Computer Vision, Deep Learning, and OpenCV step-by-step guides! - pyimagesearch

카테고리 없음 2019. 10. 10. 14:07

https://www.pyimagesearch.com/start-here/

 

 

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How Do I Get Started?

You’re interested in Computer Vision, Deep Learning, and OpenCV…but you don’t know how to get started.

Follow these steps to get OpenCV configured/installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more!

  • Step #1: Install OpenCV + Python on Your System (Beginner)
    • Before you can start learning OpenCV you first need to install the OpenCV library on your system.
    • By far the easiest way to install OpenCV is via pip:
    • However, for the full, optimized install I would recommend compiling from source:
    • Compiling from source will take longer and requires basic Unix command line and Operating System knowledge (but is worth it for the full install).
    • If you’re brand new to OpenCV and/or Computer Science in general, I would recommend you follow the pip install. Otherwise, you can compile from source.
      • If you run into any problems compiling from source you should revert to the pip install method.
    • Please note do that I do not support Windows.
      • I do not recommend Windows for Computer Vision, Deep Learning, and OpenCV.
      • Furthermore, I have not used the Windows OS in over 10+ years so I cannot provide support for it.
      • If you are using Windows and want to install OpenCV, be sure to follow the official OpenCV documentation.
        • Once you have OpenCV installed on your Windows system all code examples included in my tutorials should work (just understand that I cannot provide support for them if you are using Windows).
    • If you are struggling to configure your development environment be sure to take a look at my book, Practical Python and OpenCV, which includes a pre-configured VirtualBox Virtual Machine.
      • All you need to do is install VirtualBox, download the VM file, import it and load the pre-configured development environment.
      • And best of all, this VM will work on Linux, macOS, and Windows!
  • Step #2: Understand Command Line Arguments (Beginner)
    • Command line arguments aren’t a Computer Vision concept but they are used heavily here on PyImageSearch and elsewhere online.
    • If you intend on studying advanced Computer Science topics such as Computer Vision and Deep Learning then you need to understand command line arguments:
    • Take the time now to understand them as they are a crucial Computer Science topic that cannot, under any circumstance, be overlooked.
  • Step #3: Learn OpenCV by Example (Beginner)
    • Congrats, you are now ready to learn the fundamentals of Computer Vision and the OpenCV library!
    • This OpenCV Tutorial will teach you the basics of the OpenCV library, including:
      • Loading an image
      • Accessing individual pixels
      • Array/Region of Interest (ROI) cropping
      • Resizing images
      • Rotating an image
      • Edge detection
      • Thresholding
      • Drawing lines, rectangles, circles, and text on an image
      • Masking and bitwise operations
      • Contour and shape detection
      • …and more!
    • Additionally, if you want a consolidated review of the OpenCV library that will get you up to speed in less than a weekend, you should take a look at my book, Practical Python and OpenCV.
  • Step #4: Build OpenCV Mini-Projects (Beginner)
  • Step #5: Solve More Advanced OpenCV Projects (Intermediate)
  • Step #6: Pick Your Niche (Intermediate)
    • Congratulations, you have now learned the fundamentals of Image Processing, Computer Vision, and OpenCV!
    • The Computer Vision field is compromised of subfields (i.e., niches), including Deep Learning, Medical Computer Vision, Face Applications, and many others.
      • Many of these fields overlap and intertwine as well — they are not mutually exclusive.
      • That said, as long as you follow this page you’ll always have the proper prerequisites for a given niche, so don’t worry!
    • Most readers jump immediately into Deep Learning as it’s one of the most popular fields in Computer Science; however,
  • Where to Next?
    • If you need additional help learning the basics of OpenCV, I would recommend you read my book, Practical Python and OpenCV.
      • This book is meant to be a gentle introduction to the world of Computer Vision and Image Processing through the OpenCV library.
      • And if you don’t know Python, don’t worry!
        • Since I explain every code examples in the book line-by-line, 1000s of PyImageSearch readers have used this book to not only learn OpenCV, but also Python at the same time!
    • If you’re looking for a more in-depth treatment of the Computer Vision field, I would instead recommend the PyImageSearch Gurus course.
      • The PyImageSearch Gurus course is similar to a college survey course in Computer Vision, but much more hands-on and practical (including well documented source code examples).
    • Otherwise, my personal recommendation would be to jump into the Deep Learning section — most PyImageSearch readers who are interested in Computer Vision are also interested in Deep Learning as well.

 

Deep Learning

Deep Learning algorithms are capable of obtaining unprecedented accuracy in Computer Vision tasks, including Image Classification, Object Detection, Segmentation, and more.

Follow these steps and you’ll have enough knowledge to start applying Deep Learning to your own projects.

  • Step #1: Configure your Deep Learning environment (Beginner)
    • Before you can apply Deep Learning to your projects, you first need to configure your Deep Learning development environment.
    • The following guides will help you install Keras, TensorFlow, OpenCV, and all other necessary CV and DL libraries you need to be successful when applying Deep Learning to your own projects:
    • Again, I do not provide support for the Windows OS.
      • I do not recommend Windows for Computer Vision and Deep Learning.
      • Definitely consider using a Unix-based OS (i.e., Ubuntu, macOS, etc.) when building your Computer Vision and Deep Learning projects.
    • If you are struggling to configure your Deep Learning development environment, you can:
  • Step #2: Train Your First Neural Network (Beginner)
    • Provided that you have successfully configured your Deep Learning development environment, you can move now to training your first Neural Network!
    • I recommend starting with this tutorial which will teach you the basics of the Keras Deep Learning library:
    • After that, you should read this guide on training LeNet, a classic Convolutional Neural Network that is both simple to understand and easy to implement:
  • Step #3: Understand Convolutional Neural Networks (Beginner)
    • Convolutional Neural Networks rely on a Computer Vision/Image Processing technique called convolution.
    • A CNN automatically learns kernels that are applied to the input images during the training process.
    • But what exactly are kernels and convolution?
    • Now that you understand what kernels and convolution are, you should move on to this guide which will teach you how Keras’ utilizes convolution to build a CNN:
  • Step #4: Build Your Own Image Dataset (Intermediate)
    • So far you’ve learned how to train CNNs on pre-compiled datasets — but what if you wanted to work with your own custom data?
    • But how are you going to train a CNN to accomplish a given task if you don’t already have a dataset of such images?
    • The short answer is you can’t — you need to gather your image dataset first:
    • The Google Images method is fast and easy, but can also be a bit tedious at the same time.
    • If you are an experiencing programming you will likely prefer the Bing API method as it’s “cleaner” and you have more control over the process.
  • Step #5: Train a CNN on Your Dataset (Intermediate)
  • Step #6: Tuning Your Learning Rate (Intermediate)
    • So, you trained your own CNN from Step #5 — but your accurate isn’t as good as what you want it to be.
    • What now?
    • In order to obtain a highly accurate Deep Learning model, you need to tune your learning rate, the most important hyperparameter when training a Neural Network.
    • The following tutorial will teach you how to start training, stop training, reduce your learning rate, and continue training, a critical skill when training neural networks:
    • This guide will teach you about learning rate schedules and decay, a method that can be quickly implemented to slowly lower your learning rate when training, allowing it to descend into lower areas of the loss landscape, and ideally obtain higher accuracy:
    • You should also read about Cyclical Learning Rates (CLRs), a technique used to oscillate your learning rate between an upper and lower bound, enabling your model to break out of local minima:
    • But what if you don’t know what your initial learning rate should be?
  • Step #7: Data Augmentation (Intermediate)
    • If you haven’t already, you will run into two important terms in Deep Learning literature:
      • Generalization: The ability of your model to correctly classify images that are outside the training set used to train the model.
        • Your model is said to “generalize well” if it can correctly classify images that it has never seen before.
        • Generalization is absolutely critical when training a Deep Learning model.
          • Imagine if you were working for Tesla and needed to train a self-driving car application used to detect cars on the road.
          • Your model worked well on the training set…but when you evaluated it on the testing set you found that the model failed to detect the majority of cars on the road!
          • In such a situation we would say that your model “failed to generalize”.
            • To fix this problem you need to apply regularization.
      • Regularization: The term “regularization” is used to encompass all techniques used to (1) prevent your model from overfitting and (2) generalize well to your validation and testing sets.
    • Data augmentation is a type of regularization technique.
      • There are three types of data augmentation, including:
        • Type #1: Dataset generation and expanding an existing dataset (less common)
        • Type #2: In-place/on-the-fly data augmentation (most common)
        • Type #3: Combining dataset generation and in-place augmentation
      • Unless you have a good reason not to apply data augmentation, you should always utilize data augmentation when training your own CNNs.
      • You can read more about data augmentation here:
  • Step #8: Feature Extraction and Fine-tuning Pre-trained Networks (Intermediate)
    • So far we’ve trained our CNNs from scratch — but is it possible to take a pre-trained model and use it to classify images it was never trained on?
    • Yes, it absolutely is!
    • Taking a pre-trained model and using it to classify data it was never trained on is called transfer learning.
    • There are two types of transfer learning:
      • Feature extraction: Here we treat our CNN as an arbitrary feature extractor.
        • An input image is presented to the CNN.
        • The image is forward-propagated to an arbitrary layer of the network.
        • We take those activations as our output and treat them like a feature vector.
        • Given feature vectors for all input images in our dataset we train an arbitrary Machine Learning model (ex., Logistic Regression, Support Vector Machine, SVM) on top of our extracted features.
        • When making a prediction, we:
          • Forward-propagate the input image.
          • Take the output features.
          • Pass them to our ML classifier to obtain our output prediction.
        • You can read more about feature extraction here:
      • Fine-tuning: Here we modify the CNN architecture itself by performing network surgery.
        • Think of yourself as a “CNN Surgeon”.
        • We start by removing the Fully-Connected (FC) layer head from the pre-trained network.
        • Next, we add a brand new, randomly initialized FC layer head to the network
        • Optionally, we freeze layers earlier in the CNN prior to training
          • Keep in mind that CNNs are hierarchical feature learners:
            • Layers earlier in the CNN can detect “structural building blocks”, including blobs, edges, corners, etc.
            • Intermediate layers use these building blocks to start learning actual shapes
            • Finally, higher-level layers of the network learn abstract concepts (such as the objects themselves).
          • We freeze layers earlier in the network to ensure we retain our structural building blocks
        • Training is then started using a very low learning rate.
        • Once our new FC layer head is “warmed up” we may then optionally unfreeze our earlier layers and continue training
        • You can learn more about fine-tuning here:
    • I’ll wrap up this section by saying that transfer learning is a critical skill for you to properly learn.
      • Use the above tutorials to help you get started, but for a deeper dive into my tips, suggestions, and best practices when applying Deep Learning and Transfer Learning, be sure to read my book:
      • Inside the text I not only explain transfer learning in detail, but also provide a number of case studies to show you how to successfully apply it to your own custom datasets.
  • Step #9: Video Classification (Advanced)
    • At this point you have a good understanding of how to apply CNNs to images — but what about videos?
    • Can the same algorithms and techniques be applied?
    • Video classification is an entirely different beast — typical algorithms you may want to use here include Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs).
    • However, before you start breaking out the “big guns” you should read this guide:
  • Step #10: Multi-Input and Multi-Output Networks (Advanced)
    • Imagine you are hired by a large clothing company (ex., Nordstorms, Neiman Marcus, etc.) and are tasked with building a CNN to classify two attributes of an input clothing image:
      • Clothing Type: Shirt, dress, pants, shoes, etc.
      • Color: The actual color of the item of clothing (i.e., blue, green, red, etc.).
    • To get started building such a model, you should refer to this tutorial:
    • As you’ll find out in the above guide, building a more accurate model requires you to utilize a multi-output network:
    • Now, let’s imagine that for your next job you are hired by real estate company used to automatically predict the price of a house based solely on input images.
    • Both multi-input and multi-output networks are a bit on the “exotic” side.
      • You won’t need them often, but when you do, you’ll be happy you know how to use them!
  • Step #11: Improve Your Deep Learning Models (Advanced)
  • Step #12: AutoML and Auto-Keras (Advanced)
    • What if you…
      • Didn’t have to select and implement a Neural Network architecture?
      • Didn’t have to tune your learning?
      • Didn’t have to tune your regularization parameters?
    • What if you instead could treat the training process like a “black box”:
      • Input your data to an API
      • And let the algorithms inside automatically train the model for you!
    • Sound too good to be true?
    • In some cases it is…
    • …but in others it works just fine!
    • We call these sets of algorithms Automatic Machine Learning (AutoML) — you can read more about these algorithms here:
    • The point here is that AutoML algorithms aren’t going to be replacing you as a Deep Learning practitioner anytime soon.
      • They are super important to learn about, but they have a long way to go if they are ever going to replace you!
  • Where to Next?
    • Congratulations! If you followed the above steps then you now have enough Deep Learning knowledge to consider yourself a “practitioner”!
    • But where should you go from here?
      • If you’re interested in a deeper dive into the world of Deep Learning, I would recommend reading my book, Deep Learning for Computer Vision with Python.
      • Inside the book you’ll find:
        • Super practical walkthroughs that present solutions to actual, real-world image classification problems, challenges, and competitions.
        • Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well.
        • A no-nonsense teaching style that is guaranteed to help you master deep learning for image understanding and visual recognition
      • You can learn more about the book here.
    • Otherwise, I would recommend reading the following sections of this guide:
      • Object Detection: State-of-the-art object detectors, including Faster R-CNN, Single Shot Detectors (SSDs), YOLO, and RetinaNet all rely on Deep Learning.
        • If you want to learn how to not only classify an input image but also locate where in the object is, then you’ll want to read these guides.
      • Embedded and IoT Computer Vision and Computer Vision on the Raspberry Pi If you’re interested in applying DL to resource constrained devices such as the Raspberry Pi, Google Coral, and NVIDIA Jetson Nano, these are the sections for you!
      • Medical Computer Vision: Apply Computer Vision and Deep Learning to medical image analysis and learn how to classify blood cells and detect cancer.

 

Face Applications

Using Computer Vision we can perform a variety of facial applications, including facial recognition, building a virtual makeover system (i.e., makeup, cosmetics, eyeglasses/sunglasses, etc.), or even aiding in law enforcement to help detect, recognize, and track criminals.

Computer Vision is powering facial recognition at a massive scale — just take a second to consider that over 350 million images are uploaded to Facebook every day.

For each of those images, Facebook is running face detection (to detect the presence) of faces followed by face recognition (to actually tag people in photos).

In this section you’ll learn the basics of facial applications using Computer Vision.

  • Step #1: Install OpenCV, dlib, and face_recognition (Beginner)
    • Before you can build facial applications, you first need to configure your development environment.
    • From there, you’ll need to install the dlib and face_recognition libraries.
      • The Install your face recognition libraries of this tutorial will help you install both dlib and face_recognition.
    • Make sure you have installed OpenCV, dlib, and face_recognition before continuing!
  • Step #2: Detect Faces in Images and Video (Beginner)
    • In order to apply Computer Vision to facial applications you first need to detect and find faces in an input image.
    • Face detection is different than face recognition.
      • During face detection we are simply trying to locate where in the image faces are.
      • Our face detection algorithms do not know who is in the image, simply that a given face exists at a particular location.
      • Once we have our detected faces, we pass them into a facial recognition algorithm which outputs the actual identify of the person/face.
    • Thus, all Computer Vision and facial applications must start with face detection.
    • There are a number of face detectors that you can use, but my favorite is OpenCV’s Deep Learning-based face detector:
    • OpenCV’s face detector is accurate and able to run in real-time on modern laptops/desktops.
      • That said, if you’re using a resource constrained devices (such as the Raspberry Pi), the Deep Learning-based face detector may be too slow for your application.
      • In that case, you may want to utilize Haar cascades or HOG + Linear SVM instead:
      • Haar cascades are very fast but prone to false-positive detections.
        • It can also be a pain to properly tune the parameters to the face detector.
      • HOG + Linear SVM is a nice balance between the Haar cascades and OpenCV’s Deep Learning-based face detector.
        • This detector is slower than Haar but is also more accurate.
    • Here’s my suggestion:
      • If you need accuracy, go with OpenCV’s Deep Learning face detector.
      • If you need pure speed, go with Haar cascades.
      • And if you need a balance between the two, go with HOG + Linear SVM.
    • Finally, make sure you try all three detectors before you decide!
      • Gather a few example images and test out the face detectors.
      • Let your empirical results guide you — apply face detection using each of the algorithms, examine the results, and double-down on the algorithm that gave you the best results.
  • Step #3: Discover Facial Landmarks (Intermediate)
  • Step #4: Create Face Application Mini-Projects (Intermediate)
  • Step #5: Build a Face Recognition Dataset (Intermediate)
    • Are you ready to build your first facial recognition system?
    • Hold up — I get that you’re eager, but before you can build a face recognition system, you first need to gather your dataset of example images.
    • The following tutorials will help you create a face recognition dataset:
    • You can then take the dataset you created and proceed to the next step to build your actual face recognition system.
    • Note: If you don’t want to build your own dataset you can proceed immediately to Step #6 — I’ve provided my own personal example datasets for the tutorials in Step #6 so you can continue to learn how to apply face recognition even if you don’t gather your own images.
  • Step #6: Face Recognition (Intermediate)
    • At this point you have either (1) created your own face recognition dataset using the previous step or (2) elected to use my own example datasets I put together for the face recognition tutorials.
    • To build your first face recognition system, follow this guide:
    • The problem with the first method is that it relies on a modified k-Nearest Neighbor (k-NN) search to perform the actual face identification.
      • k-NN, while simple, can easily fail as the algorithm doesn’t “learn” any underlying patterns in the data.
    • To remedy the situation (and obtain probabilities associated with the face recognition), you should follow this guide:
      • OpenCV Face Recognition
      • You’ll note that this tutorial does not rely on the dlib and face_recognition libraries — instead, we use OpenCV’s FaceNet model.
      • A great project for you would be to:
        • Replace OpenCV’s FaceNet model with the dlib and face_recognition packages.
        • Extract the 128-d facial embeddings
        • Train a Logistic Regression or Support Vector Machine (SVM) on the embeddings extracted by dlib/face_recognition
      • Take your time whewn implementing the above project — it will be a great learning experience for you.
  • Step #7: Improve Your Face Recognition Accuracy (Intermediate)
    • Whenever I write about face recognition the #1 question I get asked is:
      • “How can I improve my face recognition accuracy?”
    • I’m glad you asked — and in fact, I’ve already covered the topic.
      • Make sure you refer to the Drawbacks, limitations, and how to obtain higher face recognition accuracy section (right before the Summary) of the following tutorial:
      • You should also read up on face alignment as proper face alignment can improve your face recognition accuracy:
      • Inside that section I discuss how you can improve your face recognition accuracy.
  • Step #8: Detect Fake Faces and Perform Anti-Face Spoofing
    • You may have noticed that it’s possible to “trick” and “fool” your face recognition system by holding up a printed photo of a person or photo of the person on your screen.
      • In those situations your face recognition correctly recognizes the person, but fails to realize that it’s a fake/spoofed face!
      • What do you do then?
    • The answer is to apply liveness detection:
      • Liveness Detection with OpenCV
      • Liveness detection algorithms are used to detect real vs. fake/spoofed faces.
        • Once you have determined that the face is indeed real, then you can pass it into your face recognition system.
  • Where to Next?
    • Congrats on making it all the way through the Facial Applications section!
      • That was quite a lot of content to cover and you did great.
      • Take a second now to be proud of yourself and your accomplishments.
    • But what now — where should you go next?
      • My recommendation would be the PyImageSearch Gurus course.
        • The PyImageSearch Gurus course includes additional modules and lessons on face recognition.
        • Additionally, you’ll also find:
          • An actionable, real-world course on OpenCV and computer vision (similar to a college survey course on Computer Vision but much more hands-on and practical).
          • The most comprehensive computer vision education online today. The PyImageSearch Gurus course covers 13 modules broken out into 168 lessons, with other 2,161 pages of content. You won’t find a more detailed computer vision course anywhere else online, I guarantee it.
          • A community of like-minded developers, researchers, and students just like you, who are eager to learn computer vision and level-up their skills.
      • To learn more about the PyImageSearch Gurus course, just use the link below:

 

Optical Character Recognition (OCR)

One of the first applications of Computer Vision was Optical Character Recognition (OCR).

OCR algorithms seek to (1) take an input image and then (2) recognize the text/characters in the image, returning a human-readable string to the user (in this case a “string” is assumed to be a variable containing the text that was recognized).

While OCR is a simple concept to comprehend (input image in, human-readable text out) it’s actually extremely challenging problem that is far from solved.

The steps in this section will arm you with the knowledge you need to build your own OCR pipelines.

  • Step #1: Install OpenCV (Beginner)
    • Before you can apply OCR to your own projects you first need to install OpenCV.
    • Follow Step #1 of the How Do I Get Started? section above to install OpenCV on your system.
    • Once you have OpenCV installed you can move on to Step #2.
  • Step #2: Discover Tesseract for OCR (Beginner)
    • Tesseract is an OCR engine/API that was originally developed by Hewlett-Packard in the 1980s.
    • The library was open-sourced in 2005 and later adopted by Google in 2006.
    • Tesseract supports over 100 written languages, ranging from English to to Punjabi to Yiddish.
    • Combining OpenCV with Tesseract is by far the fastest way to get started with OCR.
    • First, make sure you Tesseract installed on your system:
    • From there, you can create your first OCR application using OCR and Tesseract:
  • Step #3: OCR Without Tesseract (Intermediate)
    • It’s entirely possible to perform OCR without libraries such as Tesseract.
    • To accomplish this task you need to combine feature extraction along with a bit of heuristics and/or machine learning.
    • The following guide will give you experience recognizing digits on a 7-segment display using just OpenCV:
    • Take your time and practice with that tutorial — it will help you learn how to approach OCR projects.
  • Step #4: Practice OCR with Mini-Projects (Intermediate)
  • Step #5: Text Detection in Natural Scenes (Intermediate)
    • So far we’ve applied OCR to images that were captured under controlled environments (i.e., no major changes in lighting, viewpoint, etc.).
    • But what if we wanted to apply OCR to images in uncontrolled environments?
      • Imagine we were tasked with building a Computer Vision system for Facebook to handle OCR’ing the 350+ million new images uploaded to their new system.
      • In that we case, we can make zero assumptions regarding the environment in which the images were captured.
      • Some images may be captured using a high quality DSLR camera, others with a standard iPhone camera, and even others with a decade old flip phone — again, we can make no assumptions regarding the quality, viewing angle, or even contents of the image.
    • In that case, we need to break OCR into a two stage process:
      • Stage #1: Use the EAST Deep Learning-based text detector to locate where text resides in the input image.
      • Stage #2: Use an OCR engine (ex., Tesseract) to take the text locations and then actually recognize the text itself.
    • To perform Stage #1 (Text Detection) you should follow this tutorial:
    • If you’ve read the Face Applications section above you’ll note that our OCR pipeline is similar to our face recognition pipeline:
      • First, we detect the text in the input image (akin to to detecting/locating a face in an image)
      • And then we take the regions of the image that contain the text, and then actually recognize it (which is similar to taking the location of a face and then actually recognizing who is in the face).
  • Step #6: Combine Text Detection with OCR (Advanced)
    • Now that we know where in the input image text resides, we can then take those text locations and actually recognize the text.
    • To accomplish this task we’ll again be using Tesseract, but this time we’ll want to use Tesseract v4.
      • The v4 release of Tesseract contains a LSTM-based OCR engine that is far more accurate than previous releases.
    • You can learn how to combine Text Detection with OCR using Tesseract v4 here:
  • Where to Next?
    • Keep in mind that OCR, while widely popular, is still far from being solved.
      • It is likely, if not inevitable, that your OCR results will not be 100% accurate.
        • Commercial OCR engines anticipate results not being 100% correct as well.
        • These engines will sometimes apply auto-correction/spelling correction to the returned results to make them more accurate.
          • The pyspellchecker package would likely be a good starting point for you if you’re interested in spell checking the OCR results.
        • Additionally, you may want to look at the Google Vision API:
          • While the Google Vision API requires (1) an internet connection and (2) payment to utilize, in my opinion it’s one of the best OCR engines available to you.
    • OCR is undoubtedly one of the most challenging areas of Computer Vision.

 

Object Detection

Object detection algorithms seek to detect the location of where an object resides in an image.

These algorithms can be as simple as basic color thresholding or as advanced as training a complex deep neural network from scratch.

In the first part of this section we’ll look at some basic methods of object detection, working all the way up to Deep Learning-based object detectors including YOLO and SSDs.

  • Step #1: Configure Your Development Environment (Beginner)
    • Prior to working with object detection you’ll need to configure your development environment.
    • To start, make sure you:
    • Provided you have OpenCV, TensorFlow, and Keras installed, you are free to continue with the rest of this tutorial.
  • Step #2: Create a Basic Object Detector/Tracker (Beginner)
    • We’ll keep our first object detector/tracker super simple.
    • We’ll rely strictly on basic image processing concepts, namely color thresholding.
      • To apply color threshold we define an upper and lower range in a given color space (such as RGB, HSV, L*a*b*, etc.)
      • Then, for an incoming image/frame, we use OpenCV’s cv2.inRange  function to apply color thresholding, yielding a mask, where:
        • All foreground pixels are white
        • And all background pixels are black
      • Therefore, all pixels that fall into our upper and lower boundaries will be marked as foreground.
    • Color thresholding methods, as the name suggestions, are super useful when you know the color of the object you want to detect and track will be different than all other colors in the frame.
    • Furthermore, color thresholding algorithms are very fast, enabling them to run in super real-time, even on resource constrained devices, such as the Raspberry Pi.
    • Let’s go ahead and implement your first object detector now:
    • Then, when you’re done, you can extend it to track object movement (north, south, east, west, etc.):
    • Once you’ve implemented the above two guides I suggest you extend the project by attempting to track your own objects.
      • Again, keep in mind that this object detector is based on color, so make sure the object you want to detect has a different color than the other objects/background in the scene!
  • Step #3: Basic Person Detection (Beginner)
    • Color-based object detectors are fast and efficient, but they do nothing to understand the semantic contents of an image.
    • For example, how would you go about defining a color range to detect an actual person?
      • Would you attempt to track based on skin tone?
        • That would fail pretty quickly — humans have a large variety of skin tones, ranging from ethnicity, to exposure to the sun. Defining such a range would be impossible.
        • Would clothing work?
          • Well, maybe if you were at a soccer/football game and wanted to track players on the pitch via their jersey colors.
          • But for general purpose applications that wouldn’t work either — clothing comes in all shapes, sizes, colors, and designs.
    • I think you get my point here — trying to detect a person based on color thresholding methods alone simply isn’t going to work.
    • Instead, you need to use a dedicated object detection algorithm.
      • One of the most common object detectors is the Viola-Jones algorithm, also known as Haar cascades.
      • The Viola-Jones algorithm was published back in 2001 but is still used today (although Deep Learning-based object detectors obtain far better accuracy).
      • To try out a Haar cascade out, follow this guide:
    • In 2005, Dalal and Triggs published the seminal paper, Histogram of Oriented Gradients for Human Detection.
    • Let’s gain some experience applying HOG + Linear SVM to pedestrian detection:
    • You’ll then want to understand the parameters to OpenCV’s detectMultiScale function, including how to tune them obtain higher accuracy:
  • Step #4: Improving Our Basic Object Detector (Beginner)
    • Now that we’ve seen how HOG + Linear SVM works in practice, let’s dissect the algorithm a bit.
    • To start, the HOG + Linear SMV object detectors uses a combination of sliding windows, HOG features, and a Support Vector Machine to localize objects in images.
    • Finally, you need to understand the concept of non-maxima suppression, a technique used in both traditional object detection as well as Deep Learning-based object detection:
      • Non-Maxima Suppression for Object Detection in Python
      • (Faster) Non-Maxima Suppression in Python
      • When performing object detection you’ll end up locating multiple bounding boxes surrounding a single object.
      • This behavior is actually a good thing — it implies that your object detector is working correctly and is “activating” when it gets close to objects it was trained to detect.
      • The problem is that we now have multiple bounding boxes for one object.
        • To rectify the problem we can apply non-maxima suppression, which as the name suggestions, suppresses (i.e., ignores/deletes) weak, overlapping bounding boxes.
          • The term “weak” here is used to indicate bounding boxes of low confidence/probability.
    • If you are interested in learning more about the HOG + Linear SVM object detector, including:
  • Step #5: Your First Deep Learning Object Detector (Intermediate)
    • For ~10 years HOG + Linear SVM (including its variants) was considered the state-of-the-art in terms of object detection.
    • However, Deep Learning-based object detectors, including Faster R-CNN, Single Shot Detector (SSDs), You Only Look Once (YOLO), and RetinaNet have obtained unprecedented object detection accuracy.
    • The OpenCV library is compatible with a number of pre-trained object detectors — let’s start by taking a look at this SSD:
  • Step #6: Real-time Object Detection with Deep Learning (Intermediate)
    • In Step #5 you learned how to apply object detection to images — but what about video?
    • Is it possible to apply object detection to real-time video streams?
    • On modern laptops/desktops you’ll be able to run some (but not all) Deep Learning-based object detectors in real-time.
    • This tutorial will get you started:
  • Step #7: Deep Learning Object Detectors (Intermediate)
    • For a deeper dive into Deep Learning-based object detection, including how to filter/remove classes that you want to ignore/not detect, refer to this tutorial:
    • Next, you’ll want to practice applying the YOLO object detector:
    • The YOLO object detector is designed to be super fast; however, it appears that the OpenCV implementation is actually far slower than the SSD counterparts.
      • I’m not entirely sure why that is.
    • Furthermore, OpenCV’s Deep Neural Network ( dnn ) module does not yet support NVIDIA GPUs, meaning that you cannot use your GPU to improve inference speed.
      • OpenCV is reportedly working on NVIDIA GPU support but it may not be until 2020 until that support is available.
  • Step #8: Evaluate Deep Learning Object Detector Performance (Intermediate)
    • If you decide you want to train your own custom object detectors from scratch you’ll need a method to evaluate the accuracy of the model.
    • To do that we use two metrics: Intersection over Union (IoU) and mean Average Precision (mAP) — you can read about them here:
  • Step #9: From Object Detection to Semantic/Instance Segmentation (Intermediate)
    • If you’ve followed along so far, you know that object detection produces bounding boxes that report the location and class label of each detected object in an image.
    • But what if you wanted to extend object detection to produce pixel-wise masks?
      • These masks would not only report the bounding box location of each object, but would report which individual pixels belong to the object.
    • These types of algorithms are covered in the Instance Segmentation and Semantic Segmentation section.
  • Step #10: Object Detection on Embedded Devices (Advanced)
    • Deep Learning-based object detectors, while accurate, are extremely computationally hungry, making them incredibly challenging to apply them to resource constrained devices such as the Raspberry Pi, Google Coral, and NVIDIA Jetson Nano.
    • If you would like to apply object detection to these devices, make sure you read the Embedded and IoT Computer Vision and Computer Vision on the Raspberry Pi sections, respectively.
  • Where to Next?
    • Congratulations, you now have a solid foundation on how object detection algorithms work!
    • If you’re looking to study object detection in more detail, I would recommend you:
      • Join the PyImageSearch Gurus course
        • Inside the course I cover the inner-workings of the HOG + Linear SVM algorithm, including how to train your own custom HOG + Linear SVM detector.
      • Take a look at Deep Learning for Computer Vision with Python
        • That book covers Deep Learning-based object detection in-depth, including how to (1) annotate your dataset and (2) train the follow object detectors:
          • Faster R-CNNs
          • Single Shot Detectors (SSDs)
          • RetinaNet
        • If you’re interested in instance/semantic segmentation, the text covers Mask R-CNN as well.
      • Read through Raspberry Pi for Computer Vision
        • As the name suggestions, this book is dedicated to developing and optimizing Computer Vision and Deep Learning algorithms on resource constrained devices, including the:
          • Raspberry Pi
          • Google Coral
          • Intel Movidius NCS
          • NVIDIA Jetson Nano
        • Inside you’ll learn how to train your own object detectors, optimize/convert them for the RPi, Coral, NCS, and/or Nano, and then run the detectors in real-time!

 

Object Tracking

Object Tracking algorithms are typically applied after and object has already been detected; therefore, I recommend you read the Object Detection section first. Once you’ve read those sets of tutorials, come back here and learn about object tracking.

Object detection algorithms tend to be accurate, but computationally expensive to run.

It may be infeasible/impossible to run a given object detector on every frame of an incoming video stream and still maintain real-time performance.

Therefore, we need an intermediary algorithm that can accept the bounding box location of an object, track it, and then automatically update itself as the object moves about the frame.

We’ll learn about these types of object tracking algorithms in this section.

  • Step #1: Install OpenCV on Your System (Beginner)
    • Prior to working through this section you’ll need to install OpenCV on your system.
    • Additionally, I recommend reading the Object Detection section first as object detection tends to be a prerequisite to object tracking.
  • Step #2: Your First Object Tracker (Beginner)
    • The first object tracker we’ll cover is a color-based tracker.
    • This algorithm combines both object detection and tracking into a single step, and in fact, is the simplest object tracker possible.
    • You can read more about color-based detection and tracking here:
  • Step #3: Discover Centroid Tracking (Intermediate)
    • Our color-based tracker was a good start, but the algorithm will fail if there is more than one object we want to track.
    • For example, let’s assume there are multiple objects in our video stream and we want to associate unique IDs with each of them — how might we go about doing that?
  • Step #4: Better Object Tracking Algorithms (Intermediate)
    • OpenCV comes with eight object tracking algorithms built-in to the library, including:
      • BOOSTING Tracker
      • MIL Tracker
      • KCF Tracker
      • CSRT Tracker
      • MedianFlow Tracker
      • TLD Tracker
      • MOSSE Tracker
      • GOTURN Tracker
    • You can learn how to use each of them in this tutorial:
    • The dlib library also has an implementation of correlation tracking:
    • When utilizing object tracking in your own applications you need to balance speed with accuracy.
      • My persona recommendation is to:
        • Use CSRT when you need higher object detection accuracy and can tolerate slower FPS throughput.
        • Use KCF when you need faster FPS throughput but can handle slightly lower object tracking accuracy.
        • Use MOSSE when you need pure speed.
  • Step #5: Multi-Object Tracking (Intermediate)
    • Step #4 handled single object tracking using OpenCV and dlib’s object trackers — but what about multi-object tracking?
    • You should start by reading about multi-object tracking with OpenCV:
    • Multi-object tracking is, by definition, significantly more complex, both in terms of the underlying programming, API calls, and computationally efficiency.
      • Most multi-object tracking implementations instantiate a brand new Python/OpenCV class to handle object tracking, meaning that if you have N objects you want to track, you therefore have N object trackers instantiated — which quickly becomes a problem in crowded scenes.
      • Your CPU will choke on the load and your object tracking system will come to a grinding halt.
    • One way to overcome this problem is to use multiprocessing and distribute the load across multiple processes/cores, thus enabling you to reclaim some speed:
  • Step #6: Applied Object Tracking and Counting (Intermediate)
    • So far you’ve learned how to apply single object tracking and multi-object tracking.
    • Let’s put all the pieces together and build a person/footfall counter application capable of detecting, tracking, and counting the number of people that enter/exit a given area (i.e., convenience store, grocery store, etc.):
    • In particular, you’ll want to note how the above implementation takes a hybrid approach to object detection and tracking, where:
      • The object detector is only applied every N frames.
      • One object tracker is created per detected object.
      • The trackers enable us to track the objects.
      • Then, once we reach the N-th frame, we apply object detection, associate centroids, and then create new object trackers.
    • Such a hybrid implementation enables us to balance speed with accuracy.
  • Where to Next?
    • Object tracking algorithms are more of an advanced Computer Vision concept.
    • If you’re interested in studying Computer Vision in more detail, I would recommend the PyImageSearch Gurus course.
      • This course is similar to a college survey in Computer Vision, but way more practical, including hands-on coding and implementations.

 

Instance Segmentation and Semantic Segmentation

There are three primary types of algorithms used for image understanding:

  1. Image classification algorithms enable you to obtain a single label that represents the contents of an image. You can think of image classification as inputting a single image to a network and obtaining a single label as output.
  2. Object detection algorithms are capable of telling you not only what is in an image, but also where in the image a given object is. Object detectors thus accept a single input image and then returning multiple values as an output. The output itself is a list of values containing (1) the class label and (2) the bounding box (x, y)-coordinates of where the particular object is in the image.
  3. Instance segmentation and semantic segmentation take object detection farther. Instead of returning bounding box coordinates, instance/semantic segmentation methods instead yield pixel-wise masks that tell us (1) the class label of an object, (2) the bounding box coordinates of the object, and (3) the coordinates of the pixels that belong to the object. 

These segmentation algorithms are intermediate/advanced techniques, so make sure you read the Deep Learning section above to ensure you understand the fundamentals.

  • Step #1: Configure Your Development Environment (Beginner)
    • In order to perform instance segmentation you need to have OpenCV, TensorFlow, and Keras installed on your system.
    • Make sure you follow Step #1 from the How Do I Get Started? section to install OpenCV.
    • From there, follow Step #1 from the Deep Learning section to ensure TensorFlow and Keras are properly configured.
  • Step #2: Segmentation vs. Object Detection (Intermediate)
    • Now that you have your deep learning machine configured, you can learn about instance segmentation.
    • Follow this guide to utilize your first instance segmentation network using OpenCV:
    • That guide will also teach you how instance segmentation is different from object detection.
  • Step #3: Applying Mask R-CNN (Intermediate)
    • Mask R-CNN is arguably the most popular instance segmentation architecture.
      • Mask R-CNNs have been successfully applied to self-driving cars (vehicle, road, and pedestrian detection), medical applications (automatic tumor detection/segmentation), and much more!
    • This guide will show you how to use Mask R-CNN with OpenCV:
    • And this tutorial will teach you how to use the Keras implementation of Mask R-CNN:
  • Step #4: Semantic Segmentation with OpenCV (Intermediate)
    • When performing instance segmentation our goal is to (1) detect objects and then (2) compute pixel-wise masks for each object detected.
    • Semantic segmentation is a bit different — instead of labeling just the objects in an input image, semantic segmentation seeks to label every pixel in the image.
      • That means that if a given pixel doesn’t belong to any category/class, we label it as “background” (meaning that the pixel does not belong to any semantically interesting object).
    • Semantic segmentation algorithms are very popular for self-driving car applications as they can segment an input image/frame into components, including road, sidewalk, pedestrian, bicyclist, sky, building, background, etc.
    • To learn more about semantic segmentation algorithms, refer to this tutorial:
  • Where to Next?
    • Congratulations, you now understand how to work with instance segmentation and semantic segmentation algorithms!
    • However, we worked only with pre-trained segmentation networks — what if you wanted to train your own?
      • That is absolutely possible — and to do so, you’ll want to refer to Deep Learning for Computer Vision with Python.
      • Inside the book you’ll discover
        • ​The annotation tools I recommend (and how to use them) when labeling your own image dataset for instance/semantic segmentation.
        • How to train a Mask R-CNN on your own custom dataset.
        • How to take your trained Mask R-CNN and apply it to your own images.
        • My best practices, tips, and suggestions when training your own Mask R-CNN.
      • To learn more about the book just click here.

 

Embedded and IoT Computer Vision

Applying Computer Vision and Deep Learning algorithms to resource constrained devices such as the Raspberry Pi, Google Coral, and NVIDIA Jetson Nano can be super challenging due to the fact that state-of-the-art CV/DL algorithms are computationally hungry — these resource constrained devices just don’t have enough CPU power and sufficient RAM to feed these hungry algorithm beasts.

But don’t worry!

You can still apply CV and DL to these devices — you just need to follow these guides first.

 

Computer Vision on the Raspberry Pi

At only $35, the Raspberry Pi (RPi) is a cheap, affordable piece of hardware that can be used by hobbyists, educators, and professionals/industry alike.

The Raspberry Pi 4 (the current model as of this writing) includes a Quad core Cortex-A72 running at 1.5Ghz and either 1GB, 2GB, or 4GB of RAM (depending on which model you purchase) — all running on a computer the size of a credit card.

But don’t let its small size fool you!

The Raspberry Pi can absolutely be used for Computer Vision and Deep Learning (but you need to know how to tune your algorithms first).

 

Medical Computer Vision

Computer Vision and Deep Learning algorithms have touched nearly every facet of Computer Science.

One area that CV and DL algorithms are making a massive impact on is the field of Medical Computer Vision.

Using Medical Computer Vision algorithms, we can now automatically analyze cell cultures, detect tumors, and even predict cancer before it even metastasizes!

  • Step #1: Configure Your Development Environment (Beginner)
    • Step #2 and #3 of this section will require that you have OpenCV configured and installed on your machine.
    • Step #4 covers how to use Deep Learning for Medical Computer Vision.
      • You will need to have TensorFlow and Keras installed on your system for those guides.
      • You should follow Step #1 from the Deep Learning section to ensure TensorFlow and Keras are properly configured.
  • Step #2: Your First Medical Computer Vision Project (Beginner)
    • Our first Medical Computer Vision project uses only basic Computer Vision algorithms, thus demonstrating how even basic techniques can make a profound impact on the medical community:
    • Fun fact: I wrote the above tutorial in collaboration with PyImageSearch reader, Joao Paulo Folador, a PhD student from Brazil.
      • We then published a paper detailing the method in CLAIB 2019!
      • It’s just further proof that PyImageSearch tutorials can lead to publishable results!
  • Step #3: Create Medical Computer Vision Mini-Projects (Intermediate)
    • Now that you have some experience, let’s move on to a slightly more advanced Medical Computer Vision project.
    • Here you will learn how to use Deep Learning to analyze root health of plants:
  • Step #4: Solve Real-World Medical Computer Vision Projects (Advanced)
    • Our previous sections dealt with applying Deep Learning to a small medical image dataset.
    • But what about larger medical datasets?
    • Can we apply DL to those datasets as well?
    • You bet we can!
    • Take your time working through those guides and make special note of how we compute the sensitivity and specificity, of the model — two key metrics when working with medical imaging tasks that directly impact patients.
  • Where to Next?
    • As I mention in my About page, Medical Computer Vision is a topic near and dear to my heart.
    • Previously, my company has consulted with the National Cancer Institute and National Institute of Health to develop image processing and machine learning algorithms to automatically analyze breast histology images for cancer risk factors.
    • I’ve also developed methods to automatically recognize prescription pills in images, thereby reducing the number of injuries and deaths that happen each year due to the incorrect medication being taken.
    • I continue to write about Medical Computer Vision, so if you’re interested in the topic, be sure to keep an eye on the PyImageSearch blog.
    • Otherwise, you should take a look at my book, Deep Learning for Computer Vision with Python, which covers chapters on:
      • Automatic cancer/skin lesion segmentation using Mask R-CNNs
      • Prescription pill detection/localization using Mask R-CNNs
    • To learn more about my deep learning book, just click here.

 

Working with Video

Most tutorials I have on the PyImageSearch blog involve working with images — but what if you wanted to work with videos instead?

If that’s you, make sure you pay attention to this section.

  • Step #1: Install OpenCV on Your System (Beginner)
    • Prior to working with video (both on file and live video streams), you first need to install OpenCV on your system.
    • You should follow Step #1 of the How Do I Get Started? section to configure and install OpenCV on your machine.
  • Step #2: Accessing your Webcam (Beginner)
    • Now that you have OpenCV installed, let’s learn how to access your webcam.
      • If you are using either a USB webcam or built-in webcam (such as the camera on your laptop), you can use OpenCV’s cv2.VideoCapture  class.
      • The problem with this method is that it will block your main execution thread until the next frame is read from the camera sensor.
        • That can be a big problem as it can dramatically decrease the Frames Per Second (FPS) throughput of your system.
        • To resolve the issue, I have implemented a threaded  VideoStream  class that more efficiently reads frames from a camera:
        • I would also suggest reading the following tutorial which provides a direct comparison of the cv2.VideoCapture  class to my VideoStream  class:
      • If you are using a Raspberry Pi camera module then you should follow this getting started guide to access the RPi camera:
      • Once you’ve confirmed you can access the RPi camera module you can use the VideoStream  class which is compatible with both built-in/USB webcams and the RPi camera module:
      • Inevitably, there will be a time where OpenCV cannot access your camera and your script errors out, resulting in a “NoneType” error — this tutorial will help you diagnose and resolve such errors:
  • Step #3: Face Detection in Video (Beginner)
    • I’m strong believer in learning by doing through practical, hands-on applications — and it’s hard to get more practical than face detection!
    • This tutorial will teach you how to apply face detection to video streams:
  • Step #4: Face Applications in Video (Intermediate)
  • Step #5: Object Detection in Video (Intermediate)
  • Step #6: Create OpenCV and Video Mini-Projects (Beginner/Intermediate)
    • At this point you have a fair amount of experience applying Computer Vision and OpenCV to videos — let’s continue practicing using these tutorials:
    • Take you time working through them and take notes as you do so.
      • You should pay close attention to the tutorials that interest you and excite you the most.
      • Take note of them and then revisit your ideas after you finish these tutorials.
        • Ask yourself how could extend them to work with your own projects?
        • What if you tried a different video source?
        • Or how might you integrate one of these video applications into a home security system?
        • Brainstorm these ideas and then try to implement them yourself — the best way to learn is to learn by doing!
  • Step #7: Image/Video Streaming with OpenCV (Intermediate)
  • Step #8: Video Classification with Deep Learning (Advanced)
    • For this step I’ll be making the assumption that you’ve worked through the first half of the Deep Learning section.
    • Provided that you have, you may have noticed that applying image classification to video streams results in a sort of prediction flickering.
      • A “prediction flicker” occurs when an image classification model reports Label A for Frame N, but then reports Label B (i.e., a different class label) for Frame N + 1 (i.e., the next frame in the video stream), despite the frames having near-identical contents!
      • Prediction flickering is a natural phenomena in video classification.
        • It happens due to noise in the input frames confusing the classification model.
      • One simple method to rectify prediction flickering is to apply prediction averaging:
      • Using prediction averaging you can overcome the prediction flickering problem.
        • Additionally, you may want to look into more advanced Deep Learning-based image/video classifiers, including Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs).
  • Where to Next?
    • If you’re brand new to the world of Computer Vision and Image Processing, I would recommend you read Practical Python and OpenCV.
      • That book will teach you the basics of Computer Vision through the OpenCV library — and best of all, you can complete that book in only a single weekend.
      • It’s by far the fastest way to get up and running with OpenCV.
      • And furthermore, the book includes complete code templates and examples for working with video files and live video streams with OpenCV.
    • For a more detailed review of the Computer Vision field, I would recommend the PyImageSearch Gurus course.
      • The PyImageSearch Gurus course is a comprehensive dive into the world of Computer Vision.
      • You can think of the Gurus course as similar to a college survey course on CV (but much more hands-on and practical).
    • Finally, you’ll note that we utilized a number of pre-trained Deep Learning image classifiers and object detectors in this section.
      • If you’re interested in training your own custom Deep Learning models you should look no further than Deep Learning for Computer Vision with Python.
      • You’ll learn how to create your own datasets, train models on top of your data, and then deploy the trained models to solve real-world projects.
      • It’s by far the most comprehensive, detailed, and complete Computer Vision and Deep Learning education you can find online today.
      • Click here to learn more.

 

Image Search Engines

Content-based Image Retrieval (CBIR) is encompasses all algorithms, techniques, and methods to build an image search engine.

An image search engine functions similar to a text search engine (ex., Google, Bing, etc.).

A user visits the search engine website, but instead of having a text query (ex., “How do I learn OpenCV?”) they instead have an image as a query.

The goal of the image search engine is to accept the query image and find all visually similar images in a given dataset.

CBIR is the primary reason I started studying Computer Vision in the first place. I found the topic fascinating and am eager to share my knowledge with you.

  • Step #1: Install OpenCV on your System (Beginner)
    • Before you can perform CBIR or build your first image search engine, you first need to install OpenCV your system.
    • Follow Step #1 of the How Do I Get Started? section above to configure OpenCV and install it on your machine.
  • Step #2: Build Your First Image Search Engine (Beginner)
  • Step #3: Understand Image Quantification (Beginner)
    • In Step #2 we built an image search engine that characterized the contents of an image based on color — but what if we wanted to quantify the image based on texture, shape, or some combination of all three?
    • How might we go about doing that?
    • In order to describe the contents of an image, we first need to understand the concept of image quantification:
      • How To Describe and Quantify an Image Using Feature Vectors
      • Image quantification is the process of:
        • Accepting an input image
        • Applying an algorithm to characterize the contents of the image based on shape, color, texture, etc.
        • Returning a list of values representing the quantification of the image (we call this our feature vector).
        • The algorithm that performs the quantification is our image descriptor or feature descriptor.
  • Step #4: The 4 Steps of Any Image Search Engine (Beginner)
  • Step #5: Build Image Search Engine Mini-Projects (Beginner)
  • Step #6: Image Hashing (Intermediate)
    • So far we’ve learned how to build an image search engine to find visually similar images in a dataset.
    • But what if we wanted to find duplicate or near-duplicate images in a dataset?
      • Such an application is a subset of the CBIR field called image hashing:
      • Image hashing algorithms compute a single integer to quantify the contents of an image.
      • The goal of applying image hashing is to find all duplicate/near-duplicate images.
      • Practical use cases of image hashing include:
        • De-duping a set of images you obtained by crawling the web.
          • You may be using my Google Images scraper or my Bing API crawler to build a dataset of images to train your own custom Convolutional Neural Network.
          • In that case, you want want to find all duplicate/near-duplicate images in your dataset (as these duplicates provide no additional value to the dataset itself).
        • Building TinEye, a reverse image search engine.
          • Reverse image search engines:
            • Accept an input image
            • Compute its hash
            • And tell you everywhere on the web that the input image appears on
  • Step #7: Scaling Image Hashing Search Engines (Intermediate)
    • At this point you know how image hashing algorithms work — but how can we scale them like TinEye has?
    • The answer is to utilize specialized data structures, such as VP-Trees.
    • This tutorial will show you how to efficiently use VP-Trees to scale your image hashing search engine:
  • Where to Next?
    • The techniques covered here will help you build your own basic image search engines.
    • The problem with these algorithms is they do not scale.
    • If you want to build more advanced image search engines that scale to millions of images you’ll want to look into:
      • The Bag-of-Visual-Words model (BOVW)
      • k-Means clustering and forming a “codebook”
      • Vector quantization
      • Tf-idf weighting
      • Building an inverted index
    • The PyImageSearch Gurus course includes over 40+ lessons on building image search engines, including how to scale your CBIR system to millions of images.
    • If you’re interested in learning more about the course, and extending your own CBIR knowledge, just use the link below:

 

Interviews, Case Studies, and Success Stories

You can learn Computer Vision, Deep Learning, and OpenCV — I am absolutely confident in that.

And if you’ve been following this guide, you’ve seen for yourself how far you’ve progressed.

However, we cannot spend all of our time neck deep in code and implementation — we need to come up for air, rest, and recharge our batteries.

When then happens I suggest supplementing your technical education with a bit of light reading used to open your mind to what the world of Computer Vision and Deep Learning offers you.

After 5 years running the PyImageSearch blog I’ve seen countless readers dramatically change their lives, including changing their careers to CV/DL/AI, being awarded funding, winning Kaggle competitions, and even becoming CTOs of funded companies!

It’s truly a privilege and an honor to be taking this journey with you — thank you for letting me accompany you on it.

Below you’ll find some of my favorite interviews, case studies, and success stories.

  • Step #1: A Day in the Life of Adrian Rosebrock (Beginner)
    • Ever wonder what it’s like to work as a Computer Vision/Deep Learning researcher and developer?
    • You’re not alone.
    • Over the past 5 years running PyImageSearch, I have received 100s of emails and inquiries that are “outside” traditional CV, DL, and OpenCV questions.
    • They instead focus on something much more personal — my daily life.
    • To give you an idea of what it’s like to be me, I’m giving you a behind the scenes look at:
      • How I spend my day.
      • What it’s like balancing my role as a (1) computer vision researcher/developer  and (2) a writer and owner of PyImageSearch.
      • The habits and practices I’ve spent years perfecting to help me get shit done.
    • You can read the full post here:
  • Step #2: Intro to Computer Vision (Beginner)
  • Step #3: Computer Vision — Where are We Going Next? (Beginner)
    • A more recent podcast (April 2019) comes from an interview on the Super Data Science Podcast, hosted by Kirill Eremenko:
    • In the podcast we discuss Computer Vision, Deep Learning, and what the future holds for the fields.
    • I highly recommend listening to this podcast, regardless if you are brand new to Computer Vision or already a seasoned expert — it’s both entertaining and educational at the same time.
  • Step #4: From Developer to CTO (Beginner)
    • Saideep Talari’s story holds a special place in my heart.
    • He started his career as a network tester, found his first job in Computer Vision after completing the PyImageSearch Gurus course, and then after completing Deep Learning for Computer Vision with Python is now the CTO of a tech company with over $2M in funding.
    • He’s also an incredibly nice person — he used his earnings to clear his families debts and start fresh.
    • Saideep is one my favorite people I’ve ever had the privledge of knowing — there’s a lot you can learn from this interview:
  • Step #5: $30,500 in Grant Funding (Beginner)
  • Step #6: Winning Kaggle’s Most Competitive Image Classification Challenge Ever (Beginner)
  • Step #7: Landing a Research and Development (R&D) Position (Beginner)
    • Kapil Varshney was recently hired at Esri R&D as a Data Scientist focusing on Computer Vision and Deep Learning.
    • Kapil’s story is really important as it shows that, no matter what your background is, you can be successful in computer vision and deep learning — you just need the right education first!
    • You see, Kapil is a long-time PyImageSearch reader who read Deep Learning for Computer Vision with Python (DL4CV) last year.
    • Soon after reading DL4CV, Kapil competed in a challenge sponsored by Esri to detect and localize objects in satellite images (including cars, swimming pools, etc.).
    • He finished in 3rd-place out of 53 competitors.
    • Esri was so impressed with Kapil’s work that after the contest they called him in for an interview.
    • Kapil nailed the interview and was hired full-time at Esri R&D.
    • His work on satellite image analysis at Esri now impacts millions of people across the world daily — and it’s truly a testament to his hard work.
    • You can read the full interview with Kapil here:
  • Where to Next?

 

Need More Help?

I’m dedicated to helping you learn Computer Vision, Deep Learning, and OpenCV.

If you need more help from me, here are a few options:

  • Books and Courses
    • Practical Python and OpenCV
      • My gentle introduction to the world of computer vision and image processing through OpenCV.
      • If you’re brand new to the world of computer vision and image processing, start with this book so you can learn the fundamentals first.
    • Deep Learning for Computer Vision with Python
      • In-depth dive into the world of computer vision and deep learning.
      • Whether this is the first time you’ve worked with machine learning and neural networks or you’re already a seasoned deep learning practitioner, DL4CV is engineered from the ground up to help you reach expert status.
    • PyImageSearch Gurus
      • Similar to a college survey course in computer vision but much more hands-on, and practical.
      • Covers 13 modules broken out into 168 lessons, with other 2,161 pages of content.
      • Includes private community forums which I participate in daily.
        • Great way to get faster, more detailed answers to your questions.
    • Raspberry Pi for Computer Vision
      • Apply Computer Vision and Deep Learning algorithms to embedded devices, including the Raspberry Pi, Google Coral, and NVIDIA Jetson Nano.
      • I recommend reading this book together with Practical Python and OpenCV and/or Deep Learning for Computer Vision with Python.
        • RPI for CV uses both Computer Vision and Deep Learning algorithms so some previous experience is suggested but not required.
  • Blog
    • I’ve authored over 350+ free tutorials on the PyImageSearch.com blog.
    • It’s likely that I have already authored a tutorial to help you with your question or project.
      • Make sure you use the “Search” bar to search for keywords related to your topic.
      • The search bar can be found on top-right of the sidebar on every page
  • FAQ
    • I’ve compiled answers to to the most common questions I receive on my official FAQ page.
    • Please check the FAQ as it’s possible that your question has been addressed there.
  • Contact
    • Feel free to ask me a question, but kindly keep it to one question per email.
    • My contact form
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