tomtec77 practical-python-opencv: Examples and exercises from the Practical Python and OpenCV book
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tomtec77 practical-python-opencv: Examples and exercises from the Practical Python and OpenCV book

In the above tutorial you’ll learn how to combine color with locality, leading to a more accurate image search engine. Content-based Image Retrieval (CBIR) is encompasses all algorithms, techniques, and methods opencv introduction to build an image search engine. 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.

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We then advanced to more complex scenarios, addressing data alignment and managing missing values when DataFrames with different structures are concatenated. These examples showcased the robustness of pandas concat in handling datasets that do not perfectly align, illustrating its practical utility in real-world data manipulation tasks. Next we’ll set up your development environment to ensure you have all the necessary tools installed. Following that, we’ll dive into simple examples to help you get comfortable with the basic functionalities of pandas concat.

  1. The following case studies and tutorials will help you learn techniques that you can apply to your projects.
  2. 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.
  3. Object detectors can be trained to recognize just about any type of object.
  4. The goal of the image search engine is to accept the query image and find all visually similar images in a given dataset.

The fastest way to learn OpenCV, Object Detection, and Deep Learning.

It’s safe to say that I have a ton of experience in the computer vision world and know my way around a Python shell and image processing libraries. I’m here to distill all my years of experience into bite size, easy to understand chunks, while sharing the tips, tricks, and hacks I’ve learned along the way. There is just something about a hardcopy of a book that can’t be beat.

Chapter 3: Loading Displaying and Saving

In this section you’ll learn the basics of facial applications using Computer Vision. Inside you’ll learn how to use prediction averaging to reduce “prediction flickering” and create a CNN capable of applying stable video classification. 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. These algorithms utilize keypoint detection, local invariant descriptor extraction, and keypoint matching to build a program capable of stitching multiple images together, resulting in a panorama. To find the corners of an image, use­ the cornerHarris function from OpenCV.

After reading Case Studies, you’ll be able to apply these techniques to solve computer vision problems of your own. Practical Python and OpenCV covers the very basics of computer vision, starting from answering the question “what’s a pixel? ” all the way up to more challenging tasks such as edge detection, thresholding, and finding objects in images. There are some minor layout issues in my mind in that sometimes he refers to an image that either doesn’t appear soon or was inexplicably shown earlier in the chapter. There are also a couple of occasions where he is explaining some code that is not on the page. However these things don’t affect the quality of the code examples themselves.

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. Combining OpenCV with Tesseract is by far the fastest way to get started with OCR. The library was open-sourced in 2005 and later adopted by Google in 2006. The steps in this section will arm you with the knowledge you need to build your own OCR pipelines.

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. 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. One of the most common object detectors is the Viola-Jones algorithm, also known as Haar cascades. 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. 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. Before you can build facial applications, you first need to configure your development environment.

No matter whether you are a beginner or advanced computer vision developer, you’ll definitely learn something new and valuable inside the course. I highly recommend PyImageSearch Gurus to anyone interested in learning computer vision. For chapter seven, you will learn how to use histograms effectively in OpenCV. The histogram will help you determine the contrast, brightness and intensity distributions of the images that you are working with.

An introductory computer vision book that takes an example driven, hands on approach. In just a single weekend, you can learn the basics of computer vision and image processing and have solid foundation to build on. Python has firmly established itself as the go-to programming language for data science projects due to its simplicity, versatility, and rich ecosystem of libraries. With the ever-growing demand for data-driven insights across industries, mastering Python for data science is essential for aspiring data scientists.

You can think of the Gurus course as similar to a college survey course on CV (but much more hands-on and practical). 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 happens due to noise in the input frames confusing the classification model. Take note of them and then revisit your ideas after you finish these tutorials. You should pay close attention to the tutorials that interest you and excite you the most. You should follow Step #1 from the Deep Learning section to ensure TensorFlow and Keras are properly configured.