More specifically, I show how your team can leverage the rich interactive capabilities of selected MATLAB apps to prepare, label, annotate, and preprocess your data before using it as the input to your neural network – and everything else that follows in the traditional deep learning pipeline. In this blog post, I focus on integrating MATLAB apps into a Python deep learning workflow for computer vision and image analysis tasks, with emphasis on the data preparation stage of the traditional deep learning workflow (Figure 1).
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Promote code integration among team members and collaborators using different frameworks and tools.Some of the main reasons for calling MATLAB from Python can be motivated by the need to:
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These resources can be extremely valuable when learning how to call Python scripts from MATLAB and vice-versa. The interoperability of MATLAB and Python has been extensively documented in videos, webinars, blog posts, and the official MATLAB documentation. Fortunately, you can use the two languages together, which we will show next. I am also a strong proponent of always using the best available tools to get the job done in the best possible way. I know how important it is to promote and facilitate the adoption of a streamlined and well-documented deep learning workflow. I have also worked with multidisciplinary teams who adopt a variety of tools and are well-versed in diverse skill sets. In my personal experience, I have used both MATLAB (for 25 years and counting) and Python (for less than a decade) in different research projects, classes, bootcamps, and publications, mostly in the context of image processing/analysis, computer vision, and (more recently) data science, machine learning, and deep learning. MATLAB also has a vibrant community of users who contribute additional functionality (including apps and entire toolboxes) and a growing presence in popular code-sharing repositories such as GitHub. It features many specialized toolboxes which encapsulate relevant algorithms, interactive tools, and rich examples in areas such as machine learning, deep learning, image processing, and computer vision (to mention but a few).
It has a successful history of widespread adoption by engineers and researchers in industry and academia.
MATLAB is a programming and scientific computing platform used to analyze data, develop algorithms, and create models in a variety of fields of science and engineering.
It has been adopted by many data scientists and machine/deep learning researchers thanks to popular packages (e.g., scikit-learn) and frameworks (e.g., Keras, TensorFlow, PyTorch).
In this blog post, I will show how your team can use both MATLAB and Python effectively and provide an easy-to-follow recipe that you should allow you to leverage “the best of both worlds” when building computer vision solutions using deep learning.īackground Python is a programming language created by Guido van Rossum in the early 1990s. The success and popularization of DL in computer vision and related areas (e.g., medical image analysis) has been fostered, in great part, by the availability of rich tools, apps and frameworks in the Python and MATLAB ecosystems. You can follow him on Twitter ( In this blog post, Oge will cover how to do Deep Learning using both Python and MATLAB for a Computer Vision example.ĭeep Learning (DL) techniques have changed the field of computer vision significantly during the last decade, providing state-of-the-art solutions for classical tasks (e.g., object detection and image classification) and opening the doors for solving challenging new problems, such as image-to-image translation and visual question answering (VQA). Oge is a Sigma Xi Distinguished Speaker, book author, and AAAS Leshner Fellow. He also happens to be a MATLAB aficionado and has been using MATLAB in his classroom for more than 20 years. This post is from Oge Marques, PhD and Professor of Engineering and Computer Science at FAU.
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