The graphics capabilities of the CPC were very similar to those of the BBC Micro, but the CPCs benefitted greatly from having many more colours available. Everything you could ever want to know about the video capabilities of the Amstrad CPC range is explained here.
The graphics capabilities are so similar, in fact, that to create a graphics filter for Amstrad CPC in Mode 0 all I had to do was change the palette data in my BBC Micro Mode 5 filter. Instead of picking 4 colours from a range of 8 the filter simply needs to pick 16 colours from a range of 27.
The BBC Micro Mode 2 version with a 2x2 threshold matix seems much flatter and less detailed:
BBC Micro, Mode 2, Ordered Dither 2x2 threshold matrix
So what went wrong with no dithering? Well, the method I'm using to choose a palette is very crude - it picks the sixteen most used colours from the Amstrad CPC palette to go into the final image. If my method is applied an image with lots of dark areas and a few highlights the highlights will be completely missing as the light colours will not be used enough to feature in the final table of 16 colours. I obviously need to find a method that takes into account the range of luminance used on an image.
The situation gets even worse when dealing with the Amstrad CPC Mode 1. Amstrad CPC Mode 1 is very similar to the BBC Micro's Mode 1. But whereas Mode 1 on the BBC Micro can pick 4 colours from a selection of 8, Mode 1 on the Amstrad CPC can pick 4 colours from a selection of 27.
Here's a picture of Polly parrot:
Pining for the fjords
Here's the same picture put through the Amstrad CPC Mode 1 filter with no dithering:
Amstrad CPC, Mode 1, No Dither
Here's the same picture of a parrot put through the BBC Micro Mode 1 filter with no dithering.
BBC Micro, Mode 1, No Dither
Even Sierra3 error diffusion won't help the Amstrad CPC Mode 1 image:
Amstrad CPC, Mode 1, Sierra 3 Error Diffusion
Whereas the BBC Micro filter produces excellent results:
BBC Micro, Mode 1, Sierra 3 Error Diffusion
Needless to say, the Amstrad CPC filters should be producing better results than the BBC Micro filters!
So, I'm going off to find a better way to pick an Amstrad palette! In the meantime, if you want to play with the Amstrad CPC filters they can be downloaded from here. Microsoft Windows users can find out how to install and use the filters with The GIMP by following the very nice set of instructions with pictures I've found here.
Whilst doing this, I also made the Strength slider in my BBC Micro filter act upon Ordered Dithering as well as Error Diffusion. This allows for some quite interesting effects.
Here is John Liven's Somerset cottage picture. I've applied Ordered Dithering with a 2x2 threshold matrix set to 100% strength:
Mode 2, 2x2 Ordered Dither, 100% Strength
And here is the same picture with the same dithering applied at just 50% strength:
Mode 2, 2x2 Ordered Dither, 50% Strength
Once this was done the BBC Micro Mode 2 filter was finally finished off. I then turned my attention to writing a BBC Micro Mode 5 filter. Mode 5 is very similar to Mode 2, but you can only use 4 colours from a selection of 8.
This means the image filter needs an additional step in which the palette to use for the image is selected. The way I approached this was to scan the entire image pixel by pixel tallying which of the 8 possible BBC Micro colours each pixel was closest to. Then, I simply used the four most commonly found colours.
Incidentally, the way you work out how close one colour is to another is quite simple once it's explained to you: I found the answer here.
After a coding a bit of Python I loaded an image of some parrots into The GIMP:
Norwegian Blue is on the left
And tried out the filter with no dithering. It worked first time:
Mode 5, No Dithering
And when some Sierra3 error diffusion was added too, the results were incredibly good:
Mode 5, Sierra 3 Error Diffusion 100% Serpentine
I still can't get over the fact that there are just four colours (black, white, red and green) in this image.
With a Mode 5 filter under my belt it was very easy to produce a Mode 1 filter. Mode 1 is the same as Mode 5, but has square pixels:
Mode 1, Sierra 3 Error Diffusion 100% Serpentine
And a Mode 4 filter. Mode 4 is like Mode 1, but is restricted to just two colours:
Mode 4, Sierra 3 Error Diffusion 100% Serpentine
So, I have got a pretty nice suite of BBC Micro image filters, with only Mode 0 to add. As always, you can download them from here. Microsoft Windows users can find out how to install and use the filters with The GIMP by following the very nice set of instructions with pictures I've found here.
I've spent a lot of time recently converting nitrofurano's sdlBasic retro computing picture converter programs into Python-Fu image filters for The GIMP. I've also been adding a few ideas of my own - particularly in the area of half-toning techniques.
After successfully getting Error Diffusion working, I decided to see if I could get Ordered Dithering working. Ordered Dithering is a technique akin to the half-toning you see on images in newspapers. From an image made up of many colours you can create an image made up of only a handful of colours stippled such a way as to give the illusion of many colours.
There are several excellent explanations of Ordered Dither on the web for the curious written by people who actually know what they are talking about - I would recommend this one and this one.
Ordered Dithering is much simpler than Error Diffusion (which is probably why it took me longer to get it working!). As it's a much simpler process it's lightning fast in comparison, even when coded in Python.
As a test, I took John Liven's picture of a Somerset cottage.
Photo: John Livens
Then I converted the image to the BBC Micro's MODE 2 (which has just eight colours) using an ordered dither with a 2 x 2 threshold matrix.
BBC MODE2, Ordered Dither, 2x2 matrix
As you can see above, it produces a very pleasing regular patterning on the colours - to allow you to compare, here is the same image with Floyd-Steinberg Error Diffusion:
BBC MODE2, Floyd-Steinberg Error Diffusion
On a small image such as this, larger grids don't really add much. Here is an ordered dither using a 4 x 4 threshold matrix:
BBC Micro MODE2, Ordered dither, 4x4 matrix
But, the proof of the pudding is in the eating. If you have The GIMP installed on your computer you might like to try the ordered dither filter for yourself - it's available from here.
Over the past few days I've been converting nitrofurano's image filters from stand-alone sdlBasic programs to Python-Fu image filters for The GIMP. But, so far, there has been a particularly noticeable absence - the BBC Micro.
Partly that was because the BBC Micro world has already been utterly spoilt in the image conversion department by Francis G. Loch's incredible BBC Micro Image Converter. It's a highly professional piece of software and does it all. I've posted about it many times here and the work I've done for Retro Software would have been utterly impossible without it.
BBC Micro Image Converter by Francis G. Loch
But partly it was because Francis' program had inspired me to try and find out more about the mind-boggling array of dither options he had included in his program. It boasted a host of exotic sounding names like "Floyd-Steinberg", "Sierra", "Jarvis, Judice and Ninke" and "Stucki".
Paulo had used a technique called Bayer ordered dither in his filters, which is similar to the traditional half-toning used in print. It's very powerful, very fast and gives you a lovely regular patterned effect on the images, which is sometimes just what you're after.
Naturally, Francis' BBC Micro Image Converter does this as well. But these exotic sounding names were the inventors of various flavours of another technique: error diffusion.
Error diffusion works by trying to compensate for the colour information lost by turning a pixel into a value from a restricted palette by sharing it out amongst the surrounding pixels.
After looking at the Wikipedia entry for Floyd-Steinberg, it looked like even I could understand how to program it and then after finding an excellent article here I realised that all the other filters did exactly the same thing. They just shared out the lost information (or quantisation error) to different pixels in different proportions.
And, after a couple of hours messing about in Python, I managed to get out a servicable MODE 2 image (click on the images to enlarge):
Floyd-Steinberg error diffusion, 100% strength
You can see just how effective error diffusion is when you compare the results to the same image processed with no error diffusion:
The same image with no error diffusion
Here's the original image for comparison:
I'm the one on the right
I excitedly added a range of different filters into my BBC Micro image filter:
Take your pick!
There was a problem though. Sierra3 was taking well over 70 seconds. This sluggishness was caused by the inefficient way in which I was checking that a pixel was within a certain range in Python.
Sierra3, 100% Strength - and very slow!
An error message I had been getting during development rather ironically proved to be the key to solving the speed problem. Instead of using time consuming range() functions to see if pixels were inside a particular range, I could use exception handling and check for an IndexError instead. This was very fast - it sped the filter up by a factor of at least four. Mind you, it still crawls along compared to Francis' version!
The next thing I needed to add to the filter was something called "Serpentine parsing". This means that instead of processing the image from left to right as it moves down, the computer processes the image backwards and forwards. This helps to stop all the error diffusion going in just one direction - smearing all the errors to the right.
Finally, pinching another one of Francis' excellent ideas, I added a strength control to the filter to allow you to control how strongly the error diffusion works.
Here is Test Card F with 50% Floyd-Steinberg error diffusion strength:
Floyd-Steinberg error diffusion, 50% strength
And here it is with 25% Floyd-Steinberg error diffusion strength:
Floyd-Steinberg error diffusion, 25% strength
So, a BBC Micro Mode 2 image filter for The GIMP, that can be downloaded from here. However there are numerous refinements that need to be added to it. But they will have to wait for another day.
In Commodore 64 Low Resolution mode, each block of 4 x 8 2:1 aspect ratio pixels can contain four colours from a choice of 16. Only, it's a bit more complicated than that! Fortunately, this article explains how it is supposed to behave very nicely. Paulo's algorithm has to go through eight separate stages to create the finished image.
The main novelty for me in this filter was that in order to avoid having to use a three dimensional list (which would have entailed syntax to boggle the mind) I used a Python new-style class. That meant I could use a one dimensional list and let the class take care of getting and setting the right bit of it when required through method calls.
Here's an example image before:
And after conversion in The GIMP using the filter:
Commodore 64 Low Res Filter
As usual this plug-in, along with ones for the ZX Spectrum, Apple II and MSX1 can be downloaded from here.
Mind you, saying that the Apple II had a colour mode is a bit like saying that Steve Wozniak was a professional ballroom dancer. Whilst technically correct, it really is wide of the mark.
Apple II colour is a strange world in which "red or yellow" becomes orange and "blue or cyan" becomes blue. Because of this Paulo's filter was rather more complicated than the ones I've tackled previously. In all, it took me about three days to understand what was going on and finally iron out all the bugs (mine, not Paulo's!).
Because of the complexity of the filter, I decided to implement some functions to emulate the ink(), dot(), point() and line() commands of sdlBasic. This made the Python much more readable (and eaiser to debug), even if it did mean I lost a bit of speed.
In order to make up some of the lost speed, I used tuples instead of lists for the look-up tables. I should have done this in my other filters too.
Once I finished the filter I dug out my usual cottage picture as a test:
Photo: John Livens
The resulting image had me crying into my coffee:
Soundtrack from the film More?
It looked like something out of "The Lost World of Friese-Greene"! Having picked out some bugs I got something a bit closer, but the white stripes were a real pain to get rid of:
It took ages to fix...
Finally, after I had remembered how to count to six, I got a successful image:
...but the result was worth it.
The filter runs in two modes, a halftone mode or a posterised mode. The posterise mode doesn't stipple the colours. Here is the posterised output:
Posterised, it's very striking
I added a little dialogue box to the filter to allow users to pick which mode they want:
The filter's complex user interface
My overall impression is that the Apple II produced orangey mush - a bit like the NTSC pictures put through the IBA's DICE standards convertor we used to see on British television in the 70s. But, I must admit, it does have a certain kind of charm. And, above all, Paulo did an incredible job in coming up with an Apple II filter - it's an ingenious bit of coding.
If you want to try it out for yourself, the filter is available to download from here. Bear in mind that the filter is pretty slow, so it's best to stick to small images unless you have a fast computer.
The R3PLAY Arcade, Retro and Video Gaming Expo was held on the 6-7 November in Blackpool and, as I said a few days ago, it saw the official launch of Repton: The Lost Realms.
The visitors to the show had the poster I designed inflicted on them as they were queuing to get in:
Photo: Michael S. Repton
The Retro Software team were ready and waiting to sell millions of copies:
(Back L-R) Peter, Greg, Steve, Tom, Michael, Jonathan, (Front) Martin, Dave. Photo: Martin Barr.
Needless to say, the game completely sold out. And no wonder, the finished article looked gorgeous - in spite of the fact that I did all the artwork:
Kecske Bak, after Ellis Ives Sprowell
We were honoured that Repton took time out from running his HSE-free diamond mining conglomerate to attend in person:
Photo: Michael S. Repton
The Beeb performed masterfully:
Photo: Martin Barr
And so did its half brother by the milkman, the Acorn Electron:
The most impressive Acorn Electron Repton yet
Contributing to a real Repton release was a childhood ambition of mine - and one that, somehow, I've now managed to fulfil.
Paras Sidapara, Tom Walker, Michael S. Repton, Jonathan Parkin, Richard Barnard, Dave Moore, Peter Edwards, Andrew Weston, Richard Hanson, Matthew Atkinson, John Chesney and of course the durian munching genius polymath that is Tim Tyler all put a huge amount of work but most of all love into Repton: The Lost Realms and I think that really shows.
I'm really honoured I got to be a part of the team.
Now that Paulo Silva's (nitrofurano) ZX Spectrum filter for The GIMP was working nicely, I thought I'd like to try converting one of his other sdlBasic picture filters into a Python GIMP plug-in.
I chose the MSX1 Screen 2 filter, as it looked quite similar to the ZX Spectrum filter. I'd never actually seen an MSX computer working (I saw some switched off in a shop once) so I didn't really know what to expect until I read up on Wikipedia.
Whereas the ZX Spectrum suffered from attribute clash on the character square level, MSX1 suffered from attribute clash on the character row level so I was expecting the resulting images to look like slightly better ZX Spectrum images. And so it turned out.
To compare, here is John Liven's photograph of a cottage:
Cottage - Photo: John Livens
And here it is processed by the ZX Spectrum filter:
Cottage - ZX Spectrum
And finally by the MSX1 filter:
Cottage - MSX1
Converting the filter was straightforward, and I managed to find and fix a small bug in the sdlBasic original whilst I was going along.
As always, the finished MSX1 filter can be downloaded from here.
To get undo to work I needed to create a duplicate of the current layer to work on, and then merge that down into the original layer when the filter has finished its work.
From XOR to AOR
That way, The GIMP seemed to remember the original layer and could go back to it when you used undo.
So now, hopefully, it's all finished. The final version of the filter is available to download from here. People with Windows or Macs wanting to try the plug-in need to follow the instructions here. GNU/Linux users can just copy it into their ~/.gimp2.6/plug-ins folder and set the Execute permission.