Fundamentals of Image thresholding and masking

Install Anaconda to create a virtual Python environment and run interactive code with Jupiter notebook, verify with a Python interpreter, and launch notebooks.

Install OpenCV to enable image manipulation, grayscale conversion, rotation, and other transforms by compiling with Xcode, configuring Python environments, and installing Homebrew, then verify installation.

Install OpenCV part ii verifies dependencies and installs OpenCV 3.4.2, then uses a conda environment and a Jupyter notebook to import and test the package.

Explore how images are stored and apply basic operations like increasing intensity and decreasing density to build a solid foundation for understanding image convolution and thresholding.

Learn basic image processing with OpenCV by loading images, saving in formats, and analyzing pixel arrays, including splitting images into parts. Explore grayscale and color images and their RGB/BGR channels.

Analyze a 3-channel BGR image by inspecting its shape, height and width, and pixel values; split into four quadrants, extract each region, and reconstruct or display them with OpenCV.

Apply image arithmetic on pixel densities to brighten or darken a 0–255 image, using add and subtract with clipping at 0 and 255.

Explore convolution basics by applying a 3x3 kernel to a 5x5 image, performing element-wise multiplication and sum, and using padding to preserve size via the output formula.

Showcases convolution of a grayscale image with a kernel, padding by (kernel-1)/2, inflating borders with copyMakeBorder, and performing region-wise multiplications and sums to produce the output.

Explore practical convolution with common kernels: blur, emboss, outline, and sharpen; plus grayscale conversion and Sobel filters. Learn how kernel size and sequencing affect image features in a notebook workflow.

Apply simple thresholding to isolate the foreground in an image using grayscale conversion and masking. Learn to extract foreground and background with binary masks in Python using OpenCV.

Explore Otsu's thresholding for automatic, dynamic masking that separates foreground and background by analyzing pixel intensity histograms, reducing manual trial-and-error across varying lighting.

See how adaptive thresholding uses a local neighborhood to compute a variable threshold, using arithmetic or weighted means, subtracting a constant, and sliding a window for fine, binary outlines.

Master image thresholding and masking to extract foreground and background, create masks and sketch-like objects, with hands-on practice across different images and parameters.