The first step in any EDA process is to load the image data into your Integrated Development Environment (IDE) workspace, such as VS Code, Jupyter Notebook, or any other Python editor. Depending on the format of the data, you may need to use a library such as OpenCV or PIL to read in the images.
Checking the dimensions
The next step is to check the dimensions of the images. Image dimensions can affect the performance of your model, as larger images require more memory and computation. You should also check that all the images have the same dimensions, as this is a requirement for most computer vision models. If the images are not of the same size, then preprocessing is required to convert them to the same size.
Visualizing the data
Visualization is a powerful tool for understanding image data. You can use the Matplotlib or Seaborn libraries to visualize the data in various ways. You can plot histograms of pixel values to see their distributions or use scatter plots to visualize the relationship between pixel values. We will cover this later in this chapter.
Checking for outliers
Outliers can have a significant impact on your model’s performance. You should check for outliers in your image data by plotting boxplots and examining the distribution of pixel values. In the context of image data, outliers are data points (in this case, images) that significantly deviate from the expected or normal distribution of the dataset. Outliers in image data are images that have distinct characteristics or patterns that are different from the majority of images in the dataset. Images with pixel values that are much higher or lower than the typical range for the dataset can be considered outliers. These extreme values might be due to sensor malfunctions, data corruption, or other anomalies. Images with color distributions that significantly differ from the expected color distributions of the dataset can be considered outliers. These might be images with unusual color casts, saturation, or intensity.
