Ubuntu16.04,CUDA8.0,Caffe-ssd,OpenCV3.1配置指南(二)

git clone https://github.com/weiliu89/caffe.git cd caffe
git checkout ssd

cp Makefile.config.example Makefile.config
gedit Makefile.config

将
#USE_CUDNN := 1
修改成：
USE_CUDNN := 1

将
#OPENCV_VERSION := 3
修改为：
OPENCV_VERSION := 3

将
#WITH_PYTHON_LAYER := 1
修改为
WITH_PYTHON_LAYER := 1

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
修改为：
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 /usr/lib/x86_64-linux-gnu/hdf5/serial

将：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
改为：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

LIBRARIES += boost_thread stdc++后，如下

LIBRARIES += boost_thread stdc++ boost_regex

[code]将：NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)替换为：NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

将#error-- unsupported GNU version! gcc versions later than 4.9 are not supported!改为//#error-- unsupported GNU version! gcc versions later than 4.9 are not supported!

"libcudart.so.8.0 cannot open shared object file: Nosuch fileor directory"

#注意自己CUDA的版本号！cp /usr/local/cuda-8.0/lib64/libcudart.so.8.0 /usr/local/lib/libcudart.so.8.0 && sudo ldconfigcp /usr/local/cuda-8.0/lib64/libcublas.so.8.0 /usr/local/lib/libcublas.so.8.0 && sudo ldconfigcp /usr/local/cuda-8.0/lib64/libcurand.so.8.0 /usr/local/lib/libcurand.so.8.0 && sudo ldconfig

make -j8# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.make pymake test -j8# (Optional)make runtest -j8

vim ~/.bashrc4000

source ~/.bashrc

# Download the data.cd $HOME/datawget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar # Extract the data.tar -xvf VOCtrainval_11-May-2012.tartar -xvf VOCtrainval_06-Nov-2007.tartar -xvf VOCtest_06-Nov-2007.tar

cd $CAFFE_ROOT# Create the trainval.txt, test.txt, and test_name_size.txt in data/VOC0712/./data/VOC0712/create_list.sh# You can modify the parameters in create_data.sh if needed.# It will create lmdb files for trainval and test with encoded original image:#   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_trainval_lmdb#   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_test_lmdb# and make soft links at examples/VOC0712/./data/VOC0712/create_data.sh

# It will create model definition files and save snapshot models in:#   - $CAFFE_ROOT/models/VGGNet/VOC0712/SSD_300x300/# and job file, log file, and the python script in:#   - $CAFFE_ROOT/jobs/VGGNet/VOC0712/SSD_300x300/# and save temporary evaluation results in:#   - $HOME/data/VOCdevkit/results/VOC2007/SSD_300x300/# It should reach 77.* mAP at 120k iterations.python examples/ssd/ssd_pascal.py

# If you would like to test a model you trained, you can do:python examples/ssd/score_ssd_pascal.py

python examples/ssd/ssd_detect_singleImg.py

# coding: utf-8# Note: this file is expected to be in {caffe_root}/examples# ### 1. Setupfrom __future__ import print_functionimport numpy as npimport matplotlib.pyplot as pltimport pylabplt.rcParams['figure.figsize'] = (10, 10)plt.rcParams['image.interpolation'] = 'nearest'plt.rcParams['image.cmap'] = 'gray'caffe_root = '/home/yakerang/caffe'import osos.chdir(caffe_root)import syssys.path.insert(0, '/home/yakerang/caffe/python')import caffefrom google.protobuf import text_formatfrom caffe.proto import caffe_pb2caffe.set_device(0)caffe.set_mode_gpu()labelmap_file = '/home/yakerang/caffe/data/VOC0712/labelmap_voc.prototxt'file = open(labelmap_file, 'r')labelmap = caffe_pb2.LabelMap()text_format.Merge(str(file.read()), labelmap)def get_labelname(labelmap, labels):num_labels = len(labelmap.item)labelnames = []if type(labels) is not list:labels = [labels]for label in labels:found = Falsefor i in xrange(0, num_labels):if label == labelmap.item[i].label:found = Truelabelnames.append(labelmap.item[i].display_name)breakassert found == Truereturn labelnamesmodel_def = '/home/yakerang/caffe/models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt'model_weights = '/home/yakerang/caffe/models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712Plus_SSD_300x300_iter_240000.caffemodel'net = caffe.Net(model_def, model_weights, caffe.TEST)# input preprocessing: 'data' is the name of the input blob == net.inputs[0]transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})transformer.set_transpose('data', (2, 0, 1))transformer.set_mean('data', np.array([104, 117, 123]))  # mean pixeltransformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]transformer.set_channel_swap('data', (2, 1, 0))  # the reference model has channels in BGR order instead of RGB# ### 2. SSD detection# Load an image.image_resize = 300net.blobs['data'].reshape(1, 3, image_resize, image_resize)image = caffe.io.load_image('/home/yakerang/caffe/examples/images/fish-bike.jpg')plt.imshow(image)# Run the net and examine the top_k resultstransformed_image = transformer.preprocess('data', image)net.blobs['data'].data[...] = transformed_image# Forward pass.detections = net.forward()['detection_out']# Parse the outputs.det_label = detections[0, 0, :, 1]det_conf = detections[0, 0, :, 2]det_xmin = detections[0, 0, :, 3]det_ymin = detections[0, 0, :, 4]det_xmax = detections[0, 0, :, 5]det_ymax = detections[0, 0, :, 6]# Get detections with confidence higher than 0.6.top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]top_conf = det_conf[top_indices]top_label_indices = det_label[top_indices].tolist()top_labels = get_labelname(labelmap, top_label_indices)top_xmin = det_xmin[top_indices]top_ymin = det_ymin[top_indices]top_xmax = det_xmax[top_indices]top_ymax = det_ymax[top_indices]# Plot the boxescolors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()currentAxis = plt.gca()for i in xrange(top_conf.shape[0]):# bbox valuexmin = int(round(top_xmin[i] * image.shape[1]))ymin = int(round(top_ymin[i] * image.shape[0]))xmax = int(round(top_xmax[i] * image.shape[1]))ymax = int(round(top_ymax[i] * image.shape[0]))# scorescore = top_conf[i]# labellabel = int(top_label_indices[i])label_name = top_labels[i]# display info: label score xmin ymin xmax ymaxdisplay_txt = '%s: %.2f %d %d %d %d' % (label_name, score,xmin, ymin, xmax, ymax)# display_bbox_value = '%d %d %d %d' % (xmin, ymin, xmax, ymax)coords = (xmin, ymin), xmax - xmin + 1, ymax - ymin + 1color = colors[label]currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor': color, 'alpha': 0.5})# currentAxis.text((xmin+xmax)/2, (ymin+ymax)/2, display_bbox_value, bbox={'facecolor': color, 'alpha': 0.5})plt.imshow(image)pylab.show()