django + scrapy + celery + redis +

# setup virtual env
mkdir test
cd test
virtualenv .env
source .env/bin/activate

# install scrapy
pip uninstall lxml
STATIC_DEPS=true pip install -U lxml
pip install scrapy
pip install service_identity
# install mongodb
brew install mongodb --with-openssl
pip install pymongo
pip install mongoengine

#install celery
port install openssl  
pip install celery
pip install celery-with-mongodb

#install django

# install 3rd part libs
pip install pandas
pip install numpy
pip install zipline
port install cython
port install TA-Lib
easy_install TA-Lib

# install supervisor
pip install supervisor

# explore $PATH
.bash_profile

export PATH=".env/bin:/Users/seanchen/anaconda/bin:/opt/local/bin:/opt/local/sbin:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:$PATH"

export PATH="$PATH:/Users/seanchen/tool/mongodb/mongodb-osx-x86_64-2.6.4/bin:/Users/seanchen/prj/talib/src/ta-lib/bin"

export LD_LIBRARY_PATH=".env/lib:/usr/local/lib:$LD_LIBRARY_PATH" 

export C_FORCE_ROOT=true

remoteLED

• remote LED
• Server
• raspberry pi control LED on/off
• control GPIO pin 16, 18 to turn on/off led
• client 
• android phone
• send remote control command to Server
• prj path
• https://github.com/funningboy/remoteLED

webrtc + image process

• path 
• https://github.com/funningboy/remoteChat

raspberry pi + microcontroller

• RAM size 256/512 
• http://openenergymonitor.blogspot.tw/2013/10/raspberry-pi-new-type-of-ram.html
• FTDI USB 2 UART adaptor (JTAG) shared with GPIO
• http://www.ftdichip.com/Products/Modules/RPi.htm
• FT232 JTAG
• https://github.com/progranism/FT232R-JTAG
• https://github.com/dwelch67/raspberrypi/tree/master/armjtag
• openOCD
• JTAG driver
• USB or the Parallel Printer Port (USB to rs232)
• arm instruction disassembly
• http://visualgdb.com/tutorials/arm/openocd/
• http://openenergymonitor.blogspot.tw/2013/10/raspberry-pi-new-type-of-ram.html
• mount JTAG with GDB(Eclipse)
• http://www.ethernut.de/en/tools/eclipse/confdebug.html
• convert arm assembly to c
• Hex-Rays
• https://www.hex-rays.com/products/decompiler/compare_arm0.shtml
• bootloader
• 
  

• adafruit (extend hardware models,such as motor, led, mcu)
• GPIO export
• http://www.adafruit.com/
• http://learn.adafruit.com/category/learn-raspberry-pi

• 開發板教學
• http://ruten-proteus.blogspot.tw/

xbus UVM example

purpose

a sample bus UVM/SV verification env that contains direct pattern, random pattern, trx replay pattern

pattern definition

1. direct pattern : normal read/write test, like read after write test, the expected and answer had already known
2. random pattern : random test based on it's constrain rule set
3. trx replay pattern : replay trx via DPI interface supported

how to run it?

1.you can check our testsuites first, we provide three kinds of test at path ./sv/pat

1.1. for direct pattern +UVM_TESTNAME=test_xbus_normal_rw_seq_lib

1.2. for random pattern +UVM_TESTNAME=test_xbus_random_rw_seq_lib

1.3. for trx replay pattern +UVM_TESTNAME=test_xbus_adaptor_seq_lib

2.setup irun env please make sure your irun version is > 12.x

3.% irun -f run.f

https://github.com/funningboy/xbus

carCV

purpose

car and lane detection example

method

-lane detection

• houghlines
• houghlinesP
• fitlines

-car detection

• haar cascades

how to run it?

step1. get video source %python get_video.py

step2. lane detection %python detect_lane.py

step3. car detection %python detect_car.py

https://github.com/funningboy/carCV

Ref

• using color segmentation/background subtractionhttp://www.prodigyproductionsllc.com/articles/programming/lane-detection-with-opencv-and-c/
• car lane detection http://code.google.com/p/opencv-lane-vehicle-track/source/browse/trunk/main.cpp
• circle and line detection https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html

-contour features https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html Lane Detection as (line detection)

remote openCV with raspberry pi

this is an example about how to use remote object detection based on openCV and zeromq framework.

requirements

• opencv(python) as cv(opencv) and cv2(numpy)
• zeromq(python)
• gevent(light weight switch)
• knowledge about img process
• profile, logging each stage mem and cpu time

method

• Server
  • capture img
  • send img to clinet
  • wait for next command from client
  • update status by new command
• Client
  • receive img
  • run all detection
  • build up a new command based on detection results

detections

• face detect
• circle detect
• line detect
• find attribute obj

how to run it?

• python poll_camera.py (using cv to capture/show img via remote protocol)
• python poll_camera2.py (using cv2 to capture/show img ...)
• python poll_thread_run2.py (using cv2 to run all thread detection based on CPU num)
• python poll_series_run2.py (using cv2 to run all series detection ...)

https://github.com/funningboy/remoteCV