A huge amount of the data collected today is made up of images and videos. That is why effective image processing for translating and obtaining information is crucial for businesses. 

Data scientists usually preprocess the images before feeding it to machine learning models to achieve desired results. Consequently, it is paramount to understand the capabilities of various image processing libraries to streamline their workflows.

In this article, we are listing down the top image processing libraries in Python:

1. Scikit-image

Scikit-image uses NumPy arrays as image objects by transforming the original pictures. These ndarrys can either be integers (signed or unsigned) or floats. And as NumPy is built in C programming, it is very fast, making it an effective library for image processing. Among different methods, data scientists often utilise greyscale technique where each pixel is a shade of grey.

2. OpenCV

First released in 2000, OpenCV has become a popular library due to its ease of use and readability. The library is focused on image processing, face detection, object detection, and more. It is written in C++ but also comes with Python wrapper and can work in tandem with NumPy, SciPy, and Matplotlib. Backed by more than one thousand contributors on GitHub, the computer vision library keeps enhancing for an effortless image processing.

3. Mahotas

Mahotas allows developers to use its advanced features such as haralick, local binary patterns, and more. It can compute 2D and 3D images through mahotas.features.haralick module and perform advanced image processing by extracting information from pictures. Mahotas has over 100 functionalities for computer vision capabilities that can enable you to carry out processes like watershed, morphological processing, convolution, and more.

Here’s the link to the documentation and GitHub.

3. SimplelTK

Unlike other libraries that consider images as arrays, SimpleITK treats images as a set of points on a physical region in space. The region occupied by images is defined as origin, spacing, size, and direction cosine matrix. This modus operandi enables it to effectively process images. It supports a wide range of dimensions that includes 2D, 3D, and 4D.

4. SciPy

SciPy is primarily used for mathematics and scientific computations, but you can also implement algorithms for image manipulation by importing scippy.ndimage module. You can carry out binary morphology, object measurements, linear and non-linear filtering. Besides, one can draw contour lines, adjust interpolation, filter, effects, denoising, and other similar extraction and segmentation on images.SEE ALSO

5. Pillow

The library is an advanced version of PIL, which is supported by Tidelift. It includes various processes in image processing such as point operations, filtering, manipulating, and more. Pillow also supports a wide range of image formats, thus makes its must-have library for handling images.

6. Matplotlib

Matplotlib is mostly used for 2D visualisations, but it can also be leveraged for image processing. Although it does not support all the file formats, Matplotlib is effective in altering images for extracting information out of it.

Outlook

Image and video processing techniques are rapidly being adopted across the globe due to its many use cases. More recently, Indian Railways is using facial recognition for identifying criminals. Besides, it has also become an integral part of data science and artificial intelligence workflow for gathering information out of images or videos. 

While we have compiled a few most widely used libraries, there are numerous others in the technology marketplace that can be used for specific requirements. Therefore, you should identify your needs and based on that you can determine the best-fit image processing library.

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It can be hard to know how much we should care about our digital footprint – but like it or not, most of us have one.

From the first check of the phone in the morning, to the ride in to work, to lunchtime swiping of loyalty cards, we leave a trail of information behind us as we go about our lives.

Our movements, online activity and spending habits all paint a picture of who we are and – of special interest to marketers – what we buy.

Data collection can be a scary thought to some; a digital dystopia where our private information is collected without us really noticing and sold on.

But then again, maybe it’s a good thing.

Stuff journalist Katie Kenny, who reports on digital trends, says data collection has pros and cons.

 “It can make society a much fairer place if Governments are making really data-driven decisions around infrastructure and allocation of resources and things like that.

“On an individual level, there are conveniences. Even something as simple as a discount is an incentive to sign up for a loyalty club.

“The way that ads will pop up that are tailored to your previous browsing habits; some people think it’s creepy, some people think it’s helpful… but it can make life easier.”

But Kenny says the downside is that we run the risk of people knowing too much about us.

“At the extreme end, are we heading towards a surveillance society?

“We are very much in a society that I wouldn’t say is zero-privacy, but there’s certainly not a lot of privacy.”

While data collection is far from a new thing, Stats NZ’s latest information venture is looking to shake up the data game by accessing information held by phone companies.

Data Ventures is a commercial start-up from within Stats NZ. Its first project, Population Density, measures what the name suggests: how many people are in one place, at one time.

It does this by taking information provided by telcos from cell phone towers, and then compiles that information to show where all the people are.

Then that information’s sold on – initially, back to Government departments, but Data Ventures isn’t ruling out venturing into the private data market, either.

Kenny says that information will be used to plan for tourism peaks and troughs, and emergency planning too.

“You can use a big event like a rugby game, where you have 70,000 people gathered in open space, and then suddenly they’re leaving – this data would show hour by hour which highways are getting overloaded … and how quickly they’re able to get out of a central city area.  

The project’s attracted few privacy concerns. The Privacy Commissioner, Government ministers and phone providers Spark and Vodafone are all confident the anonymised nature of the data means no individual can be identified.

“It’s not the same as Google data, or other movement data, because it doesn’t track movements from A to B, it just provides a map of the number of people in a portion of a country,” says Kenny.

Not everyone is comfortable with it however.

A notable exception to this data train is 2 Degrees, which has opted out of the Population Density project over potential privacy concerns with future projects.

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Google regularly gets voted as the best company to work for in USA – its employees get generous paid holidays, free food and are even encouraged to take power naps during the work day in those ‘nap pods’. Google has been providing an excellent workplace atmosphere to its staff – not because they are lovely people. As with everything they do, these decisions are purely backed by data – data showed that treating their employees like this would improve employee satisfaction and ultimately their bottom lines.

Although most businesses have adopted big data as a critical component of their customer experience enhancement and market analytics practices, there are more applications that are being overlooked by many. Big data can effectively be used to improve employee management in an organization which would ultimately improve their operational efficiency and employee retention rates among countless other benefits.

Most companies still use the traditional method of measuring employee performance which uses the key performance indicators (KPI). Although this method may still work to an extent, it does not factor in some of the crucial points like the motivation level, derived work satisfaction and the actual potential of the employee. Tying the organization’s long-term HR goals to certain data points that can be tracked from the employees would be the first step towards innovating employee management. Let’s see how this can be achieved.

Don’t just collect data, listen

Although organizations use employee surveys with good intentions, this process is riddled with deficiencies. For one thing, getting access to the real data is always better than data provided via surveys as it tends to be less reliable.

Just like listening to the customer isn’t a one-time thing, listening to your employees shouldn’t be limited to an employee satisfaction survey. The data is never static, which means your data gathering pursuit should be a never-ending one. It helps to make your employees actively participate in the discussion. The prime focus should be on gathering multiple data points from different internal departments which should give you the bigger picture. It should also be noted that there’s a thin line between employee data collection and privacy intrusion. The data collection activities should be ethical and within the limits.

Make the KPIs Employee-Centric

The KPIs being used by high-level executives fail to address some of the key factors that affect employee performance like the satisfaction level and whether or not the employee is incentivized enough to make things happen. Simply put, you should make the KPIs more employee centric and also make sure to include them in the process. If long-term metrics like tenure and retention rates are what you seek, it’s important to define a list of KPIs that factor in employee satisfaction and motivation.

You can begin by quizzing them about what they consider to be the indicators of motivation and satisfaction and then incorporate the responses into your metrics to track. Considering the cost and challenges associated with hiring and training, ignoring this would be a big mistake.

Measure, Recalibrate and Adjust

It’s important to revisit your HR objectives once in a while and check if the data backed employee management strategy is helping you improve the retention rates and employee performance. It’s better to tie the employee KPIs into your HR objectives as it helps assess the progress better. The review periods will also help you stay on track or recalibrate when needed.

Collect the data, review and analyze it and make the necessary adjustments on a continuous basis. Making big data work internally will take exactly the same commitment as it would externally. It’s understandable if you track the wrong data in the initial stages, since employee metrics to be tracked can vary across industries and organizations, it’s tough to establish a standard set of metrics here. You will have to go via the trial and error method to find out what metrics give you the most accurate results in terms of employee management.

Intel used the same approach in their organization and found out certain attributes that make employees leave or stay loyal to reduce attrition by 20 percent during a six-month trial, according to The Wall Street Journal.

Filling the gaps

Data collection, as we discussed above is a continuous pursuit. As you collect data, it’s important to revisit the collected data in order to evaluate its quality. This should be followed by filling the gaps in your big data funnel. Analysis is the final and most crucial step in the process. It’s important to be unbiased while deriving insights from the data collected as this could skew your results and lead you to bad decisions.

Conclusion

Gathering data from employees is a controversial topic and many employees might resent this level of analysis of their activities. But the issue can be resolved by implementing it the right way. In fact, there are many less provocative uses for employee data collection and analysis.

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