Optimize pictures for visual recognition with openCV and gimp

Visual Recognition

Watson result

Computer Vision or Visual Recognition is part of cognitive computing (CC) aka Artificial Intelligence. One of the main concepts is to extract information out of unstructured data. For example you have a webcam pointing on a highway. As a human you see if there is a traffic jam or not. For a computer it’s only 640x480x3x8 (7.372.800) bit. Visual Recognition helps you to extract information out of this data. For example “This is a highway”. Out of the box systems like Watson are able to give you information what do you see on the picture. You can try it here https://visual-recognition-demo.mybluemix.net. The result can be seen on the left picture. So Watson knows it is a highway and even it’s a divided highway but it does not tell you there is a traffic jam or even a blocked road. Fortunately Watson is always eager to learn, let us see how we can teach him what is a traffic jam. This article only focuses on the picture preparation part not the train Watson part. See next postings for the Watson part.

Get pictures

There are many traffic cameras all around but I am not sure about the licence, so it is hard to use it here as a demo. But let us assume we can take pictures like this one from Wikimedia: Cars in I-70.If you live in south Germany there are nice traffic cameras from Strassenverkehrszentrale BaWue. Unfortunately they don’t offer the pictures with the right licence for my blog. If you know a great source for traffic cameras with the right licence please let me know.

Prepare pictures for training

Visual Recognition works a little bit like magic. You give watson 100 pictures of a traffic jam and 100 without traffic jam and he learns the difference. But how do we make sure he really learns traffic jam and not the weather or the light conditions. And furthermore only one lane in case the camera shows both lanes? So first we need to make sure we find enough different pictures of the road with traffic jam under different weather and light conditions. The second part can be done with OpenCV. OpenCV stands for open computer vision and helps you to manipulate images. The idea is to mask out parts we don’t want Watson to learn. In our case the second part of the lane and the sky. We can use GIMP to create a mask we can apply with openCV automatically to each picture.



First step is obvious to load the image in GIMP. Then open the layers dialog. It’s located under Windows/Dockable Dialogs/Layers or cmd-L. Here we add a new layer and select this one to paint on. Then we select in the tools menu the Paintbrush Tool and just paint the parts black we don’t want Watson to learn.


Then we hide the original image by pressing the eye symbol in the layer dialog. This should leave us with only the black painting we did before. This will be our mask for openCV to be applied to all pictures. Under File/Export you can save it as mask.jpg. Make sure it is only the black mask and not the picture with the black painting.

Use openCV in docker

As openCV is quite a lot to install, we could easily use it within docker to work with our pictures. We can mount host directories inside a docker container, so in this case our directory with pictures:

docker run --name opencv --rm -it -v $(pwd):/host victorhcm/opencv /bin/bash

This brings up the openCV docker container from victorhcm and opens a shell with our current directory mounted under /host. As soon es you exit the container it will be removed because of the “–rm” parameter. Don’t worry only the docker container will be deleted, everything under /host is mounted from the host system and will remain. Everything you save in other directories will be deleted.

How to mask out part of the picture

The python program to use openCV to mask out all pictures in a directory is then really easy to use:

import cv2
import glob

mask = cv2.imread("mask.jpg", cv2.IMREAD_GRAYSCALE)

for fullname in glob.glob("pics/*.jpg"):
    filename  = fullname.split('/')[-1]
    image     = cv2.imread(fullname, cv2.IMREAD_COLOR)
    dst       = cv2.bitwise_and(image, image, mask=mask)
    cv2.imwrite("masked/" + filename, dst)
    print filename

Basically the program iterate through all “jpg” pictures in the subfolder “pics” and saves the masked pictures with the same name in the “masked” folder. Both directories have to exists before you start the script. In order to keep the script reduced to the important parts I left the create and check directory part out of this script.

Line 4

Loads the mask images as a grayscale image.

Line  8

Loads the image to work on as a colour image.

Line 9

Here is the real work done, this applies the mask with bitwise add of all pixels. Therefore the blank will win and the transparent will let the normal picture gets through.

Line 10

Saves the new masked picture in the “maksed” folder.

Preselect pictures

For the learning process we need to sort the pictures by hand. One bucked with traffic jam and the other with ok.

How to use travis-ci to check your application code and the smart contract

Travis CI – Build summary

Hyperledger projects code structure

Travis-CI is one of the most used continuous delivery platforms for open source projects. In order to check your hyperledger blockchain application you need to check at least two different languages in the same travis-ci configuration, as your smart contract is written in GOlang and your application most likely in Javascript or Python or whatever(tm).

Lets assume you have a project with:

  • Smart Contract written in GO
  • Backend Server written in Python
  • Frontend written in Javascript (NodeJS)

If you run your hyperledger blockchain for development purpose on Bluemix you will need to compile your code against v0.6. This leads to a problem because you can not specify a branch when you checkout a repository in “go get”. Therefore we need to checkout the code “by hand”. Let us assume your project looks like this:





Each part of the project (smart contract, backend, frontend) is in a separate directory and has a code part and a unit test part. Without getting into details on how to run unit tests for each language we concentrate now on the .travis file.

Configure travis.yml

    - language: python
      python: 2.7
        - cd python
        - python -m unittest discover -p Test.py

    - language: python
      python: nightly
        - cd python
        - python -m unittest discover -p Test.py

    - language: node_js
      node_js: 7
        - cd javascript
        - ls

    - language: go
      go: master
        - cd go
        - mkdir -p $GOPATH/src/github.com/hyperledger
        - git   -C $GOPATH/src/github.com/hyperledger clone -b v0.6 http://gerrit.hyperledger.org/r/fabric
        - go build ./

    - travis@mydomain.eu

We have two tests for python (one with 2.7 the other one with the latest nightly build), one for the javascript (no unit test in this example) and the GO part.

Important are lines 29 and 30. Before we run the go build we create the directory for hyperledger and check out manually the official hyperledger code with the v0.6 branch. After that we can normally build the smart contract by calling go build ./



BC95 Board

NB-Iot with the BC95 with arduino

Hardware hacking with the mobile c-lab

Last weekend the c-base and friends team had a great time doing some hacking with Narrow Band IoT (NB-IoT) from Deutsche Telekom at the nbiot-hackathon at hub:raum. We could put our hands on the BC95 chip, a dev board and access to the Telekom test network. Beside hacking we had a great time with funny hats and our obligatory overalls.

The BC95 Board

The board we could use was the BC95-B8 board which we mounted on a development board with a support controller and serial converter. Beside the board setup we also soldered a PCB with some sensors and a Teensy board to control the BC95 and the sensors.



The dev board itself has a RS232 converter to give access via “normal” RS232 connectors. This is convenient for older laptops or desktops but luckily they also give you access via 3.3V pegel to the same UART interface. The pins are pre soldered on a 10 pin header so it’s easy to connect this to arduino or Raspberry PI via the serial connection. As you can see in the picture it is pin 1,2 and 6. No level converter necessary.

AT- Command Set

AT      // First handshake will be used for out speed detection may be result in ERROR
AT      // Repeat sending "AT" until you receive an "OK"
AT+NRB  // Reboot the device in order to have a clean start

//wait for the reboot

AT+NBAND=8                        // Set the communication band in this case 900Mhz
AT+CGDCONT=1,"IP","NBIOT.Telekom" // Sets the APN (Access Point Name)
AT+CEREG=2                        // Connect to the IoT Core
AT+CFUN=1                         // Power on the module
AT+COPS=1,2,"12345"               // Force the module to connect to the network

// wait for the network connection (some seconds)

AT+NPING=                          // Ping a server to check if it works
AT+NSOCR=DGRAM,17,16666                   // Open a UDP socket
AT+NSOST=0,ansi.23-5.eu,16666,4,414E5349  // Send out the data

Line 1,2

The serial protocol can be any speed at 8N1, the board auto detects the speed at first communication, therefore you need to send the AT command several times (normally 2) to set the speed. Normally the first AT command is answered with ERROR and the second one with AT. Make sure you get an OK before you continue.

Line 3

In order to get a clean setup we first reboot the board with “NRB”. It takes some seconds and it will come back with OK.

Line 7

Depending on your network you need to set the band with “NBAND”. The Telekom test network is on 900 MHz so we go with 8. Other bands are 5 (850 Mhz) and 20 (800 MHz).

Line 8

Depending on your setup and provider you need to set the APN with the CGDCONT command.

Line 9

Connect to the IoT Core

Line 10

Power on the module

Line 11

Connect to the network. This can take several seconds even a minute. You can always check the connection with at+cgatt?” and check for “+CGATT:1”. You can double check the existing connection by asking for the IP address which is assigned to you by sending “at+cgpaddr=1” to get for example “+CGPADDR:1,”.

Line 15

Ping a server to test everything is fine. (In this case the google DNS server)

Line 16

Open an UDP socket to receive answers. In our case the DGRAM and 17 are mandatory but the port you are using (in our case 16666) is up to you.

Line 17

Send your UDP data package. First parameter is the out port (0). The second one is the address you want to send the data to (ip or name). Third one is the receivers port (16666). Fours is the amount of data you want to send (keep it below 100 bytes) and the last one is your data in hex notification. I recommend asciitohex.com to convert a string you want to send.


uint8_t sensordata[10];

void setup() {
  while (!Serial) {}
  while (!Serial1) {}
  Serial.println("c-base starts");

void sensorfake(){
  for (int i = 0; i < 10; i++){
    uint8_t d = sensordata[i];
    sensordata[i] = d+1;

void sendit(String in) {
  while(Serial1.available() > 0){
    if(Serial1.available() > 0){
      byte c = Serial1.read();

void initModem(){

void sendData(){

  String datastring = "AT+NSOST=0,ansi.23-5.eu,16666,11,01";

  for(uint8_t i = 0; i < 10; i++){
    String hexstring = String(sensordata[i], HEX);
    if(hexstring.length() < 2){
      hexstring = "0" + hexstring;


void loop() {

This arduino code is really a fast and ugly hack for the hackathon in order to send out the data. It does not listen for the AT returns or anything else. So this is only an example on how NOT to do coding but it worked for the hackathon.

WordPress docker setup with nginx proxy

Setup a wordpress blog on docker with nginx as reverse proxy

docker setup with wordpress, nginx and mysql
Docker setup with wordpress, nginx and mysql containern

My friend und very experienced colleague Niklas Heidloff convinced me to start also a blog with all the geeky things I am doing all day long. In order to make it more interesting for me I decided to host wordpress on my own instead of using wordpress as a service (WaaS ?). Sylwester from Fablab.berlin pointed me to Vultr for hosting and so here we start. The idea is to have a simple docker setup with 3 containers. One for the Nginx webserver facing the evil internet and proxying the wordpress which is located in the local private docker network. Doing so I can also redirect to other web services (containers) later. Luckily there are already maintained docker container available for all 3 parts, so I only need to customize the nginx container with a dockerfile but can use the two others right as they are.

Install Docker on Ubuntu

Either you go with a docker provider like Bluemix or you get a virtual machine from softlayer or any other provider. In my case I have chosen a virtual server so I had to install docker on Ubuntu LTS. Which is really easy. Basically you add a new repository entry to your apt sources and install latest stable docker packages. There is also a script available on get.docker.com but I don’t feel comfortable to execute a shell script right from the net with root access. But it’s up to you.

wget -qO- https://get.docker.com/ | sh

Docker on linux does not contain docker-compose compared to the docker installation for example on mac. Installing docker compose is straightforward. The docker compose script can be downloaded from github here: https://github.com/docker/compose/releases.

curl -L https://github.com/docker/compose/releases/download/1.14.0/docker-compose-`uname -s`-`uname -m` > /usr/local/bin/docker-compose
chmod +x /usr/local/bin/docker-compose


Docker-compose takes care of a docker setup containing more than one docker container, including network and also basic monitoring. The following script starts and builds all docker container with nginx, mysql and wordpress. It also exports the volumes on the host file system for easy backup and persistence along docker container rebuilds and monitors if the docker containers are up and running.

version: '3'

     image: mysql:latest
       - ./db:/var/lib/mysql
     restart: always
       MYSQL_ROOT_PASSWORD: easytoguess
       MYSQL_DATABASE: wordpress
       MYSQL_USER: wordpress
       MYSQL_PASSWORD: eveneasier

       - db
     image: wordpress:latest
     restart: always
       - ./wordpress:/var/www/html/wp-content
       WORDPRESS_DB_HOST: db:3306
       WORDPRESS_DB_USER: wordpress
       WORDPRESS_DB_PASSWORD: eveneasier
       WORDPRESS_DB_NAME: wordpress

       - wordpress
     restart: always
       context: .
       dockerfile: Dockerfile-nginx
       - "80:80"

Mysql is the first container we bring up with environment variables for the database like username, password and database name. Line 7 takes care to save the database file outside the docker container so you can delete the docker container, start a new one and still have the same database up and running. Point this where you want to have it. In this case in “db” under the same directory. Also make sure you come up with decent passwords.

The second container is wordpress. Same here with the host folder on line 21. Furthermore make sure you have the same user, password and db name configured as in the mysql container configuration.

Last one is nginx as internet facing container. You expose the port 80 here. While you just specify a container in the other two, in this one you configure a Dockerfile and a build context to customize your nginx regarding to the network setup. If you only want to host static files you can add this via volume mounts, but in our case we need to configure nginx itself so we need a customized Dockerfile as described below.

Dockerfile for nginx setup

FROM nginx:latest
COPY   default.conf /etc/nginx/conf.d/default.conf
VOLUME /var/log/nginx/log/

This dockerfile inherits everything from the latest nginx and copies the default.conf file into it. See next chapter for how to setup the config file.

Nginx config file

server {
    listen            80;
    listen       [::]:80;
    server_name  www.23-5.eu ansi.23-5.eu;
    access_log  /var/log/nginx/log/unsecure.access.log  main;

    location / {
        proxy_read_timeout    90;
        proxy_connect_timeout 90;
        proxy_redirect        off;
        proxy_pass http://wordpress;

        proxy_set_header      X-Real-IP $remote_addr;
        proxy_set_header      X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header      Host $host;

Line 2 and 3 configures the port we want to listen on. We need one for ip4 and one for ip6. Important is the proxy configuration in line 8 to 15. Line 11 redirect all calls to “/” (so without a path in the URL) to the server wordpress. As we used docker-compose for it docker takes care to make the address available via the internal DNS server. Line 13-15 rewrites the http header in order to map everything to the different URL, otherwise we would end up with auto generated links in docker pointing to http://wordpress

Start the System

If everything is configured and the docker-compose.yml, default.conf, Dockerfile-nginx and the folders db and wordpress are in the same folder, we can start everything being in this folder with:

docker-compose up --build -d

The parameter “-d” starts the setup in the background (daemon). For the very first run I would recommend using it without the “-d” parameter to see all debug messages.