segnet运行记录

xiaoxiao2022-07-05 203

安装步骤

1.

git clone https://github.com/alexgkendall/caffe-segnet.git

2.

cd caffe-segnet cp Makefile.config.example Makefile.config

去掉WITH_PYTHON_LAYER:=1的注释

make all

报错：

1.hdf5.h:没有那个文件或目录

解决方法：在Makefile.config文件中，添加/usr/include/hdf5/serial/ 到 INCLUDE_DIRS，也就是把下面第一行代码改为第二行代码。

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/

2.nvcc warning:

解决方法：删除下面两句

3.奇怪的报错

解决方法：

https://blog.csdn.net/hhg337372083/article/details/82897620

这篇博客中说是opencv的问题，我安装的是opencv3，要换成opencv2，还没尝试

换完了，没用，哼！！！但是学会了opencv3和opencv2如何共存，感谢博主https://blog.csdn.net/denghecsdn/article/details/77876832

将src/caffe/layers/contrastive_loss_layer.cpp的56行改成下面这样：

Dtype dist = std::max(margin - (float)sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0));

/usr/bin/ld: 找不到 -lhdf5_hl /usr/bin/ld: 找不到 -lhdf5

将/usr/lib/x86_64-linux-gnu/hdf5/openmpi/目录下的两个文件copy到/usr/lib下

.build_release/lib/libcaffe.so：对‘cv::imread(cv::String const&, int)’未定义的引用 .build_release/lib/libcaffe.so：对‘cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator<unsigned char> >&, std::vector<int, std::allocator<int> > const&)’未定义的引用

加上glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m opencv_imgcodecs

ok啦

3.

make pycaffe

报错：

make: *** No rule to make target 'include/caffe/layers/python_layer.hpp', needed by 'python/caffe/_caffe.so'。停止。

/home/wang/caffe/include/caffe/layers复制到当前工程的/include/caffe/下

./include/caffe/python_layer.hpp:23:34: error: ‘solver_count’ is not a member of ‘caffe::Caffe’ if (this->phase_ == TRAIN && Caffe::solver_count() > 1 ^ ./include/caffe/python_layer.hpp:24:13: error: ‘multiprocess’ is not a member of ‘caffe::Caffe’ && !Caffe::multiprocess()) { ^ ./include/caffe/python_layer.hpp: In instantiation of ‘void caffe::PythonLayer<Dtype>::LayerSetUp(const std::vector<caffe::Blob<Dtype>*>&, const std::vector<caffe::Blob<Dtype>*>&) [with Dtype = float]’: python/caffe/_caffe.cpp:301:1: required from here ./include/caffe/python_layer.hpp:27:29: error: ‘const class caffe::PythonParameter’ has no member named ‘param_str’ self_.attr("param_str") = bp::str( ^

make clean 然后全部重新编译一下;

报错：

touch python/caffe/proto/__init__.py CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp PROTOC (python) src/caffe/proto/caffe.proto python/caffe/_caffe.cpp:10:31: fatal error: numpy/arrayobject.h: 没有那个文件或目录 compilation terminated. Makefile:453: recipe for target 'python/caffe/_caffe.so' failed make: *** [python/caffe/_caffe.so] Error 1

解决方法：https://blog.csdn.net/wuzuyu365/article/details/52430657

4.

make test

一些max参数类型错误,加一些强制类型转换.

5.

https://github.com/alexgkendall/SegNet-Tutorial下载目录并解压。

http://mi.eng.cam.ac.uk/~agk34/resources/SegNet/下载对应的权重参数

python Scripts/webcam_demo.py --model Example_Models/segnet_model_driving_webdemo.prototxt --weights caffe-segnet/segnet_weights_driving_webdemo.caffemodel --colours Scripts/camvid12.png

晕了晕了

WARNING: Logging before InitGoogleLogging() is written to STDERR W0524 11:10:36.958748 4861 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface W0524 11:10:36.958777 4861 _caffe.cpp:140] Use this instead (with the named "weights" parameter): W0524 11:10:36.958781 4861 _caffe.cpp:142] Net('Example_Models/segnet_model_driving_webdemo.prototxt', 1, weights='caffe-segnet/segnet_weights_driving_webdemo.caffemodel') [libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 47:12: Message type "caffe.LayerParameter" has no field named "bn_param". F0524 11:10:36.960089 4861 upgrade_proto.cpp:90] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: Example_Models/segnet_model_driving_webdemo.prototxt *** Check failure stack trace: *** 已放弃 (核心已转储)

解决方法：webcam_demo.py文件的14行，路径最后少了一个‘ / ’;（令人窒息，教训就是有事还是找google，别找百度了！！！）

6.

修改webcam_demo.py(参照https://www.cnblogs.com/kanuore/p/7588846.html)

# -*- coding: utf-8 -* import numpy as np import matplotlib.pyplot as plt import os.path import scipy import argparse import math import cv2 import sys import time sys.path.append('/usr/local/lib/python2.7/site-packages') # Make sure that caffe is on the python path: caffe_root = '/home/wang/ZJF/SegNet-Tutorial/caffe-segnet/' sys.path.insert(0, caffe_root + 'python') import caffe # Import arguments #deploy='Example_Models/segnet_model_driving_webdemo.prototxt' #weights='Example_Moudels/segnet_weights_driving_webdemo.caffemodel' #colours='Scripts/camvid12.png' #net = caffe.Net(deploy,weights,caffe.TEST) # Import arguments parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, required=True) parser.add_argument('--weights', type=str, required=True) parser.add_argument('--colours', type=str, required=True) args = parser.parse_args() net = caffe.Net(args.model, args.weights, caffe.TEST) caffe.set_mode_gpu() input_shape = net.blobs['data'].data.shape output_shape = net.blobs['argmax'].data.shape label_colours = cv2.imread(args.colours).astype(np.uint8) #cv2.namedWindow("Input") #cv2.namedWindow("SegNet") cap = cv2.VideoCapture(0) # Change this to your webcam ID, or file name for your video file rval = True frame = cv2.imread('/home/wang/ZJF/SegNet-Tutorial/CamVid/test/0001TP_008550.png') frame = cv2.resize(frame, (input_shape[3],input_shape[2])) input_image = frame.transpose((2,0,1)) # input_image = input_image[(2,1,0),:,:] # May be required, if you do not open your data with opencv input_image = np.asarray([input_image]) out = net.forward_all(data=input_image) segmentation_ind = np.squeeze(net.blobs['argmax'].data) segmentation_ind_3ch = np.resize(segmentation_ind,(3,input_shape[2],input_shape[3])) segmentation_ind_3ch = segmentation_ind_3ch.transpose(1,2,0).astype(np.uint8) segmentation_rgb = np.zeros(segmentation_ind_3ch.shape, dtype=np.uint8) cv2.LUT(segmentation_ind_3ch,label_colours,segmentation_rgb) #这里应该变成小数存储了，看来opencv对于小数也是热图显示，但是保存还是黑白的图 segmentation_rgb = segmentation_rgb.astype(float)/255 #cv2.imwrite('output.jpg',segmentation_rgb) #cv2.imshow("Input", frame) #cv2.imshow("SegNet", segmentation_rgb) #cv2.imwrite('output.jpg',segmentation_rgb) #这里使用plt显示与保存，比cv2好点，并且不会出现进程卡住的情况 plt.imshow(segmentation_rgb) plt.savefig('output.png') plt.show()

7.

python Scripts/webcam_demo1.py --model Example_Models/segnet_model_driving_webdemo.prototxt --weights caffe-segnet/segnet_weights_driving_webdemo.caffemodel --colours Scripts/camvid12.png

一开始识别很慢，发现是没有设置caffe.set_mode_gpu(),加上之后就快很多啦！！！

8.

python cv2.imshow正常显示,cv2.imwrite结果保存了一张全黑的图片,原因是归一化了,要将图片像素乘以255后再保存:

img = img.astype("float") * 255.0

9.

运行以下命令,测试segnet在camvid上的效果

python Scripts/test_segmentation_camvid.py --model Example_Models/bayesian_segnet_camvid.prototxt --weights bayesian_segnet_camvid.caffemodel --iter 233

在caffe-segnet下运行:

make runtest

报错:

CUDA driver version is insufficient for CUDA runtime version

暂定修改Makefile.config不使用GPU编译通过

参考博客

后自己训练，解决https://blog.csdn.net/zjf2543771964/article/details/93905075

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