Hello World in SystemC

Create two files and run it at the Shell prompt.

1. Edit the CPP file first.

// All systemc modules should include systemc.h header file
 #include "systemc.h"
 // Hello_world is module name
 SC_MODULE (hello_world) {
   SC_CTOR (hello_world) {
     // Nothing in constructor 
   }
   void say_hello() {
     //Print "Hello World" to the console.
     cout << "Hello World.\n";
   }
 };
 
 // sc_main in top level function like in C++ main
 int sc_main(int argc, char* argv[]) {
   hello_world hello("HELLO");
   // Print the hello world
   hello.say_hello();
   return(0);
 }

2. Then create a Makefile. This makefile is reusable for all SystemC projects.

SYSTEMC=/usr/local/systemc
LDFLAGS= -L$(SYSTEMC)/lib-linux -lsystemc
CXXFLAGS=-Wno-deprecated -I$(SYSTEMC)/include -fpermissive

all:
 g++ $(CXXFLAGS) *.cpp $(LDFLAGS)
 ./a.out

3. The command "make" compiles and runs the Hello World program.

$ make
g++ -Wno-deprecated -I/usr/local/systemc/include  *.cpp -L/usr/local/systemc/lib-linux -lsystemc
./a.out

             SystemC 2.2.0 --- Aug 25 2011 10:30:16
        Copyright (c) 1996-2006 by all Contributors
                    ALL RIGHTS RESERVED
Hello World.

Install SystemC 2.2.0 on Ubuntu 11.04

Thanks to these tips from http://archive.pfb.no/2010/10/13/systemc-ubuntu-1010/ and http://noxim.sourceforge.net/pub/Noxim_User_Guide.pdf, I was able to install SystemC on 11.04 Natty Narwhal.

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Step 0: Prepare.

Obviously, you need to have a compiler. Do this step in case you haven't done so.

$ sudo apt-get install build-essential

Step 1: Download.

Next: register, and download from http://www.systemc.org using your Web browser then unpack it....but the tgz file has wrong extension. Do these steps to unpack the file:

$ mv systemc-2.2.0.tgz systemc-2.2.0.tar
$ tar xvf systemc-2.2.0.tar

Alternatively, download the file using wget from another site with the correct extension:

$ wget http://panoramis.free.fr/search.systemc.org/?download=sc220/systemc-2.2.0.tgz
$ tar xvfz systemc-2.2.0.tgz

Ooops! Turns out you can't use wget. Copy the URL to your browser location bar, answer a simple a question and the file will be downloaded. In any case, no need to reveal you email for this option!

In any case, create a build directory and enter into it for the following steps:

$ cd systemc-2.2.0
$ sudo mkdir /usr/local/systemc
$ mkdir objdir
$ cd objdir
$ export CXX=g++
$ sudo ../configure --prefix=/usr/local/systemc CPPFLAGS=-fpermissive

Step 2: Patch.

Using new versions of GCC such as GCC 4.4, we will fail to compile because 2 lines of code were left out of systemc-2.2.0/src/sysc/utils/sc_utils_ids.cpp.

Method 1: You can just just open the file and add these includes at the top of the file (after the header comments):

#include "cstdlib"
#include "cstring"
#include "sysc/utils/sc_report.h"

Replace the " signs with less than and greater than signs (because this blog site treats the the pair of characters as HTML tags).

Method 2: You patch it using a prewritten patch file.

$ wget http://www.pfb.no/files/systemc-2.2.0-ubuntu10.10.patch
$ patch -p1 < ../systemc-2.2.0-ubuntu10.10.patch

Method 3: Patch it using sed:

$ sed -i '1 i #include "cstdlib"\n#include "cstring"' ../src/sysc/utils/sc_utils_ids.cpp

Step 3: Compile

$ make
$ sudo make install
$ make check
$ cd ..
$ rm -rf objdir

The command make check is optional. What is does is to compile SystemC source files to see if the files can run. I strongly suggest that you run it.

Step 4: Tell your compiler where to find SystemC

Since we do not install SystemC with a standard location we need to specifically tell the compiler where to look for the libraries. We do this with an environment variable.

$ export SYSTEMC=/usr/local/systemc/

This, however will disappear on the next login. To permanently add it to your environment, alter ~/.profile or ~/.bash_profile if it exists. For system wide changes, edit /etc/environment. (newline with expression: SYSTEMC_HOME=”/usr/local/systemc/“) To compile a systemC program simply use this expression:

$ g++ -I. -I$SYSTEMC/include -L. -L$SYSTEMC/lib-linux -o OUTFILE INPUT.cpp -lsystemc -lm

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I still could not get SystemC installed in Fedora. :(

Handbrake for Linux

$ maczulu@ubuntu:~$ sudo add-apt-repository ppa:stebbins/handbrake-releases
$ maczulu@ubuntu:~$ sudo apt-get update
$ maczulu@ubuntu:~$ sudo apt-get install handbrake

To run it from the command line, enter:

$ ghb &

How intiutive is that? Anyhow, it's available form the Application menu.

Another Review of Dalal & Triggs

Writeup HOG Descriptor from the blog of Yet Another Blogger,

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It gives a working example on choosing of various modules at the recognition pipeline for human figure (pedestrians).

Much simplified summary

It uses Histogram of Gradient Orientations as a descriptor in a 'dense' setting. Meaning that it does not detect key-Points like SIFT detectors (sparse). Each feature vector is computed from a window (64x128) placed across an input image. Each vector element is a histogram of gradient orientations (9 bins from 0-180 degrees, +/- directions count as the same). The histogram is collected within a cell of pixels (8x8). The contrasts are locally normalized by a block of size 2x2 cells (16x16 pixels). Normalization is an important enhancement. The block moves in 8-pixel steps - half the block size. Meaning that each cell contributes to 4 different normalization blocks. A linear SVM is trained to classify whether a window is human-figure or not. The output from a trained linear SVM is a set of coefficient for each element in a feature vector.

I presume Linear SVM means the Kernel Method is linear, and no projections to higher dimension. The paper by Hsu, et al suggests that linear method is enough when the feature dimension is already high.

OpenCV implementation (hog.cpp, objdetect.hpp)

The HOGDescriptor class is not found in the API documentation. Here is notable points judging by the source code and sample program(people_detect.cpp):

• Comes with a default human-detector. It says at the file comment that it is "compatible with the INRIA Object Detection and Localization toolkit. I presume this is a trained linear SVM classifier represented as a vector of coefficients;
• No need to call SVM code. The HOGDescriptor.detect() function simply uses the coefficients on the input feature-vector to compute the weight-sum. If the sum is greated than the user specified 'hitThreshold' (default to 0), then it is a human-figure.
• 'hitThreshold' argument could be negative.
• 'winStride' argument (default 8x8)- controls how the window is slide across the input window.
• detectMultiScale() arguments
• 'groupThreshold' pass-through to cv::groupRectangles() API - non-Max-Suppression?
• 'scale0' controls how much down-sampling is performed on the input image before calling 'detect()'. It is repeated for 'nlevels' number of times. Default is 64. All levels could be done in parallel.

Sample (people_detect.cpp)

• Uses the built-in trained coefficients.
• Actually needs to eliminate for duplicate rectangles from the results of detectMultiScale(). Is it because it's calling to match at multiple-scales?
• detect() return list of detected points. The size is the detector window size.

Observations

• With GrabCut BSDS300 test images - only able to detect one human figure (89072.jpg). The rest could be either too small or big or obscured. Interestingly, it detected a few long-narrow upright trees as human figure. It takes about 2 seconds to process each picture.
• With GrabCut Data_GT test images - able to detect human figure from 3 images: tennis.jpg, bool.jpg (left), person5.jpg (right), _not_ person7.jpg though. An interesting false-positive is from grave.jpg. The cut-off tomb-stone on the right edge is detected. Most pictures took about 4.5 seconds to process.
• MIT Pedestrian Database (64x128 pedestrian shots):
• The default HOG detector window (feature-vector) is the same size as the test images.
• Recognized 72 out of 925 images with detectMultiScale() using default parameters. Takes about 15 ms for each image.
• Recognized 595 out of 925 images with detect() using default parameters. Takes about 3 ms for each image.
• Turning off gamma-correction reduces the hits from 595 to 549.
• INRIA Person images (Test Batch)
• (First half) Negative samples are smaller in size at (1 / 4) of Positives, 800 - 1000 ms, the others takes about 5 seconds.
• Are the 'bike_and_person' samples there for testing occlusion?
• Recognized 232/288 positive images. 65 / 453 negative images - Takes 10-20 secs for each image.
• Again cut-off boxes resulting in long vertical shape becomes false positives
• Lamp Poles, Trees, Rounded-Top Extrances, Top part of a tower, long windows are typical false positives. Should upright statue considered 'negative' sample?
• Picked a few false-negatives to re-run with changing parameters. I picked those with large human-figure and stands mostly upright. (crop_00001.jpg, crop001688.jpg, crop001706.jpg, person_107.jpg).
• Increased the nLevels from default(64) to 256.
• Decrease 'hitThreshold' to -2: a lot more small size hits.
• Half the input image size from the original.
• Decrease the scaleFactor from 1.05 to 1.01.
• Tried all the above individually - still unable to recognize the tall figure. I suppose this has something to do with their pose, like how they placed their arms.

Resources

• MIT Pedestrian Database: http://cbcl.mit.edu/cbcl/software-datasets/PedestrianData.html
• INRIA Toolkit http://pascal.inrialpes.fr/soft/olt/ ; DataSet: http://pascal.inrialpes.fr/data/human/ (There are links to other image databases)
• More INRIA images: http://lear.inrialpes.fr/data
• Fast Alternative Site for INRIA image: http://yoshi.cs.ucla.edu/yao/data/PASCAL_human/
• SVM Light : http://svmlight.joachims.org/ (free for scientific use)
• Wikipedia on this topic: http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
• Histograms of Oriented Gradients for Human Detection, Dalal & Triggs.
• A Practical Guide to Support Vector Classifier, Hsu, Chang & Lin

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Installing OpenCV 2.2 in Ubuntu 11.04

Instructions by Sebastian Montabone, author Beginning Digital Image Processing: Using Free Tools for Photographers are here:

• Installing OpenCV 2.2 in Ubuntu 11.04

Volvo Pedestrian Detection with Full Auto Brake system

• First announced Feb 2010
• Developed by Mobileye
  
• Available as option on Volvo S60 and XC60 (above)


• Uses radar and camera technology to watch out for pedestrians ahead of the car, designed to save lives on urban streets


• In case a collision is imminent, the system sends an audio warning to alert the driver, and if there is no response the car is immediately brought to an emergency stop


• Completely avoid any collision below 35km/h


• Drivers speeding above 35km/h will be slowed down