OpenSource GUI Tool For OpenCV And DeepLearning

February 28, 2020 by Inderpreet Singh 4 Comments

AI and Deep Learning for computer vision projects has come to the masses. This can be attributed partly to the community projects that help ease the pain for newbies. [Abhishek] contributes one such project called Monk AI which comes with a GUI for transfer learning.

Monk AI is essentially a wrapper for Computer Vision and deep learning experiments. It facilitates users to finetune deep neural networks using transfer learning and is written in Python. Out of the box, it supports Keras and Pytorch and it comes with a few lines of code; you can get started with your very first AI experiment.

[Abhishek] also has an Object Detection wrapper(GitHub) that has some useful examples as well as a Monk GUI(GitHub) tool that looks similar to the tools available in commercial packages for running, training and inference experiments.

The documentation is a work in progress though it seems like an excellent concept to build on. We need more tools like these to help more people getting started with Deep Learning. Hardware such as the Nvidia Jetson Nano and Google Coral are affordable and facilitate the learning and experimentation.

Now You Can Be Big Brother Too, With A Raspberry Pi License Plate Reader

February 26, 2020 by Jenny List 60 Comments

If you are wowed by some of the abilities of a Tesla but can’t quite afford one, perhaps you can enhance your current ride with a few upgrades. This was what [Robert Lucian Chiriac] did with his Land Rover, to gain some insight into automotive machine vision he fitted it with a Raspberry Pi and camera with an automatic number plate recognition system.

This bracket should find a use in a few projects.

His exceptionally comprehensive write-up takes us through the entire process, from creating a rather useful set of 3D-printed brackets for a Pi and camera through deciding the combination of artificial intelligence software components required, to making the eventual decision to offload part of the processing to a cloud service through a 4G mobile phone link. In this he used Cortex, a system designed for easy deployment of machine learning models, which he is very impressed with.

The result is a camera in his car that identifies and reads the plates on the vehicles around it. Which in a way has something of the Big Brother about it, but in another way points to a future in which ever more accessible AI applications self-contained without a cloud service become possible that aren’t quite so sinister. It’s an inevitable progression whose privacy questions may go beyond a Hackaday piece, but it’s also a fascinating area of our remit that should be available at our level.

You can see the system in action in the video below the break, as well as find the code in his GitHub repository.

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Machine Learning System Uses Images To Teach Itself Morse Code

January 27, 2020 by Dan Maloney 19 Comments

Conventional wisdom holds that the best way to learn a new language is immersion: just throw someone into a situation where they have no choice, and they’ll learn by context. Militaries use immersion language instruction, as do diplomats and journalists, and apparently computers can now use it to teach themselves Morse code.

The blog entry by the delightfully callsigned [Mauri Niininen (AG1LE)] reads like a scientific paper, with good reason: [Mauri] really seems to know a thing or two about machine learning. His method uses curated training data to build a model, namely Morse snippets and their translations, as is the usual approach with such systems. But things take an unexpected turn right from the start, as [Mauri] uses a Tensorflow handwriting recognition implementation to train his model.

Using a few lines of Python, he converts short, known snippets of Morse to a grayscale image that looks a little like a barcode, with the light areas being the dits and dahs and the dark bars being silence. The first training run only resulted in about 36% accuracy, but a subsequent run with shorter snippets ended up being 99.5% accurate. The model was also able to pull Morse out of a signal with -6 dB signal-to-noise ratio, even though it had been trained with a much cleaner signal.

Other Morse decoders use lookup tables to convert sound to text, but it’s important to note that this one doesn’t. By comparing patterns to labels in the training data, it inferred what the characters mean, and essentially taught itself Morse code in about an hour. We find that fascinating, and wonder what other applications this would be good for.

Thanks to [Gordon Shephard] for the tip.

Generating Beetles From Public Domain Images

January 12, 2020 by Sharon Lin 11 Comments

Ever since [Ian Goodfellow] and his colleagues invented the generative adversarial network (GAN) in 2014, hundreds of projects, from style transfers to poetry generators, have been produced using the concept of contesting neural networks. Unlike traditional neural networks, GANs can generate new data that fits statistically within the same set as the training set.

[Bernat Cuni], the one-man design team behind [cunicode] came up with the idea to generate beetles using this technique. Inspired by material published on Machine Learning for Artists, he decided to deploy some visual experiments with zoological illustrations. The training data was found from a public domain book hosted at archive.org, found through the Biodiversity Heritage Library. A combination of OpenCV and ImageMagick helped with individually extracting illustrations to squared images.

[Cuni] then ran a DCGAN with the data set, generating the first set of quasi-beetles after some tinkering with epochs and settings. After the failed first experiment, he went with StyleGAN, setting up a machine at PaperSpace with 1 GPU and running the training for >3 days on 128 px images. The results were much better, but fairly small and the cost of running the machine was quite expensive (>€125).

Given the success of the previous experiment, he decided to transfer over to Google CoLab, using their 12 hours of K80 GPU per run for free to generate some more beetles. With the intent on producing more HD beetles, he used Runway trained on 1024 px beetles, discovering much better results after 3000 steps. The model was moved over to Google CoLab to produce HD outputs.

He has since continued to experiment with the beetles, producing some confusing generated images and fun collectibles.

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It Turns Out, Robots Need Tough Love Too

January 8, 2020 by Sharon Lin 5 Comments

Showing robots adversarial behavior may be the key to improving their performance, according to a study conducted by the University of Southern California. While a generative adversarial network (GAN), where two neural networks compete in a game, has been demonstrated, this is the first time adversarial human users have been used in a learning effort.

The report was presented at the International Conference on Intelligent Robots and Systems, describing the experiment in which reinforcement learning was used to train robotic systems to create a general purpose system. For most robots, a huge amount of training data is necessary in order to manipulate objects in a human-like way.

A line of research that has been successful in overcoming this problem is having a “human in the loop”, in which a human provides feedback to the system in regards to its abilities. Most algorithms have assumed a cooperating human assistant, but by acting against the system the robot may be more inclined to develop robustness towards real world complexities.

The experiment that was conducted involved a robot attempting to grasp an object in a computer simulation. The human observer observes the simulated grasp and attempts to snatch the object away from the robot if the grasp is successful. This helps the robot discern weak and firm grasps, a crazy idea from the researchers that managed to work. The system trained with the adversary rejected unstable grasps, quickly learning robust grasps for different objects.

Experiments like these can test the assumptions made in the learning task for robotic applications, leading to better stress-tested systems more inclined to work in real-world situations. Take a look at the interview in the video below the break.

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Lego Machine Uses Machine Learning To Sort Itself Out

December 16, 2019 by Dan Maloney 8 Comments

In our opinion, the primary evidence of a properly lived childhood is an enormous box of every conceivable Lego piece, from simple bricks to girders and gears, all with a small town’s worth of minifigs swimming through it. It takes years of birthdays and Christmases to accumulate a Lego collection best measured by the pound, but like anything worth doing, it’s worth overdoing.

But what to do with such a collection? Digging through it to find Just the Right Piece™ can be frustrating, and bringing order to the chaos with manual sorting is just so impractical. How about putting some of those bricks to work with a machine-vision Lego sorter built from Lego?

[Daniel West]’s approach is hardly new – we’ve even featured brick-built Lego sorters before – but we’re impressed by its architecture. First, the mechanical system is amazing. It uses a series of conveyors to transport bricks from a hopper, winnowing the stream down as it goes. The final step is a vibratory feeder that places one piece on a conveyor at a time. Those pass under a camera attached to a Raspberry Pi, where OpenCV does background subtraction from the video stream, applies bounding boxes to the parts, and runs the images through a convolutional neural network (CNN) that’s been trained on a database of every Lego part. Servo-controlled gates then direct the parts into one of 18 bins. See it in action in the video below.

We must admit that we’re not sure what the sorting criteria are, as some bins seem nearly as chaotic as the input mix. Still, we appreciate the fine engineering, and award extra style points for all the Lego goodness.

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AI Knows If The Pitch Is On Target Before You Do

December 10, 2019 by Lewin Day 21 Comments

Pitching a baseball is about accuracy and speed. A swift ball on target is the goal, allowing the pitcher to strike out the batter. [Nick Bild] created an AI system that can determine a ball’s trajectory in mid-flight, based on a camera feed.

The system uses an NVIDIA Jetson AGX Xavier, fitted with a USB camera running at 100FPS. A Nerf tennis ball launcher is used to fire a ball towards the batter. Once triggered, the AI uses the camera to capture two successive images of the ball in flight. These images are fed into a convolutional neural network (CNN), and the software determines whether the ball is heading for the strike zone, or moving off-target. It uses this information to light a green or red LED respectively to alert the batter.

While such a system is unlikely to appear in professional baseball anytime soon, it shows the sheer capability of neural network systems to quickly and effectively analyse data in ways simply impossible for mere humans. [Nick]’s future goals involve running the system on faster hardware, and expanding it to determine effects like spin and more accurate positioning within the strike zone.

We’ve seen CNNs do everything from naming tomatoes to finding parking spaces. Video after the break.

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