PyImageSearch

You mention image recognition, I can recommend this website that does a good job with python calling OpenCV: https://pyimagesearch.com/

If you know python, this is an excellent resource: https://www.pyimagesearch.com/

PyImageSearch [1] is one of the best resources for doing actual stuff with opencv. I personally find the writing style a little patronising sometimes, but the content is excellent and kept up to date. [1] https://www.pyimagesearch.com/

So many cool projects on HN this week! For OpenCV classification tutorials this is another great resource to keep playing around with DIY projects. FYI avoid his email list unless you enjoy 3-4 sales emails every week. https://www.pyimagesearch.com/

I absolutely love Adrian's blog. http://www.pyimagesearch.com/ He has articles on solving actual problems with OpenCV, dlib and tensorflow. I subscribe to the blog and try to do some of the tutorials myself. Udacity is another great resource. Their self driving and robotics nanodegrees are great. I am on the same path as you trying to pivot my career from full stack engineer and add CV + ML skills to it. When we have decent robot hardware, I want to be the one programming them, not the one getting replaced by them.

I may be off base here but wouldn't CV be part of what you're interested in? If so I've found Adrian Rosebrock has some great paid for and free options: https://www.pyimagesearch.com (free tutorials in the blog) https://www.pyimagesearch.com/pyimagesearch-gurus/ (paid for "guru" course)

If you're willing to expend funds for a good hands-on tutorial that takes you from the cradle to the crown I'd recommend http://www.pyimagesearch.com/ The 21 day crash course should fat you up to speed. Good luck

Totally not what you asked, but making an OCR model is a learning exercise for AI research students. Using the Kaggle-hosted dataset https://www.kaggle.com/datasets/landlord/handwriting-recognition/data and a tutorial, eg https://pyimagesearch.com/2020/08/17/ocr-with-keras-tensorflow-and-dee… you can follow along and train your own OCR model!

To produce scientific work, one needs to follow the scientific method. This involves stating a hypothesis, designing an experiment that would test this hypothesis, conducting the experiment with controls, and analyzing the data w.r.t to the hypothesis being tested. Access to past works is only useful in informing what is a good hypothesis worth testing. And data is only useful when generated by an experiment that is testing for causality (see [1]). [1] https://pyimagesearch.com/2023/11/27/a-brief-introduction-to-do-calcul…

A nice mix of application and background: https://pyimagesearch.com/

I recommmend PyimagesearchGuru (https://www.pyimagesearch.com/) for real-world applications.

Hi ! I think you can use my code to detect and extract the grid, then run a "color detector" script on all the regions instead of the Tesseract recognizer, and return the RBG/HSL/... values in a text file. I don't know if the region's pre-processing (denoizing, clear border, ...) will be useful though. For the color detection, I found this tutorial : https://www.pyimagesearch.com/2014/08/04/opencv-python-color-detection… which comes from Adrian Rosebrock (https://github.com/jrosebr1) who makes very great Python tutorials. Hope it'll work !

PyImageSearch by Adrian Rosebrock. http://www.pyimagesearch.com/

OpenCV and http://www.pyimagesearch.com/ disclaimer: not related to any of these

To start on OpenCV and deep learning and image recognition you should check out http://www.pyimagesearch.com/ I've found his tutorials and books to be very good.

There's a guy who has a book on computer vision, and he sells more expensive versions that come with test images, so it might actually be worth it. See- https://www.pyimagesearch.com/practical-python-opencv/ I've been working on a search engine for lectures (https://www.findlectures.com) and accessibility is an area I'd like to explore, but it's tough to know how to test something as an end user would see it.

Adrian Rosebrock of PyImageSearch explained his rationale for purchasing a pre-configured Nvidia box in [1] and talked about other build considerations in [2] (I'm not affiliated in any way; just a subscriber of his blog who thinks you may find the info there useful). [1]: http://www.pyimagesearch.com/2016/06/06/hands-on-with-the-nvidia-digit… [2]: http://www.pyimagesearch.com/2016/06/13/considerations-when-setting-up…

I would add to this comment. I recently thought face detection was a difficult service, but its actually really easy to implement yourself. I found these really useful: http://www.pyimagesearch.com/

On a related note I started doing some of the tutorials on this site [1]. It uses OpenCV as opposed to PCV. [1] : http://www.pyimagesearch.com

About a year and a half ago I was looking to learn more about the openCV library and computer vision. Searching through HN turned me on to: https://pyimagesearch.com/ Their approach to hands on learning with code samples you can download and run proved very effective (for me) at demystifying the current AI fields of image classification and object detection. It's all python based and leans heavily on existing libraries. But he does cover the basic math and references various books and research papers that are influential in the field. I ultimately signed up for their paid courses but you could learn quite a bit from the free stuff. After a few months of learning from pyimagesearch.com I ultimately decided to enroll in the GT OMSCS program to really dig into the field. @Pinegulf is right - AI is just code. There is no "one AI program to rule them all" and there never will be. Better to think of AI as a collection of various tools (really cool tools!) that have been invented to solve various problems - image classification, or voice processing to name two of the more recent hot AI tools.

I've built several object detection systems and you definitely want to fine-tune your model to get good results, even if the object class you want to track is available in a pre-trained model (often it isn't). For example, pre-trained models don't work too well at detecting people in surveillance footage when the camera is mounted on the ceiling and people are at an angle in the footage. Off-the-shelf object detection models also don't work well in poor lighting conditions but can work well if you fine-tune one of the pre-trained models for your dataset and lighting conditions. Here's a great and fun tutorial on how to fine-tune an object detection model using TensorFlow (it's a surprisingly fiddly process but worth the headache): https://towardsdatascience.com/how-to-train-your-own-object-detector-w… I suspect you can use your fine-tuned model with this repo by replacing the `get_ssd_model.sh` compiled graph with your fine-tuned model graph. It's also a fun exercise to implement the tracking yourself - here's a solid tutorial to get you started: https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/

Here is a guide I have developed over 6 years when I dove into copmuter vision around 2011. My path has been self taught until recently I took a graduate course. I started from wanting to develop AR apps during my undergrad, Here are the best resources I have found to date: Computer Vision is very theoretical and experimental, so the more hands on, the better! My approach has been to go top-down, overview the landscape and slowly progress deeper. Begin with the best library for CV in my opinion: OpenCV. The tutorials are amazing! Python tutorials: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorials.html C++ tutorials: http://docs.opencv.org/3.0-beta/doc/tutorials/tutorials.html Immerse yourself in these and build any apps you think of! Then go into: pyimagesearch tutorials http://www.pyimagesearch.com/ and aishack.in http://aishack.in/, tons of great tutorials to learn different topics of vision with coding walkthroughs. Understand the examples and rewrite applications. Then Dive Deep: Get the new OpenCV3 book, a nice deep overview of many topics in computer vision. https://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/1491… And watch this course on youtube: https://www.youtube.com/watch?v=skaQfPQFSyY&list=PL4B3F8D4A5CAD8DA… I feel like then, you will have so much exposure that when you dive into formal classes and textbooks, you will really…

I'm relatively new to Python so I don't have anything that I've closely studied...mostly things I've bookmarked that have been submitted to HN. The attractiveness of Python to me is the huge scientific programming community behind it, plus its human-friendly syntax...if R was more readable by me, I'd prefer to work with just one language. So in terms of Python resources: - The classic NLTK book: http://www.nltk.org/ - A Programmer's Guide to Data Mining: http://guidetodatamining.com/ - Hitchhiker's Guide to Python: http://docs.python-guide.org/en/latest/index.html - Statistical Inference for Everyone: http://web.bryant.edu/~bblais/statistical-inference-for-everyone-sie.h… - Frequentism and Bayesianism: A Python-driven Primer: http://arxiv.org/abs/1411.5018 - Probabilistic Programming and Bayesian Methods for Hackers: http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Prog… - Software Carpentry's primer: http://software-carpentry.org/v5/novice/python/index.html - And of course, LPTHW: http://learnpythonthehardway.org/ I also came across Practical Python and OpenCV book and have found it really useful for implementing computer-vision exercises...it's not free, but the author has a blog where he regularly posts insightful examples: http://www.pyimagesearch.com/ (I haven't created any class-specific lessons but will definitely post them when I have…

I don't mean literally flipping pixels, but subtle changes in the image that cause a misclassification. See this post for examples: https://pyimagesearch.com/2020/10/19/adversarial-images-and-attacks-wi…

Someone has written a comprehensive howto: https://pyimagesearch.com/2018/09/24/opencv-face-recognition/ Someone else has managed to get face recognition running in real time on a Raspberry Pi, which is mind-blowing on its own given how lackluster performance was before the Pi 4 came along: https://towardsdatascience.com/real-time-face-recognition-an-end-to-en… And all of this is from 2018 - hard to imagine a company the scale of Pornhub could not hire a bunch of ten developers and let them google "opencv video face recognition". For something like Content ID, I'd use ffmpeg to get raw stills of the video, normalize them into 720p resolution and then go with a visual hash implementation (again OpenCV has enough howtos on that topic) over these. Yes it's a ton of data to store but it should not be too difficult to implement at the scale of pornhub.

This is cool, though I wonder how much more accurate this is compared to dlib's built-in Correlation Tracker: http://dlib.net/correlation_tracker.py.html Problem areas are usually when objects cross over each other, and when the tracked object starts moving at a faster speed (eg: sudden speed up in runners, or so on). *Edit: There's a good gif of dlib's implementation here: https://www.pyimagesearch.com/2018/10/22/object-tracking-with-dlib/

Here's an interview I came across with the creator (Davis King) of dlib: https://www.pyimagesearch.com/2017/03/13/an-interview-with-davis-king-… Goes into some interesting details, and is a library that I'm definitely going to check out.

It would be interesting to train OpenCV on the different UPC / QR / etc. code formats, and have it capture whatever codes it sees (even multiple formats at the same time, which is pretty common in shipping) and send the images to a reader to be processed. This would probably be a good Raspberry Pi project for someone thus inclined. Edit: I found this project that says it can, so it's at least a 2-year old still-unanswered question of who will program it: https://www.pyimagesearch.com/2018/05/21/an-opencv-barcode-and-qr-code…

I found a pretty good free Computer Vision (in python) email course by pyimagesearch.com (https://www.pyimagesearch.com/free-opencv-computer-vision-deep-learnin… It's just a compilation of tutorial articles from the blog (which is why it's free), but in a curated order to make some more sense.

The ones I really enjoyed reading were: https://www.pyimagesearch.com/deep-learning-computer-vision-python-boo… https://www.machinelearningisfun.com/ and https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/161729…