Deep Learning for Computer Vision with Python as well as MATLAB

Deep Learning (DL) techniques have transformed computing vision in a significant way in the past decade, providing cutting-edge solutions for traditional tasks (e.g., object detection as well as image classification) and opening the way to tackle new and challenging problems such as image-to-image translation and visual answer (VQA).

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The growth and popularity of DL in computer vision and related fields (e.g., imaging analysis for medical purposes) has been facilitated by the availability of a wide range of tools and apps.

Through the abundance of powerful frameworks, tools, and apps within the Python and MATLAB ecosystems.

In this blog entry, I'll demonstrate how your team could efficiently use each of MATLAB and Python and offer an easy-to-follow formula that will be able to use "the greatest of both of them" in the development of computer vision applications using deep learning.

Python is a programming language invented by Guido van Rossum in the early 1990s. It has been used by numerous data scientists as well as deep learning researchers because of popular programs (e.g., Scikit-learn, etc.)) as well as Frameworks (e.g., Keras, TensorFlow, PyTorch ).

MATLAB is an application for scientific computing and programming platform used to analyze data, create algorithms, and develop models in various areas of engineering and science. It has had a long and successful track record of use by researchers and engineers in academia and industry.

It has a variety of particular toolboxes that provide practical algorithms and interactive software and numerous examples from areas like deep learning, machine imaging, learning, and even computer vision (to name just a few). MATLAB also has a thriving community of users who provide additional features (including tools and apps) and has a growing presence in the most popular repositories for code sharing like GitHub.

In my own experience, I've utilized both MATLAB (for over 25 decades and counting) as well as Python (for more than ten years) in numerous research projects, boot camps, classes and research papers generally in the field of computer vision and image processing as well as (more lately) Data Science, machine learning and deep learning.

I've also worked with multidisciplinary teams that employ a range of software and tools, and I am skilled in various abilities. I understand how crucial it is to encourage and encourage the use of a well-documented and streamlined deep learning process. Additionally, I'm also a firm advocate of always using the most efficient tools available to complete the task efficiently.

It is a good thing that you can utilize both languages in conjunction, and we'll show it in the next.

Context and its scope

The interoperability between MATLAB and Python is extensively explained in webinars, videos, blog posts, and the officially published MATLAB documentation. These sources can prove extremely useful when learning to run Python scripts in MATLAB and vice versa.

Some of the most important reasons to call MATLAB from Python are based on the necessity to:

1. Facilitate code integration among team members and collaborators using various frameworks and tools.

2. The leverage functionality is only accessible within MATLAB applications, like applications and toolboxes (including those from third-party sources) created by the MATLAB community).

The blog in this article will concentrate on integrating MATLAB applications to a Python deep-learning workflow that can be used for computer imaging and image analysis tasks, focusing on the data preparation phase of the traditional deep-learning workflow.

Mainly, I explain the way your group can use the interactive capabilities of specific MATLAB applications to create, label, annotate, label, and process your data before applying it as input for the neural network you have made - as well as everything else that follows within the traditional deep learning pipeline.

If you've got MATLAB running and set up on your computer and you have a favorite Python configuration (e.g., with Jupyter notebooks) and calling MATLAB using the Python script can be a simple procedure, and the main steps include:

1. (In MATLAB) Install the MATLAB Engine API for Python, which comes with Matlab's Python program. Matlab lets you call MATLAB functions and exchange the data in Python and MATLAB.

2. (In Python) Configure paths and the working directory.

(In MATLAB) Use MATLAB to work (interactively) using the chosen application and export the results to variables within your workspace.

(In Python) Save the required variables to continue this workflow process, e.g., images name and label (and its bounding boxes).

(In Python) Utilize the variables as you need, e.g., processing tabular data using pandas or using images-related labels as the base truth.

Repeat steps 3-7 as many times as you need within your process.

(In Python) Stop using your MATLAB engines:

Here's one example of how you can make use of Python and MATLAB in conjunction for two distinct tasks that fall under the category of image processing for medicine (using deep-learning) skin segmentation of lesions as well as (medical) images (ROI) marking.

Despite the distinct differences between the tasks, they all follow the same recipe presented earlier. The details of each lesson will be discussed in the next.

Task A: Skin Lesion Segmentation

The Goal: Given an image set containing skin lesions, our goal is to create a deep learning solution to separate every photo, i.e., classifying each pixel to be part of one of the lesions (foreground) or the remaining portion of the picture (background).

The Issue is to develop and validate the deep neural network used to segment images. It is necessary to input both images and the segmentation masks, which are fundamentally binary. In this instance, the foreground pixels (those that correspond to the lesion) are labeled as white, while background pixels are marked black. The neural network's task is to train the segmentation masks used in new images.

The fundamental workflow typically involves using convolutional network structures, like U-net and its variants and variations, with many examples of their implementations in Python or MATLAB. The most crucial element of the solution is the manual creation of binary masks required for training and verification.

Apart from a handful of publicly accessible datasets, interactive software must accomplish this laborious and complex task.

The solution is to use this MATLAB Image Segmenter app to create the binary masks and use the existing (Python for this instance) workflow to create everything else. Image Segmenter lets you create covers by hand and offers various (semi-)automatic methods to accelerate the process and improve the final results. The final segmentation image and the segmented image from the picture can export onto the MATLAB workspace and then downloaded to disk.

Task B: (Medical) Image (ROI) Labeling

Similarly, the Objective is identical to task A above; we would like to develop a deep-learning solution for detecting regions' interest (ROIs) within each photograph, i.e., placing an outline around each pertinent. The most frequent ROI will be a lesion. Other potential ROIs include ruler markers, stickers, and water bubbles. Different possible ROIs include ink marks and other artifacts.

The Challenge is: To train and verify a deep neural network to perform ROI/object detection, it is necessary to input both images and the coordinates and labels of the ROIs that are relevant, which could be expressed in the form of rectangles (most popular) or polygons or masks that are based on pixels (similarly similar to the shows that are employed in segmentation). The task for the neural network is to discover the location and label of the ROIs relevant to new images.

Similar to what we observed in Task A, an essential component of the process is the manual creation of ROIs (polygons and labels) required for training and validation. Except for a handful of publicly accessible datasets, the time-consuming and complex job must be done using an interactive and powerful tool.

The solution: Use Image Labeler MATLAB Image Labeler app to label and create ROIs and use the workflow already in place for everything else. Image Labeler lets you make ROI labels with a variety of shapes, assign them different names and colors, and offers different algorithms that can help speed up and simplify the process and improve the outcomes. The ROIs created are exported into the MATLAB workspace and later be used as variables within Python code (see an example on GitHub to get specifics).

Key takeaways

Deep Learning projects are often collaborative and require top-quality tools available that allow for efficient code integration, development, and testing strategies, encouraging communication and ensuring that code is reproducible. Your team could (and should) utilize the best features tools like MATLAB and Python to develop Deep Learning projects. In this blog post, I have demonstrated how you can use Python and MATLAB to accomplish several tasks that relate to medical and computer vision image analysis issues.

The integration of Python and MATLAB extends far beyond the content of this blog post. Take a look at the resources that follow for more information.