Contents:

• How to Install TensorFlow GPU?
• How to convert an image sequence into a 3D array in MATLAB?
• How to find the image spatial resolution?
• How to locally run Colab notebooks (seamlessly)?
• How to create publication-quality plots in LaTeX via MATLAB?

How to Install TensorFlow GPU?

Here's a step-by-step tutorial on how to install TensorFlow GPU using Conda:

Step 1: Install Anaconda or Miniconda

• Download and install Anaconda or Miniconda from the official website: https://www.anaconda.com/products/individual

Step 2: Create a new Conda environment

• Open a terminal or Anaconda Prompt.
• Create a new Conda environment with a specific Python version by running the following command:

conda create --name tf_gpu python=3.9

• Activate the newly created environment:

conda activate tf_gpu

Step 3: Install TensorFlow GPU

• With the environment activated, run the following command to install TensorFlow GPU:

conda install tensorflow-gpu

• Conda will automatically resolve and install the compatible versions of CUDA Toolkit and cuDNN required for TensorFlow GPU.

Step 4: Verify the installation

• Still within the activated environment, open a Python interactive shell:

python

• Import TensorFlow and check the version:

import tensorflow as tf 
print(tf.__version__)

• Verify that TensorFlow is using the GPU:

print(tf.test.is_gpu_available())

If TensorFlow GPU is properly installed and the GPU is recognized, it should output True.

That's it! You have successfully installed TensorFlow GPU using Conda. Remember to activate the Conda environment (conda activate tf_gpu) whenever you want to use TensorFlow GPU in your projects.

Note: Make sure you have a compatible NVIDIA GPU and the appropriate GPU drivers installed on your system before installing TensorFlow GPU.

How to convert an image sequence into a 3D array in MATLAB?

1. Load the image sequence:
   • Place all the images from the sequence in a single folder.
   • In MATLAB, set the current working directory to the folder containing the images using the cd command:

   cd('path/to/your/image/folder')

   • Get a list of all the image files in the folder using the dir command:

   files = dir('*.jpg'); % Replace '*.jpg' with the appropriate image file extension

2. Determine the image dimensions:
   • Read the first image and convert it to grayscale using the imread and rgb2gray functions:

   firstImage = rgb2gray(imread(files(1).name)); [height, width] = size(firstImage);

   • The height and width variables will store the dimensions of the grayscale images.
3. Preallocate the 3D array:
   • Create a 3D array to store the grayscale image sequence using the zeros function:

   numFrames = length(files); imageArray = zeros(height, width, numFrames, 'uint8');

   • The numFrames variable represents the number of images in the sequence.
4. Load the images into the 3D array:
   • Use a loop to iterate over the image files, convert them to grayscale, and store them in the 3D array:

   for i = 1:numFrames
    imageArray(:,:,i) = rgb2gray(imread(files(i).name)); end

   • Each image is loaded using the imread function, converted to grayscale using rgb2gray, and stored in the corresponding position in the imageArray.
5. Verify the 3D array:
   • Display the dimensions of the resulting 3D array:

   size(imageArray)

   • You can also visualize individual frames from the 3D array using the imshow function:

   imshow(imageArray(:,:,1));

   • Replace 1 with the desired frame number to display different frames.
6. Save the 3D array as a MAT file:
   • Use the save function to save the 3D array as a MAT file:

   save('imageSequence.mat', 'imageArray');

   • The 'imageSequence.mat' argument specifies the filename of the MAT file, and 'imageArray' is the variable name of the 3D array to be saved.

How to find the image spatial resolution?

Here's how to calculate the spatial resolution of an image using Windows Paint:

Step 1: Open the image in Windows Paint

• Right-click on the image file and select "Open with" -> "Paint".
• The image will open in the Windows Paint application.

Step 2: Locate the scale bar in the image

• Find the scale bar within the image, which is typically a line or bar with a known length (e.g., 100 μm, 1 mm).
• The scale bar should have a label indicating its actual size.

Step 3: Measure the length of the scale bar in pixels

• Select the "Rectangle" or "Line" selection tool from the toolbar in Paint.
• Click and drag the selection tool to draw a rectangle or line that precisely covers the length of the scale bar.
• In the status bar at the bottom of the Paint window, you will see the dimensions of the selection (width x height) in pixels.
• Take note of the width (for a horizontal scale bar) or height (for a vertical scale bar) of the selection, which represents the length of the scale bar in pixels.

Step 4: Calculate the spatial resolution

• Divide the actual size of the scale bar by the number of pixels you measured:

spatial_resolution = actual_size / number_of_pixels

• For example, if the scale bar represents 100 μm and you measured it to be 200 pixels long, the spatial resolution would be:

spatial_resolution = 100  / 200  = 0.5 #μm/pixel

• The spatial resolution is now expressed in units of length per pixel (e.g., μm/pixel, mm/pixel).

Step 5: Interpret the spatial resolution

• The spatial resolution indicates the physical size that each pixel in the image represents.
• In the example above, each pixel in the image corresponds to a physical size of 0.5 μm.
• A lower spatial resolution value means that each pixel represents a smaller physical size, indicating higher image resolution and more detail captured per pixel.

By following these steps, you can easily calculate the spatial resolution of an image using Windows Paint and gain a better understanding of the physical scale represented in the image.

How to locally run Colab notebooks (seamlessly)?

Here's how to run notebooks seamlessly on Colab or locally:

 

Step 1: Set up Google Drive

• Create a folder in your Google Drive where you will store your notebook files.
• Place your notebook files in this folder.

 

Step 2: Mount Google Drive in Colab (if using Colab)

• Open a new notebook in Colab.
• Run the following code in a cell to mount your Google Drive:

from google.colab import drive drive.mount('/content/drive')

• Authorize Colab to access your Google Drive by following the provided link and entering the authorization code.

 

Step 3: Set up the notebook

• Add the following code at the beginning of your notebook:

import os
if os.path.exists("C:\"): 
  colab = 0; prepath = 'F:/My Drive/' 
else: 
  colab = 1; prepath = '/content/drive/MyDrive/'
if colab == 1: 
  from google.colab import drive 
  drive.mount('/content/drive')

• Adjust the prepath variable to match the path to your notebook files in Google Drive (if using Colab) or on your local machine. From now on, every time reading or writing a file, you can use prepath before its relative path to make to direct it to the correct location on your machine or on cloud.

  

This code snippet automatically detects whether the notebook is running on Colab or locally by checking for the existence of the "C:" drive. It sets the colab variable accordingly and adjusts the prepath variable to the appropriate path for accessing the notebook files.

 

How to create publication-quality plots in LaTeX via MATLAB?

Here's how to have (very!) high quality plots in Latex:

 

Step 1: Draw the plots in MATLAB and copy the figures with metadata

• Create your plots using MATLAB, ensuring that the figures are of high quality and properly formatted.
• Copy the figures from MATLAB, including the metadata, to ensure that the resolution and other properties are preserved.

 

Step 2: Paste the plot in PowerPoint and add final touches

• Open a new PowerPoint presentation.
• Paste the copied plot from MATLAB into a slide in PowerPoint.
• Add any final touches or annotations to the plot using PowerPoint's tools, such as text boxes, arrows, or shapes.

 

Step 3: Save the plot as a PDF

• Once you are satisfied with the plot in PowerPoint, save the slide as a PDF file.
• Ensure that the PDF is saved with high quality settings to maintain the clarity and resolution of the plot.

 

Step 4: Crop the PDF automatically using the pdfcropmargin package in Python

• Install the pdfCropMargins package in Python using pip:

pip install pdfCropMargins

• Use the following command to automatically crop the PDF file:

pdf-crop-margins -v Figure.pdf

• You can create a batch file (`.bat`) that automates this process:

@echo on call
 C:\anaconda3\Scripts\activate.bat 
pip install pdfCropMargins 
pdf-crop-margins -v Figure.pdf

 

Step 5: Insert the cropped plot into your LaTeX document

• In your LaTeX document, use the \includegraphics command to insert the cropped PDF plot.
• Specify the page number that corresponds to the desired figure within the PDF file.
• For example: \includegraphics[page=1]{Figure.pdf}

 

Step 6: (Optional) Define a counter in LaTeX to automatically increment the page number

• To take it a step further, you can define a counter in LaTeX that automatically increments the page number each time you import a figure from the PDF file.
• This eliminates the need to manually keep track of the page numbers.
• Here's an example of how you can define the counter:

\newcounter{figurepage} 
\setcounter{figurepage}{0} 
\newcommand{\nextfigure}{% \stepcounter{figurepage}% \includegraphics[page=\thefigurepage]{Figure.pdf}% }

• Now, instead of using \includegraphics directly, you can use the \nextfigure command to automatically include the next figure from the PDF file.

 

By following these steps, you can ensure that your plots in LaTeX are of very high quality, with the added convenience of automating the cropping and insertion process.