Category: Image recognition python github

image recognition python github

In this article, we will briefly introduce the field of artificial intelligence, particularly in computer vision, the challenges involved, the existing modern solutions to these challenges and how you can apply these solutions conveniently and easily without taking much time and effort. Artificial Intelligence has for decades been a field of research in which both scientists and engineers have been making intense efforts to unravel the mystery of getting machines and computers to perceive and understand our world well enough to act properly and serve humanity.

One of the most important aspect of this research work is getting computers to understand visual information images and videos generated everyday around us.

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This field of getting computers to perceive and understand visual information is known as computer vision. During the rise of artificial intelligence research in the s to the s, computers were manually given instructions on how to recognize images, objects in images and what features to look out for. This method are traditional algorithms and were called Expert Systemsas they require that humans take the pain of identifying features for each unique scene of object that has to be recognize and representing these features in mathematical models that the computer can understand.

That involves a whole lot of tedious work because there are hundreds and thousands of various ways an object can be represented and there are thousands or even millions of different scenes and objects that uniquely exist, and therefore finding the optimized and accurate mathematical models to represent all the possible features of each objects or scene, and for all possible objects or scene is more of work that will last forever.

Machine learning opened the way for computers to learn to recognize almost any scene or object we want them too. With the emergence of powerful computers such as the NVIDIA GPU s and state-of-the-art Deep Learning algorithms for image recognition such as AlexNet in by Alex Krizhevsky et al, ResNet in by Kaeming He et al, SqueezeNet in by Forrest Landola et al, DenseNet in by Gao Huang et al, to mention a few, it is possible to put together a number of pictures more like image books for computers and define an artificial intelligence model to learn features of scenes and objects in these pictures by itself and use the knowledge gained from the learning process to recognize all other instance of the type of scene or objects it will encounter after.

To train an artificial intelligence model that can recognize whatever you want it to recognize in pictures, it traditional involves lots of expertise in Applied Mathematics and use of Deep Learning libraries, not to mention the amount of time involved and stress you have to go through to write the code for the algorithm and fit the code to your images.

This is where we have provided our solutions. Our team at AI Commons has developed a python library that can let you train an artificial intelligence model that can recognize any object you want it to recognize in images using just 5 simple lines of python code. The python library is ImageAIa library built to let students, developers and researchers with all levels of expertise to build systems and applications with state-of-the-art computer vision capabilities using between 5 to 15 simple lines of code.

Now, let us walk you through creating your first artificial intelligence model that can recognize whatever you want it to. To train your artificial intelligence model, you need a collection of images called a dataset.

A dataset contains hundreds to thousands of sample images of objects you want your artificial intelligence model to recognize. We are not asking you to go and download thousands of pictures right now just to train your artificial intelligence model. For this tutorial, we have provided a dataset called IdenProf.

IdenProf Identifiable Professionals is a dataset that contains 11, pictures of 10 different professionals that humans can see and recognize their jobs by their mode of dressing. The classes of professionals whose pictures are in this dataset are as below:. This dataset is split into pictures for each profession pictures to train the artificial intelligence model and pictures for each profession pictures to test the performance of the artificial intelligence model as it is training.

IdenProf has been properly arranged and made ready for training your artificial intelligence model to recognize professionals by their mode of dressing. For reference purposes, if you are using your own image dataset, you must collect at least pictures for each object or scene you want your artificial intelligence model to recognize.

To train any image dataset you collect yourself with ImageAI, you must arrange the images in folders as seen in the example below:. Now that you have understand how to prepare own image dataset for training artificial intelligence models, we will now proceed with guiding you training an artificial intelligence model to recognize professionals using ImageAI.

Also you can view all the details and sample results of artificial intelligence models trained to recognize professions in the IdenProf GitHub repository whose link is below. Then unzip it into the same folder. Before you run the code to start the training, let us explain the code. In the second line we, created an instance of the model training class. In the fourth line, we set the data directory dataset directory to the folder of the dataset zip file you unzipped.

Now you can start run the Python file and start the training. When the training starts, you will see results like the one below:.Using this model we were able to detect and localize the bounding box coordinates of text contained in an image. Tesseract, a highly popular OCR engine, was originally developed by Hewlett Packard in the s and was then open-sourced in Google adopted the project in and has been sponsoring it ever since.

Just as deep learning has impacted nearly every facet of computer vision, the same is true for character recognition and handwriting recognition. Deep learning-based models have managed to obtain unprecedented text recognition accuracy, far beyond traditional feature extraction and machine learning approaches.

It was only a matter of time until Tesseract incorporated a deep learning model to further boost OCR accuracy — and in fact, that time has come. The latest release of Tesseract v4 supports deep learning-based OCR that is significantly more accurate.

Version 3. The exact commands used to install Tesseract 4 on Ubuntu will be different depending on whether you are using Ubuntu As you can see, I am running Ubuntu For Ubuntu Once you have Tesseract installed on your machine you should execute the following command to verify your Tesseract version:. They are tested, but mileage may vary on your own Raspberry Pi.

If the cv2. For whatever reason, the trained English language data file was missing from the install so I needed to download and move it into the proper directory:. Now that we have OpenCV and Tesseract successfully installed on our system we need to briefly review our pipeline and the associated commands. From there unzip the file and navigate into the directory.

The function:. The function processes this input data, resulting in a tuple containing 1 the bounding box locations of the text and 2 the corresponding probability of that region containing text:. For further details on the code block above, please see this blog post. Using both the original and new dimensions, we calculate ratios used to scale our bounding box coordinates later in the script Lines 89 and I cannot emphasize this enough: you need OpenCV 3. In two lines of code, you have used Tesseract v4 to recognize a text ROI in an image.

Notice how our OpenCV OCR system was able to correctly 1 detect the text in the image and then 2 recognize the text as well. Keep in mind that no OCR system is perfect in all cases. Can we do better by changing some parameters, though? By adding a bit of padding we can expand the bounding box coordinates of the ROI and correctly recognize the text:. See the previous figure for the first, failed attempt.

Our OCR system is far from perfect. But the smaller words are a lost cause likely due to the similar color of the letters to the background.Skip to content. Instantly share code, notes, and snippets. Code Revisions 1. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist.

Learn more about clone URLs. Download ZIP. VideoCapture 0 Load a sample picture and learn how to recognize it. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Load a sample picture and learn how to recognize it. VideoCapture 0. Load a second sample picture and learn how to recognize it. Create arrays of known face encodings and their names. Initialize some variables. Grab a single frame of video.

Only process every other frame of video to save time. Find all the faces and face encodings in the current frame of video. See if the face is a match for the known face s. Or instead, use the known face with the smallest distance to the new face. Display the results. Draw a box around the face. Draw a label with a name below the face.

Display the resulting image.There are some great articles covering these topics for example here or here. You can find plenty of speculation and some premature fearmongering elsewhere.

This post is simply a detailed description of how to get started in Machine Learning by building a system that is somewhat able to recognize what it sees in an image. I am currently on a journey to learn about Artificial Intelligence and Machine Learning. And the way I learn best is by not only reading stuff, but by actually building things and getting some hands-on experience. I want to show you how you can build a system that performs a simple computer vision task: recognizing image content.

If, on the other hand, you find mistakes or have suggestions for improvements, please let me know, so that I can learn from you. The example code is written in Python, so a basic knowledge of Python would be great, but knowledge of any other programming language is probably enough.

Image recognition is a great task for developing and testing machine learning approaches. Vision is debatably our most powerful sense and comes naturally to us humans. But how do we actually do it?

How does the brain translate the image on our retina into a mental model of our surroundings? More than half of our brain seems to be directly or indirectly involved in vision. The goal of machine learning is to give computers the ability to do something without being explicitly told how to do it. We just provide some kind of general structure and give the computer the opportunity to learn from experience, similar to how we humans learn from experience too.

We will try to solve a problem which is as simple and small as possible while still being difficult enough to teach us valuable lessons.

All we want the computer to do is the following: when presented with an image with specific image dimensionsour system should analyze it and assign a single label to it.

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Our goal is for our model to pick the correct category as often as possible. This task is called image classification. CIFAR consists of images. There are 10 different categories and images per category.

Each image has a size of only 32 by 32 pixels. The small size makes it sometimes difficult for us humans to recognize the correct category, but it simplifies things for our computer model and reduces the computational load required to analyze the images.

Because of their small resolution humans too would have trouble labeling all of them correctly. The way we input these images into our model is by feeding the model a whole bunch of numbers.

Each pixel is described by three floating point numbers representing the red, green and blue values for this pixel. Apart from CIFAR, there are plenty of other image datasets which are commonly used in the computer vision community.

Using standardized datasets serves two purposes. First, it is a lot of work to create such a dataset. You need to find the images, process them to fit your needs and label all of them individually.

The second reason is that using the same dataset allows us to objectively compare different approaches with each other. In addition, standardized image datasets have lead to the creation of computer vision high score lists and competitions. The most famous competition is probably the Image-Net Competitionin which there are different categories to detect.

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This was the first time the winning approach was using a convolutional neural network, which had a great impact on the research community. Convolutional neural networks are artificial neural networks loosely modeled after the visual cortex found in animals. This technique had been around for a while, but at the time most people did not yet see its potential to be useful.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am new to TensorFlow and to Deep Learning. I am trying to recognize text in naturel scene images.

image recognition python github

It means that I cropped the image around every character so each picture gives me 10 characters to build my training and test set and they build a two conv neural networks. But I want to go further. What I would like to do is just to give the full pictures and output the entire text not one character such as in my previous model.

The difficulty is that you don't know where the text is. The solution is, given an image, you need to use a sliding window to crop different part of the image, then use a classifier to decide if there are texts in the cropped area.

So you need to train another classifer: given a cropped image the size of cropped images should be slightly larger than that of your text areadecide if there are texts inside. Learn more.

image recognition python github

TensorFlow - Text recognition in image [closed] Ask Question. Asked 3 years, 1 month ago. Active 2 years, 5 months ago. Viewed 11k times. Thank you in advance for any help. Attia A. Attia 3 3 gold badges 13 13 silver badges 24 24 bronze badges.

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Active Oldest Votes. Thanks but should this classifier sliding window must be a convnet? The training set must contained multi character text areas or just one character?GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

image recognition python github

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If nothing happens, download the GitHub extension for Visual Studio and try again. You have at least one image of the person you are looking for and a clue about their name. These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. If Firefox is installed, download the latest release of the Geckodriver for you Architecture. Skip to content.

Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Stalk your Friends. Python Branch: master. Find file.

How to Build a Simple Image Recognition System with TensorFlow (Part 1)

Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. ThoughtfulDev Fixed Latest commit bdf7a7e Mar 22, You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window. Fixed It is the fastest and the simplest way to do image recognition on your laptop or computer without any GPU because it is just an API and your CPU is good enough for this.

For more details refer this tensorflow page.

How to Make an Image Classifier - Intro to Deep Learning #6

Before I start showing you guys how to implement this API with any image, here is an example The whole process will be done in 4 steps :. Open the Command prompt as Admin.

Train Image Recognition AI with 5 lines of code

This will download a mb model which will help you in recognising your image. If everything worked perfectly you will see in your command prompt:. Now just to make sure that we understand how to use this properly we will do this twice. The image I used is below. After coming in the imagenet directory, open the command prompt and type…. Now, obviously results for both the Images were same which is given as below.

As you can see the score is pretty accurate i. Note: Feel free to use any image that you want and keep it in any directory. Just keep in mind to type correct path of the image. I have tried to keep the article as exact and easy to understand as possible. Any comments, suggestions or if you have any questions, write it in the comments.

Clap it! Share it! Follow Me! Happy to be helpful. Epoch vs Batch Size vs Iterations. What the Hell is Perceptron? Activation Function Explained: Neural Networks. Sign in. The whole process will be done in 4 steps : 1. Download the model from tensorflow repository. Command line Open the Command prompt as Admin.

Epoch vs Batch Size vs Iterations 3. Towards Data Science A Medium publication sharing concepts, ideas, and codes.

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