segnet 編譯與測試
參考：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、編譯caffe-segnet：
1.1下載caffe-segnet(適用於segnet的caffe版本，下成scaffe)
git clone https://github.com/alexgkendall/caffe-segnet
1.2更改一些編譯選項：
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
處加入后面的opencv_imgcodecs，因為opencv3.0.0把imread相關函數放到imgcodecs.lib中了（原來是imgproc.lib
make -j8
make pycaffe
1.3、下載segnet,建議放置在caffe-segnet文件中：
git clone https://github.com/alexgkendall/SegNet-Tutorial
文件很大，因為其中包含一些圖片
下載模型文件：[http://mi.eng.cam.ac.uk/~agk34/resources/SegNet/segnet_weights_driving_webdemo.caffemodel
這是用於攝像頭的模型文件，不過圖片也能使用,不過需要改變測試文件
使用文件中自帶的圖片測試結果
Example——Moudel下有相應的模型描述文件prototxt
Scripts文件夾下有相應的測試文件：*p
更改相應路徑即可顯示結果，這里我更改了使用上面那個攝像頭模型的測試文件，可以用於測試單張圖片：
# -*- 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 time
sys.path.append('/usr/local/lib/python2.7/site-packages')
# Make sure that caffe is on the python path:
caffe_root = '/media/lbk/娛樂/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.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('/media/lbk/娛樂/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)
#這里應該變成小數存儲了，看來opencv對於小數也是熱圖顯示，但是保存還是黑白的圖
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)
#這里使用plt顯示與保存，比cv2好點，並且不會出現進程卡住的情況
plt.imshow(segmentation_rgb)
plt.savefig('output.png')
plt.show()
運行：進入到SegNet-Tutorial-master文件夾
python Scripts/*.py --model Example_Models/segnet_model_driving_webdemo.prototxt --weights Example_Models/segnet_weights_driving_webdemo.caffemodel --colours Scripts/camvid12.png
即可得到結果