Fundamentals of image contours

Install Anaconda to create a virtual Python environment, launch a Python interpreter and Jupyter notebook, and verify a working setup for developing image contour projects.

Install OpenCV to enable image manipulation, including grayscale conversion and geometric transforms, by setting up Python 3.x, Xcode command line tools, and Homebrew, then compile OpenCV and verify installation.

install opencv part ii guides you through installing dependencies, verifying the setup, using a jupiter notebook to import and test, and checking results from the command line.

Explore basic image operations, how images are stored, and simple adjustments to intensity and density before tackling convolutions, establishing a foundation for understanding image storage and image mathematics.

Explore basic image processing with OpenCV, loading and inspecting images, and understanding images as pixel arrays with width, height, and color channels (grayscale and BGR color) and pixel intensities 0–255.

learn basic image operations with open cv: inspect image shape and channels, print pixel values, manipulate bgr data, split into four quadrants, and reconstruct and save images.

Learn how to manipulate the image map and pixel intensities by adding or subtracting density values with clipping at 0 and 255, using a matrix-based approach in a C example.

This lecture explains how convolution uses a 3x3 kernel to scan an image, producing a smaller 3x3 output from a 5x5 input, and introduces padding to preserve the original size.

Walks you through implementing a grayscale image convolution with a kernel, using padding and border replication to preserve size, and computing the output via region-of-interest multiplications and sums.

Apply convolution to color and grayscale images using 3x3 and 7x7 kernels, exploring blur, emboss, outline, and sharpen effects with Sobel and other filters in a notebook.

Draw bounding boxes around contours by computing boundingRect on a cloned image, then apply a rotated bounding box method to keep boxes aligned with rotated objects.

Learn how contour properties differentiate objects by using aspect ratio, extent, convex hull, and solidity, with bounding rectangles and convex defects explained.

Identify shapes by computing contour properties—aspect ratio, extent, solidity, and convex hull—using OpenCV and contour analysis.

Master contour approximation to identify shapes by polygonal contours and epsilon, distinguish rectangles from circles, and filter noise by keeping the largest contours with bounding rectangles.