156 count: 12000
time: 3.4331839084625244 count: 13000
time: 3.3643810749053955 count: 14000
time: 3.428262233734131 count: 15000
time: 3.4237232208251953 count: 16000
"–set"参数用于指定生成的列表文件名，本例中为：test/trainval
“no-shuffle”代表不对数据随机打乱;
“–pack-label”代表打包标签信息到RecordIO文件。
在Main/下生成的文件如下图所示：
- creat_list.py
import os
import argparse
from PIL import Image
import xml.etree.ElementTree as ET
import random
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--set', type=str, default='train')
parser.add_argument('--save-path', type=str, default='')
parser.add_argument('--dataset-path', type=str, default='')
parser.add_argument('--shuffle', type=bool, default=False)
args = parser.parse_args()
return args
def main():
label_dic = {"aeroplane": 0, "bicycle": 1, "bird": 2, "boat": 3, "bottle": 4, "bus": 5,
"car": 6, "cat": 7, "chair": 8, "cow": 9, "diningtable": 10, "dog": 11,
"horse": 12, "motorbike": 13, "person": 14, "pottedplant": 15, "sheep": 16,
"sofa": 17, "train": 18, "tvmonitor": 19}
args = parse_args()
if not os.path.exists(os.path.join(args.save_path, "{}.lst".format(args.set))):
os.mknod(os.path.join(args.save_path, "{}.lst".format(args.set)))
with open(os.path.join(args.save_path, "{}.txt".format(args.set)), "r") as input_file:
lines = input_file.readlines()
if args.shuffle:
random.shuffle(lines)
with open(os.path.join(args.save_path, "{}.lst".format(args.set)), "w") as output_file:
index = 0
for line in lines:
line = line.strip()
out_str = "\t".join([str(index), "2", "6"])
img = Image.open(os.path.join(args.dataset_path, "JPEGImages", line+".jpg"))
width, height = img.size
xml_path = os.path.join(args.dataset_path, "Annotations", line+".xml")
tree = ET.parse(xml_path)
root = tree.getroot()
objects = root.findall('object')
for object in objects:
name = object.find('name').text
difficult = ("%.4f" % int(object.find('difficult').text))
label_idx = ("%.4f" % label_dic[name])
bndbox = object.find('bndbox')
xmin = ("%.4f" % (int(bndbox.find('xmin').text)/width))
ymin = ("%.4f" % (int(bndbox.find('ymin').text)/height))
xmax = ("%.4f" % (int(bndbox.find('xmax').text)/width))
ymax = ("%.4f" % (int(bndbox.find('ymax').text)/height))
object_str = "\t".join([label_idx, xmin, ymin, xmax, ymax, difficult])
out_str = "\t".join([out_str, object_str])
out_str = "\t".join([out_str, "{}/JPEGImages/".format(args.dataset_path.split("/")[-1])+line+".jpg"+"\n"])
output_file.writelines(out_str)
index += 1
if __name__ == '__main__':
main()
- im2rec.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import print_function
import os
import sys
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(curr_path, "../python"))
import mxnet as mx
import random
import argparse
import cv2
import time
import traceback
try:
import multiprocessing
except ImportError:
multiprocessing = None
def list_image(root, recursive, exts):
"""Traverses the root of directory that contains images and
generates image list iterator.
Parameters
----------
root: string
recursive: bool
exts: string
Returns
-------
image iterator that contains all the image under the specified path
"""
i = 0
if recursive:
cat = {}
for path, dirs, files in os.walk(root, followlinks=True):
dirs.sort()
files.sort()
for fname in files:
fpath = os.path.join(path, fname)
suffix = os.path.splitext(fname)[1].lower()
if os.path.isfile(fpath) and (suffix in exts):
if path not in cat:
cat[path] = len(cat)
yield (i, os.path.relpath(fpath, root), cat[path])
i += 1
for k, v in sorted(cat.items(), key=lambda x: x[1]):
print(os.path.relpath(k, root), v)
else:
for fname in sorted(os.listdir(root)):
fpath = os.path.join(root, fname)
suffix = os.path.splitext(fname)[1].lower()
if os.path.isfile(fpath) and (suffix in exts):
yield (i, os.path.relpath(fpath, root), 0)
i += 1
def write_list(path_out, image_list):
"""Hepler function to write image list into the file.
The format is as below,
integer_image_index \t float_label_index \t path_to_image
Note that the blank between number and tab is only used for readability.
Parameters
----------
path_out: string
image_list: list
"""
with open(path_out, 'w') as fout:
for i, item in enumerate(image_list):
line = '%d\t' % item[0]
for j in item[2:]:
line += '%f\t' % j
line += '%s\n' % item[1]
fout.write(line)
def make_list(args):
"""Generates .lst file.
Parameters
----------
args: object that contains all the arguments
"""
image_list = list_image(args.root, args.recursive, args.exts)
image_list = list(image_list)
if args.shuffle is True:
random.seed(100)
random.shuffle(image_list)
N = len(image_list)
chunk_size = (N + args.chunks - 1) // args.chunks
for i in range(args.chunks):
chunk = image_list[i * chunk_size:(i + 1) * chunk_size]
if args.chunks > 1:
str_chunk = '_%d' % i
else:
str_chunk = ''
sep = int(chunk_size * args.train_ratio)
sep_test = int(chunk_size * args.test_ratio)
if args.train_ratio == 1.0:
write_list(args.prefix + str_chunk + '.lst', chunk)
else:
if args.test_ratio:
write_list(args.prefix + str_chunk + '_test.lst', chunk[:sep_test])
if args.train_ratio + args.test_ratio < 1.0:
write_list(args.prefix + str_chunk + '_val.lst', chunk[sep_test + sep:])
write_list(args.prefix + str_chunk + '_train.lst', chunk[sep_test:sep_test + sep])
def read_list(path_in):
"""Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
"""
with open(path_in) as fin:
while True:
line = fin.readline()
if not line:
break
line = [i.strip() for i in line.strip().split('\t')]
line_len = len(line)
# check the data format of .lst file
if line_len < 3:
print('lst should have at least has three parts, but only has %s parts for %s' % (line_len, line))
continue
try:
item = [int(line[0])] + [line[-1]] + [float(i) for i in line[1:-1]]
except Exception as e:
print('Parsing lst met error for %s, detail: %s' % (line, e))
continue
yield item
def image_encode(args, i, item, q_out):
"""Reads, preprocesses, packs the image and put it back in output queue.
Parameters
----------
args: object
i: int
item: list
q_out: queue
"""
fullpath = os.path.join(args.root, item[1])
if len(item) > 3 and args.pack_label:
header = mx.recordio.IRHeader(0, item[2:], item[0], 0)
else:
header = mx.recordio.IRHeader(0, item[2], item[0], 0)
if args.pass_through:
try:
with open(fullpath, 'rb') as fin:
img = fin.read()
s = mx.recordio.pack(header, img)
q_out.put((i, s, item))
except Exception as e:
traceback.print_exc()
print('pack_img error:', item[1], e)
q_out.put((i, None, item))
return
try:
img = cv2.imread(fullpath, args.color)
except:
traceback.print_exc()
print('imread error trying to load file: %s ' % fullpath)
q_out.put((i, None, item))
return
if img is None:
print('imread read blank (None) image for file: %s' % fullpath)
q_out.put((i, None, item))
return
if args.center_crop:
if img.shape[0] > img.shape[1]:
margin = (img.shape[0] - img.shape[1]) // 2
img = img[margin:margin + img.shape[1], :]
else:
margin = (img.shape[1] - img.shape[0]) // 2
img = img[:, margin:margin + img.shape[0]]
if args.resize:
if img.shape[0] > img.shape[1]:
newsize = (args.resize, img.shape[0] * args.resize // img.shape[1])
else:
newsize = (img.shape[1] * args.resize // img.shape[0], args.resize)
img = cv2.resize(img, newsize)
try:
s = mx.recordio.pack_img(header, img, quality=args.quality, img_fmt=args.encoding)
q_out.put((i, s, item))
except Exception as e:
traceback.print_exc()
print('pack_img error on file: %s' % fullpath, e)
q_out.put((i, None, item))
return
def read_worker(args, q_in, q_out):
"""Function that will be spawned to fetch the image
from the input queue and put it back to output queue.
Parameters
----------
args: object
q_in: queue
q_out: queue
"""
while True:
deq = q_in.get()
if deq is None:
break
i, item = deq
image_encode(args, i, item, q_out)
def write_worker(q_out, fname, working_dir):
"""Function that will be spawned to fetch processed image
from the output queue and write to the .rec file.
Parameters
----------
q_out: queue
fname: string
working_dir: string
"""
pre_time = time.time()
count = 0
fname = os.path.basename(fname)
fname_rec = os.path.splitext(fname)[0] + '.rec'
fname_idx = os.path.splitext(fname)[0] + '.idx'
record = mx.recordio.MXIndexedRecordIO(os.path.join(working_dir, fname_idx),
os.path.join(working_dir, fname_rec), 'w')
buf = {}
more = True
while more:
deq = q_out.get()
if deq is not None:
i, s, item = deq
buf[i] = (s, item)
else:
more = False
while count in buf:
s, item = buf[count]
del buf[count]
if s is not None:
record.write_idx(item[0], s)
if count % 1000 == 0:
cur_time = time.time()
print('time:', cur_time - pre_time, ' count:', count)
pre_time = cur_time
count += 1
def parse_args():
"""Defines all arguments.
Returns
-------
args object that contains all the params
"""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Create an image list or \
make a record database by reading from an image list')
parser.add_argument('prefix', help='prefix of input/output lst and rec files.')
parser.add_argument('root', help='path to folder containing images.')
cgroup = parser.add_argument_group('Options for creating image lists')
cgroup.add_argument('--list', action='store_true',
help='If this is set im2rec will create image list(s) by traversing root folder\
and output to <prefix>.lst.\
Otherwise im2rec will read <prefix>.lst and create a database at <prefix>.rec')
cgroup.add_argument('--exts', nargs='+', default=['.jpeg', '.jpg', '.png'],
help='list of acceptable image extensions.')
cgroup.add_argument('--chunks', type=int, default=1, help='number of chunks.')
cgroup.add_argument('--train-ratio', type=float, default=1.0,
help='Ratio of images to use for training.')
cgroup.add_argument('--test-ratio', type=float, default=0,
help='Ratio of images to use for testing.')
cgroup.add_argument('--recursive', action='store_true',
help='If true recursively walk through subdirs and assign an unique label\
to images in each folder. Otherwise only include images in the root folder\
and give them label 0.')
cgroup.add_argument('--no-shuffle', dest='shuffle', action='store_false',
help='If this is passed, \
im2rec will not randomize the image order in <prefix>.lst')
rgroup = parser.add_argument_group('Options for creating database')
rgroup.add_argument('--pass-through', action='store_true',
help='whether to skip transformation and save image as is')
rgroup.add_argument('--resize', type=int, default=0,
help='resize the shorter edge of image to the newsize, original images will\
be packed by default.')
rgroup.add_argument('--center-crop', action='store_true',
help='specify whether to crop the center image to make it rectangular.')
rgroup.add_argument('--quality', type=int, default=95,
help='JPEG quality for encoding, 1-100; or PNG compression for encoding, 1-9')
rgroup.add_argument('--num-thread', type=int, default=1,
help='number of thread to use for encoding. order of images will be different\
from the input list if >1. the input list will be modified to match the\
resulting order.')
rgroup.add_argument('--color', type=int, default=1, choices=[-1, 0, 1],
help='specify the color mode of the loaded image.\
1: Loads a color image. Any transparency of image will be neglected. It is the default flag.\
0: Loads image in grayscale mode.\
-1:Loads image as such including alpha channel.')
rgroup.add_argument('--encoding', type=str, default='.jpg', choices=['.jpg', '.png'],
help='specify the encoding of the images.')
rgroup.add_argument('--pack-label', action='store_true',
help='Whether to also pack multi dimensional label in the record file')
args = parser.parse_args()
args.prefix = os.path.abspath(args.prefix)
args.root = os.path.abspath(args.root)
return args
if __name__ == '__main__':
args = parse_args()
# if the '--list' is used, it generates .lst file
if args.list:
make_list(args)
# otherwise read .lst file to generates .rec file
else:
if os.path.isdir(args.prefix):
working_dir = args.prefix
else:
working_dir = os.path.dirname(args.prefix)
files = [os.path.join(working_dir, fname) for fname in os.listdir(working_dir)
if os.path.isfile(os.path.join(working_dir, fname))]
count = 0
for fname in files:
if fname.startswith(args.prefix) and fname.endswith('.lst'):
print('Creating .rec file from', fname, 'in', working_dir)
count += 1
image_list = read_list(fname)
# -- write_record -- #
if args.num_thread > 1 and multiprocessing is not None:
q_in = [multiprocessing.Queue(1024) for i in range(args.num_thread)]
q_out = multiprocessing.Queue(1024)
# define the process
read_process = [multiprocessing.Process(target=read_worker, args=(args, q_in[i], q_out)) \
for i in range(args.num_thread)]
# process images with num_thread process
for p in read_process:
p.start()
# only use one process to write .rec to avoid race-condtion
write_process = multiprocessing.Process(target=write_worker, args=(q_out, fname, working_dir))
write_process.start()
# put the image list into input queue
for i, item in enumerate(image_list):
q_in[i % len(q_in)].put((i, item))
for q in q_in:
q.put(None)
for p in read_process:
p.join()
q_out.put(None)
write_process.join()
else:
print('multiprocessing not available, fall back to single threaded encoding')
try:
import Queue as queue
except ImportError:
import queue
q_out = queue.Queue()
fname = os.path.basename(fname)
fname_rec = os.path.splitext(fname)[0] + '.rec'
fname_idx = os.path.splitext(fname)[0] + '.idx'
record = mx.recordio.MXIndexedRecordIO(os.path.join(working_dir, fname_idx),
os.path.join(working_dir, fname_rec), 'w')
cnt = 0
pre_time = time.time()
for i, item in enumerate(image_list):
image_encode(args, i, item, q_out)
if q_out.empty():
continue
_, s, _ = q_out.get()
record.write_idx(item[0], s)
if cnt % 1000 == 0:
cur_time = time.time()
print('time:', cur_time - pre_time, ' count:', cnt)
pre_time = cur_time
cnt += 1
if not count:
print('Did not find and list file with prefix %s'%args.prefix)
2.训练参数及配置
参见本人github，下载即可运行。
SSD
扫一扫
651
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
