Computer Vision

OpenCV is a library for image processing and computer vision that can be used to resize images. Resizing images using OpenCV can be useful in a number of ways, some of which include:  

• Image compression: to reduce the file size of an image.  
• Image processing: as a pre-processing step in image processing algorithms, such as object detection, segmentation, and feature extraction.  
• Computer vision: to adjust the resolution of an image to match the requirements of a computer vision algorithm, such as object detection or image recognition.  
• Data augmentation: as a data augmentation technique to increase the diversity of the training data, which can improve the performance of machine learning models  
• Printing: To adjust the resolution of an image to match the requirements of a printing device.  
• Video editing: To adjust the resolution of an image to match the requirements of video editing software.  

Here is how you can print coloured text in Terminal:  

Preview of the output that you will get on running this code in your ide

Code

In this solution we have used Imread function in python,

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Modify the name, location of the image to be read in the code.
3. Run the file to resize the image.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for " Re-Size the image in Open Cv using python" in kandi. You can try any such use case!

Environment Tested

I have tested this solution in the following version. Be mindful of changes when working with other versions.

1. This solution is created and executed in Python 3.7.15 version
2. This solution is tested in Opencv 4.6.0 version

Using this solution we able to re size the image in python with the help of OpenCv library with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us resize an image in Python.

Dependent Library

If you don't have the opencv library which is required to run this code, click the above link and install opencv and copying the pip install command from the OpenCv page in Kandi. You can search for any dependent library like Opencv using kandi.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

C++ is the main programming language used in embedded devices, and if you use embedded operating systems such as Linux and Android, C++ will be one of the most common languages used to program these devices. Due to this, C++ is ideal for a wide variety of applications, from web servers and mobile apps to machine learning, image processing and computer vision. There are several popular open source C++ Computer Vision libraries available for developers: Cvui - A simple UI lib built on top of OpenCV drawing primitives; Superviseddescent - generic algorithm to perform optimisation of arbitrary functions. The library contains an implementation of the Robust Cascaded Regression facial landmark detection and features a pre-trained detection model; AutoAnnotationTool - label tool aim to reduce semantic segmentation label time. The following is a list of the most popular libraries.

Java Computer Vision libraries are intended for developing applications that provide image and video processing. In other words, these Java Computer Vision libraries allow you to access the camera of the device and use it as an input for different projects. The Java Computer Vision libraries are used for visual inspection systems, medical imaging platforms, document analysis, face identification systems, motion detection platforms, and many more. Here are some Java Computer Vision libraries: BoofCV - written from scratch for real-time computer vision and robotics applications in the Java programming language. It has no external dependencies other than a few basic libraries (Java 8+, Apache Commons, JUnit 4+) and can run on desktop, Android, and embedded systems. Opencv-processing - Creative coding computer vision library based on the official OpenCV Java API. The following is a list of the most popular libraries.

Go Computer Vision is a relatively new technology, first releasing in the 1990s. It's an artificial intelligence technology that lets users take images and turn them into data. This is accomplished by giving the computer system what's called "image recognition" abilities. The fundamental idea behind computer vision is that computers can help people accomplish tasks that would otherwise be impossible. Go Computer Vision has numerous applications, including: Self-driving cars, Object detection and tracking, Image classification, Robotics, Medical image analysis and more... There are several popular open source libraries available for developers: Gocv - Go package for computer vision using OpenCV 4 and beyond; Onepanel - open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises. The following is a comprehensive list of the best open source libraries.

Computer vision is a discipline within computer science that focuses on how computers can gain an understanding of the visual world. Typically, this involves capturing image data from a camera and using specialized algorithms to extract information about objects, locations, and people involved in an image. This can range from detecting whether a face is smiling or frowning to identifying what kind of car is parked in front of a building. Computer vision has applications in many fields including medicine, entertainment, law enforcement, and more—and C# is one of the most popular programming languages used for computer vision. Some of the most popular among developers are: Opencvsharp - Provides functions for converting from Mat into Bitmap(GDI+) or WriteableBitmap(WPF); OptiKey - An on-screen keyboard that is designed to help Motor Neuron Disease (MND) patients interact with Windows computers; OpenCV - ComputerVision Demos. The entire list of open source C# Computer Vision libraries are provided below.

Computer vision is a field of computer science that aims to process digital images with the help of a computer. Computer vision has been used for various purposes including processing medical images and performing image recognition tasks in applications. Computer vision is used to analyze and automate various types of visual content by extracting data from real-world images. Image analysis may include image classification, object detection, and other image processing tasks such as noise reduction, blurring, etc. Popular JavaScript open source libraries include: JS-objectdetect - computer vision in your browser - javascript real-time object detection; Imageprocessing-labs - computer vision, image processing, and machine learning on the web browser or node; speedy-vision - GPU-accelerated Computer Vision for JavaScript. Full list of the best open source libraries below.

Python programming language employs NumPy arrays in order to achieve image processing and computer vision tasks in the applications of artificial intelligence. The gamut of computer vision tasks can be executed efficiently with Python's scikit image processing libraries that work in tandem with NumPy arrays to produce high-quality filtered images. In fact, a Python imaging library can be used to accomplish complex tasks such as feature extraction, face recognition, and image manipulation. In addition, the scikit learn library in Python provides deep learning modules to employ in business operations (for example, for consumer satisfaction analysis).

Check out these libraries for Python computer vision to create a state-of-the-art code to deploy in data science fields. The face_recognition library touts to be the simplest API in the world for Python and command-line. If you are aiming to achieve image-to-image translation, check out the pytorch-CycleGAN-and-pix2pix library. One of the highest utility libraries in the list is perhaps the EasyOCR - a code pack for optical character recognition in over 80 languages. If machine learning is your objective, the NumPy_ML library carries pre-coded modules to insert into your code.

Python object detection libraries are used for detecting an object in an image. It is a computer vision library that can detect the objects present in images and videos. The Python object detection libraries can be used to build a machine learning model for detecting objects in the images or videos. One of the best in class, Detectron, is Facebook AI Research’s software system that performs object detection with various state-of-the-art machine learning algorithms like Mask R-CNN. It is powered by the Caffe2 deep learning framework with the goal to provide a high-quality codebase for object detection research. Pillow or PIL is another open-source Python library for image processing. With it, you can read, rescale, and save images in different formats. Part of the OpenMMLab project, MMDetection is a PyTorch-based object detection toolbox. The following is a comprehensive list of the best open-source libraries that you can use for object detection:

Face detection and recognition is one of the most revolutionary technologies of the 21st century. We have deployed them on phones for user authentication, digital cameras for auto-focus, and webcams for a quick passport-size photograph, and now, it’s possible to recognize any specific face from the crowd. But this isn’t as straightforward as it sounds. So, professional programmers have developed various AI-powered Python libraries which we can use to develop our own face recognition software.

Face recognition is a type of computer vision that uses optical input to analyse an image—in this case, it looks particularly at faces that appear in the image. Facial recognition technology can be used as a building block to support other capabilities like face identification.

Face recognition software can support many practical uses, both for businesses and for users at home.

Face recognition is a form of artificial intelligence (AI) that mimics a human capability to recognise human faces. Just like when a human recognises a face, facial recognition software captures facial features and creates a pattern of facial features which it uses to identify or group a face.

Libraries useful for this solution

Development Environment

Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments

Kit Solution Source

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .

Use the open source, cloud APIs, or public libraries listed in your application development based on your technology preferences, such as primary language. The list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications.

OpenCV (Open Source Computer Vision) is free under the open-source BSD license. This library is used for face detection, object detection, motion estimation, image recognition, segmentation, and others. OpenCV was designed for computational efficiency and strongly focused on real-time applications. 

You can use the Pillow library to convert an OpenCV image to a PIL image in Python. The process involves using the tostring method in OpenCV to get the image as an array of bytes and then using the image.frombuffer method in Pillow to create the PIL image from the array. 

• PIL (Python Imaging Library) is an open-source library for image processing tasks that require sophisticated file format support, an efficient internal representation, and powerful image manipulation capabilities. 

Here is an example of how an OpenCV image can be converted to PIL. 

Fig 1: Preview of the output that you will get on running this code from your IDE

Code

In this solution, we use the cvtColor Function of the Opencv library

Instructions

Follow the steps carefully to get the output easily.

1. Install Jupyter Notebook on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install numpy - pip install numpy.
4. Install opencv - pip install opencv.
5. Install pillow - pip install pillow.
6. Copy the code using the "Copy" button above, and paste it into your IDE's Python file.
7. Add "im_pil.show()' in last to view the output.
8. Use '/' instead of '\' in the image path or add 'r' as prefix to the path like (r"path").
9. Run the file to convert the OpenCV image into PIL image.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to convert an OpenCV image to PIL image in Python?" in kandi. You can try any such use case!

Dependent Libraries

If you do not have OpenCV or numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV.

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.9.6
2. The solution is tested on OpenCV-Python 4.7.0.72 version.

Using this solution, we are able to Convert OpenCV images in PIL image format using the OpenCV and numpy libraries in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Denoising colored images remove unwanted noise or artifacts from an image to improve its visual quality or make it more suitable for further analysis or processing. In computer vision and image processing, noise can arise from various sources, such as image sensors, transmission or storage errors, or digital processing algorithms. 

OpenCV is a popular open-source computer vision library with many functions and methods to denoise colored images in Python. Some of the most commonly used techniques for denoising include: 

• Bilateral filtering: a non-linear filtering method that preserves edges while smoothing noise by applying a weighted average of nearby pixels. 
• Non-local means denoising: a method that replaces the value of each pixel with a weighted average of similar pixels in the image based on their distance and similarity. 
• Median filtering: a simple but effective method that replaces every pixel with the median value of its neighboring pixels. 

OpenCV provides implementations of these methods in Python, which can be used to denoise colored images. To apply these methods, one needs first to read the image in OpenCV, then apply the denoising method of choice with appropriate parameters, and finally, display or save the denoised image. 

Denoising colored images using OpenCV in Python can help improve the quality and clarity of images, making them more suitable for various computer vision applications, such as object recognition, segmentation, or tracking. It can also help reduce the effects of noise on subsequent image analysis or processing, leading to more accurate and reliable results. 

Here is an example of how to Denoise the colored image using an open cv in Python: 

Preview of the output that you will get on running this code from your IDE

Code

In this solution we use the numPy and openCV library.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Modify the name, location of the image to be read in the code.
3. Run the file to get the Output

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to denoise the image" in kandi. You can try any such use case!

Note

Add your Image path in line 14 .

Dependent Library

If you do not have openCv and numPy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Spacy page in kandi.

You can search for any dependent library on kandi like numPy and OpenCv

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.7.15. Version
2. The solution is tested on numPy 1.21.6 Version
3. The solution is tested on OpenCV 4.6.0 Version

Using this solution, we can able to Denoise the image using python with the help of OpenCV library. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Denoise the image in python.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

OpenCV is a computer vision library written in C++ and widely used for image and video processing. It offers a range of features for working with photographs and movies, including the ability to load and save images, use filters, find edges, and find and track objects. In collaboration, applications involving image and video processing are frequently created using Python and OpenCV. This combination enables you to develop solid and adaptable programs that can address various computer vision issues.  

In our work as developers, we frequently must read and rotate the photos in our applications to complete various image processing activities, such as recognition, upload, augmentation, training, and many more. There are numerous libraries for Python that enable working with images. Python has features for manipulating, enhancing, and creating more images. In addition to using additional OpenCV functions to apply other transformations to the image, such as scaling, cropping, and applying filters, you can modify the angle of rotation and the image's size to get the desired effect.  

Here is an example of how we can draw a line beyond the second point using opencv

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the numpy and open cv library

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name and Length of the points.
4. Run the file to draw a line.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

ifound this code snippet by searching for "Draw a line in open cv and python beyond given points" in kandi. You can try any such use case!

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Environment Test

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0 version
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to draw a line beyond the second given point using the OpenCv library and numpy library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us draw a image in Python

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Converting RGB to YCbCr can provide better results for image and video compression, color space conversions, and HDR processing. There are several reasons why we might need to convert RGB to YCbCr

Compression efficiency: YCbCr provides better compression results compared to RGB, especially in preserving image quality after compression. This is because the human visual system is more sensitive to changes in brightness (luma, Y) than to changes in color (chroma, Cb and Cr). Color space conversion: Some image processing tasks, such as color correction and color space conversion, may require transforming the image from one color space to another. For example, many image sensors capture the image in the YCbCr color space, and it may be necessary to convert it to RGB for display purposes. 

OpenCV (Open Source Computer Vision Library) is an open-source and machine-learning software library. OpenCV is a computer vision library written in C++ and widely used for image and video processing. OpenCV provides a vast array of image and video processing functions that can be used in various domains such as:

• Object detection and recognition
• Image and video segmentation
• Face and feature detection
• Object tracking
• Image restoration and enhancement
• Stereoscopic vision
• Motion analysis and object tracking
• 3D reconstruction

RGB and YCbCr are color spaces used in digital image processing.

RGB stands for Blue, Green, Red, and is an encoding of the RGB (Red, Green, Blue) color space. BGR is used in computer vision and image processing applications and is the default color format for the OpenCV library in Python.

YCbCr, on the other hand, stands for Luma (Y) and Chrominance (Cb, Cr), and is a color space used in digital video processing. YCbCr separates the brightness information (luma) from the color information (chroma), which allows for more efficient compression. YCbCr is used in many image and video compression standards, such as JPEG and MPEG. In summary, BGR is used in computer vision and image processing, while YCbCr is used in video processing and compression.

In this solution, we are going to learn how to convert the RGB image to YcbCr using opencv.

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and numpy library
3. Modify the name, and location of the image in the code.
4. Run the file to get the Output

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

i found this code snippet by searching for "OpenCV Python converting color-space image to YCbCr" in kandi. You can try any such use case!

Note:-

If the user wants to Display the output use this command

cv2.imshow('after', YCrbCrImage)

cv2.waitkey(0)

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to convert BGR image to YCBCR using the OpenCv library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us convert BGR to YCBCR in Python

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

This code demonstrates how to use the OpenCV library in Python to modify a transparent image. It's useful for various applications that require the manipulation of transparent images, such as image editing software or computer vision projects. 

This code imports the cv2 (OpenCV) module, a Python wrapper for the OpenCV library, and the NumPy module, which supports multi-dimensional arrays and matrices. This code prepares the image file for further processing by loading it into a numpy array and obtaining its dimensions. The loaded image can then be modified or processed in various ways, such as resizing, cropping, or applying image filters. 

This code draws a straight line on the image result using the line() function from the cv2 module. These modules can perform various image processing tasks, such as loading, manipulating, and saving images and applying various image processing operations like filtering, thresholding, and segmentation. This code modifies the image result by adding a blue line to it. The resulting array can then be saved as a new image file or used for further image processing. 

Here is an example of how to draw a line in a transparent Image 

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name, location of the image in the code.
4. Run the file to draw a line.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "Drawing a line on PNG image OpenCV2 Python' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to draw a line in PNG image using the OpenCv library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us draw a line in a image in Python

Dependent Library

If you do not have OpenCV that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV ,numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Attempting to create a new image from an input image by swapping the positions of the rows and columns using nested loops, and then writing the resulting image to a file using the OpenCV library

OpenCV (Open Source Computer Vision) and NumPy are two powerful libraries in Python that are widely used in computer vision, image processing, and machine learning applications. Here's a brief overview of how each library can be used. OpenCV provides a variety of computer vision algorithms and functions for image and video processing.

• These functions range from basic image filtering, resizing, and rotation to advanced feature detection, object recognition, and video analysis.
• OpenCV can read and write a variety of image and video formats, making it easy to work with different types of media.
• OpenCV has interfaces for several programming languages, including

h, w, c where h, w, and c represent the height, width, and number of color channels in the new array. A nested loop is a loop inside another loop. It is a common programming construct used to iterate over multiple levels of data, such as two-dimensional arrays or matrices. cv2.imwrite is a function provided by the OpenCV library that is used to write an image to a file on a disk. The function takes two arguments: the filename of the image to be saved, and the image data to be written.

Here is the example how to rotate the image:

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv and Numpy library
3. Modify the name, location of the image to be rotate in the code.
4. Run the file to rotate the image.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "Image rotation using OpenCV" in kandi. You can try any such use case!

Dependent Libraries

If you do not have OpenCV that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV, numpy

Envorinment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0

Using this solution, we are able to rotate an image using the OpenCv library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us rotate an image in Python

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

This kit contains libraries (modules in python) that can be used to set up a Live Django Web Server with Facial Recognition Features.

Server Setup

Online Django Server Setup can be done using these modules of python

Face Recognition

These modules of python help in using the Face Recognition feature

Image Manipulation and Handling

Following Libraries help in image manipulation and handling images in the back-end

Files Serving

Following Libraries help in hosting files with the Django Server

Using the best Object Detection libraries, a developer can make the object detection process efficient and less complex by leveraging the additional functionalities they offer. Some libraries have less complex code, provide faster results, and use low RAM storage.

Developers can use this by looking for instant models with accurate results. Some libraries support image and video processing, eliminating the requirement for any other library to process the image or video. Developers who look for object detection models use libraries for their implementations. Some libraries also offer a good research platform for developers about their implementations. Also, some libraries support transformers for end-to-end object detection, which developers can use to make the process easier and quicker. 

Here is a list of the top 10 Object Detection libraries handpicked for developers to use for Object Detection tasks in Python. 

Detectron: 

• Facebook AI Research’s software system that helps implement state-of-the-art object detection algorithms.  
• Designed to be flexible for supporting rapid evaluation and implementation of novel research.  
• The goal is to offer a high-performance, high-quality codebase for object detection research. 

Mask_RCNN: 

• This model generates segmentation masks and bounding boxes for every instance of an object in an image, based on the Feature Pyramid Network and a ResNet101 backbone. 
• Includes training code for MS COCO, Jupyter notebooks for visualizing the detection pipeline at each step, evaluation of MS COCO metrics, and source code of Mask R-CNN built on FPN and ResNet101. 
• Created from 3D reconstructed spaces captured by our customers, who made them publicly available for academic purposes. 

labelImg: 

• Is an open source graphical image annotation tool used for creating bounding boxes and labeling images for object detection tasks. 
• Users can manually draw bounding boxes around objects in the image and label them with categorial representations. 
• Offers an intuitive graphical interface that allows users to label images easily and quickly without requiring any programming knowledge.  

deep_learning_object_detection: 

• Is a Python library that is built on top of TensorFlow and offers a simple and easy-to-use interface for object detection tasks.  
• A pre-trained model which can be easily downloaded and used for object detection tasks.  
• Is useful for those looking to quickly implement object detection tasks in their project without spending time on it. 

Pillow: 

• Provides efficient internal representation, powerful image processing, and extensive file format support.  
• Is designed for quick access to data stored in a few basic pixel formats, offering a solid foundation for a general image processing tool. 
• Used in different projects, like computer vision, scientific computing, and web development. 

detr: 

• Is an open source project developed by Facebook AI Research that offers various object detection tasks. 
• Is built on top of the PyTorch framework and uses a transformer architecture for processing the image and predicting object labels and locations.  
• Is a key idea detr is treating object detection like a set prediction problem, where the goal is predicting various object instances with their corresponding bounding boxes and class labels.  

kornia: 

• Is an open source computer vision library designed to offer various differentiable operations for video and image processing tasks. 
• Includes various operations like filtering, transformations, feature extraction, and many more. 
• Provides a fully differentiable interface that integrates computer vision operations directly into deep learning pipelines. 

ImageAI: 

• Is a Python library built to empower developers to create systems and applications with self-contained Computer Vision capabilities. 
• Supports various state-of-the-art Machine Learning algorithms for custom image prediction, video detection, image prediction training, image prediction, object detection, and video object detection.  
• Allows us to train custom models for recognizing and detecting new objections. 

darkflow: 

• Is a Python library that offers a TensorFlow implementation of the Darknet Neural Network Framework.  
• Allows for easy implementation of Darknet models for different computer vision tasks, like segmentation, object detection, and image classification. 
• Can be a useful library for those who want to implement efficient computer vision models using the Darknet framework in their Python projects.  

YOLOv3_TensorFlow: 

• Is a Python library that implements the YOLOv3 object detection algorithm using TensorFlow.  
• Offers pre-trained models for object detection in videos and images and tools for training custom object detection models.  
• An easy-to-use API for running object detection on user-provided videos and images.  

Here are some of the famous C++ Computer Vision Libraries. Some of the use cases of C++ Computer Vision Libraries include Image Classification, Object Tracking, Augmented Reality, Facial Recognition, and Image Processing.

C++ computer vision libraries are collections of code libraries and algorithms used to create applications that can interpret, analyze, and manipulate digital images and videos. They are used in a variety of fields, including robotics, automotive, security, and image processing. The libraries often contain functions for feature detection, object tracking, image segmentation, and stereo vision, among many other features.

Let us have a look at some of the famous C++ Computer Vision Libraries.

opencv

• Supports deep learning frameworks such as TensorFlow, Caffe, and Torch.
• Optimized C/C++, and can take advantage of multi-core processing and GPUs.
• Wide range of both classic and state-of-the-art computer vision and machine learning algorithms.

pcl

• Only C++ computer vision library that has a dedicated library for 3D point cloud processing.
• Implementation of the Kinect Fusion algorithm, which allows for real-time 3D mapping and tracking.
• Provides bindings to popular scripting languages such as Python and JavaScript.

VTK

• Ability to create custom data structures and objects to enable rapid prototyping and development of advanced algorithms.
• Support for parallel computing and distributed memory architectures.
• Ability to easily integrate with other software packages.

CImg

• Easy-to-use graphical user interface (GUI) for interactive image processing.
• Provides a range of advanced mathematical and statistical functions for image analysis.
• One of the few libraries to offer support for multi-threaded processing.

libcvd

• Optimized C++ code to provide fast image processing, feature detection, and tracking capabilities.
• Allows for concurrent execution of tasks, providing improved performance and scalability.
• Provides a transparent and accessible development environment.

vxl

• Designed for robustness, flexibility, and portability, providing a clean and efficient API.
• Provides tools for testing the correctness and performance of algorithms.
• Designed to be extensible and can be easily integrated with other software packages.

openvx-samples

• Designed to provide the highest level of performance on a variety of hardware platforms.
• Provides a wide range of advanced computer vision algorithms.
• Users have full access to the source code and can modify and improve it as they see fit.

Here are some famous C++ Image Processing Libraries. Some C++ Image Processing Libraries' use cases include Image Enhancement, Image Segmentation, Image Filtering, and Image Recognition.

C++ image processing libraries are collections of programming tools used for manipulating and analyzing images in the C++ programming language. They are typically used for tasks such as image filtering, color correction, object recognition, image segmentation, and more. They provide a set of functions and classes that can be used to build applications that can process images.

Let us have a look at these libraries in detail below.

opencv

• Vast library of computer vision algorithms, including machine learning, object detection, image processing, and more.
• Has an intuitive and easy-to-use C++ API which makes it easy to integrate into existing codebases.
• Highly optimized for speed and performance.

cgal

• Includes C++ interfaces for easy integration into application code.
• Actively maintained and updated with new features.
• Supports the development of geometric algorithms in a generic and unified framework.

oiio

• Supports high dynamic range (HDR) images and provides tools for tone mapping and exposure control.
• Optimized for performance, with SIMD vectorization and multi-threaded processing.
• Offers a powerful command-line interface, allowing users to quickly batch-process large numbers of images.

CImg

• Supports numerous image formats, including JPG, PNG, BMP, TGA, GIF, and HDR.
• Supports a wide range of image processing operations, such as transformations, filtering, blurring, and more.
• Portable, allowing you to use it on various platforms, such as Linux, MacOS, and Windows.

ITK

• Powerful set of algorithms for image processing, segmentation, registration, and analysis.
• Robust testing infrastructure, new features are implemented correctly.
• Has a strong emphasis on scalability

DevIL

• Offers a comprehensive API and comprehensive documentation.
• Powerful image manipulation capabilities, including image scaling, flipping, blurring, sharpening, and more.
• Support for advanced features such as mipmap generation, cube maps, and volume textures.

cximage

• Supports several advanced features, such as image filtering, and image compression.
• Includes support for EXIF and IPTC metadata.
• Provides an easy-to-use, high-level API

FreeImage

• Support for loading, saving, and manipulating pixel data.
• Offers a wide range of tools for image processing, including resizing, cropping, rotating, flipping, and others.
• Supports a variety of color depth, from 8-bit grayscale to 16-bit true color and even 32-bit floating point color.

The top Python OCR libraries can extract text from images and perform searching and other analysis operations.  

The procedure used to transform an image of text into a machine-readable text format is known as optical character recognition (OCR). It is a commercial system for automating data extraction from printed or written text from scanned documents or picture files, then turning the text into a machine-readable form for data processing like editing or searching. For instance, if you scan a form or a receipt, your computer stores the scan as an image file. The information can then be used to automate processes, streamline operations, and increase productivity.  

OCR libraries developed using python are listed below. These are optimized so that the process of OCR is simplified. 

PaddleOCR- 

• Multilingual OCR tools to train better models. 
• Layout analysis and Table Recognition optimization. 
• A visual independent model for key information extraction. 

EasyOCR- 

• Supports 80+ languages and is ready to use.  
• Scripts of all popular languages, including Chinese, Arabic, etc.  
• The output will be presented as a list, with each item denoting a bounding box, the amount of text detected, and the confidence level. 

OCRmyPDF- 

• Makes a pdf searchable by adding an OCR layer. 
• The exact resolution of the original image is maintained. 
• Highly scalable and can handle pdfs with multiple pages. 
• Can also validate input and output files. 

ocropy- 

• Can be used for document analysis alongside OCR. 
• The text-line recognizer is robust, while the layout analysis is resolution dependent. 
• Image pre-processing and training models are required. 

ExtractTable-py- 

• Specifically for extracting tabular data from images or pdf.  
• Table area, column coordinates, and other specifications are taken care of.  
• It is an API authorized using an API key. 

LiPlate- 

• OpenCV script that takes images of cars as input. 
• Reads the license plate number extracted from the image. 
• The Tesseract library is needed for the Tesseract-OCR version.

ocr- 

• Uses neural networks for Optical Character Recognition.  
• Implemented using NumPy and OpenCV. 
• Noises can be removed and segmented for better OCR. 

keras-ocr- 

• High-level API for text detection and OCR pipeline. 
• Inspired by CRAFT text detection model. 
• Punctuation and letter case is ignored. 

pytesseract- 

• Python version of Google’s Tesseract. 
• Stand-alone invocation script to Tesseract. 
• The recognized text can be printed instead of written into a file.

calamari- 

• ATR engine-based Optical character recognition.  
• Operates on the text-line level, and line segmentation is required.  
• Modular, customizable, and command line interface. 

LaTeX-OCR- 

• Extract an image of a formula and convert it into latex code. 
• Already existing images, as well as images in the clipboard, can be analyzed. 
• Efficient and user-friendly interface for better model prediction.