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. - Source: dev.to / 5 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/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months 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... Source: 9 months 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++. Source: 11 months 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/. Source: 12 months 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. - Source: dev.to / about 1 year ago
Everything you describe can be done rather simply with opencv. Source: about 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. - Source: dev.to / about 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... Source: about 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. - Source: dev.to / about 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). Source: 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... Source: 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. Source: over 1 year 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). Source: over 1 year 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. Source: over 1 year 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... Source: over 1 year ago
You should be able to do this with opencv. https://opencv.org/. Source: over 1 year ago
If you need the precise values shown on the meter then you'll need to rig up a camera. You'll also need to space it far enough away that it can focus on the meter + have sufficient illumination. Take stills and then feed it into OpenCV. Something like https://github.com/mirogta/dial-meter-reader-opencv-py. Source: over 1 year ago
In terms of software, what you linked used optical tracking. The underlying tech is commonly called machine/computer vision. You can develop your own tracking software, but there's some open source solutions, like OpenCV than make it faster to build apps. (You can use OpenCV with Python bindings to learn the basics somewhat quickly). What you linked uses Kalidokit, an open source solution using TensorFlow.js and... Source: over 1 year ago
OpenCV (https://opencv.org/) was pretty standard last I was doing image processing in c/c++. Source: over 1 year ago
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