Computer Vision Made Simple: Latest Trends and Python Tools

AI has given us quite a few things to be grateful about and computer vision is one of them. Computer vision works as a bridge between machines and human perception. Hence, opening the doors for endless creative opportunities across the industries. Grasping the correct concepts of computer vision and getting hold of some really good libraries will be the perfect kickstart. From analyzing medical images till building a self-driving car, everything is worth the effort. So, let’s learn a little, install some libraries and step into the world of computer vision.

See Like a Machine, Think Like a Human

Humans heavily rely on their vision to interact with the world hence enabling machines to do the same has transformative potential. By allowing computers to process and understand visual information, computer vision powers innovations in automation, safety, and efficiency across industries like healthcare, automotive, entertainment, and retail. The core of computer vision answers the following question – What is in this image or video? What actions are taking place?

All in all computer vision enables machines to read and interpret visual data. This entire process, therefore, is a structured workflow that involves several steps; moreover, each step is pivotal in converting the raw visual inputs into meaningful insights

1. Image Acquisition

The primary step is to collect visual data using cameras, sensors, or any other devices. Now this can take various forms-

Image- A single photo or frame, like a passport photo or maybe an image of a moving car captured by a traffic CCTV.

Videos- Any continuous stream of frames as those of a surveillance footage or live feed.

Other visual data sources can be from devices like depth sensors or infrared cameras.

2. Preprocessing

Just like any other data that is collected in raw form, these visual data also contain noise, imperfections and irrelevant information. Hence, removing these makes preprocessing a crucial step in the entire ladder. Once preprocessing is done we can be sure that the data is clean, consistent and ready to use. Preprocessing is done by

Resizing: Changing the size of the image to fit the needs of algorithms or models.

Denoising: Eliminating unwanted noise, like rough textures or unclear images.

Adjusting colour: Turning images into black and white or boosting brightness and contrast for clearer viewing.

Normalization: Aligning pixel values to enhance how well the model works. Also referred to as Standardization.

Say, suppose, in License plate recognition systems, preprocessing may involve- adjusting brightness and contrast of the image to make the text on the plate more readable. Mostly used when we have low-light conditions.

3. Feature Extraction

In this stage, the system finds important features of the image that help tell apart different objects or patterns. This simplifies the data while keeping useful information. Common methods include:

Edge Detection: Finding the edges or borders of objects in the image. Two of the most prominent techniques to do edge detection are – Canny and Sobel Detection.

Texture Analysis: Identifying patterns or surfaces, like smooth versus rough textures.

Colour Features: Spotting main colours or colour patterns in an image. For instance, in a security system, feature extraction might concentrate on the shapes of objects to tell a person from a car.

Normalization: Adjusting pixel values to enhance model performance.

4. Analysis and Decision Making

The system analyzes the visual data and uses that information to make choices or produce results based on the task it is working on. When the system detects and recognizes a vehicle’s license plate, it can check it against a database of registered plates. This helps identify the owner, spot unauthorized vehicles, or confirm access to restricted areas.

For instance, in a parking lot, if the system recognizes a plate linked to an authorized vehicle, it may open the gate for entry. If the plate is not recognized or is marked as restricted, the system could send an alert or block access. To make this concept even more clear, take a peek at the below images –

Trending Applications Of Computer Vision

Facial recognition is a specialized type of object detection that aims to identify and verify individuals by analyzing their distinct facial characteristics. By pinpointing facial landmarks and matching them against a database, computer vision technologies can effectively recognize individuals. Facial recognition can be observed in technologies like Unlocking smartphones or devices using facial authentication. Improving security at airports, workplaces, or events by recognizing individuals. Social media platforms utilize facial recognition to recommend tags or categorize photos based on the people in them.

In the realm of healthcare, computer vision plays a crucial role in identifying diseases and irregularities by examining Medical images such as X-rays, MRIs, and CT scans. This technology facilitates quicker and more precise diagnoses, significantly minimizing the chances of human error. Identifying fractures, tumours, or lesions within medical scans. Tracking the progression of diseases, like spotting diabetic retinopathy in eye images. Supporting radiologists by pinpointing areas of concern for additional investigation.

Object detection is the process of recognizing and pinpointing particular objects in images or videos. It extends beyond simple classification by also establishing the location and outlines of these objects, frequently employing methods such as bounding boxes. Example Applications: In security systems, object detection plays a crucial role in spotting suspicious objects or unauthorized individuals. In traffic management, it identifies vehicles, pedestrians, and traffic signals to help monitor congestion and enforce regulations.

Computer Vision Essentials: Must have Python Tools

1. OpenCV

OpenCV, or Open Source Computer Vision Library, is a robust and extensively adopted library designed for real-time computer vision and image processing applications. It thus facilitates a variety of tasks, including image filtering, edge detection, object detection, and video analysis.

Notable Features: – Analysis of images and videos, including capabilities such as face detection and object tracking. Along with this OpenCV also provides Preprocessing functionalities, which encompass resizing, denoising, and color adjustments. Seamless integration with deep learning models is yet another usage of this wonderful library.

OpenCV can be installed using the following command:

pip install opencv-python

To add features of OpenCV like GUI support, install:

pip install opencv-python-headless

For official documentation and installation process of OpenCV refer here – OpenCV

2. TensorFlow

TensorFlow, a library for machine learning and deep learning, was created by Google and is extensively utilized for the training and implementation of computer vision models, especially neural networks such as Convolutional Neural Networks (CNNs).

It is efficient in handling large datasets and complex models. Apart from this, Pre-trained models are available through TensorFlow Hub for various tasks of Image Classification and Object-Detection.

The installation requires the following command:

pip install tensorflow

For GPU support (NVIDIA CUDA required), you may use:

pip install tensorflow[and-cuda]

Follow the official documentation and procedure of installation here – TensorFlow

3. PyTorch

PyTorch is an open-source deep-learning framework created by Facebook. Renowned for its dynamic computational graph and adaptability, PyTorch has gained significant popularity in both research and practical applications within the field of computer vision.

Key Features of PyTorch include –

• Adaptable model development and troubleshooting capabilities.
• Frameworks such as TorchVision offer access to pre-trained models and tools for image transformation.
• Robust community backing for various computer vision applications, including image segmentation and object detection.

Installation command for PyTorch:

pip install torch torchvision torchaudio

Follow the official documentation and procedure of installation here – PyTorch

4. scikit-image

Scikit-image is a streamlined and user-friendly library for image processing, developed as an extension of SciPy. It offers a range of tools for fundamental image manipulation and analysis tasks. It allows user to tackle tasks such as – Image segmentation, filtering, and transformations.

scikit also has tools for edge detection, object counting, and feature extraction. If a project requires preprocessing and small-scale computer vision tasks then this is the best library to pick.

Install scikit-image using:

pip install scikit-image

Follow the official documentation and procedure of installation here – scikit-image

The tools OpenCV, TensorFlow, PyTorch, and scikit-image play a crucial role in a wide array of computer vision applications, encompassing simple image processing to sophisticated deep learning tasks. Unique advantages are possessed by each of these libraries and can be readily installed for seamless incorporation into Python-based workflows. Check out our complete guide on another major Python tool – Pandas

Conclusion

Visual information interpretation is transformed by computer vision, a vital area of artificial intelligence. Its applications, ranging from facial recognition and medical imaging to autonomous vehicle navigation, impact various sectors significantly. The process involves stages such as image acquisition, preprocessing, feature extraction, interpretation, and decision-making, all enhanced by deep learning and machine learning techniques.

Tools like OpenCV for image processing, TensorFlow and PyTorch for deep learning, and scikit-image for image manipulation empower developers in Python to create innovative solutions. The future of computer vision holds immense potential as trends like edge AI and real-time video processing emerge. Ultimately, the interpretation of environments and enhancement of human capabilities in remarkable ways are enabled, going beyond mere “seeing” by machines.