【TensorFlow学习笔记】目标识别篇（五）：基于Faster-rcnn-tensorflow-python3.5-master打造属于自己的物体检测模型
作者：互联网
一、环境配置
win10系统，显卡GeForce GTX 960M；
TensorFlow-gpu 1.13.0-rc2，CUDA 10.0，Cudnn 7.4.2；
python 3.6
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。
- Faster RCNN下载
下载地址https://github.com/dBeker/Faster-RCNN-TensorFlow-Python3.5
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、准备数据集
三、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
- resnet模型下载
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：
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
填坑
1.运行train.py 报错No module named cython_bbox 这是由于fasterrcnn为基于pyhton3.5的代码，但是之前安装的python版本为3.6，解决方法很简单
Faster-RCNN：ModuleNotFoundError: No module named
‘lib.utils.cython_bbox’
【已解决】
https://blog.csdn.net/memories_sunset/article/details/821176282.error: Unable to find vcvarsall.bat 这个错误的原因为我的vs版本为2013，但是程序运行需要vs2015的环境，解决这个错误不需要重新安装vs2015.
只需要安装其building tools即可，下载链接在这里
https://devblogs.microsoft.com/python/unable-to-find-vcvarsall-bat/#comments
选择python3.5 or latter下载即可
下载之后直接安装，选择自定义，选择win10版本安装过程中可能会提示安装包损坏，不用管，跳过即可
安装成功后，反过来去解决第一个问题
之后即可成功运行train.py来进行faster-rcnn训练了
https://blog.csdn.net/kellyroslyn/article/details/92799174
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()
标签：Faster,voc,vgg16,python3.5,im,bbox,tf,master,DEFINE 来源： https://blog.csdn.net/Jarvis_lele/article/details/114926079