HIPI meets OpenCV
Problem
This project tries to solve the problem of processing big data of images on Apache Hadoop using Hadoop Image Processing Interface (HIPI) for storing and efficient distributed processing, combined with OpenCV, an open source library of rich image processing algorithms. A program to count number of faces in collection of images is demonstrated.
Background
Processing large set of images on a single machine can be very time consuming and costly. HIPI is an image processing library designed to be used with the Apache Hadoop MapReduce, a software framework for sorting and processing big data in a distributed fashion on large cluster of commodity hardware. HIPI facilitates efficient and high-throughput image processing with MapReduce style parallel programs typically executed on a cluster. It provides a solution for how to store a large collection of images on the Hadoop Distributed File System (HDFS) and make them available for efficient distributed processing.
OpenCV (Open Source Computer Vision) is an open source library of rich image processing algorithms, mainly aimed at real time computer vision. Starting with OpenCV 2.4.4, OpenCV supports Java Development which can be used with Apache Hadoop.
Goal
This project demonstrates how HIPI and OpenCV can be used together to count total number of faces in big image dataset.
Overview of Steps
Big Data Set
Test images for face detection
Input: Image data containing 158 image (34MB)
Format: png image files
Downloaded image were in gif format, I used Mac OSX Preview program to convert these to png format.
Other sources for face detection image datasets:
Technologies Used:
Software Used
|
Purpose
|
VMWare Fusion
|
Software hypervisor for running Cloudera Quickstart VM
|
Cloudera Quickstart VM
|
VM for single node Hadoop cluster for testing and running map/reduce programs
|
IntelliJ IDEA 14 CE
|
Java IDE for editing and compiling Java code
|
Hadoop Image Processing Interface (HIPI)
|
Image processing library designed to be used with the Apache Hadoop MapReduce parallel programming framework, for storing large collection of images on HDFS and efficient distributed processing.
|
OpenCV
|
An image processing library aimed at real-time computer vision
|
Apache Hadoop
|
Distributed processing of large data sets
|
References:
Steps
1. Download VMWare Fusion
VMware Fusion is a software hypervisor developed by VMware for computers running OS X with Intel processors. Fusion allows Intel-based Macs to run operating systems such as Microsoft Windows, Linux, NetWare, or Solaris on virtual machines, along with their Mac OS X operating system using a combination of paravirtualization,hardware virtualization and dynamic recompilation. (http://en.wikipedia.org/wiki/VMware_Fusion)
Download and install VMWare Fusion from following URL, this will be used to run Cloudera Quickstart VM.
2. Download and Setup Cloudera Quickstart VM 5.4.x
The Cloudera QuickStart VMs contain a single-node Apache Hadoop cluster, complete with example data, queries, scripts, and Cloudera Manager to manage your cluster. The VMs run CentOS 6.4 and are available for VMware, VirtualBox, and KVM. This will help us gettings started with all the tools needed to run image processing using Hadoop.
Download Cloudera Quickstart VM 5.4.x from following URL. The Quickstart VM 5.4 has Hadoop 2.6 installed on it, which is needed for HIPI.
Open VMWare Fusion and open the VM
3. Getting started with HIPI
Following steps demonstrates how to setup HIPI to run MapReduce job on Apache Hadoop.
Setup Hadoop
The Cloudera Quickstart VM 5.4.x comes pre-installed with Hadoop 2.6 which is needed needed for running HIPI.
Check Hadoop is installed and has correct version:
[cloudera@quickstart Project]$ which hadoop
/usr/bin/hadoop
[cloudera@quickstart Project]$ hadoop version
Hadoop 2.6.0-cdh5.4.0
Subversion http://github.com/cloudera/hadoop -r c788a14a5de9ecd968d1e2666e8765c5f018c271
Compiled by jenkins on 2015-04-21T19:18Z
Compiled with protoc 2.5.0
From source with checksum cd78f139c66c13ab5cee96e15a629025
This command was run using /usr/lib/hadoop/hadoop-common-2.6.0-cdh5.4.0.jar
Install Apache Ant
Install Apache Ant and check it added to PATH:
[cloudera@quickstart Project]$ which ant
/usr/local/apache-ant/apache-ant-1.9.2/bin/ant
Install and build HIPI
There are two ways to install HIPI
-
-
Clone HIPI GitHub repository
The best way to check and verify that your system is properly setup is to clone the official GitHub repository and build the tools and example programs.
[cloudera@quickstart Project]$ git clone https://github.com/uvagfx/hipi.git
Initialized empty Git repository in /home/cloudera/Project/hipi/.git/
remote: Counting objects: 2882, done.
remote: Total 2882 (delta 0), reused 0 (delta 0), pack-reused 2882
Receiving objects: 100% (2882/2882), 222.33 MiB | 7.03 MiB/s, done.
Resolving deltas: 100% (1767/1767), done.
Download Apache Hadoop tarball
Download Apache Hadoop tarball from following URL and untar it, this is needed to build HIPI.
[cloudera@quickstart Project]$ tar -xvzf /mnt/hgfs/CSCEI63/project/hadoop-2.6.0-cdh5.4.0.tar.gz
[cloudera@quickstart Project]$ ls
hadoop-2.6.0-cdh5.4.0 hipi
Build HIPI binaries
Change directory to hipi repo and build HIPI.
[cloudera@quickstart Project]$ cd hipi/
[cloudera@quickstart hipi]$ ls
3rdparty data examples license.txt release
build.xml doc libsrc README.md util
Before building the HIPI, hadoop.home and hadoop.version properties in build.xml file should be updated to the path to Hadoop installation and the version of Hadoop we are using. Change:
build.xml
<!-- IMPORTANT: You must update the following two properties according to your Hadoop setup -->
<!-- <property name="hadoop.home" value="/usr/local/Cellar/hadoop/2.6.0/libexec/share/hadoop" /> -->
<!-- <property name="hadoop.version" value="2.6.0" /> -->
to
<!-- IMPORTANT: You must update the following two properties according to your Hadoop setup -->
<property name="hadoop.home" value="/home/cloudera/Project/hadoop-2.6.0-cdh5.4.0/share/hadoop" />
<property name="hadoop.version" value="2.6.0-cdh5.4.0" />
Build HIPI using ant
[cloudera@quickstart hipi]$ ant
Buildfile: /home/cloudera/Project/hipi/build.xml
…
hipi:
[javac] Compiling 30 source files to /home/cloudera/Project/hipi/lib
[jar] Building jar: /home/cloudera/Project/hipi/lib/hipi-2.0.jar
[echo] Hipi library built.
compile:
[javac] Compiling 1 source file to /home/cloudera/Project/hipi/bin
[jar] Building jar: /home/cloudera/Project/hipi/examples/covariance.jar
[echo] Covariance built.
all:
BUILD SUCCESSFUL
Total time: 36 seconds
Make sure it builds tools and examples:
[cloudera@quickstart hipi]$ ls
3rdparty build.xml doc lib license.txt release util
bin data examples libsrc README.md tool
[cloudera@quickstart hipi]$ ls tool/
hibimport.jar
[cloudera@quickstart hipi]$ ls examples/
covariance.jar hipi runCreateSequenceFile.sh
createsequencefile.jar jpegfromhib.jar runDownloader.sh
downloader.jar rumDumpHib.sh runJpegFromHib.sh
dumphib.jar runCovariance.sh testimages.txt
Sample MapReduce Java Program
Create SampleProgram.java class in “sample” folder to run a simple map/reduce program using sample.hib created in above step:
[cloudera@quickstart hipi]$ mkdir sample
[cloudera@quickstart hipi]$ vi sample/SampleProgram.java
SampleProgram.java
import hipi.image.FloatImage;
import hipi.image.ImageHeader;
import hipi.imagebundle.mapreduce.ImageBundleInputFormat;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class SampleProgram extends Configured implements Tool {
public static class SampleProgramMapper extends Mapper<ImageHeader, FloatImage, IntWritable, FloatImage> {
public void map(ImageHeader key, FloatImage value, Context context)
throws IOException, InterruptedException {
// Verify that image was properly decoded, is of sufficient size, and has three color channels (RGB)
if (value != null && value.getWidth() > 1 && value.getHeight() > 1 && value.getBands() == 3) {
// Get dimensions of image
int w = value.getWidth();
int h = value.getHeight();
// Get pointer to image data
float[] valData = value.getData();
// Initialize 3 element array to hold RGB pixel average
float[] avgData = {0,0,0};
// Traverse image pixel data in raster-scan order and update running average
for (int j = 0; j < h; j++) {
for (int i = 0; i < w; i++) {
avgData[0] += valData[(j*w+i)*3+0]; // R
avgData[1] += valData[(j*w+i)*3+1]; // G
avgData[2] += valData[(j*w+i)*3+2]; // B
}
}
// Create a FloatImage to store the average value
FloatImage avg = new FloatImage(1, 1, 3, avgData);
// Divide by number of pixels in image
avg.scale(1.0f/(float)(w*h));
// Emit record to reducer
context.write(new IntWritable(1), avg);
} // If (value != null...
} // map()
}
public static class SampleProgramReducer extends Reducer<IntWritable, FloatImage, IntWritable, Text> {
public void reduce(IntWritable key, Iterable<FloatImage> values, Context context)
throws IOException, InterruptedException {
// Create FloatImage object to hold final result
FloatImage avg = new FloatImage(1, 1, 3);
// Initialize a counter and iterate over IntWritable/FloatImage records from mapper
int total = 0;
for (FloatImage val : values) {
avg.add(val);
total++;
}
if (total > 0) {
// Normalize sum to obtain average
avg.scale(1.0f / total);
// Assemble final output as string
float[] avgData = avg.getData();
String result = String.format("Average pixel value: %f %f %f", avgData[0], avgData[1], avgData[2]);
// Emit output of job which will be written to HDFS
context.write(key, new Text(result));
}
} // reduce()
}
public int run(String[] args) throws Exception {
// Check input arguments
if (args.length != 2) {
System.out.println("Usage: firstprog <input HIB> <output directory>");
System.exit(0);
}
// Initialize and configure MapReduce job
Job job = Job.getInstance();
// Set input format class which parses the input HIB and spawns map tasks
job.setInputFormatClass(ImageBundleInputFormat.class);
// Set the driver, mapper, and reducer classes which express the computation
job.setJarByClass(SampleProgram.class);
job.setMapperClass(SampleProgramMapper.class);
job.setReducerClass(SampleProgramReducer.class);
// Set the types for the key/value pairs passed to/from map and reduce layers
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(FloatImage.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Text.class);
// Set the input and output paths on the HDFS
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// Execute the MapReduce job and block until it complets
boolean success = job.waitForCompletion(true);
// Return success or failure
return success ? 0 : 1;
}
public static void main(String[] args) throws Exception {
ToolRunner.run(new SampleProgram(), args);
System.exit(0);
}
}
Add a new build target to hipi/build.xml to build SampleProgram.java and create a sample.jar library:
build.xml
…
<target name="sample">
<antcall target="compile">
<param name="srcdir" value="sample" />
<param name="jarfilename" value="sample.jar" />
<param name="jardir" value="sample" />
<param name="mainclass" value="SampleProgram" />
</antcall>
</target>
...
Build SampleProgram
[cloudera@quickstart hipi]$ ant sample
Buildfile: /home/cloudera/Project/hipi/build.xml
...
compile:
[jar] Building jar: /home/cloudera/Project/hipi/sample/sample.jar
BUILD SUCCESSFUL
Total time: 16 seconds
Running a sample HIPI MapReduce Program
Create a sample.hib on HDFS file from sample images provides with HIPI using hibimport tool, this will be the input to MapReduce program.
[cloudera@quickstart hipi]$ hadoop jar tool/hibimport.jar data/test/ImageBundleTestCase/read/
0.jpg 1.jpg 2.jpg 3.jpg
[cloudera@quickstart hipi]$ hadoop jar tool/hibimport.jar data/test/ImageBundleTestCase/read examples/sample.hib
** added: 2.jpg
** added: 3.jpg
** added: 0.jpg
** added: 1.jpg
Created: examples/sample.hib and examples/sample.hib.dat
[cloudera@quickstart hipi]$ hadoop fs -ls examples
Found 2 items
-rw-r--r-- 1 cloudera cloudera 80 2015-05-09 22:19 examples/sample.hib
-rw-r--r-- 1 cloudera cloudera 1479345 2015-05-09 22:19 examples/sample.hib.dat
Running Hadoop MapReduce program
[cloudera@quickstart hipi]$ hadoop jar sample/sample.jar examples/sample.hib examples/output
15/05/09 23:05:10 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
…
15/05/09 23:07:05 INFO mapreduce.Job: Job job_1431127776378_0001 running in uber mode : false
15/05/09 23:07:05 INFO mapreduce.Job: map 0% reduce 0%
15/05/09 23:08:55 INFO mapreduce.Job: map 57% reduce 0%
15/05/09 23:09:04 INFO mapreduce.Job: map 100% reduce 0%
15/05/09 23:09:38 INFO mapreduce.Job: map 100% reduce 100%
15/05/09 23:09:39 INFO mapreduce.Job: Job job_1431127776378_0001 completed successfully
…
File Output Format Counters
Bytes Written=50
Check the program output:
[cloudera@quickstart hipi]$ hadoop fs -ls examples/output
Found 2 items
-rw-r--r-- 1 cloudera cloudera 0 2015-05-09 23:09 examples/output/_SUCCESS
-rw-r--r-- 1 cloudera cloudera 50 2015-05-09 23:09 examples/output/part-r-00000
The average pixel value calculated for all the image is:
[cloudera@quickstart hipi]$ hadoop fs -cat examples/output/part-r-00000
1 Average pixel value: 0.420624 0.404933 0.380449
4. Getting started with OpenCV using Java
OpenCV is an image processing library. It contains a large collection of image processing functions. Starting from version 2.4.4 OpenCV includes desktop Java bindings. We will use version 2.4.11 to build Java bindings and use it with HIPI to run image processing.
Download OpenCV source:
The zip bundle for OpenCV 2.4.11 source can be download from following URL and unzip it to ~/Project/opencv directory.
[cloudera@quickstart Project]$ mkdir opencv && cd opencv
[cloudera@quickstart opencv]$ unzip /mnt/hgfs/CSCEI63/project/opencv-2.4.11.zip
CMake build system
CMake is a family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files. CMake generates native makefiles and workspaces that can be used in the compiler environment of your choice. (http://www.cmake.org/)
[cloudera@quickstart opencv]$ tar -xvzf cmake-3.2.2-Linux-x86_64.tar.gz
Build OpenCV for Java
Following steps details how to build OpenCV on linux
Configure OpenCV for builds on Linux
[cloudera@quickstart opencv-2.4.11]$ ../cmake-3.2.2-Linux-x86_64/bin/cmake -DBUILD_SHARED_LIBS=OFF
.
.
. Target "opencv_haartraining_engine" links to itself.
This warning is for project developers. Use -Wno-dev to suppress it.
-- Generating done
-- Build files have been written to: /home/cloudera/Project/opencv/opencv-2.4.11
Build OpenCV
[cloudera@quickstart opencv-2.4.11]$ make
.
.
.
[100%] Building CXX object apps/traincascade/CMakeFiles/opencv_traincascade.dir/imagestorage.cpp.o
Linking CXX executable ../../bin/opencv_traincascade
[100%] Built target opencv_traincascade
Scanning dependencies of target opencv_annotation
[100%] Building CXX object apps/annotation/CMakeFiles/opencv_annotation.dir/opencv_annotation.cpp.o
Linking CXX executable ../../bin/opencv_annotation
[100%] Built target opencv_annotation
This will create a jar containing the Java interface (bin/opencv-2411.jar) and a native dynamic library containing Java bindings and all the OpenCV stuff (lib/libopencv_java2411.so). We’ll use these files to build and run OpenCV program.
[cloudera@quickstart opencv-2.4.11]$ ls lib | grep .so
libopencv_java2411.so
[cloudera@quickstart opencv-2.4.11]$ ls bin | grep .jar
opencv-2411.jar
Running OpenCV Face Detection Program
Following steps are run to make sure OpenCV is setup correctly and works as expected.
Create a new directory sample and Create an ant build.xml file in it.
[cloudera@quickstart opencv]$ mkdir sample && cd sample
[cloudera@quickstart sample]$ vi build.xml
build.xml
<project name="Main" basedir="." default="rebuild-run">
<property name="src.dir" value="src"/>
<property name="lib.dir" value="${ocvJarDir}"/>
<path id="classpath">
<fileset dir="${lib.dir}" includes="**/*.jar"/>
</path>
<property name="build.dir" value="build"/>
<property name="classes.dir" value="${build.dir}/classes"/>
<property name="jar.dir" value="${build.dir}/jar"/>
<property name="main-class" value="${ant.project.name}"/>
<target name="clean">
<delete dir="${build.dir}"/>
</target>
<target name="compile">
<mkdir dir="${classes.dir}"/>
<javac includeantruntime="false" srcdir="${src.dir}" destdir="${classes.dir}" classpathref="classpath"/>
</target>
<target name="jar" depends="compile">
<mkdir dir="${jar.dir}"/>
<jar destfile="${jar.dir}/${ant.project.name}.jar" basedir="${classes.dir}">
<manifest>
<attribute name="Main-Class" value="${main-class}"/>
</manifest>
</jar>
</target>
<target name="run" depends="jar">
<java fork="true" classname="${main-class}">
<sysproperty key="java.library.path" path="${ocvLibDir}"/>
<classpath>
<path refid="classpath"/>
<path location="${jar.dir}/${ant.project.name}.jar"/>
</classpath>
</java>
</target>
<target name="rebuild" depends="clean,jar"/>
<target name="rebuild-run" depends="clean,run"/>
</project>
Write a program using OpenCV to detect number of faces in an image.
DetectFaces.java
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.highgui.*;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.objdetect.CascadeClassifier;
import java.io.File;
/**
* Created by dmalav on 4/30/15.
*/
public class DetectFaces {
public void run(String imageFile) {
System.out.println("\nRunning DetectFaceDemo");
// Create a face detector from the cascade file in the resources
// directory.
String xmlPath = "/home/cloudera/project/opencv-examples/lbpcascade_frontalface.xml";
System.out.println(xmlPath);
CascadeClassifier faceDetector = new CascadeClassifier(xmlPath);
Mat image = Highgui.imread(imageFile);
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
File f = new File(imageFile);
System.out.println(f.getName());
// Save the visualized detection.
String filename = f.getName();
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);
}
}
Main.java
import org.opencv.core.Core;
import java.io.File;
public class Main {
public static void main(String... args) {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
if (args.length == 0) {
System.err.println("Usage Main /path/to/images");
System.exit(1);
}
File[] files = new File(args[0]).listFiles();
showFiles(files);
}
public static void showFiles(File[] files) {
DetectFaces faces = new DetectFaces();
for (File file : files) {
if (file.isDirectory()) {
System.out.println("Directory: " + file.getName());
showFiles(file.listFiles()); // Calls same method again.
} else {
System.out.println("File: " + file.getAbsolutePath());
faces.run(file.getAbsolutePath());
}
}
}
}
Build Face Detection Java Program
[cloudera@quickstart sample]$ant -DocvJarDir=/home/cloudera/Project/opencv/opencv-2.4.11/bin -DocvLibDir=/home/cloudera/Project/opencv/opencv-2.4.11/lib jar
Buildfile: /home/cloudera/Project/opencv/sample/build.xml
compile:
[mkdir] Created dir: /home/cloudera/Project/opencv/sample/build/classes
[javac] Compiling 2 source files to /home/cloudera/Project/opencv/sample/build/classes
jar:
[mkdir] Created dir: /home/cloudera/Project/opencv/sample/build/jar
[jar] Building jar: /home/cloudera/Project/opencv/sample/build/jar/Main.jar
BUILD SUCCESSFUL
Total time: 3 seconds
This build creates a build/jar/Main.jar file which can be used to detect faces from images stored in a directory:
[cloudera@quickstart sample]$ java -cp ../opencv-2.4.11/bin/opencv-2411.jar:build/jar/Main.jar -Djava.library.path=../opencv-2.4.11/lib Main /mnt/hgfs/CSCEI63/project/images2
File: /mnt/hgfs/CSCEI63/project/images2/addams-family.png
Running DetectFaceDemo
/home/cloudera/Project/opencv/sample/lbpcascade_frontalface.xml
Detected 7 faces
addams-family.png
Writing addams-family.png
OpenCV detected faces
OpenCV does fairly good job detecting front facing faces when lbpcascade_frontalface.xml classifier is used. There are other classifier provided by OpenCV which can detect rotate faces and other face orientations.
5. Configure Hadoop for OpenCV
To run OpenCV Java code the native library Core.NATIVE_LIBRARY_NAME must be added to Hadoop java.library.path, following steps details how to setup OpenCV native library with Hadoop.
Copy OpenCV native library libopencv_java2411.so to /etc/opencv/lib
[cloudera@quickstart opencv]$ pwd
/home/cloudera/Project/opencv
[cloudera@quickstart opencv]$ ls
cmake-3.2.2-Linux-x86_64 opencv-2.4.11 sample test
[cloudera@quickstart opencv]$ sudo cp opencv-2.4.11/lib/libopencv_java2411.so /etc/opencv/lib/
Setup JAVA_LIBRARY_PATH in /usr/lib/hadoop/libexec/hadoop-config.sh file to point to OpenCV native library.
[cloudera@quickstart Project]$ vi /usr/lib/hadoop/libexec/hadoop-config.sh
.
.
# setup 'java.library.path' for native-hadoop code if necessary
if [ -d "${HADOOP_PREFIX}/build/native" -o -d "${HADOOP_PREFIX}/$HADOOP_COMMON_LIB_NATIVE_DIR" ]; then
if [ -d "${HADOOP_PREFIX}/$HADOOP_COMMON_LIB_NATIVE_DIR" ]; then
if [ "x$JAVA_LIBRARY_PATH" != "x" ]; then
JAVA_LIBRARY_PATH=${JAVA_LIBRARY_PATH}:${HADOOP_PREFIX}/$HADOOP_COMMON_LIB_NATIVE_DIR
else
JAVA_LIBRARY_PATH=${HADOOP_PREFIX}/$HADOOP_COMMON_LIB_NATIVE_DIR
fi
fi
fi
# setup opencv native library path
JAVA_LIBRARY_PATH=${JAVA_LIBRARY_PATH}:/etc/opencv/lib
.
.
6. HIPI with OpenCV
This step details Java code for combining HIPI with OpenCV.
HIPI uses HipiImageBundle class to represent collection of images on HDFS, and FloatImage for representing the image in memory. This FloatImage must be converted to OpenCV Mat format for image processing, counting face in this case.
Following method is used to convert FloatImage to Mat:
// Convert HIPI FloatImage to OpenCV Mat
public Mat convertFloatImageToOpenCVMat(FloatImage floatImage) {
// Get dimensions of image
int w = floatImage.getWidth();
int h = floatImage.getHeight();
// Get pointer to image data
float[] valData = floatImage.getData();
// Initialize 3 element array to hold RGB pixel average
double[] rgb = {0.0,0.0,0.0};
Mat mat = new Mat(h, w, CvType.CV_8UC3);
// Traverse image pixel data in raster-scan order and update running average
for (int j = 0; j < h; j++) {
for (int i = 0; i < w; i++) {
rgb[0] = (double) valData[(j*w+i)*3+0] * 255.0; // R
rgb[1] = (double) valData[(j*w+i)*3+1] * 255.0; // G
rgb[2] = (double) valData[(j*w+i)*3+2] * 255.0; // B
mat.put(j, i, rgb);
}
}
return mat;
}
To count the number of faces from an image we need to create a CascadeClassifier which uses a classifier file. This file must be present on HDFS, this can be accomplished by using Job.addCacheFile method and later retrieve it in Mapper class.
public int run(String[] args) throws Exception {
....
// Initialize and configure MapReduce job
Job job = Job.getInstance();
....
// add cascade file
job.addCacheFile(new URI("/user/cloudera/lbpcascade_frontalface.xml#lbpcascade_frontalface.xml"));
// Execute the MapReduce job and block until it complets
boolean success = job.waitForCompletion(true);
// Return success or failure
return success ? 0 : 1;
}
Override Mapper::setup method to load OpenCV native library and create CascadeClassifier for face detections:
public static class FaceCountMapper extends Mapper<ImageHeader, FloatImage, IntWritable, IntWritable> {
// Create a face detector from the cascade file in the resources
// directory.
private CascadeClassifier faceDetector;
public void setup(Context context)
throws IOException, InterruptedException {
// Load OpenCV native library
try {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
} catch (UnsatisfiedLinkError e) {
System.err.println("Native code library failed to load.\n" + e + Core.NATIVE_LIBRARY_NAME);
System.exit(1);
}
// Load cached cascade file for front face detection and create CascadeClassifier
if (context.getCacheFiles() != null && context.getCacheFiles().length > 0) {
URI mappingFileUri = context.getCacheFiles()[0];
if (mappingFileUri != null) {
faceDetector = new CascadeClassifier("./lbpcascade_frontalface.xml");
} else {
System.out.println(">>>>>> NO MAPPING FILE");
}
} else {
System.out.println(">>>>>> NO CACHE FILES AT ALL");
}
super.setup(context);
} // setup()
....
}
Full listing of FaceCount.java.
Mapper:
Load OpenCV native library
Create CascadeClassifier
Convert HIPI FloatImage to OpenCV Mat
Detect and count faces in the image
Write number of faces detected to context
Reducer:
Count number of files processed
Count number of faces detected
Output number of files and faces detected
FaceCount.java
import hipi.image.FloatImage;
import hipi.image.ImageHeader;
import hipi.imagebundle.mapreduce.ImageBundleInputFormat;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.opencv.core.*;
import org.opencv.objdetect.CascadeClassifier;
import java.io.IOException;
import java.net.URI;
public class FaceCount extends Configured implements Tool {
public static class FaceCountMapper extends Mapper<ImageHeader, FloatImage, IntWritable, IntWritable> {
// Create a face detector from the cascade file in the resources
// directory.
private CascadeClassifier faceDetector;
// Convert HIPI FloatImage to OpenCV Mat
public Mat convertFloatImageToOpenCVMat(FloatImage floatImage) {
// Get dimensions of image
int w = floatImage.getWidth();
int h = floatImage.getHeight();
// Get pointer to image data
float[] valData = floatImage.getData();
// Initialize 3 element array to hold RGB pixel average
double[] rgb = {0.0,0.0,0.0};
Mat mat = new Mat(h, w, CvType.CV_8UC3);
// Traverse image pixel data in raster-scan order and update running average
for (int j = 0; j < h; j++) {
for (int i = 0; i < w; i++) {
rgb[0] = (double) valData[(j*w+i)*3+0] * 255.0; // R
rgb[1] = (double) valData[(j*w+i)*3+1] * 255.0; // G
rgb[2] = (double) valData[(j*w+i)*3+2] * 255.0; // B
mat.put(j, i, rgb);
}
}
return mat;
}
// Count faces in image
public int countFaces(Mat image) {
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
return faceDetections.toArray().length;
}
public void setup(Context context)
throws IOException, InterruptedException {
// Load OpenCV native library
try {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
} catch (UnsatisfiedLinkError e) {
System.err.println("Native code library failed to load.\n" + e + Core.NATIVE_LIBRARY_NAME);
System.exit(1);
}
// Load cached cascade file for front face detection and create CascadeClassifier
if (context.getCacheFiles() != null && context.getCacheFiles().length > 0) {
URI mappingFileUri = context.getCacheFiles()[0];
if (mappingFileUri != null) {
faceDetector = new CascadeClassifier("./lbpcascade_frontalface.xml");
} else {
System.out.println(">>>>>> NO MAPPING FILE");
}
} else {
System.out.println(">>>>>> NO CACHE FILES AT ALL");
}
super.setup(context);
} // setup()
public void map(ImageHeader key, FloatImage value, Context context)
throws IOException, InterruptedException {
// Verify that image was properly decoded, is of sufficient size, and has three color channels (RGB)
if (value != null && value.getWidth() > 1 && value.getHeight() > 1 && value.getBands() == 3) {
Mat cvImage = this.convertFloatImageToOpenCVMat(value);
int faces = this.countFaces(cvImage);
System.out.println(">>>>>> Detected Faces: " + Integer.toString(faces));
// Emit record to reducer
context.write(new IntWritable(1), new IntWritable(faces));
} // If (value != null...
} // map()
}
public static class FaceCountReducer extends Reducer<IntWritable, IntWritable, IntWritable, Text> {
public void reduce(IntWritable key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
// Initialize a counter and iterate over IntWritable/FloatImage records from mapper
int total = 0;
int images = 0;
for (IntWritable val : values) {
total += val.get();
images++;
}
String result = String.format("Total face detected: %d", total);
// Emit output of job which will be written to HDFS
context.write(new IntWritable(images), new Text(result));
} // reduce()
}
public int run(String[] args) throws Exception {
// Check input arguments
if (args.length != 2) {
System.out.println("Usage: firstprog <input HIB> <output directory>");
System.exit(0);
}
// Initialize and configure MapReduce job
Job job = Job.getInstance();
// Set input format class which parses the input HIB and spawns map tasks
job.setInputFormatClass(ImageBundleInputFormat.class);
// Set the driver, mapper, and reducer classes which express the computation
job.setJarByClass(FaceCount.class);
job.setMapperClass(FaceCountMapper.class);
job.setReducerClass(FaceCountReducer.class);
// Set the types for the key/value pairs passed to/from map and reduce layers
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Text.class);
// Set the input and output paths on the HDFS
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// add cascade file
job.addCacheFile(new URI("/user/cloudera/lbpcascade_frontalface.xml#lbpcascade_frontalface.xml"));
// Execute the MapReduce job and block until it complets
boolean success = job.waitForCompletion(true);
// Return success or failure
return success ? 0 : 1;
}
public static void main(String[] args) throws Exception {
ToolRunner.run(new FaceCount(), args);
System.exit(0);
}
}
7. Build FaceCount.java as facecount.jar
Create new facecount directory in hipi folder (where HIPI was built) and copy FaceCount.java from previous step.
[cloudera@quickstart hipi]$ pwd
/home/cloudera/Project/hipi
[cloudera@quickstart hipi]$ mkdir facecount
[cloudera@quickstart hipi]$ cp /mnt/hgfs/CSCEI63/project/hipi/src/FaceCount.java facecount/
[cloudera@quickstart hipi]$ ls
3rdparty data facecount libsrc README.md sample
bin doc hipiwrapper license.txt release tool
build.xml examples lib my.diff run.sh util
[cloudera@quickstart hipi]$ ls facecount/
FaceCount.java
Make changes to HIPI build.xml ant script to build link to OpenCV jar file and add new build target facecount.
build.xml
<project basedir="." default="all">
<target name="setup">
....
<!-- opencv dependencies -->
<property name="opencv.jar" value="../opencv/opencv-2.4.11/bin/opencv-2411.jar" />
<echo message="Properties set."/>
</target>
<target name="compile" depends="setup,test_settings,hipi">
<mkdir dir="bin" />
<!-- Compile -->
<javac debug="yes" nowarn="on" includeantruntime="no" srcdir="${srcdir}" destdir="./bin" classpath="${hadoop.classpath}:./lib/hipi-${hipi.version}.jar:${opencv.jar}">
<compilerarg value="-Xlint:deprecation" />
</javac>
<!-- Create the jar -->
<jar destfile="${jardir}/${jarfilename}" basedir="./bin">
<zipfileset src="./lib/hipi-${hipi.version}.jar" />
<zipfileset src="${opencv.jar}" />
<manifest>
<attribute name="Main-Class" value="${mainclass}" />
</manifest>
</jar>
</target>
....
<target name="facecount">
<antcall target="compile">
<param name="srcdir" value="facecount" />
<param name="jarfilename" value="facecount.jar" />
<param name="jardir" value="facecount" />
<param name="mainclass" value="FaceCount" />
</antcall>
</target>
<target name="all" depends="hipi,hibimport,downloader,dumphib,jpegfromhib,createsequencefile,covariance" />
<!-- Clean -->
<target name="clean">
<delete dir="lib" />
<delete dir="bin" />
<delete>
<fileset dir="." includes="examples/*.jar,experiments/*.jar" />
</delete>
</target>
</project>
Build FaceCount.java
[cloudera@quickstart hipi]$ ant facecount
Buildfile: /home/cloudera/Project/hipi/build.xml
facecount:
setup:
[echo] Setting properties for build task...
[echo] Properties set.
test_settings:
[echo] Confirming that hadoop settings are set...
[echo] Properties are specified properly.
hipi:
[echo] Building the hipi library...
hipi:
[javac] Compiling 30 source files to /home/cloudera/Project/hipi/lib
[jar] Building jar: /home/cloudera/Project/hipi/lib/hipi-2.0.jar
[echo] Hipi library built.
compile:
[jar] Building jar: /home/cloudera/Project/hipi/facecount/facecount.jar
BUILD SUCCESSFUL
Total time: 12 seconds
Check facecount.jar is built under facecount directory
[cloudera@quickstart hipi]$ ls facecount/
facecount.jar FaceCount.java
8. Run FaceCount MapReduce job
Setup input images
[cloudera@quickstart hipi]$ ls /mnt/hgfs/CSCEI63/project/images-png/
217.png eugene.png patio.png
221.png ew-courtney-david.png people.png
3.png ew-friends.png pict_28.png
….
Create HIB
The primary input type to a HIPI program is a HipiImageBundle (HIB), which stores a collection of images on the Hadoop Distributed File System (HDFS). Use the hibimport tool to create a HIB (project/input.hib) from a collection of images on your local file system located in the directory /mnt/hgfs/CSCEI63/project/images-png/ by executing the following command from the HIPI root directory
[cloudera@quickstart hipi]$ hadoop fs -mkdir project
[cloudera@quickstart hipi]$ hadoop jar tool/hibimport.jar /mnt/hgfs/CSCEI63/project/images-png/ project/input.hib
** added: 217.png
** added: 221.png
** added: 3.png
** added: addams-family.png
** added: aeon1a.png
** added: aerosmith-double.png
.
.
.
** added: werbg04.png
** added: window.png
** added: wxm.png
** added: yellow-pages.png
** added: ysato.png
Created: project/input.hib and project/input.hib.dat
Run MapReduce
Create a run-facecount.sh script to clean previous output file and execute mapreduce job:
run-facecount.sh
#!/bin/bash
hadoop fs -rm -R project/output
hadoop jar facecount/facecount.jar project/input.hib project/output
[cloudera@quickstart hipi]$ bash run-facecount.sh
15/05/12 16:48:06 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.
Deleted project/output
15/05/12 16:48:17 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
15/05/12 16:48:20 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
15/05/12 16:48:21 INFO input.FileInputFormat: Total input paths to process : 1
Spawned 1map tasks
15/05/12 16:48:22 INFO mapreduce.JobSubmitter: number of splits:1
15/05/12 16:48:22 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1431127776378_0049
15/05/12 16:48:25 INFO impl.YarnClientImpl: Submitted application application_1431127776378_0049
15/05/12 16:48:25 INFO mapreduce.Job: The url to track the job: http://quickstart.cloudera:8088/proxy/application_1431127776378_0049/
15/05/12 16:48:25 INFO mapreduce.Job: Running job: job_1431127776378_0049
15/05/12 16:48:58 INFO mapreduce.Job: Job job_1431127776378_0049 running in uber mode : false
15/05/12 16:48:58 INFO mapreduce.Job: map 0% reduce 0%
15/05/12 16:49:45 INFO mapreduce.Job: map 3% reduce 0%
15/05/12 16:50:53 INFO mapreduce.Job: map 5% reduce 0%
15/05/12 16:50:57 INFO mapreduce.Job: map 8% reduce 0%
15/05/12 16:51:01 INFO mapreduce.Job: map 11% reduce 0%
15/05/12 16:51:09 INFO mapreduce.Job: map 15% reduce 0%
15/05/12 16:51:13 INFO mapreduce.Job: map 22% reduce 0%
15/05/12 16:51:18 INFO mapreduce.Job: map 25% reduce 0%
15/05/12 16:51:21 INFO mapreduce.Job: map 28% reduce 0%
15/05/12 16:51:29 INFO mapreduce.Job: map 31% reduce 0%
15/05/12 16:51:32 INFO mapreduce.Job: map 33% reduce 0%
15/05/12 16:51:45 INFO mapreduce.Job: map 38% reduce 0%
15/05/12 16:51:57 INFO mapreduce.Job: map 51% reduce 0%
15/05/12 16:52:07 INFO mapreduce.Job: map 55% reduce 0%
15/05/12 16:52:10 INFO mapreduce.Job: map 58% reduce 0%
15/05/12 16:52:14 INFO mapreduce.Job: map 60% reduce 0%
15/05/12 16:52:18 INFO mapreduce.Job: map 63% reduce 0%
15/05/12 16:52:26 INFO mapreduce.Job: map 100% reduce 0%
15/05/12 16:52:59 INFO mapreduce.Job: map 100% reduce 100%
15/05/12 16:53:01 INFO mapreduce.Job: Job job_1431127776378_0049 completed successfully
15/05/12 16:53:02 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=1576
FILE: Number of bytes written=226139
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=35726474
HDFS: Number of bytes written=27
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=205324
Total time spent by all reduces in occupied slots (ms)=29585
Total time spent by all map tasks (ms)=205324
Total time spent by all reduce tasks (ms)=29585
Total vcore-seconds taken by all map tasks=205324
Total vcore-seconds taken by all reduce tasks=29585
Total megabyte-seconds taken by all map tasks=210251776
Total megabyte-seconds taken by all reduce tasks=30295040
Map-Reduce Framework
Map input records=157
Map output records=157
Map output bytes=1256
Map output materialized bytes=1576
Input split bytes=132
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=1576
Reduce input records=157
Reduce output records=1
Spilled Records=314
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=11772
CPU time spent (ms)=45440
Physical memory (bytes) snapshot=564613120
Virtual memory (bytes) snapshot=3050717184
Total committed heap usage (bytes)=506802176
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=35726342
File Output Format Counters
Bytes Written=27
Check results:
[cloudera@quickstart hipi]$ hadoop fs -ls project/output
Found 2 items
-rw-r--r-- 1 cloudera cloudera 0 2015-05-12 16:52 project/output/_SUCCESS
-rw-r--r-- 1 cloudera cloudera 27 2015-05-12 16:52 project/output/part-r-00000
[cloudera@quickstart hipi]$ hadoop fs -cat project/output/part-r-00000
157 Total face detected: 0
9. Summary
OpenCV provides very rich set of tools for image processing, when combined with HIPI’s efficient and high-throughput parallel image processing power can be a great solution for processing very large image dataset very fast. These tools can help researcher and engineers alike to achieve high performance image processing.
10. Issues
The wrapper function to convert HIPI FloatImage to OpenCV did not work for some reason and not producing the correct image when converted. This was causing a bug of no faces detected. I had contacted HIPI members but did not receive timely reply before finishing this project. This bug is causing my results to show “0” faces detected.
11. Benefits (Pros/Cons)
Pros: HIPI is a great tool for processing very large volume of images in hadoop cluster, when combined with OpenCV it can be very powerful.
Cons: Converting the image format (HIPI FloatImage) to OpenCV Mat format is not straightforward and causing the issues for OpenCV to process images correctly.
12. Lessons Learned
Setting up Cloudera Quickstart VM
Using HIPI to run MapReduce on large volume of images
Using OpenCV in Java environment
Settings up hadoop to load native libraries
Using cached files on HDFS
