博客原文链接：https://blog.csdn.net/kebi199312/article/details/88368904
最近在学习深度学习的目标检测(Objection Detection)，对比了YOLO V3、Faster R-CNN、SSD及其它的目标检测算法，觉得Faster RCNN的性能不错，并且安装也比较简单，于是就自己动手在win10系统下安装了Faster RCNN。
本文分为两部分：
- 在win10系统上配置Tensorflow版本的Faster RCNN
- 运行Faster RCNN程序，测试了图片和视频
一、环境配置：
1、环境
- win10系统，显卡GeForce GTX 960M；
- TensorFlow-gpu 1.13.0-rc2，CUDA 10.0，Cudnn 7.4.2；
- python 3.5.2
安装Tensorflow-gpu版本可以参考博主的另一篇博客：https://blog.csdn.net/kebi199312/article/details/86549637，虽然CUDA的版本不同，但是安装步骤大同小异。
Tensorflow-gpu是在windows PowerShell里用pip安装的，同时安装一些必要的库：cython、easydict、matplotlib、python-opencv等，可直接使用pip安装或者下载相应的.whl离线文件安装。
2、源码下载
Faster RCNN的下载地址：
https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3.5
也可通过git下载，在命令行中打开cmd，cd到你的目录，输入：
git clone https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3.5.git
下载完成后，项目的根目录为：Faster-RCNN-TensorFlow-Python3.5-master
cd到Faster-RCNN-TensorFlow-Python3.5-master\data\coco\PythonAPI目录下，打开cmd，运行编译提供的代码：
-
python
setup
.py
build_ext
--inplace
-
python
setup
.py
build_ext
install
二、数据集VOC2007下载：
数据集使用的是VOC2007，下载地址：
由于被墙，可以下载百度云盘的数据集，链接：https://pan.baidu.com/s/1Y_RzqLvW4CAzTEq4ICFVUA ，提取码：m9dl
将下载后的三个压缩包解压到同一个文件夹，同时选中这三个压缩包，选择解压到当前文件夹，可得到VOCDevkit文件夹，如图1所示，将VOCDevkit重命名为VOCDevkit2007，然后将这个文件夹复制到data目录下 。文件夹目录
Faster-RCNN-TensorFlow-Python3.5-master\data\VOCDevkit2007
图1 数据集VOC2007的文件夹
三、VGG16模型下载：
VGG16模型的下载地址：http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz，也可去百度云盘下载，
链接：https://pan.baidu.com/s/11Ty10NJ-rgXkkvM92SVVKw ，提取码：d2jz
下载完后解压，文件重命名为vgg16.ckpt，如图2所示。新建文件夹imagenet_weights，把vgg16.ckpt放到imagenet_weights下，再将imagenet_weights文件夹复制到data文件夹下。文件夹目录：
Faster-RCNN-TensorFlow-Python3.5-master\data\imagenet_weights\vgg16.ckpt
图2 重命名后的vgg16.ckpt
四、 训练模型
训练模型的参数可以在Faster-RCNN-TensorFlow-Python3.5-master\lib\config文件夹里的config.py修改，包括训练的总步数、权重衰减、学习率、batch_size等参数。
-
tf.app.flags.DEFINE_float(
'weight_decay',
0.0005,
"Weight decay, for regularization")
-
tf.app.flags.DEFINE_float(
'learning_rate',
0.001,
"Learning rate")
-
tf.app.flags.DEFINE_float(
'momentum',
0.9,
"Momentum")
-
tf.app.flags.DEFINE_float(
'gamma',
0.1,
"Factor for reducing the learning rate")
-
-
tf.app.flags.DEFINE_integer(
'batch_size',
128,
"Network batch size during training")
-
tf.app.flags.DEFINE_integer(
'max_iters',
40000,
"Max iteration")
-
tf.app.flags.DEFINE_integer(
'step_size',
30000,
"Step size for reducing the learning rate, currently only support one step")
-
tf.app.flags.DEFINE_integer(
'display',
20,
"Iteration intervals for showing the loss during training, on command line interface")
-
-
tf.app.flags.DEFINE_string(
'initializer',
"truncated",
"Network initialization parameters")
-
tf.app.flags.DEFINE_string(
'pretrained_model',
"./data/imagenet_weights/vgg16.ckpt",
"Pretrained network weights")
-
-
tf.app.flags.DEFINE_boolean(
'bias_decay',
False,
"Whether to have weight decay on bias as well")
-
tf.app.flags.DEFINE_boolean(
'double_bias',
True,
"Whether to double the learning rate for bias")
-
tf.app.flags.DEFINE_boolean(
'use_all_gt',
True,
"Whether to use all ground truth bounding boxes for training, "
-
"For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd''")
-
tf.app.flags.DEFINE_integer(
'max_size',
1000,
"Max pixel size of the longest side of a scaled input image")
-
tf.app.flags.DEFINE_integer(
'test_max_size',
1000,
"Max pixel size of the longest side of a scaled input image")
-
tf.app.flags.DEFINE_integer(
'ims_per_batch',
1,
"Images to use per minibatch")
-
tf.app.flags.DEFINE_integer(
'snapshot_iterations',
5000,
"Iteration to take snapshot")
参数调整完后，在Faster-RCNN-TensorFlow-Python3.5-master的目录下，运行 python train.py，就可以训练生成模型了。
模型训练结束后，在 Faster-RCNN-TensorFlow-Python3.5-master\default\voc_2007_trainval\default目录下可以看到训练的模型，一个迭代了40000次，迭代次数可在Faster-RCNN-TensorFlow-Python3.5-master\lib\config文件夹里的config.py修改。
在目录下新建output\vgg16\voc_2007_trainval\default文件，将训练生成的文件复制到该文件下，并改名如下:“vgg16.ckpt.meta”，如图4所示：
图4 改名后的vgg16.ckpt.meta
五、测试模型
对demo.py进行如下的修改
1、将NETS中的“vgg16_faster_rcnn_iter_70000.ckpt”改成“vgg16”，如下所示；
NETS = {'vgg16': ('vgg16.ckpt',), 'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
2、将DATASETS中的“voc_2007_trainval+voc_2012_trainval”改为“voc_2007_trainval”，如下所示；
DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval',)}
3、将def parse_args()函数的两个default分别改成vgg16和pascal_voc，如下所示；
-
def parse_args():
-
"""Parse input arguments."""
-
parser = argparse.ArgumentParser(description=
'Tensorflow Faster R-CNN demo')
-
parser.add_argument(
'--net', dest=
'demo_net', help=
'Network to use [vgg16 res101]',
-
choices=NETS.keys(), default=
'vgg16')
-
parser.add_argument(
'--dataset', dest=
'dataset', help=
'Trained dataset [pascal_voc pascal_voc_0712]',
-
choices=DATASETS.keys(), default=
'pascal_voc')
-
args = parser.parse_args()
-
-
return args
4、修改上述参数后，运行demo.py，出现错误：
-
E:\Software\Python\python.exe E:/liukang/Faster-RCNN-TensorFlow-Python3.5-master/demo.py
--net vgg16
-
Traceback (most recent
call
last):
-
File
"E:/liukang/Faster-RCNN-TensorFlow-Python3.5-master/demo.py", line
142,
in <
module>
-
tag=
'default', anchor_scales=[
8,
16,
32])
-
File
"E:\liukang\Faster-RCNN-TensorFlow-Python3.5-master\lib\nets\network.py", line
283,
in create_architecture
-
weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.FLAGS.weight_decay)
-
File
"E:\Software\Python\lib\site-packages\tensorflow\python\platform\flags.py", line
84,
in __getattr__
-
wrapped(_sys.argv)
-
File
"E:\Software\Python\lib\site-packages\absl\flags\_flagvalues.py", line
633,
in __call__
-
name,
value, suggestions=suggestions)
-
absl.flags._exceptions.UnrecognizedFlagError:
Unknown command line flag
'net'. Did you mean: network ?
解决方法：
新建一个py文件，把demo.py脚本内容复制到里面就好了；新建一个脚本temp.py，测试多张图片，运行的结果如图5所示：
图5 运行demo.py的结果
测试视频，代码如下，截取视频的一张图片如图6所示：
-
# -*- coding: utf-8 -*-
-
# --------------------------------------------------------
-
# Faster R-CNN
-
#author lk
-
# --------------------------------------------------------
-
"""
-
Demo script showing detections in videos.
-
"""
-
from __future__
import absolute_import
-
from __future__
import division
-
from __future__
import print_function
-
-
import argparse
-
import os
-
import cv2
-
import tensorflow
as tf
-
from lib.config
import config
as cfg
-
from lib.utils.test
import im_detect
-
from lib.utils.nms_wrapper
import nms
-
from lib.utils.timer
import Timer
-
from lib.nets.vgg16
import vgg16
-
import matplotlib.pyplot
as plt
-
import numpy
as np
-
import sys
-
import time
-
-
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split(
'.')
-
-
CLASSES = (
'__background__',
-
'aeroplane',
'bicycle',
'bird',
'boat',
-
'bottle',
'bus',
'car',
'cat',
'chair',
-
'cow',
'diningtable',
'dog',
'horse',
-
'motorbike',
'person',
'pottedplant',
-
'sheep',
'sofa',
'train',
'tvmonitor')
-
-
NETS = {
'vgg16': (
'vgg16.ckpt',),
'res101': (
'res101_faster_rcnn_iter_110000.ckpt',)}
-
DATASETS = {
'pascal_voc': (
'voc_2007_trainval',),
'pascal_voc_0712': (
'voc_2007_trainval',)}
-
-
def vis_detections(im, class_name, dets, thresh=0.5):
-
"""Draw detected bounding boxes."""
-
inds = np.where(dets[:,
-1] >= thresh)[
0]
-
if len(inds) ==
0:
-
return
-
-
im = im[:, :, (
2,
1,
0)]
-
fig, ax = plt.subplots(figsize=(
12,
12))
-
ax.imshow(im, aspect=
'equal')
-
for i
in inds:
-
bbox = dets[i, :
4]
-
score = dets[i,
-1]
-
-
ax.add_patch(
-
plt.Rectangle((bbox[
0], bbox[
1]),
-
bbox[
2] - bbox[
0],
-
bbox[
3] - bbox[
1], fill=
False,
-
edgecolor=
'red', linewidth=
3.5)
-
)
-
ax.text(bbox[
0], bbox[
1] -
2,
-
'{:s} {:.3f}'.format(class_name, score),
-
bbox=dict(facecolor=
'blue', alpha=
0.5),
-
fontsize=
14, color=
'white')
-
-
ax.set_title((
'{} detections with '
-
'p({} | box) >= {:.1f}').format(class_name, class_name,
-
thresh),
-
fontsize=
14)
-
-
plt.axis(
'off')
-
plt.tight_layout()
-
plt.draw()
-
-
def vis_detections_video(im, class_name, dets, thresh=0.5):
-
"""Draw detected bounding boxes."""
-
#np.where判断语句
-
inds = np.where(dets[:,
-1] >= thresh)[
0]
-
-
if len(inds) ==
0:
-
return im
-
-
for i
in inds:
-
bbox = dets[i, :
4]
-
score = dets[i,
-1]
-
cv2.rectangle(im, (bbox[
0], bbox[
1]), (bbox[
2], bbox[
3]), (
0,
0,
255),
2)
-
cv2.rectangle(im, (int(bbox[
0]), int(bbox[
1] -
20)), (int(bbox[
0] +
200), int(bbox[
1])), (
10,
10,
10),
-1)
-
-
cv2.putText(im,
'{:s} {:.3f}'.format(class_name, score), (int(bbox[
0]), int(bbox[
1] -
2)),
-
cv2.FONT_HERSHEY_SIMPLEX,
.75, (
0,
0,
255))
-
-
return im
-
-
def demo(net, im):
-
"""Detect object classes in an image using pre-computed object proposals."""
-
global frameRate
-
global fps
-
-
# Detect all object classes and regress object bounds
-
-
timer = Timer()
-
timer.tic()
-
scores, boxes = im_detect(sess,net, im)
-
timer.toc()
-
-
print(
'Detection took {:.3f}s for '
-
'{:d} object proposals'.format(timer.total_time, boxes.shape[
0]))
-
frameRate =
1.0 / timer.total_time
-
print(
'fps:'+str(float(frameRate)))
-
-
# Visualize detections for each class
-
CONF_THRESH =
0.8
-
NMS_THRESH =
0.3
-
for cls_ind, cls
in enumerate(CLASSES[
1:]):
-
# because we skipped background
-
cls_ind +=
1
-
cls_boxes = boxes[:,
4 * cls_ind:
4 * (cls_ind +
1)]
-
cls_scores = scores[:, cls_ind]
-
dets = np.hstack((cls_boxes,
-
cls_scores[:, np.newaxis])).astype(np.float32)
-
keep = nms(dets, NMS_THRESH)
-
dets = dets[keep, :]
-
vis_detections_video(im, cls, dets, thresh=CONF_THRESH)
-
-
text=
'{:s} {:.2f}'.format(
"FPS:", frameRate)
-
position=(
50,
50)
-
cv2.putText(im, text, position, cv2.FONT_HERSHEY_SIMPLEX,
1, (
0,
0,
255))
-
cv2.imshow(videoFilePath.split(
'/')[len(videoFilePath.split(
'/')) -
1], im)
-
cv2.waitKey(
50)
-
-
def parse_args():
-
"""Parse input arguments."""
-
parser = argparse.ArgumentParser(description=
'Tensorflow Faster R-CNN demo')
-
parser.add_argument(
'--net', dest=
'demo_net', help=
'Network to use [vgg16 res101]',
-
choices=NETS.keys(), default=
'vgg16')
-
parser.add_argument(
'--dataset', dest=
'dataset', help=
'Trained dataset [pascal_voc pascal_voc_0712]',
-
choices=DATASETS.keys(), default=
'pascal_voc')
-
args = parser.parse_args()
-
-
return args
-
-
if __name__ ==
'__main__':
-
args = parse_args()
-
-
# model path
-
demonet = args.demo_net
-
dataset = args.dataset
-
tfmodel = os.path.join(
'output', demonet, DATASETS[dataset][
0],
'default', NETS[demonet][
0])
-
-
if
not os.path.isfile(tfmodel +
'.meta'):
-
print(tfmodel)
-
raise IOError((
'{:s} not found.\nDid you download the proper networks from '
-
'our server and place them properly?').format(tfmodel +
'.meta'))
-
-
# set config
-
tfconfig = tf.ConfigProto(allow_soft_placement=
True)
-
tfconfig.gpu_options.allow_growth =
True
-
-
# load network
-
if demonet ==
'vgg16':
-
net = vgg16(batch_size=
1)
-
-
else:
-
raise NotImplementedError
-
-
# init session
-
sess = tf.Session(config=tfconfig)
-
-
net.create_architecture(sess,
"TEST",
21,
-
tag=
'default', anchor_scales=[
8,
16,
32])
-
saver = tf.train.Saver()
-
saver.restore(sess, tfmodel)
-
-
print(
'\n\nLoaded network {:s}'.format(tfmodel))
-
-
# Warmup on a dummy image
-
im =
128 * np.ones((
300,
500,
3), dtype=np.uint8)
-
for i
in range(
2):
-
_, _ = im_detect(sess,net, im)
-
-
videoFilePath =
'Camera Road 01.avi'
-
videoCapture = cv2.VideoCapture(videoFilePath)
-
-
while
True:
-
success, im = videoCapture.read()
-
demo(net, im)
-
-
if cv2.waitKey(
10) &
0xFF == ord(
'q'):
-
break
-
videoCapture.release()
-
cv2.destroyAllWindows()
-
sess.close()
-
大家在运行该程序时，要将 videoFilePath改成自己的路径。
图6 测试视频
最近在学习深度学习的目标检测(Objection Detection)，对比了YOLO V3、Faster R-CNN、SSD及其它的目标检测算法，觉得Faster RCNN的性能不错，并且安装也比较简单，于是就自己动手在win10系统下安装了Faster RCNN。