## Image processing and data analysis

However, our eyes do not work like this. Instead, our eyes perceive double the amount of light as only a fraction brighter. Thus, while a digital camera has a linear relationship between brightness our eyes have a non-linear relationship. In order to account for this relationship, we apply gamma correction.

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Now, depending on the resolution and size of the image, it will see a 32 x 32 x 3 array of numbers where the 3 refers to RGB values or channels. The representative array will be x x 3. Each of these numbers is given a value from 0 to which describes the pixel intensity at that point. Like we mentioned before, the input is a 32 x 32 x 3 array of pixel values.

## Basic Image Data Analysis Using Python: Part 1

Now, the best way to explain a convolution is to imagine a flashlight that is shining over the top left of the image. Now, this filter is also an array of numbers where the numbers are called weights or parameters. A very important note is that the depth of this filter has to be the same as the depth of the input, so the dimensions of this filter are 3 x 3 x 3.

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• Basic Image Data Analysis Using Python – Part 3.

These multiplications are all summed up. So now we have a single number. Remember, this number is just representative of when the filter is at the top left of the image. Now, we repeat this process for every location on the input volume. Every unique location on the input volume produces a number. We can also choose stride or the step size 2 or more, but we have to care whether it will fit or not on the input image. The reason we get a 30 x 30 array is that there are different locations that a 3 x 3 filter can fit on a 32 x 32 input image.

These numbers are mapped to a 30 x 30 array. We can calculate the convolved image by following:. Do this using the scale function. To make the resulting array values near the center of array to be close to 1 and closer to the edges of the array are values closer to be 0, subtract the previous array from 1.

Test your functions in the main function, which should create, using matplotlib, a figure that has three subfigures stacked vertically. On top the original painting. Although this method we used was very simple, it could still be used for example to automatically count number of birds or stars in an image. Of course, humans can do this easily, but when there are hundreds or thousands of images, then it is better to use machines to do this mechanical work.

The are several libraries written in Python that allow easy processing of images. Few examples of these:. Data analysis with Python - Summer Number of clusters is Few examples of these: pillow scikit-image In Scipy there is the subpackage ndimage that also contains routines for processing images. Here, we'll observe some of the following, which is very basic fundamental image data analysis with Numpy and some concern Python pacakges, like imageio , matplotlib etc.

The shape of the ndarray shows that it is a three-layered matrix.

## Image Processing and Text-Audio-Video Analytics - Services

The first two numbers here are length and width, and the third number i. So, if we calculate the size of an RGB image, the total size will be counted as height x width x 3. These values are important to verify since the eight-bit color intensity cannot be outside of the 0 to range. Now, using the picture assigned variable, we can also access any particular pixel value of an image and can further access each RGB channel separately.

Now, we could have also selected one of these numbers specifically by giving the index value of these three channels. Now we know for this:. Now, we can also able to change the number of RGB values. Now, we know that each pixel of the image is represented by three integers. Splitting the image into separate color components is just a matter of pulling out the correct slice of the image array. Black and white images are stored in 2-Dimensional arrays. Now, Greyscaling is a process by which an image is converted from a full color to shades of grey.

There are a couple of ways to do this in python to convert an image to grayscale , but a straightforward way of using matplotlib is to take the weighted mean of the RGB value of original image using this formula. However, the GIMP converting color to grayscale image software has three algorithms to do the task.

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We can create a bullion ndarray in the same size by using a logical operator. Now as we said, a host variable is not traditionally used, but I refer it because it behaves. It just holds the True value and nothing else. We generated that low-value filter using a global comparison operator for all the values less than Image masking is an image processing technique that is used to remove the background from which photographs those have fuzzy edges, transparent or hair portions.

Like many other visualizations, the colors in each RGB layer mean something.