Author: spqr

Programming to process images : coding

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People take photographs for a number of reasons. People process images for a number of reasons. Some people (like me) spend as little time as possible post-processing images, others spend hours in applications like Photoshop, tweaking every possible part of an image. To each their own. There are others still who like to tinker with the techniques themselves, writing their own software to process their images. There are many different things to consider, and my hope in this post is to try and look at what it means to create your own image processing programs to manipulate images.

Methods of image manipulation

1. What do you want to achieve?

The first thing to ask is what do you want to achieve? Do you want to implement algorithms you can’t find in regular photo manipulating programs? Do you want to batch process images? There are various different approaches here. If you want to batch process images, for example converting hundreds of images to another file format, or automatically generating histograms of images, then it is possible to learn some basic scripting, and use existing image manipulation programs such as ImageMagick. If you want to write heavier algorithms, for example some fancy new Instagram-like filter, then you will have to learn how to program using a programming language.

2. To program you need to understand algorithms

Programming of course is not a trivial thing, as much as people like to make it sound easy. It is all about taking an algorithm, which is a precise description of a method for solving a problem, and translating it into a program using a programming language. The algorithms can already exist, or they can be created from scratch. For example, a Clarendon-like (Instagram) image filter can be reproduced with the following algorithm (shown visually below):

  1. Read in an image from file.
  2. Add a blue tint to the image by blending it with a blue image of the same size (e.g. R=127, G=187, B=227, opacity = 20%).
  3. Increase the contrast of the image by 20%.
  4. Increase the saturation of the image by 35%.
  5. Save the new Clarendon-like filtered image in a new file.
A visual depiction of the algorithm for a Clarendon filter-like effect

To reproduce any of these tasks, we need to have an understanding of how to translate the algorithm into a program using a programming language, which is not always a trivial task. For example to perform the task of increasing the saturation of an image, we have to perform a number of steps:

  1. Convert the image to a colour model which allows saturation to be easily modified, for example HSI, which has three component layers: Hue, Saturation, and Intensity.
  2. Increase in saturation by manipulating the Saturation layer, i.e. multiplying all values by 1.2.
  3. Convert the image from HSI back to RGB.

Each of these tasks in turn requires additional steps. You can see this could become somewhat long-winded if the algorithm is complex.

3. To implement algorithms you need a programming language

The next trick is the programming language. Most people don’t really want to get bogged down in programming because of the idiosyncrasies of programming languages. But in order to convert an algorithm to a program, you have to choose a programming language, and learn how to code in it.

There are many programming languages, some are old, some are new. Some are easier to use than others. Novice programmers often opt for a language such as Python which is easy to learn, and offers a wide array of existing libraries or algorithms. The trick with programming is that you don’t necessarily want to reinvent the wheel. You don’t want to have to implement an algorithm that already exists. That’s why programming languages provide functions like sqrt() and log(), so people don’t have to implement them. For example, Akiomi Kamakura has already created a Python library called Pilgram, which contains a series of mimicked Instragram filters (26 of them), some CSS filters, and blend modes. So choosing Python means that you don’t have to build these from scratch, if anything they might provide inspiration to build your own filters. For example the following Python program uses Pilgram to apply the Clarendon filter to a JPG image:

from PIL import Image
import pilgram
import os

inputfile = input('Enter image filename (jpg): ')
base = os.path.splitext(inputfile)[0]
outputfile = base + '-clarendon.jpg'
print(base)
print(outputfile)

im = Image.open(inputfile)
pilgram.clarendon(im).save(outputfile)

The top part of the program imports the libraries, the middle portion deals with obtaining the filename of the image to be processed, and producing an output filename, and the last two lines actually open the image, process it with the filter, and save the filtered image. Fun right? But you still have to learn how to code in Python. Python comes with it’s own baggage (like dependencies, and being uber slow), but overall it is still a solid language, and easy to learn for novices. There are also other computer vision/image processing libraries such as scikit-image, SimpleCV and OpenCV. And in reality that is the crux here, programming for beginners.

If you really want to program in a more “complex” language, like C or C++ there is often a much steeper learning curve, and fewer libraries. I would not advocate for a fledgling programmer to learn any C-based language, only because it will result in being bogged down by the language itself. For the individual hell-bent on learning one of these languages I would suggest Fortran. Fortran was the first high-level programming language, introduced in 1957, however it has evolved, and modern Fortran is easy to learn, and use. It doesn’t come with much in the way of image processing libraries, but if you persevere you can build them.

The Pentax (Asahi) 17mm fish-eye lens – 160 or 180°?

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The closest Pentax came to a fisheye prior to the 17mm was the Takumar 18mm, which had an angle of view of 148°. In 1967, Pentax introduced the 17mm fish-eye. There are some discrepancies with whether the Asahi fish-eye lenses had an angle-of-view of 160° or 180°. During the period when Asahi Pentax produced the 17mm lens, it seems there were three versions.

  • Fish-eye-Takumar 17mm f/4 (1967-1971)
    • This seems to be referred to in the literature as a Super-Takumar.
  • Super-Multi-Coated FISH-EYE-TAKUMAR 17mm f/4 (1971-1975)
  • SMC PENTAX FISH-EYE 17mm f/4 (1975-1985)
All three variants of the 17mm lens

Many people assume every variant is 180°, but the literature such as brochures seems to tell another story. As you can see from the snippets of various catalog’s shown below, the earliest version seems to be 160°, with some transition between the Super-Takumar and Super-Multicoated being either 160° or 180°, with the later SMC versions being all 180°. What’s the real story? I haven’t been able to find out. Short of physically measuring the earlier two versions it’s hard to tell whether the early versions were indeed 160°, or was it a typo?

Specs from various pieces of literature

Using vintage fisheye lenses on a crop-sensor

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I love vintage lenses, and in the future, I will be posting much more on them. The question I want to look at here is the usefulness of vintage fish-eye lenses on crop sensors. Typically 35mm fisheye lenses are categorized into circular, and full-frame (or diagonal). A circular fisheye is typically in the range 8-10mm, with full-frame fisheye’s typically 15-17mm. The difference is shown in Figure 1.

Fig. 1: Circular 7.5mm versus full-frame 17mm

The problem arises with the fact that fish-eye lenses are different. So different that the projection itself can be one of a number of differing types, for example equidistant, and equisolid. That aside, using a fisheye lens on a crop-sensor format produces much different results. This of course has to do with the crop factor. An 8mm circular fisheye on a camera with an APS-C sensor will have an AOV (Angle-of-View) equivalent to a 12mm lens. A 15mm full-frame fisheye will similarly have an AOV equivalent of a 22.5mm lens. A camera with a MFT sensor will produce an even smaller image. The effect of crop-sensors on both circular and full-frame fisheye lenses is shown in Figure 2.

Fig.2: Picture areas in circular and full-frame fisheye lenses on full-frame, and crop-sensors

In particular, let’s look at the Asahi Super Takumar 17mm f/4 fish-eye lens. Produced from 1967-1971, in a couple of renditions, this lens has a 160° angle of view, in the diagonal, 130° in the horizontal. This is a popular vintage full-frame fisheye lens.

Fig.3: The Super-Takumar 17mm

The effect of using this lens on a crop-sensor camera is shown in Figure 4. It effectively looses a lot of its fisheye-ness. In the case of an APS-C sensor, the 160° in the diagonal reduces to 100°, which is on the cusp of being an ultra-wide. When associated with a MFT sensor, the AOV reduces again to 75°, now a wide angle lens. Figure 4 also shows the horizontal AOV, which is easier to comprehend.

Fig.4: The Angle-of-View of the Super-Takumar 17mm of various sensors

The bottom line is, that a full-frame camera is the best place to use a vintage fish-eye lens. Using one on a crop-sensor will limit its “fisheye-ness”. Is it then worthwhile to purchase a 17mm Takumar? Sure if you want to play with the lens, experiment with it’s cool built-in filters (good for B&W), or are looking for a wide-angle lens equivalent, any sort of fisheye effect will never be achieved. In many circumstances, if you want a more pronounced fisheye effect on a crop-sensor, it may be better to use a modern fisheye instead.

NB: Some Asahi Pentax catalogs suggest the 17mm has an AOV of 160°, while others suggest 180°.

Pixel peeping and why you should avoid it

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In recent years there has been a lot of of hoopla about this idea of pixel peeping. But what is it? Pixel peeping is essentially magnifying an image until individual pixels are perceivable by the viewer. The concept has been around for many years, but was really restricted to those that post-process their images. In the pre-digital era, the closest photographers would come to pixel peeping was the use of a loupe to view negatives, and slides in greater detail. It is the evolution of digital cameras that spurned the widespread use of pixel peeping.

Fig.1: Peeping at the pixels

For some people, pixel-peeping just offers a vehicle for finding flaws, particularly in lenses. But here’s the thing, there is no such thing as a perfect lens. There will always be flaws. A zoomed in picture will contain noise, and grain, unsharp, and unfocused regions. But sometimes these are only a problem because they are being viewed at 800%. Yes, image quality is important, but if you spend all your time worrying about every single pixel, you will miss the broader context – photography is suppose to be fun.

Pixel-peeping is also limited by the resolution of the sensor, or put another way, some objects won’t look good when viewed at 1:1 at 16MP. They might look better at 24MP, and very good at 96MP, but a picture is the sum of all its pixels. My Ricoh GR III allows 16× zooming when viewing an image. Sometimes I use it just to find out it the detail has enough sharpness in close-up or macro shots. Beyond that I find little use for it. The reality is that in the field, there usually isn’t the time to deep dive into the pixel content of a 24MP image.

Of course apps allow diving down to the level of the individual pixels. There are some circumstances where it is appropriate to look this deep. For example viewing the subtle effects of changing settings such as noise reduction, or sharpening. Or perhaps viewing the effect of using a vintage lens on a digital camera, to check the validity of manual focusing. There are legitimate reasons. Pixel peeping on the whole is really only helpful for people who are developing or finetuning image processing algorithms.

Fig.2: Pixel peeping = meaningless detail

One of the problems with looking at pixels 1:1 is that a 24MP image was never meant to be viewed using the granularity of a pixel. Given the size of the image, and the distance it should be viewed at, micro-issues are all but trivial. The 16MP picture in Figure 2 shows pixel-peeping of one of the ducks under the steam engine. The entire picture has a lot of detail in it, but dig closer, and the detail goes away. That makes complete sense because there are not enough pixels to represent everything in complete detail. Pixel-peeping shows the ducks eye – but it’s not exactly that easy to decipher what it is?

People that pixel-peep are too obsessed with looking at small details, when they should be more concerned with the picture as a whole.

The different Angle-of-View measurements

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Look at any lens spec, and they will normally talk about the angle-of-view (AOV), sometimes used interchangeably (and incorectly) with field-of-view (FOV). But there are three forms of AOV, and they can be somewhat confusing. The first form is the diagonal AOV. It is one of the most common ones found in lens literature, but it isn’t very easy to comprehend without viewing the picture across the diagonal. Next is the vertical AOV, which makes the least sense, because we generally don’t take pictures, or even visualize the vertical. Lastly is the horizontal AOV, which makes the most sense, because of how humans perceive the world in front of them.

Showing the diagonal AOV of a lens is hard to conceptualize. It’s a bit like the way TV’s are described as being, say 50″, which is the diagonal measurement. In reality through, the TV is only 43.6″ wide. Horizontal is how people generally conceptualize things. As an example of a lens, consider a 24mm full-frame lens – it has a diagonal AOV of 84°, and a horizontal AOV of 74°. This isn’t really a lot, but enough to get a little confusing. A 16mm lens that has a AOV of 180° in the vertical, may only have a horizontal AOV of 140° An example of this is shown below.

Why are there no 3D colour histograms?

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Some people probably wonder why there aren’t any 3D colour histograms. I mean if a colour image is comprised of red, green, and blue components, why not provide those in a combined manner rather than separate 2D histograms or a single 2D histogram with the R,G,B overlaid? Well, it’s not that simple.

A 2D histogram has 256 pieces of information (grayscale). A 24-bit colour image contains 2563 colours in it – that’s 16,777,216 pieces of information. So a three-dimensional “histogram” would contain the same number of elements. Well, it’s not really a histogram, more of a 3D representation of the diversity of colours in the image. Consider the example shown in Figure 1. The sample image contains 428,763 unique colours, representing just 2.5% of all available colours. Two different views of the colour cube (rotated) show the dispersion of colours. Both show the vastness of the 3D space, and conversely the sparsity of the image colour information.

Figure 1: A colour image and 3D colour distribution cubes shown at different angles

It is extremely hard to create a true 3D histogram. A true 3D histogram would have a count of the number of pixels with a particular RGB triplet at every point. For example, how many times does the colour (23,157,87) occur? It’s hard to visualize this in a 3D sense, because unlike the 2D histogram which displays frequency as the number of occurrences of each grayscale intensity, the same is not possible in 3D. Well it is, kind-of.

In a 3D histogram which already uses the three dimensions to represent R, G, and B, there would have to be a fourth dimension to hold the number of times a colour occurs. To obtain a true 3D histogram, we would have to group the colours into “cells” which are essentially clusters representing similar colours. An example of the frequency-weighted histogram with for the image in Figure 2, using 500 cells, is shown in Figure 2. You can see that while in the colour distribution cube in Figure 1 shows a large band of reds, because these colours exist in the image, the frequency weighted histogram shows that objects with red colours actually comprise a small number of pixels in the image.

Figure 2: The frequency-distributed histogram of the image in Fig.1

The bigger problem is that it is quite hard to visualize a 3D anything and actively manipulate it. There are very few tools for this. Theoretically it makes sense to deal with 3D data in 3D. The application ImageJ (Fiji) does offer an add-on called Color Inspector 3D, which facilitates viewing and manipulating an image in 3D, in a number of differing colour spaces. Consider another example, shown in Figure 3. The aerial image, taken above Montreal lacks contrast. From the example shown, you can see that the colour image takes up quite a thin band of colours, almost on the black-white diagonal (it has 186,322 uniques colours).

Figure 3: Another sample colour image and its 3D colour distribution cube

Using the contrast tool provided in ImageJ, it is possible to manipulate the contrast in 3D. Here we have increased the contrast by 2.1 times. You can easily see the result in Figure 4. difference working in 3D makes. This is something that is much harder to do in two dimensions, manipulating each colour independently.

Figure 4: Increasing contrast via the 3D cube

Another example of increasing colour saturation 2 times, and the associated 3D colour distribution is shown in Figure 5. The Color Inspector 3D also allows viewing and manipulating the image in other colour spaces such as HSB and CieLab. For example in HSB the true effect of manipulating saturation can be gauged. The downside is that it does not actually process the full-resolution image, but rather one reduced in size, largely because I imagine it can’t handle the size of the image, and allow manipulation in real-time.

Figure 5: Increasing saturation via the 3D cube

the image histogram (ii) – grayscale vs colour

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In terms of image processing there are two basic types of histogram: (i) colour, and (ii) intensity (or luminance/grayscale) histograms. Figure 1 shows a colour image (an aerial shot of Montreal), and its associated RGB and intensity histograms. Colour histograms are essentially RGB histograms, typically represented by three separate histograms, one for each of the components – Red, Green, and Blue. The three R,G,B histograms are sometimes shown in one mixed histogram with all three R,G,B, components overlaid with one another (sometimes including an intensity histogram).

Fig.1: Colour and grayscale histograms

Both RGB and intensity histograms contain the same basic information – the distribution of values. The difference lies in what the values represent. In an intensity histogram, the values represent the intensity values in a grayscale image (typically 0 to 255). In an RGB histogram, divided into individual R, G, B histograms, each colour channel is just a graph of the frequencies of each of the RGB component values of each pixel.

An example is shown in Figure 2. Here a single pixel is extracted from an image. The RGB triplet for the pixel is (230,154,182) i.e. it has a red value of 230, a green value of 154, and a blue value of 182. Each value is counted in its respective bin in the associated component histogram. So red value 230 is counted in the bin marked as “230” in the red histogram. The three R,G, B histograms are visually no different than an intensity histogram. The individual R, G, and B histograms do not represent distributions of colours, but merely distributions of components – for that you need a 3D histogram (see bottom).

Fig.2: How an RGB histogram works: From single RGB pixel to RGB component histograms

Applications portray colour histograms in many different forms. Figure 3 shows the RGB histograms from three differing applications: Apple Photos, ImageJ, and ImageMagick. Apple Photos provides the user with the option of showing the luminance histogram, the mixed RGB, or the individual R, G, B histograms. The combined histogram shows all the overlaid R, G, B histograms, and a gray region showing where all three overlap. ImageJ shows the three components in separate histograms, and ImageMagick provides an option for their combined or separate. Note that some histograms (ImageMagick) seem a little “compressed”, because of the chosen x-scale.

Fig.3: How RGB histograms are depicted in applications

One thing you may notice when comparing intensity and RGB histograms is that the intensity histogram is very similar to the green channel or the RGB image (see Figure 4). The human eye is more sensitive to green light than red or blue light. Typically the green intensity levels within an image are most representative of the brightness distribution of the colour image.

Fig.4: The RGB-green histogram verus intensity histogram

An intensity image is normally created from an RGB image by converting each pixel so that it represents a value based on a weighted average of the three colours at that pixel. This weighting assumes that green represents 59% of the perceived intensity, while the red and blue channels account for just 30% and 11%, respectively. Here is the actual formula used:

gray = 0.299R + 0.587G + 0.114B

Once you have a grayscale image, it can be used to derive an intensity histogram. Figure 5 illustrates how a grayscale image is created from an RGB image using this formula.

Fig.5: Deriving a grayscale image from an RGB image

Honestly there isn’t really that much useful data in RGB histograms, although they seem to be very common in image manipulation applications, and digital cameras. The problem lies with the notion of the RGB colour space. It is a space in which chrominance and luminance are coupled together, and as such it is difficult to manipulate any one of the channels without causing shifts in colour. Typically, applications that allow manipulation of the histogram do so by first converting the image to a decoupled colour space such as HSB (Hue-Saturation-Brightness), where the brightness can be manipulated independently of the colour information.

A Note on 3D RGB: Although it would be somewhat useful, there are very few applications that provide a 3D histogram, constructed from the R, G, and B information. One reason is that these 3D matrices could be very sparse. Instead of three 2D histograms, each with 256 pieces of information, there is now a 3D histogram with 2563 or 16,777,216 pieces of information. The other reason is that 3D histograms are hard to visualize.

What is an RGB colour image?

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Most colour images are stored using a colour model, and RGB is the most commonly used one. Digital cameras typically offer a specific RGB colour space such as sRGB. It is commonly used because it is based on how humans perceive colours, and has a good amount of theory underpinning it. For instance, a camera sensor detects the wavelength of light reflected from an object and differentiates it into the primary colours red, green, and blue.

An RGB image is represented by M×N colour pixels (M = width, N = height). When viewed on a screen, each pixel is displayed as a specific colour. However, deconstructed, an RGB image is actually composed of three layers. These layers, or component images are all M×N pixels in size, and represent the values associated with Red, Green and Blue. An example of an RGB image decoupled into its R-G-B component images is shown in Figure 1. None of the component images contain any colour, and are actually grayscale. An RGB image may then be viewed as a stack of three grayscale images. Corresponding pixels in all three R, G, B images help form the colour that is seen when the image is visualized.

A Decoupled RGB image
Fig.1: A “deconstructed” RGB image

The component images typically have pixels with values in the range 0 to 2B-1, where B is the number of bits of the image. If B=8, the values in each component image would range from 0..255. The number of bits used to represent the pixel values of the component images determines the bit depth of the RGB image. For example if a component image is 8-bit, then the corresponding RGB image would be a 24-bit RGB image (generally the standard). The number of possible colours in an RGB image is then (2B)3, so for B=8, there would be 16,777,216 possible colours.

Coupled together, each RGB pixel is described using a triplet of values, each of which is in the range 0 to 255. It is this triplet value that is interpreted by the output system to produce a colour which is perceived by the human visual system. An example of an RGB pixel’s triplet value, and the associated R-G-B component values is shown in Figure 2. The RGB value visualized as a lime-green colour is composed of the RGB triplet (193, 201, 64), i.e. Red=193, Green=201 and Blue=64.

Fig.2: Component values of an RGB pixel

One way of visualizing the R,G,B, components of an image is by means of a 3D colour cube. An example is shown in Figure 3. The RGB image shown has 310×510, or 158,100 pixels. Next to it is a colour cube with the three axes, R, G, and B, each with a range of values 0-255, producing a cube with 16,777,216 elements. Each of the images 122,113 unique colours is represented as a point in the cube (representing only 0.7% of available colours).

Fig 2 Example of colours in an RGB 3D cube

The caveat of the RGB colour model is that it is not a perceptual one, i.e. chrominance and luminance are not separated from one another, they are coupled together. Note that there are some colour models/space that are decoupled, i.e. they separate luminance information from chrominance information. A good example is HSV (Hue, Saturation, Value).

the image histogram (i) – what is it?

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An image is really just a collection of pixels of differing intensities, regardless of whether it is a grayscale (achromatic) or colour image. Exploring the pixels collectively helps provide an insight into the statistical attributes of an image. One way of doing this is by means of a histogram, which represents statistical information in a visual format. Using a histogram it is easy to determine whether there are issues with an image, such as over-exposure. In fact histograms are so useful that most digital cameras offer some form of real-time histogram in order to prevent poorly exposed photographs. Histograms can also be used in post-processing situations to improve the aesthetic appeal of an image.

Fig.1: A colour image with its intensity histogram overlaid.

A histogram is simply a frequency distribution, represented in the form of a graph. An image histogram, sometimes called an intensity histogram, describes the frequency of intensity (brightness) values that occur in an image. Sometimes as in Figure 1, the histogram is represented as a bar graph, while other times it appears as a line graph. The graph typically has “brightness” on the horizontal axis, and “number of pixels” on the vertical axis. The “brightness” scale describes a series of values in a linear scale from 0, which represents black, to some value N, which represents white.

Fig.2: A grayscale image and its histogram.

A image histogram, H, contains N bins, with each bin containing a value representing the number of times an intensity value occurs in an image. So a histogram for a typical 8-bit grayscale image with 256 gray levels would have N=256 bins. Each bin in the histogram, H[i] represents the number of pixels in the image with intensity i. Therefore H[0] is the number of pixels with intensity 0 (black), H[1] the number of pixels with intensity 1, and so forth until H[255] which is the number of pixels with the maximum intensity value, 255 (i.e. white).

A histogram can be used to explore the overall information in an image. It provides a visual characterization of the intensities, but does not confer any spatial information, i.e. how the pixels physically relate to one another in the image. This is normal because the main function of a histogram is to represent statistical information in a compact form. The frequency data can be used to calculate the minimum and maximum intensity values, the mean, and even the median.

This series will look at the various types of histograms, how they can be used to produce better pictures, and how they can be manipulated to improve the aesthetics of an image.

Things to consider when choosing a digital camera

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There is always a lot to think about when on the path to purchasing a new camera. In fact it may be one of the most challenging parts of getting started in photography, apart from choosing which lenses will be in your kit. It was frankly easier when there was less in the way of choices. You could make a list of 100 different things with which to compare cameras, but better to start with a simple series of things to consider.

Some people are likely swayed by fancy advertising, or cool features. Others think only of megapixels. There are of course many things to consider. This post aims to provide a simple insight into the sort of things you should consider when buying a digital camera. It is aimed at the pictorialist, or hobby/travel photographer. The first thing people think about when considering a camera is megapixels. These are important from a marketing perspective, mainly because they are a quantifiable number that can be sold to potential buyers. It is much harder to sell ISO or dynamic range. But megapixels aren’t everything, as I mentioned in a previous post, anywhere from 16-24 megapixels is fine. So if we move beyond the need for megapixels, what should we look for in a camera?

Perhaps the core requirement for a non-professional photographer is an understanding of what the camera is to be used for – landscapes, street photography, macro shooting, travel, blogging, video? This plays a large role in determining the type of camera from the perspective of the sensor. Full frame (FF) cameras are only required by the most dedicated of amateur photographers. For everyday shooting they can be far too bulky and heavy. At the other end of the spectrum is Micro-Four-Thirds (MFT), which is great for travelling because of it is compact size. In the middle are the cameras with APS-C sensors, sometimes often found in mirrorless cameras, and even compact fixed-lens format cameras. If you predominantly make videos, then a camera geared towards maybe less MP and more video features is essential. For street photography, perhaps something compact and unobtrusive. Many people also travel with a back-up camera, so there is that to consider as well.

Next is price, because obviously if I could afford it I would love a Leica… but in the real world it’s hard to justify. As the sensor gets larger, the price goes up accordingly. Large sensors cost more to make, and mechanisms such as image stabilization have to be scaled accordingly. Lenses for FF are also more expensive because they contain larger pieces of glass. It’s all relative – spend what you feel comfortable spending. It’s also about lifespan – how long will you use this camera? It was once about upgrading for more megapixels or fancy new features – it’s less about that now. Good cameras aren’t cheap – nothing in life is, neither are good lenses… but spend more for better quality and buy fewer lenses.

Then there are lenses. You don’t need dozens of them. Look at what lenses there are for what you want to do. You don’t need a macro lens if you are never going to take closeup shots, and fisheye lenses are in reality not very practical. Zoom lenses are the standard lenses supplied with many cameras, but the reality is a 24-80 is practical (although you honestly won’t use the telephoto function that much), anything beyond 80mm is likely not needed. Choose a good quality all round prime lens. There are also a variety of price points with lenses. Cheaper lenses will work fine but may not be as optically nice, have weather proofing or contain plastic instead of metal bodies. You can also go the vintage lens route – lots of inexpensive lenses to play with.

Now we get to the real Pandora’s Box – features. What extra features do you want? Are they features that you will use a lot? Focus stacking perhaps, for well focused macro shots. Manual focus helpers like focus peaking for use with manual lenses. High resolution mode? Image stabilization (IS)? I would definitely recommend IS but lean perhaps towards the in-body rather than the in-lens. In body means any lens will work with IS, even vintage ones. In lens is just too specialized and I favour less tech inside lenses. Features usually come at a price- battery drain, so think carefully about what makes sense for your particular situation.

So what to choose? Ultimately you can read dozens of reviews, watch reviews on YouTube, but you have to make the decision. If you’re unsure, try renting one for a weekend and try it out. There is no definitive guide to buying a digital camera, because there is so much to choose from, and everyone’s needs are so different.