Cam-ou-flage

Categories AWS, Computer Vision, Docker, Photography, Raspberry Pi

Hey Everyone,
Sorry for the lack of updates, I have been working on something so awesome it should technically be 3 blog posts and not one. It was such an intense project that I ended up bricking one of my Raspberry PIs by corrupting the memory card and causing segmentation faults. The entire fiasco is also what slowed down my progress. But anyways to start off this new year I wanted to shift my focus on upcoming and bleeding edge technologies like OpenCV. The overall idea is to find the most dominant color in a given frame so that if something was to remain camouflaged it would have the best chances with the chosen color. To implement this I used K-means clustering to divide the image into two sections and determine which color occupied the most space. The efficiency of this algorithm improves as we increase the value of K (the number of clusters). But for the sake of speed I chose to use only 2 clusters. Here is what the algorithm looks like

  1. Capture video using RPI camera
  2. Stream the video as a supported format MJPEG
  3. Load the video into OpenCV
  4. Process every frame as a Numpy Array
  5. Reduce the size of the Image for easier computation
  6. Using K Means Cluster create a histogram with K sections
  7. Determine largest section in histogram
  8. Render color on 8×8 LED Grid

The solution architecture is as follows
Abra Kedabra!

At first, I tried to everything using only my 2 raspberry pi’s but the problems I face was that it took 14 hours to compile! and the performance was incredibly poor. So I thought it was best to delegate the responsibilities to a container in the cloud which was very easy to setup and configure. They are 3 main components in the system.

  1. MJPEG streamer (here)
  2. AWS EC2 CV instance
  3. REST API for the SenseHat (by yours truly)

Check it out in action.

So after installing the MJPEG streaming module on my Pi2 I wrote a simple wrapper shell script for it.

This would create a MJPEG stream at 'http://< rpi-ip >:8080/?action=stream'
The next step was to consume this stream in AWS. I created a simple base container using the anaconda framework for python. setting OpenCV was as easy as conda install opencv . Next is the meat of the project code for which is shared below.

So this is what the EC2 container sees.

Input
Input

And this is the histogram generated after K Means clustering.
Output
Output

As you can see Red seems to be the most dominant color in the frame. You can tell by the amount of time taken for the neural network to compute the dominant color that this project is in an infancy stage. Let me mention the scope for improvement for this project.

  1. It is fundamentally wrong to use a value of k=2, I need k to be the exact number of different colors
  2. To provide the color for the LED board I should use a pub-sub system instead of REST as acknowledgment of request is not necessary
  3. In order to achieve true camouflage only computing to colour is not enough I need to figure out patterns and textures
  4. Overall performance of the system must improve by using a distributed system approach like (MPI) or tweaking the algorithm

Hope you guys liked my project. Look forward to more bleeding edge projects in the year ahead

Raspberry Pi Timelapse.

Categories Photography, Raspberry Pi

Here is my attempt at shooting a time lapse video on the raspberry pi 2.

This serene sequence is a fantastic fusion of art and technology shot and processed on hardware that costs < 60$. Let me first show you the camera I used to create this time lapse.

Rpi2
Say Cheese

The Raspberry Pi2 comes with a dedicated CSI (Camera Serial Interface) that takes a ribbon cable. Thankfully the camera I used had native support on the RPi2 so I didn’t have to install any other drivers. It was literally plug and play. Luckily I had a case with an opening that allowed for the ribbon cable to pass through it.

The Setup
The Setup

The Raspberry Pi2 was connected to a 10,000 maH power bank. I originally expected it to last about 24 hours but later learned things the hard way. The Rpi2 pulls about 400mA of power meaning it should Ideally have run for 10000/400 = 25 hours on a full charge. How ever I forgot to compute the battery efficiency of 70% which cause it to die about an hour before sunset during a previous attempt, footage of which has been attached below.

Once the camera is plugged in we do

$ sudo raspi-config

and make sure we enable the camera interface and restart the device. then a simple

 $vgencmd get_camera 

# which should return 

supported=1 detected=1

Else check all connections including the ribbon connected on the camera module below the lens that has to be pressed firmly in place. To test the quality of the camera by taking a full photo. we can use raspistill.


$raspistill -o test.jpg -vf -awb auto -ex auto 

# What this means is 

# 

# -o is to specify the output file for the picture. 

# -vf is to vertically flip the image (since my camera was attached upside-down). 

# -awb sets the auto white balance on. 

# -ex sets automatic exposure.

Since the camera is interfaced at a GPU level we wont be able to get a preview of the camera using a VNC server which makes framing the time-lapse difficult. To over come this we install vlc media player to create a live stream on the pi 

$sudo apt-get install vlc

and then we simply run


$raspivid -o - -t 0 -vf -w 640 -h 480 -fps 30 | cvlc -vvv stream:///dev/stdin --sout '#rtp{sdp=rtsp://:8554}' :demux=h264 

#Which means use raspivid to take a video that is vertically flipped 

#with a resolution of 640 x 480 at 30fps 

# Then pipe the video to vlc media player and create 

# an RTSP stream at rtsp://'raspberrypi ip':8554/ 

# which is encoded using h264

This stream can be opened using vlc media player on any tablet/computer or device as far as it is on the same network.
There is a very nice python library that I used to create the time lapse.
Here is the github gist of the program I used to create this timelapse.

I store all images in an S3 bucket because it makes viewing the images a lot easier. Because the camera can only be used by one application at a time so its not possible to access the live feed and run a time lapse at the same time. Upload the images to an S3 bucket means I can see the recently taken images with ease by accessing the public url of the content.
Scope for improvement:

  • Complete support for sunrise time-lapses
  • Add ability to change camera settings at a given time of day
  • Add support to share images/video outside of AWS.