Too-Big Data? That don’t impress me much

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On a whim, I spent the evening in Edinburgh at a meetup presentation/meeting concerning Big Data, the talk given by a “Big Data hero” (IBM’s term), employed by a huge multinational corporation with a lot of fingers in a lot of pies (including the UK Welfare system).

I think I was supposed to be awed by the scale of data under discussion, but mostly what I heard was all immodest massive business-speak and buzzwords and acronyms. A few scattered examples to claim “we did that”, “look at the size of our supercomputer”, but the only technical word he uttered all evening was “Hadoop”.

In the absence of a clear directed message, I’ve come away with my own thoughts instead.

So the idea of Big Data is altogether a source of disappointment and concern.

There seems to be a discrepancy: on the one hand, one’s fitbit and phone are rich sources of data; the thought of analyzing it all thoroughly sets my data-geek senses twitching in excitement. However, the Internet of Things experience relies on huge companies doing the analysis – outsourced to the cloud – which forms a disjoint as they proceed to do inter-company business based on one’s personal data (read: sell it, however aggregated it might be – the presenter this evening scoffed at the idea of “anonymized”), above one’s head and outwith one’s control. The power flows upwards.

To people such as this evening’s speaker, privacy and ethics are just more buzzwords to bolt on to a “data value pipeline” to tout the profit optimizations of “data-driven companies”. So are the terms data, information, knowledge and even wisdom.

But I think he’s lost direction in the process. We’ve come a long way from sitting on the sofa making choices how to spend the evening pushing buttons on the mobile.

And that is where I break contact with The Matrix.

I believe in appreciating the value of little things. In people, humanity and compassion more than companies. In substance. In the genuine kind of Quality sought by Pirsig, not as “defined” by ISO code 9000. Value may arise from people taking care in their craft: one might put a price on a carved wooden bowl in order to sell it, but the brain that contains the skill required to make it is precious beyond the scope of the dollar.

Data is data and insights are a way to lead to knowledge, but real wisdom is not just knowing how to guide analysis – it’s understanding that human intervention is sometimes required, and knowing when to deploy it, awareness, critical thinking to see and choose.

The story goes that a salesman once approached a pianist, offering a new keyboard “with eight nuances”. The response came back: “but my playing requires nine”.

Knowing Your Lenses

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Analysing Lens Performance

Background

Typically, when a manufacturer produces new lenses, they include an MTF chart to demonstrate the lens’s performance – sharpness measured as the number of line-pairs per millimetre it can resolve (to a given contrast tolerance), radially and sagitally, varying with distance from the centre of the image. While this might be useful before purchasing a lens, it does not make for easy comparisons (what if another manufacturer uses a different contrast tolerance?) nor does it necessarily well reflect the use to which it’ll be put in practice. (What if they quote a zoom’s performance at 28mm and 70mm f/5.6, but you shoot it most at 35mm f/8? Is the difference between radial and sagittal sharpness useful for a real-world scene?)
Further, if your lens has been around in the kit-bag a bit, is its performance still up to scratch or has it gone soft, with internal elements slightly out of alignment? When you’re out in the field, does it matter whether you use 50mm in the middle of a zoom or a fixed focal-length prime 50mm instead?
If you’re shooting landscape, would it be better to hand-hold at f/5.6 or stop-down to f/16 for depth of field, use a tripod and risk losing sharpness to diffraction?
Does that blob of dust on the front element really make a difference?
How bad is the vignetting when used wide-open?

Here’s a fairly quick experiment to measure and compare lenses’ performance by aperture, two ways. First, we design a test-chart. It’s most useful if the pattern is even across the frame, reflecting a mixture of scales of detail – thick and thin lines – at a variety of angles – at least perpendicular and maybe crazy pseudo-random designs. Here’s a couple of ideas:

Method

Decide which lenses, at which apertures, you want to profile. Make your own design, print it out at least A4 size, and affix it to a wall. We want the lighting to be as even as possible, so ideally use indoor artificial light after dark (ie this is a good project for a dark evening). Carefully, set up the camera on a tripod facing the chart square-on, and move close enough so the test-chart is just filling the frame.

Camera settings: use the lowest ISO setting possible (normally around 100), to minimize sensor noise. Use aperture-priority mode so it chooses the shutter-speed itself and fix the white-balance to counteract the indoor lighting. (For a daylight lightbulb, use daylight; otherwise, fluorescent or tungsten. Avoid auto-white-balance.)
Either use a remote-release cable or wireless trigger, or enable a 2-second self-timer mode to allow shaking to die down after pushing the shutter. Assuming the paper with the test-chart is still mostly white, use exposure-compensation (+2/3 EV).
For each lens and focal-length, start with the widest aperture and close-down by a third or half a stop, taking two photos at each aperture.
Open a small text-document and list each lens and focal-length in order as you go, along with any other salient features. (For example, with old manual prime lenses, note the start and final apertures and the interval size – may be half-stops, may be third of a stop.)
Use a RAW converter to process all the images identically: auto-exposure, fixed white-balance, and disable any sharpening, noise-reduction and rescaling you might ordinarily do. Output to 16-bit TIFF files.

Now, it is a given that a reasonable measure of pixel-level sharpness in an image, or part thereof, is its standard deviation. We can use the following Python script (requires numpy and OpenCV modules) to load image(s) and output the standard deviations of subsets of the images:

#!/usr/bin/env python

import sys, glob, cv, cv2
import numpy as np

def imageContrasts(fname):
  img = cv2.imread(fname, cv.CV_LOAD_IMAGE_GRAYSCALE)
  a=np.asarray(img)
  width=len(img[0])
  height=len(img[1])
  half=a[0:height//3, 0:width//3]
  corner=a[0:height//8, 0:width//8]
  return np.std(a), np.std(half), np.std(corner)

def main():
	files=sys.argv[1:]
	if len(files)==0:
		files=glob.glob("*.jpg")
	for f in files:
		co,cc,cf=imageContrasts(f)
		print "%s contrast %f %f %f" % (f, co, cc, cf)

if __name__=="__main__":
	main()

We can build a spreadsheet listing the files, their apertures and sharpnesses overall and in the corners, where vignetting typically occurs. We can easily make a CSV file by looping the above script and some exiftool magic across all the output TIFF files:

bash$ for f in *.tif
do
ap=$(exiftool $f |awk '/^Aperture/ {print $NF}' )
speed=$( exiftool $f |awk '/^Shutter Speed/ {print $NF}' )
conts=$(~/python/image-interpolation/image-sharpness-cv.py $f | sed 's/ /,/g')
echo $f,$ap,=$speed,$conts
done

Typical output might look like:

P1440770.tif,,=1/20,P1440770.tif,contrast,29.235214,29.918323,22.694936
P1440771.tif,,=1/20,P1440771.tif,contrast,29.253372,29.943765,22.739748
P1440772.tif,,=1/15,P1440772.tif,contrast,29.572350,30.566767,25.006098
P1440773.tif,,=1/15,P1440773.tif,contrast,29.513443,30.529055,24.942437

Note the extra `=’ signs; on opening this in LibreOffice Spreadsheet, the formulae will be evaluated and fractions converted to floating-point values in seconds instead. Remove the spurious `contrast’ column and add a header column (fname,aperture,speed,fname,overall,half,corner).

Analysis

Let’s draw some graphs. If you wish to stay with the spreadsheet, use a pivot table to average the half-image contrast values per aperture per lens and work off that. Alternatively, a bit of interactive R can lead to some very pretty graphs:

> install.package(ggplot2)
> library(ggplot2)
> data<-read.csv("sharpness.csv") 
> aggs<-aggregate(cbind(speed,entire,third,corner) ~lens+aperture, data, FUN=mean)
> qplot(aggs$aperture, aggs$third, col=aggs$lens, data=aggs, asp=.5)+geom_smooth()

This will give a comparison of overall sharpness by aperture, grouped by lens. Typically we expect every lens to have a sweetspot aperture at which it is sharpest; my own examples are no exception: third There are 5 lenses at play here: a kit 14-42mm zoom, measured at both 30mm and 42mm; a Minolta Rokkor 55mm prime; a Pentacon 30mm prime; and two Pentacon 50mm f/1.8 prime lenses, one brand new off eBay and one that’s been in the bag for 4 years.

The old Pentacon 50mm was sharpest at f/5.6 but is now the second-lowest at almost every aperture – we’ll come back to this. The new Pentacon 50mm is sharpest of all the lenses from f/5 onwards, peaking at around f/7.1. The kit zoom lens is obviously designed to be used around f/8; the Pentacon 30mm prime is ludicrously unsharp at all apertures – given a choice of kit zoom at 30mm or prime, it would have to be the unconventional choice every time. And the odd one out, the Rokkor 55mm, peaks at a mere f/4.

How about the drop-off, the factor by which the extreme corners are less sharp than the overall image? Again, a quick calculation and plot in R shows:

> aggs$dropoff < - aggs$corner / aggs$third
> qplot(aggs$aperture, aggs$dropoff, col=aggs$lens, data=aggs, asp=.5)+geom_smooth()

dropoff Some observations:

  • all the lenses show a similar pattern, worst vignetting at widest apertures, peaking somewhere in the middle and then attenuating slightly;
  • there is a drastic difference between the kit zoom lens at 30mm (among the best performances) and 42mm (the worst, by far);
  • the old Pentacon 50mm lens had best drop-off around f/8;
  • the new Pentacon 50mm has least drop-of at around f/11;
  • the Minolta Rokkor 55mm peaks at f/4 again.

So, why do the old and new Pentacon 50mm lenses differ so badly? Let’s conclude by examining the shutter-speeds; by allowing the camera to automate the exposure in aperture-priority mode, whilst keeping the scene and its illumination constant, we can plot a graph showing each lens’s transmission against aperture.

> qplot(aggs$aperture, log2(aggs$speed/min(aggs$speed)), col=aggs$lens, data=aggs, asp=.5)+geom_smooth()

speed Here we see the new Pentacon 50mm lens seems to require the least increase in shutter-speed per stop aperture, while, above around f/7.1, the old Pentacon 50mm lens requires the greatest – rocketing off at a different gradient to everything else, such that by f/11 it’s fully 2 stops slower than its replacements.

There’s a reason for this: sadly, the anti-reflective coating is starting to come off in the centre of the front element of the old lens, in the form of a rough circle approximately 5mm diameter. At around f/10, the shutter iris itself is 5mm, so this artifact becomes a significant contributor to the overall exposure.

Conclusion

With the above data, informed choices can be made as to which lens and aperture suit particular scenes. When shooting a wide-angle landscape, I can use the kit zoom at f/8; for nature and woodland closeup work where the close-focussing distance allows, the Minolta Rokkor 55mm at f/4 is ideal.