Ubuntu18.04 + Yolov3 + OpenCV
一、YoloV3安装
１．进入官网
根据说明：下载Darknet：
在自己喜欢的位置解压Darknet，进入Darknet目录并编译：
cd darknet
make
等待完成即可：
下载权重文件：wget https://pjreddie.com/media/files/yolov3.weights特别慢
下载完成后，将权重文件yolov3.weights拷贝到Darknet根目录，执行：./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
简单说明：
配置文件路径：cfg/yolov3.cfg
权重文件路径：yolov3.weights
图片问价路径：data/dog.jpg
运行结果：
zhx@zhx:~/Yolo/darknet-master$ ./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg
layer filters size input output
0 conv 32 3 x 3 / 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BFLOPs
1 conv 64 3 x 3 / 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BFLOPs
2 conv 32 1 x 1 / 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BFLOPs
3 conv 64 3 x 3 / 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BFLOPs
4 res 1 304 x 304 x 64 -> 304 x 304 x 64
5 conv 128 3 x 3 / 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BFLOPs
6 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BFLOPs
7 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 3.407 BFLOPs
8 res 5 152 x 152 x 128 -> 152 x 152 x 128
9 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BFLOPs
10 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 3.407 BFLOPs
11 res 8 152 x 152 x 128 -> 152 x 152 x 128
12 conv 256 3 x 3 / 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BFLOPs
13 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
14 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
15 res 12 76 x 76 x 256 -> 76 x 76 x 256
16 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
17 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
18 res 15 76 x 76 x 256 -> 76 x 76 x 256
19 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
20 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
21 res 18 76 x 76 x 256 -> 76 x 76 x 256
22 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
23 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
24 res 21 76 x 76 x 256 -> 76 x 76 x 256
25 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
26 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
27 res 24 76 x 76 x 256 -> 76 x 76 x 256
28 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
29 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
30 res 27 76 x 76 x 256 -> 76 x 76 x 256
31 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
32 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
33 res 30 76 x 76 x 256 -> 76 x 76 x 256
34 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
35 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
36 res 33 76 x 76 x 256 -> 76 x 76 x 256
37 conv 512 3 x 3 / 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BFLOPs
38 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
39 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
40 res 37 38 x 38 x 512 -> 38 x 38 x 512
41 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
42 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
43 res 40 38 x 38 x 512 -> 38 x 38 x 512
44 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
45 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
46 res 43 38 x 38 x 512 -> 38 x 38 x 512
47 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
48 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
49 res 46 38 x 38 x 512 -> 38 x 38 x 512
50 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
51 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
52 res 49 38 x 38 x 512 -> 38 x 38 x 512
53 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
54 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
55 res 52 38 x 38 x 512 -> 38 x 38 x 512
56 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
57 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
58 res 55 38 x 38 x 512 -> 38 x 38 x 512
59 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
60 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
61 res 58 38 x 38 x 512 -> 38 x 38 x 512
62 conv 1024 3 x 3 / 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BFLOPs
63 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
64 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
65 res 62 19 x 19 x1024 -> 19 x 19 x1024
66 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
67 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
68 res 65 19 x 19 x1024 -> 19 x 19 x1024
69 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
70 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
71 res 68 19 x 19 x1024 -> 19 x 19 x1024
72 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
73 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
74 res 71 19 x 19 x1024 -> 19 x 19 x1024
75 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
76 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
77 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
78 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
79 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BFLOPs
80 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BFLOPs
81 conv 255 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BFLOPs
82 yolo
83 route 79
84 conv 256 1 x 1 / 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BFLOPs
85 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 38 x 38 x 768 -> 38 x 38 x 256 0.568 BFLOPs
88 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
89 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
90 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
91 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BFLOPs
92 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BFLOPs
93 conv 255 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BFLOPs
94 yolo
95 route 91
96 conv 128 1 x 1 / 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BFLOPs
97 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 76 x 76 x 384 -> 76 x 76 x 128 0.568 BFLOPs
100 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
101 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
102 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
103 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BFLOPs
104 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BFLOPs
105 conv 255 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BFLOPs
106 yolo
Loading weights from data/yolov3.weights...Done!
data/dog.jpg: Predicted in 13.315695 seconds.
dog: 100%
truck: 92%
bicycle: 99%
zhx@zhx:~/Yolo/darknet-master$ ./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg
二、安装OpenCV
1.安装cmake（编译器）和依赖库
$ sudo apt-get install cmake #如果已经安装过cmake，则该步骤省略
$ sudo apt-get install build-essential libgtk2.0-dev libgtk-3-dev libavcodec-dev libavformat-dev libjpeg-dev libswscale-dev libtiff5-dev
$ sudo apt-get install libgtk2.0-dev pkg-config python-dev python-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev
2.添加Python3的支持，在终端执行相关命令即可，此处不在截图
# python3支持
$ sudo apt install python3-dev python3-numpy
# streamer支持
$ sudo apt install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev
# 可选的依赖
$ sudo apt install libpng-dev libopenexr-dev libtiff-dev libwebp-dev
３．下载 OpenCV 源文件Sources，并解压缩
创建一个build文件夹，并编译
$ mkdir build
$ cd build/
编译时，OpenCV４默认不使用pkg-config，可以再编译命令中开启生成opencv4.pc文件，支持pkg-config功能，自定义安装位置，方便管理OpenCV(不要遗漏命令末尾的点)
$ cmake -D CMAKE_BUILD_TYPE=Release -D OPENCV_GENERATE_PKGCONFIG=YES -D CMAKE_INSTALL_PREFIX=/usr/local/opencv4 ..
开始纠错
如果出现以下情况，下载失败，可以手动下载ippicv_2019_lnx_intel64_general_20180723.tgz：
其他版本下载
-- IPPICV: Download: ippicv_2019_lnx_intel64_general_20180723.tgz
-- Try 1 failed
下载完成后，修改ippicv.cmake文件，先进入OpenCV根目录在执行：gedit /3rdparty/ippicv/ippicv.cmake
在ocv_download(FILENAME ${OPENCV_ICV_NAME}上面加入：“set(OPENCV_IPPICV_URL “file:///home/zhx/下载/”)”
替换为本地文件路径：
重新执行cmake命令：
出现：-- Looking for ccache - not found
解决办法：sudo apt-get install ccache
结果：
出现：-- Could NOT find Jasper (missing: JASPER_LIBRARIES JASPER_INCLUDE_DIR)
解决办法：
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update
sudo apt install libjasper1 libjasper-dev
出现：
--Could not find OpenBLAS include. Turning OpenBLAS_FOUND off -- Could not find OpenBLAS lib. Turning OpenBLAS_FOUND off -- Could NOT find Atlas (missing: Atlas_CBLAS_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR Atlas_CBLAS_LIBRARY Atlas_BLAS_LIBRARY Atlas_LAPACK_LIBRARY)
解决办法，依次运行如下命令：
git clone git://github.com/xianyi/OpenBLAS
cd OpenBLAS
sudo apt-get install gfortran
sudo make FC=gfortran
sudo make install
创建同步文件链接：sudo ln -s /opt/OpenBLAS/lib/libopenblas.so.0 /usr/lib/libopenblas.so.0
如果出现：--Could NOT find JNI
解决办法：安装Java，并配置环境变量即可，可以参考我另一篇博客
如果出现：VTK is not found.
解决办法：
sudo apt install libvtk6-dev python-vtk6
sudo update-alternatives --install /usr/bin/vtk vtk /usr/bin/vtk6 10
sudo ln -s /usr/lib/python2.7/dist-packages/vtk/libvtkRenderingPythonTkWidgets.x86_64-linux-gnu.so /usr/lib/x86_64-linux-gnu/libvtkRenderingPythonTkWidgets.so
如果出现：-- Checking for module 'libavresample'-- No package 'libavresample' found
解决办法：sudo apt-get install libavresample-dev
如果出现：No package 'libdc1394-2' found
解决办法：
sudo apt-get install libdc1394-22
sudo apt-get install libdc1394-22-dev
纠错结束
4.进行make编译
在OpenCV根目录下执行：$ sudo make -j6 # runs 6 jobs in parallel
5.进行安装：$ sudo make install
6.配置OpenCV 的 pgk-config环境
找到：$ sudo find / -iname opencv4.pc
将/usr/local/OpenCV4/lib/pkgconfig/路径加入PKG_CONFIG_PATH：$ sudo gedit /etc/profile.d/pkgconfig.sh
写入以下内容：export PKG_CONFIG_PATH=/usr/local/OpenCV4/lib/pkgconfig:$PKG_CONFIG_PATH
更新：$ source /etc/profile
验证是否配置成功：$ pkg-config --libs opencv4
结果如下，配置成功：
7.配置动态库环境
配置OpenCV动态库环境——程序执行时加载动态库*.so的路径：$ sudo gedit /etc/ld.so.conf.d/opencv4.conf
在该文件（可能是空文件）末尾加上：/usr/local/OpenCV4/lib
使配置的路径生效：$ sudo ldconfig
8.Python-opencv环境
找到编译好的python cv库：$sudo find / -iname cv2*.so，结果如下：
/home/zhx/OpenCV/opencv-4.2.0/build/lib/python3/cv2.cpython-36m-x86_64-linux-gnu.so
/usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so
cv2.cpython-35m-x86_64-linux-gnu.so就是编译好的python3的opencv库
我们把它复制到对应python解释器的/path/to/dist-packages/(系统自带的python解释器)
和/path/to/site-packages(用户安装的python解释器)目录下，之后就能在该python解释器中使用python-opencv库
链接到系统自带的python3解释器中：$ sudo ln -s /usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so /usr/lib/python3/dist-packages/cv2.so
链接到Anaconda创建的虚拟环境python3解释器中：ln -s /usr/local/OpenCV4/lib/python3.6/dist-packages/cv2/python-3.6/cv2.cpython-36m-x86_64-linux-gnu.so ~/anaconda3/lib/python3.7/site-packages/cv2.so
9.测试：
环境配置完成，对我们安装的OpenCV进行测试：
cd 到/opencv-4.0.0/samples/cpp/example_cmake目录下，查看当前目录内容：
通过Makefile测试
因为OpenCV 4.0需要C++11支持，且生成的pkg-config文件名为opencv4.pc，所以需要对当前目录下的Malefile文件进行修改：
$ gedit Makefile
原文如下：
CXX ?= g++
CXXFLAGS += -c -Wall $(shell pkg-config --cflags opencv)
LDFLAGS += $(shell pkg-config --libs --static opencv)
all: opencv_example
opencv_example: example.o; $(CXX) $< -o $@ $(LDFLAGS)
%.o: %.cpp; $(CXX) $< -o $@ $(CXXFLAGS)
clean: ; rm -f example.o opencv_example
修改为：
CXX ?= g++
CXXFLAGS += -c -std=c++11 -Wall $(shell pkg-config --cflags opencv4)
LDFLAGS += $(shell pkg-config --libs --static opencv4)
all: opencv_example
opencv_example: example.o; $(CXX) $< -o $@ $(LDFLAGS)
%.o: %.cpp; $(CXX) $< -o $@ $(CXXFLAGS)
clean: ; rm -f example.o opencv_example
当前路径，执行make命令生成可执行文件opencv_example：
$ make
结果如下：
g++ example.cpp -o example.o -c -std=c++11 -Wall -I/usr/local/opencv4/include/opencv4/opencv -I/usr/local/opencv4/include/opencv4
g++ example.o -o opencv_example -L/usr/local/opencv4/lib -lopencv_ml -lopencv_dnn -lopencv_video -lopencv_stitching -lopencv_objdetect -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_flann -lopencv_photo -lopencv_gapi -lopencv_imgproc -lopencv_core -ldl -lm -lpthread -lrt
运行生成的文件opencv_example：$ ./opencv_example
会打开计算机摄像头，并带有Hello OpenCV字样
通过Cmake测试
因为我们更改了opencv的安装路径（/usr/local/OpenCV4），所以测试前我们需要在CMakeLists.txt文件的14行find_package(OpenCV REQUIRED)前面加上：set(OpenCV_DIR /usr/local/OpenCV4/lib/cmake/opencv4)
修改后文件内容：
# cmake needs this line
cmake_minimum_required(VERSION 3.1)
# Enable C++11
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED TRUE)
# Define project name
project(opencv_example_project)
# Find OpenCV, you may need to set OpenCV_DIR variable
# to the absolute path to the directory containing OpenCVConfig.cmake file
# via the command line or GUI
set(OpenCV_DIR /usr/local/OpenCV4/lib/cmake/opencv4)
find_package(OpenCV REQUIRED)
# If the package has been found, several variables will
# be set, you can find the full list with descriptions
# in the OpenCVConfig.cmake file.
# Print some message showing some of them
message(STATUS "OpenCV library status:")
message(STATUS " config: ${OpenCV_DIR}")
message(STATUS " version: ${OpenCV_VERSION}")
message(STATUS " libraries: ${OpenCV_LIBS}")
message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}")
# Declare the executable target built from your sources
add_executable(opencv_example example.cpp)
# Link your application with OpenCV libraries
target_link_libraries(opencv_example PRIVATE ${
OpenCV_LIBS})
然后当前路径下执行：
$ mkdir build && cd build
$ cmake ..
$ make
$ ./opencv_example
弹出和之前一样效果的视频框
10.在Python里引用：
$ python3
Python 3.5.2 (default, Oct 8 2019, 13:06:37)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2 as cv
>>> print(cv.__version__)
4.2.0
如果报错，缺少numpy，就执行pin3 install numpy，如果没有pip3，根据提示执行：sudo apt install python3-pip
三、YoloV3在GPU运行
1.进入Darknet路径，修改MakeFile文件：sudo gedit Makefile
GPU/CUDNN/OPENCV的值改为1
修改前：
修改后，如下并保存：
2.找到：
LDFLAGS+= `pkg-config --libs opencv` -lstdc++
COMMON+= `pkg-config --cflags opencv`
改为：
LDFLAGS+= `pkg-config --libs opencv4` -lstdc++
COMMON+= `pkg-config --cflags opencv4`
3.重新编译：
make clean
make
此时出现这样一个错误：
./src/image_opencv.cpp:12:1: error: ‘IplImage’ does not name a type; did you mean ‘image’?
IplImage *image_to_ipl(image im)
^~~~~~~~
image
compilation terminated due to -Wfatal-errors.
Makefile:86: recipe for target 'obj/image_opencv.o' failed
make: *** [obj/image_opencv.o] Error 1
开始着手解决吧，先找到报错位置：sudo gedit ./src/image_opencv.cpp
修改以下两个函数（注释掉的为原代码）：
Mat image_to_mat(image im)
{
image copy = copy_image(im);
constrain_image(copy);
if(im.c == 3) rgbgr_image(copy);
//IplImage *ipl = image_to_ipl(copy);
//Mat m = cvarrToMat(ipl, true);
//cvReleaseImage(&ipl);
Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
int x,y,c;
int step = m.step;
for(y = 0; y < im.h; ++y){
for(x = 0; x < im.w; ++x){
for(c= 0; c < im.c; ++c){
float val = im.data[c*im.h*im.w + y*im.w + x];
m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
}
}
}
free_image(copy);
return m;
}
image mat_to_image(Mat m)
{
//IplImage ipl = m;
//image im = ipl_to_image(&ipl);
int h = m.rows;
int w = m.cols;
int c = m.channels();
image im = make_image(w, h, c);
unsigned char *data = (unsigned char *)m.data;
int step = m.step;
int i, j, k;
for(i = 0; i < h; ++i){
for(k= 0; k < c; ++k){
for(j = 0; j < w; ++j){
im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
}
}
}
rgbgr_image(im);
return im;
}
删除其余所有包含IplImage的函数
在把以：CV_ 开头的变量，去掉CV_，新版本不带CV_
最终修改后的image_opencv.cpp代码如下：
#ifdef OPENCV
#include "stdio.h"
#include "stdlib.h"
#include "opencv2/opencv.hpp"
#include "image.h"
using namespace cv;
extern "C" {
/*
IplImage *image_to_ipl(image im)
{
int x,y,c;
IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c);
int step = disp->widthStep;
for(y = 0; y < im.h; ++y){
for(x = 0; x < im.w; ++x){
for(c= 0; c < im.c; ++c){
float val = im.data[c*im.h*im.w + y*im.w + x];
disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255);
}
}
}
return disp;
}
image ipl_to_image(IplImage* src)
{
int h = src->height;
int w = src->width;
int c = src->nChannels;
image im = make_image(w, h, c);
unsigned char *data = (unsigned char *)src->imageData;
int step = src->widthStep;
int i, j, k;
for(i = 0; i < h; ++i){
for(k= 0; k < c; ++k){
for(j = 0; j < w; ++j){
im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
}
}
}
return im;
}
*/
Mat image_to_mat(image im)
{
image copy = copy_image(im);
constrain_image(copy);
if(im.c == 3) rgbgr_image(copy);
//IplImage *ipl = image_to_ipl(copy);
//Mat m = cvarrToMat(ipl, true);
//cvReleaseImage(&ipl);
Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
int x,y,c;
int step = m.step;
for(y = 0; y < im.h; ++y){
for(x = 0; x < im.w; ++x){
for(c= 0; c < im.c; ++c){
float val = im.data[c*im.h*im.w + y*im.w + x];
m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
}
}
}
free_image(copy);
return m;
}
image mat_to_image(Mat m)
{
//IplImage ipl = m;
//image im = ipl_to_image(&ipl);
int h = m.rows;
int w = m.cols;
int c = m.channels();
image im = make_image(w, h, c);
unsigned char *data = (unsigned char *)m.data;
int step = m.step;
int i, j, k;
for(i = 0; i < h; ++i){
for(k= 0; k < c; ++k){
for(j = 0; j < w; ++j){
im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
}
}
}
rgbgr_image(im);
return im;
}
void *open_video_stream(const char *f, int c, int w, int h, int fps)
{
VideoCapture *cap;
if(f) cap = new VideoCapture(f);
else cap = new VideoCapture(c);
if(!cap->isOpened()) return 0;
//if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w);
//if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w);
//if(fps) cap->set(CV_CAP_PROP_FPS, w);
if(w) cap->set(CAP_PROP_FRAME_WIDTH, w);
if(h) cap->set(CAP_PROP_FRAME_HEIGHT, w);
if(fps) cap->set(CAP_PROP_FPS, w);
return (void *) cap;
}
image get_image_from_stream(void *p)
{
VideoCapture *cap = (VideoCapture *)p;
Mat m;
*cap >> m;
if(m.empty()) return make_empty_image(0,0,0);
return mat_to_image(m);
}
image load_image_cv(char *filename, int channels)
{
int flag = -1;
if (channels == 0) flag = -1;
else if (channels == 1) flag = 0;
else if (channels == 3) flag = 1;
else {
fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
}
Mat m;
m = imread(filename, flag);
if(!m.data){
fprintf(stderr, "Cannot load image \"%s\"\n", filename);
char buff[256];
sprintf(buff, "echo %s >> bad.list", filename);
system(buff);
return make_image(10,10,3);
//exit(0);
}
image im = mat_to_image(m);
return im;
}
int show_image_cv(image im, const char* name, int ms)
{
Mat m = image_to_mat(im);
imshow(name, m);
int c = waitKey(ms);
if (c != -1) c = c%256;
return c;
}
void make_window(char *name, int w, int h, int fullscreen)
{
namedWindow(name, WINDOW_NORMAL);
if (fullscreen) {
//setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
} else {
resizeWindow(name, w, h);
if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0);
}
}
}
#endif
再次编译：
make clean
make
通过：
进行YoloV3测试：./darknet detect cfg/yolov3.cfg data/yolov3.weights data/dog.jpg
报错：
CUDA Error: out of memory darknet: ./src/cuda.c:36: check_error: Assertion 0 failed. 已放弃 (核心已转储)
出现这问题的原因就是GPU内存不够大，解决方法就是减小内存的使用：
修改cfg/yolov3.cfg如下：
[net]
# Testing
batch=1 //取消注释
subdivisions=1 //取消注释
# Training
batch=64 //可以添加注释
subdivisions=16 //可以添加注释
width=608 //可以适当减小width
height=608 //可以适当减小height
修改后：
运行通过：
至此，结束结束结束，如有错误，欢迎指正