Skip to main content

Gaussian Blur







Trying to work out why the gaussian blur in OpenCV is different from that of SciPy...
The differences are too small to see, but are still there.

Following is an imshow of each channel of the difference image.

And by looking at a singe row, we see that the difference spikes the whole intensity range.

Edit: That was pretty much staring me in the face wasn't it... uint8's are prone to integer overflow!

Comparing the OpenCV implementation with two versions in SciPy now gives:

Doing a pixel for pixel comparison on each channel between the SciPy and OpenCV examples:



The second one is comparing an IIR filter implementation to a ndfilt.

Comments

Popular posts from this blog

Matplotlib in Django

The official django tutorial is very good, it stops short of displaying
data with matplotlib - which could be very handy for dsp or automated
testing. This is an extension to the tutorial. So first you must do the
official tutorial!
Complete the tutorial (as of writing this up to part 4).

Adding an image to a view

To start with we will take a static image from the hard drive and
display it on the polls index page.
Usually if it really is a static image this would be managed by the
webserver eg apache. For introduction purposes we will get django to
serve the static image. To do this we first need to change the
template.



Change the template
At the moment poll_list.html probably looks something like this:


<h1>Django test app - Polls</h1> {% if object_list %} <ul> {% for object in object_list %} <li><a href="/polls/{{object.id}}">{{ object.question }}</a></li> {% endfor %} </ul> {% else %} <p>No polls are available.</p> …

Homomorphic encryption using RSA

I recently had cause to briefly look into Homomorphic Encryption, the process of carrying out computations on encrypted data. This technique allows for privacy preserving computation. Fully homomorphic encryption (FHE) allows both addition and multiplication, but is (currently) impractically slow.

Partially homomorphic encryption just has to meet one of these criteria and can be much more efficient.
An unintended, but well-known, malleability in the common RSA algorithm means that the multiplication of ciphertexts is equal to the multiplication of the original messages. So unpadded RSA is a partially homomorphic encryption system.

RSA is beautiful in how simple it is. See wikipedia to see how to generate the public (e, m) and private keys (d, m).

Given a message x it is encrypted with the public keys it to get the ciphertext C(x)with:

C(x)=xemodm
To decrypt a ciphertext

Bluetooth with Python 3.3

Since about version 3.3 Python supports Bluetooth sockets natively. To put this to the test I got hold of an iRacer from sparkfun. To send to New Zealand the cost was $60. The toy has an on-board Bluetooth radio that supports the RFCOMM transport protocol.



The drive protocol is dead easy, you send single byte instructions when a direction or speed change is required. The bytes are broken into two nibbles: 0xXY where X is the direction and Y is the speed. For example the byte 0x16 means forwards at mid-speed. I was surprised to note the car continues carrying out the last given demand!

I let pairing get dealt with by the operating system. The code to create a Car object that is drivable over Bluetooth is very straight forward in pure Python:

importsocketimporttimeclassBluetoothCar:def__init__(self,mac_address="00:12:05:09:98:36"):self.socket=socket.socket(socket.AF_BLUETOOTH,socket.SOCK_STREAM,socket.BTPROTO_RFCOMM)self.socket.connect((mac_address,1))def_write(self,data_byte):…