segnet 编译与测试

segnet 编译与测试
参考：http://sunxg13.github.io/2015/09/10/caffe/
http://m.blog.csdn.net/lemianli/article/details/76687508
http://blog.h5min.cn/u010069760/article/details/75258539
（注意：nakefile而非makefile.config）
1、编译caffe-segnet：
1.1下载caffe-segnet(适用于segnet的caffe版本，下成scaffe)
git clone https://github.com/alexgkendall/caffe-segnet
1.2更改一些编译选项：
Makefile.config:
#USE_CUDNN := 1 (scaffe不支持高版本cudnn)
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
WITH_PYTHON_LAYER := 1(需要PYTHON_LAYER)

修改Makefile，在
LIBRARIES += glog gflags protobuf leveldb snappy \
lmdb boost_system hdf5_hl hdf5 m \
opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
处加入后面的opencv_imgcodecs，因为opencv3.0.0把imread相关函数放到imgcodecs.lib中了（原来是imgproc.lib
make -j8
make pycaffe

1.3、下载segnet,建议放置在caffe-segnet文件中：
git clone https://github.com/alexgkendall/SegNet-Tutorial
文件很大，因为其中包含一些图片
下载模型文件：[http://mi.eng.cam.ac.uk/~agk34/resources/SegNet/segnet_weights_driving_webdemo.caffemodel
这是用于摄像头的模型文件，不过图片也能使用,不过需要改变测试文件
使用文件中自带的图片测试结果
Example——Moudel下有相应的模型描述文件prototxt
Scripts文件夹下有相应的测试文件：*p

更改相应路径即可显示结果，这里我更改了使用上面那个摄像头模型的测试文件，可以用于测试单张图片：

# -*- 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 timesys.path.append('/usr/local/lib/python2.7/site-packages')
# Make sure that caffe is on the python path:
caffe_root = '/media/lbk/娱乐/seg-env/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.shapelabel_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 filerval = Trueframe = cv2.imread('/media/lbk/娱乐/seg-env/caffe-segnet/segnet/Example_Models/123.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()

运行：进入到SegNet-Tutorial-master文件夹
python Scripts/*.py --model Example_Models/segnet_model_driving_webdemo.prototxt --weights Example_Models/segnet_weights_driving_webdemo.caffemodel --colours Scripts/camvid12.png

即可得到结果