Cara menggunakan downsample image python

It is thus no doubt that pictures play an important part in our communications—not just general photographs, but also specialized images like MRIs or ultrasounds.

We can obtain photos through different acquisition devices. For instance, melanoma (skin cancer) images are retrieved using a dermatoscope. We take photos of ourselves or friends using a digital camera or a smartphone. Sometimes, however, we notice some issues in our pictures, like blurring for instance, which may be due to the acquisition device used.

But, what to do in this case? You were sent some medical images to analyze, and you don't have the choice of retaking such images. Even if you retook an image, the resolution you see will not change, nor any other issues you face. Image processing comes into play in such situations.

image processing: The analysis and manipulation of a digitized image, especially in order to improve its quality. — Oxford Dictionaries

"Digitized image" here refers to the fact that the image is processed by a computer. Getting the computer in this game means using a programming language.

In this tutorial I will show you how we can use the Python programming language to perform image-processing tasks on an image.

scikit-image

The library we are going to use in order to carry out our image-processing tasks is

from skimage import io

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators.

The first thing we need to do is install

from skimage import io

As

from skimage import io

A package management system used to install and manage software packages written in Python. Many packages can be found in the Python Package Index (PyPI).

You can follow the steps mentioned in the Python Packaging User Guide for installing

from skimage import io

from skimage import io

from skimage import io

from skimage import io

from skimage import io

from skimage import io

We now have the library installed and ready for some image processing fun!

The test image we will be using in this tutorial is a pizzeria illustration. Go ahead and download it, or simply use the image of your choice. The image looks as follows:

Dimensions of an Image

Sometimes we need to know the dimensions of an image (more on that in the filtering section). Once we have loaded the image into our memory from a file using the

from skimage import io

from skimage import io

The reason this technique works is because images in the

from skimage import io

from skimage import io

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from skimage import io

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img = io.imread('baboon.png')

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# Outputs: (512, 512, 3)

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print(img.shape)

from skimage import io

from skimage import io

from skimage import io

from skimage import io

from skimage import io

The shape attribute gives us a tuple where the first element is the height of the image, the second element is the width of the image, and the third element represents the number of channels. In our case, the baboon.png image has three channels for red, green, and blue values so we got the value 3.

Here is an example which loads another image:

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print(img.shape)

from skimage import io

from skimage import io

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from skimage import io

You can also load your images as grayscale by setting the value of the second parameter

from skimage import io

from skimage import io

from skimage import io

from skimage import io

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print(img.shape)

from skimage import io

from skimage import io

img = io.imread('baboon.png')

from skimage import io

from skimage import io

Manipulating Individual Pixels

You can easily modify the individual pixels of any images loaded using the scikit-image library. There are a few conventions that should be kept in mind.

When directly accessing the pixels of an image, the first value indicates the row number, and the second value indicates the column number. The origin or the position that corresponds to

from skimage import io

from skimage import io

It is also possible to modify a set of pixels together. Here is an example that adds a red border to our image.

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# Outputs: (512, 512, 3)

# Outputs: (512, 512, 3)

# Outputs: (512, 512, 3)

This is the result:

Color to Grayscale

In this section, we would like to convert the original colored pizzeria image into a grayscale 2D image (black and white). This can be simply done using the following script:

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# Outputs: (512, 512, 3)

We simply passed

from skimage import io

from skimage import io

from skimage import io

The

from skimage import io

Another way to make an image grayscale is with the help of the

from skimage import io

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Here is the Python code that creates the grayscale image:

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print(img.shape)

In order to show the new grayscale image, add the following to the end of the script:

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print(img.shape)

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print(img.shape)

The result looks like this:

Applying a Filter to an Image

In image processing, filtering is performed to make some enhancements to the image. In general, filtering encompasses the following operations: edge enhancement, sharpening, and smoothing.

In this section, I'm going to show you how we can apply the Sobel filter to our image and see what the output looks like after performing such an operation.

The script for applying the Sobel filter on our image looks as follows:

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print(img.shape)

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from skimage import io

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from skimage import io

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from skimage import io

You will most probably get a warning while trying to execute the above script. We couldn't apply the operation since the image has to be a 2D image. One solution to this problem is to use a second parameter and set

from skimage import io

from skimage import io

There are many other filters that you can apply, such as the gaussian filter for blurring. It accepts many parameters, with the first one being the source image and the second one being the standard deviation for the gaussian filter. You can either pass a single value or a sequence of values (one for each axis). Here are two examples:

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from skimage import io

Here is the result of applying the Gaussian filter with a standard deviation of 10 to the pizzeria image:

Here is the result of applying the Gaussian filter with a standard deviation of 20 and 1 for the vertical and horizontal axes:

Now, let's see how we can apply the threshold filter to our image. First, we calculate the threshold value based on the mean of all the grayscale values in our image by using the

from skimage import io

from skimage import io

from skimage import io

from skimage import io

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from skimage import io

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from skimage import io

The above code produces the following result for our image:

Conclusion

There are many image-processing operations, and the

from skimage import io

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