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1. Getting Started with PIL and OpenCV
Firstly, it is significant to understand how images work in the natural world and how they are perceived by computers to process and analyze these digital visuals. All images are interpreted in the format of 0’s and a range until 255’s. The format of colored images is in the form of RGB, where a value is interpreted in a three-dimensional array. Similarly, for grayscale images, we only have two spectrums consisting of white and black counterparts.
The Python Imaging Library (PIL) is one of the main methods to add image processing capabilities to your Python interpreter. Thanks to this library which provides extensive file format support, you can perform most tasks efficiently. It has an effective internal representation and fairly powerful image processing capabilities. The overall core image library is designed for the purpose of having faster access to data elements stored in a few basic pixel formats. Hence, this library is a great starting point because it provides a solid foundation for the users with an accessible, general image processing tool (check documentation link provided below for more information).
Below is a simple code block to understand some of the basic features of the PIL library.
# Importing the required libraries
import numpy as np
from PIL import Image
import PIL# Opening and analyzing an image
image1 = Image.open('Red.png')
For further experimentation and understanding of the pillow library, I would recommend checking out the official documentation and experimenting with more images and modules available to you with this tool.
The next library to learn to create wonderful projects is with the help of the open-cv computer vision library. Once you are familiar with the pillow library, you can start experimenting with your knowledge of these images with the help of the cv2 library. With the help of this tool, you can manipulate images, performing resizing by changing their dimensions, convert their colors from one format to another, and so much more. It is worth exploring from scratch and gaining the most knowledge that you can out of this library.
If you are interested in learning most of the essential aspects of computer vision from scratch, along with all the respective codes to solve some complex tasks, I would recommend all of you check out the article provided below. It covers most of the essentials required for beginners to get started with computer vision and eventually master it.
2. Image Based Attendance System
The traditional method of raising your hand in a classroom to say “present ma’am” or “yes ma’am” or whatever other things you would say is kind of fading away. With the introduction of online classes where students and teachers interact through an online platform, it would be harder to take attendance in the more traditional way. However, computer vision comes to the rescue to help us create an image-based attendance system for taking attendance online with the help of your pixelated pictures!
Let us discuss some methodologies in which you could potentially approach this problem. One classic method is to ensure that you have a few images of all the respective students and classmates. If you cannot encompass a larger dataset, you can use methods of data augmentation to increase the amount of data that you have stored. Once you are able to collect a decent number of datasets for this particular task, you can process these images and build a deep learning model for achieving top-notch results.
If you are interested in exploring the theoretical aspects related to the task of the Image-Based Attendance System, then the Research paper should be a fantastic starting point for you to explore more theoretical knowledge and understanding of the concept. However, if you are more so interested in the practical coding implementation of the procedure, then this article guide should help you as a reference for implementing your own solutions as well.
3. Face Mask Detection
During the time of this pandemic, there are some strict regulations that need to be followed to maintain the decorum of the city, state, or country. Since we can’t always have the official authority on the lookout for some people not abiding by the rules, we can construct a face mask detection project that will enable us to figure out if a particular person is wearing a mask or not. During this time, with strict regulations of the lockdown, it would be a brilliant idea to implement this project to contribute to the upkeeping of the society.
Hence, a project in which you can process images of an entire area or region by tracking people on the road or streets to analyze if they are wearing masks or not would be a spectacular idea. With the help of image processing algorithms and deep learning techniques, you can compute images of people who are wearing masks. The following Kaggle dataset for face mask detection would be a great starting point to analyze the training images for achieving an overall high accuracy.
One of the best ways to approach this problem would be to make use of transfer learning models such as VGG-16, face-net, RESNET-50, and other similar architectures to see what method helps you to achieve the best results. As a starting point, I would highly recommend checking out one of my previous articles on smart face lock systems, where we construct some high-level face recognition systems. You can use a similar method for faces with no mask and faces with a mask to solve this type of task.
4. Number Plate Recognition
One of the best projects to work with alphanumeric character identification is with the help of number plate images. There are several methods that we can employ to solve the problems that have letters, digits, and numbers embedded in images. We can use deep learning techniques, Optical character recognition (OCR) technologies, a combination of image processing and natural language processing (NLP), computer vision methods, and so much more.
The vast methodologies in which you can approach this problem provide you with the opportunity to explore all these methods by yourself with the models you develop. Finding out what technique will help you achieve the best results is rather intriguing. With a deep learning approach, you can collect the required datasets and information from Kaggle for the Vehicle Number Plate Detection. Once you collect enough information, you can build your own custom models or use transfer learning models to see what gives you the desired results.
If you want to use a more unique approach to solve problems, It is recommended that you check out one of my previous articles on optical character recognition (OCR). Using the OCR technology, you can interpret most of the data present in an image with relative ease. The OCR engine tries to analyze the characters in the image and find the appropriate solutions. To learn more about this topic in detail, check out the link provided below. You can also try out other unique methods to see which technique yields the best results.
5. Medical Image Segmentations
One of the most significant contributions of image processing, computer vision, machine learning, and deep learning is in the medical field. They contribute to analyzing and visualizing many of the highly complex abnormalities that could occur in human beings. Tasks such as diabetic retinopathy, cancer detections, x-ray analysis, and other crucial medical processing tasks require the use of deep learning models with image processing for highly accurate results.
While most projects require high accuracy of prediction, this statement becomes much more critical in the tasks of image segmentation in the medical field. From the time of biomedical image segmentation in 2015 with the U-Net architecture, there have been more variations of this architecture as well as many different types of models that are continuously being constructed for obtaining the best possible results in every scenario.
One of the best places to receive images and video files for any task related to medical image segmentation can be obtained from the DICOM library. By accessing this link, you will be directed to a section where you can download medical images and videos for performing scientific computations.
You can also utilize the Diabetic Retinopathy dataset from Kaggle to get started with a popular challenge on computing the image segmentation of the eyes as well as detecting if a person suffers from a condition of eyes. Apart from the tasks mentioned above, there are tons of biomedical image processing and tasks that are available at your disposal. Feel free to test them out and experiment with them.
6. Emotion and Gesture Recognition
Looking at the above image, one might wonder what that particular hand sign could be classified as. There are several gestures that people throw out as a form of communication. With the help of the appropriate images, one can figure out the best methods of classifying the gestures accordingly. Similarly, you might want to figure out the emotions on one face. Whether the person shows signs of happiness, sadness, anger, or any other similar emotion, you can build an AI model that will perform the following classification.
Emotions and gestures are integral parts of human activities. Albeit a bit harder in comparison to some of the other projects mentioned in this article, we can construct a computer vision and deep learning model to perform the following task. To approach this problem, you can make use of the facial emotions recognition ( Kaggle’s fer2013 dataset) for emotions classification and the American sign language ( ASL Alphabet dataset) for performing the computation of gestures.
Once we have all the required datasets, you can construct your deep learning architectures with the help of computer vision for the implementation of these projects. With the combination of neural networks and image processing, you can start working on both emotions and gesture detection to get high-quality results with decent losses and accuracy.
The links provided below are two of the best guides in which you can perform the activity of human emotion and gesture recognition from scratch. I have covered almost every single aspect required for the perfect computation of these tasks, including the pre-processing of datasets, visualization of the data, and the construction of the architecture from scratch. Feel free to refer to them to obtain the best possible information on performing these tasks.
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