Comprehensive Library
OpenCV offers a wide range of tools for various aspects of computer vision, including image processing, machine learning, and video analysis.
Cross-Platform Compatibility
OpenCV is designed to run on multiple platforms, including Windows, Linux, macOS, Android, and iOS, which makes it versatile for development across different environments.
Open Source
Being open-source, OpenCV is freely available for use and allows developers to inspect, modify, and enhance the code according to their needs.
Large Community Support
A large community of developers and researchers actively contributes to OpenCV, providing extensive support, tutorials, forums, and continuously updated documentation.
Real-Time Performance
OpenCV is highly optimized for real-time applications, making it suitable for performance-critical tasks in various industries such as robotics and interactive installations.
Extensive Integration
OpenCV can easily be integrated with other libraries and frameworks such as TensorFlow, PyTorch, and OpenCL, enhancing its capabilities in deep learning and GPU acceleration.
Rich Collection of examples
OpenCV provides a large number of example codes and sample applications, which can significantly reduce the learning curve for beginners.
In addition to installing the YOLO implementation from Ultralytics, this also installs OpenCV for image processing.
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about 2 months ago
OpenCV – The official site for OpenCV, featuring tutorials and documentation.
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3 months ago
How to Accomplish: Use statistical analysis tools and libraries (e.g., Pandas for tabular data) to calculate and visualize these characteristics. For image datasets, custom scripts to analyze object sizes or mask distributions can be useful. Tools like OpenCV can assist in analyzing image properties, while libraries like Pandas and NumPy are excellent for tabular and numerical analysis. To address class…
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3 months ago
Open the camera feed — and use the OpenCV library for real-time computer vision processing.
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4 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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9 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).
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10 months ago
– [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection.
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about 1 year ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: “My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…” / X ) made by Mckay Wrigley and I was wondering what’s the easiest…
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about 1 year ago
You also need C++ if you’re going to do things which aren’t built in as part of the engine. As an example if you’re looking at using compute shaders, inbuilt native APIs such as a mobile phone’s location services, or a third-party library such as OpenCV, then you’re going to need C++.
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over 1 year ago
Another option is using the OpenCV library with Python to extract features from images and compare them using feature matching algorithms like SIFT, SURF, or ORB. This approach is more computationally intensive but might be more accurate in identifying near-duplicates. You can find more information about OpenCV here: https://opencv.org/.
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over 1 year ago
If you’re reading this, you’re probably curious about computer vision, or just starting out. You’ve come to the right place. In this tutorial, we’ll learn to blur images using OpenCV and Python.
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over 1 year ago
Everything you describe can be done rather simply with opencv.
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over 1 year ago
If you find this cost prohibitive, you could try using some compute resources outside of Azure and a library like OpenCV or Azure Form Recognizer to try to extract meaning from image documents and then store the extracted information in Azure Cognitive Search instead of the source document. This typically is not worth the effort, however.
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over 1 year ago
To capture your eyes and make the determination of whether they are open or not (or to even copy the images to the LCD in realtime) will require a processor running much much faster than the average Arduino. Probably something making use of openCV (an extremely capable open source Computer Vision and image processing library). Arduino’s (most) run at 16MHz. A Raspberry Pi or an ESP32 would be much more suited to…
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over 1 year ago
You first import the OpenCV library. It will pre-process the selected flower image to ensure it has the same dimensions as the images that trained the deep learning model.
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over 1 year ago
Hmm without the hardware support for AR-kit/Core, the camera has to do more of the heavy lifting. Not sure if there’s a complete framework, but I know I heavily utilized https://opencv.org/ for camera based tracking for AR back in 2017 (before AR-kit/core were released).
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over 1 year ago
Alternatively, you can get into the nitty gritty of face detection yourself. OpenCV is a massive, open source project for all kinds computer vision tasks. Without jumping into the proprietary world, this is one of the most popular and capable computer vision libraries available. While powerful, OpenCV is comparatively low level; giving you the tools you need to accomplish your task, rather than a direct, single…
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over 1 year ago
OpenCV can do this for you, in fact it’s probably better for the money and effort than solving your photogrammetry problem with hardware. I use it to create 3D images with two side-by-side GoPros. What you’re looking for is the camera calibration process.
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almost 2 years ago
Many people use a Raspberry Pi single board computer (£10 – £50) and a cheap camera module as a security system. For example, I have one that uses OpenCV to tell me if someone or a vehicle I don’t know is visiting (and different alerts for people or vehicles it recognises).
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almost 2 years ago
Yeah you can overlay images with geometric transformations to make them flat using software like OpenCV (https://opencv.org/), then upsample, but based on the video it would be unlikely that the frames have enough resolution to do so.
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almost 2 years ago
By the way, my problem was much easier because it was on a track my car essentially had lanes to stay in. Yes, I stuck an iphone on thecar, and used the iphones camera to center the runner’s face in the center of the screen using a library called OpenCV. I also used a rangefinder to determine distance from the car, and then you can basically triangulate with dynamics where the car needs to turn. There wasn’t…
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almost 2 years ago
