zionsrogue

It could very well be. It's hard to say without seeing shapes that you have drawn as well.

This method is just for face recognition. If you're interested in tracking eye movements take a look at face detection + facial landmarks:

https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dl...

And then techniques to localize the center of the eye:

http://thume.ca/projects/2012/11/04/simple-accurate-eye-cent...

The model used in the post (LeNet) is very small compared to what we typically think of in terms of modern deep neural networks. The LeNet architecture itself was published in the 1990s and was designed for character recognition so this is a natural application of it. kNN would work for many characters in this example, but you would run into a problem with overlapping text. kNN on raw pixel intensities requires a nice segmentation of the ROI. More image processing/segmentation/morphological operations could be applied to help in that case, but given a small network architecture that will naturally learn these filters a tiny CNN works well with little preprocessing.

Thanks for pointing this out! Typo has been fixed. I also appreciate the suggestion.

Thanks, I'm glad you've found the site helpful! :-)

A/B testing is absolutely on my roadmap. I'm finishing up a few bonus chapters for the book/course now. Based on previous A/B tests on the site (for other products) I have a good idea of what works well for the general audience of my viewers. But I totally understand that the messaging will not work for everyone and it's something I hope I can address in 2018. Thanks for the comment.

Hi, Adrian here, author of the book you are referencing. You are referring to the highest tier of the book. There are other lower tiers as well that are cheaper.

The highest tier (again, which you are referring to) includes 800+ pages, detailed experiment journals on how to reproduce the state-of-the-art publications (ResNet, SqueezeNet, VGG, etc.) on ImageNet (which is 1.2 million images). I demonstrate how to implement each model from scratch and then train them, detailing which parameters to change and when. The highest tier is for people looking to train really large networks on massive datasets where you could be spending thousands of dollars in the cloud for GPU costs (you can't train these networks without a GPU, or ideally multiple GPUs). I've also included the pre-trained models as well if people want to get started with them and skip training. This tier is really for researchers/practitioners who need to save time and finances by starting with experiment journals that detail how to replicate the results.

The lower tiers are for people just (1) getting started with deep learning in context of computer vision and/or (2) looking to apply best practices. Each book also includes video tutorials/lectures once I have finished putting them together. Realistically I should rebrand the book as a course as it's much more in line with something you would get from Udacity (only with more theory and more detailed code and implementations).

If anyone has any questions about the book do feel free to ask.

Adrian here, author of the PyImageSearch blog. I'll add doing a tutorial on cheque recognition (at least the routing and account numbers) to my queue. Thanks for the great suggestion.

I'm not sure what you mean by "algorithms distinguishable for documents and natural images" -- can you elaborate? OpenCV itself doesn't have builtin functionality to take documents and fit them to a pre-defined template, that tends to be part of a specific use-case/niche of computer vision for document processing. The general idea is to take a document a user has filled out and "fit" it to a blank template, where you know exactly where each field is. That way you can exact the information from the document.

Adrian here, author of PyImageSearch. Thanks for mentioning the blog. If anyone has any questions regarding learning computer vision, please see my reply to "sphix0r" below.

Adrian here, author of the PyImageSearch blog. Thank you for mentioning it, I appreciate it. If anyone has any questions about computer vision, deep learning, or OpenCV, please let me know.

In regards to OPs original question, I'm actually working on solving your very problem right now. About 1.5 years ago I created the PyImageSearch Gurus course (https://www.pyimagesearch.com/pyimagesearch-gurus/) with the aim of bridging academia with actual real-world computer vision problems. The course has helped readers in their academic careers, such as securing grants (http://www.pyimagesearch.com/2016/03/14/pyimagesearch-gurus-...) as well as students become practitioners and land jobs in the CV startup space (http://www.pyimagesearch.com/2017/06/12/pyimagesearch-gurus-...)

Within the next month I'll be launching PyImageJobs which will connect PyImageSearch readers (especially the Gurus course graduates) with companies/startups that are looking to hire.

Finally, I'm also working on my upcoming "Deep Learning for Computer Vision with Python" book (https://www.pyimagesearch.com/deep-learning-computer-vision-...) which is now 100% outlined and I'm on to the writing phase.

Definitely take a look and if you have any questions, please let me know or use the contact form on my website if you want to talk in private.

Hey, Adrian here -- I actually run PyImageSearch.com. I don't want to sell you on the course, I think the content speaks for itself. But if you want to talk to members who have gone through the course and see the value they got out of it, just let me know. Send me a message (http://www.pyimagesearch.com/contact/) and then I'll connect you with graduates. Also, if there is a specific topic you're looking to study just let me know and I can see if the course is a good fit for you.

Correct, I am the author of Practical Python and OpenCV.

Combining the Raspberry Pi + Python + OpenCV is a lot of fun. If you feel like sharing the details on your project I might be able to point you in the right direction.

Adrian, the author of the PyImageSearch blog here. Thank you for linking to the original article, I appreciate it.

In that case, you might want to go with an infrared camera. If you're using a Raspberry Pi, the Pi NoIR camera is really great.

Adrian here, author of the post and book. Thanks for picking up a copy of Practical Python and OpenCV -- and it's really awesome to hear you liked it so much.

If you have any questions about computer vision, feel free to reach out!

I personally didn't run into any issues with power consumption or slowing down. I've let this setup run for multiple days at a time when I'm on vacation and use it to monitor my apartment. Overall, it works well (but can obviously be improved).

Adrian here, author of the post.

Thanks so much for the kind words, and I'm happy the posts have helped! And as a fellow dog lover, it's awesome to see how you put together a real application for treat dispensing.

Actually, finding human faces is a little bit easier (or at least comparable) to finding logos in images. The human face has a standard shape, whereas logos can be quite different shapes, sizes, and styles. The standard approaches to human face detection include Haar cascades, HOG + Linear SVM, and now we're even using deep learning. These techniques could also be applied to the drone video as well.

Agreed, it's certainly not novel. Although I personally find it interesting to see the source code behind these solutions and how the pieces are glued together.

This is great, thanks for sharing.

Actually, the only reason I mentioned movies/TV shows was because I had just watched the Black Friday episodes of South Park and used those as inspiration for the introduction.

Hey, that's very cool! Thanks for putting the API together :-)

I have heard of ZBar but have never used it. It looks like the Python package wraps a binary, correct? Anyway, it looks like it could do the job.

Great observation. But if you erode too early you run the risk of not being able to perform a closing and fill in the gaps between the vertical stripes of the barcode.

It only works for horizontal barcodes. It's certainly possible to extend this code to work for vertical ones as well. A Radon transform might be a good start. And if you want something more powerful there is Haar cascades or HOG descriptors + Linear SVM.

Something to keep in mind for the next time.

Hey, author of the post here. Detecting the barcode is actually the easy part. Reading the barcode is substantially harder. And there are many different types of barcode variations. It's not a simple problem, unfortunately. However, this approach could be used to read the barcode. I'll add a post related to reading the barcode to queue though! Thanks for the great feedback.

Author of the post here. Sorry to offend you. Certainly wasn't trying to be an ass. But when you write tons and tons of programming tutorials, sometimes it's fun to introduce a little humor, otherwise it becomes extremely dull. If anything, the introduction simply pointed out the consumerism flaws related to Black Friday. I didn't mean to single out a group of people.