Mastering OpenCV: A Practical Guide to Computer Vision

Set up OpenCV by installing Anaconda, creating a dedicated Python virtual environment, installing cv2, and validating the library with a test in Jupyter notebook.

Learn how to read, view, and save images with OpenCV, convert images to grayscale or color, and handle alpha channels and imread flags.

Explore how image attributes define matrix representations, focusing on shapes and dimensions such as 18x18 and 84x84, and explain why larger images increase the shape with an integer data type.

Show how to display grayscale and color images with matplotlib, explain 0–255 gray values and RGB channels, and note OpenCV uses BGR, requiring channel swap for correct colors.

Learn to split and merge image color channels in OpenCV, understand BGR versus RGB order, and convert between color spaces (including HSV) to modify and visualize individual channels.

Master OpenCV by learning how to save images with imwrite using a filename and an image, handling BGR color channels and confirming the save by re-reading the file.

Manipulate images at the pixel level by accessing and changing matrix-based representations. Resize, crop, flip, and annotate images with practical examples using Python and matplotlib.

Explore resizing images with OpenCV by using the resize method, experimenting with scale factors fx and fy, exact output size, and maintaining aspect ratio through ratio-based calculations.

Learn to crop images with OpenCV by reading an image, converting from BGR to RGB for display, and slicing the array using two points to form a rectangle.

Learn to annotate images using OpenCV by drawing lines, circles, and rectangles, and by adding text, with practical steps for color handling and display.

Access vision devices with OpenCV and read frames from camera or video files. Write the frames to new videos via a video writer with codec, fps, and frame size.

Access the camera in real time with OpenCV, capture frames from the main camera, and display them in a window. Quit with q or save stream with a video writer.

Learn OpenCV image arithmetic for basic enhancement by adjusting brightness and contrast using addition, subtraction and multiplication, with grayscale pixel matrices and clipping to 0-255 to preserve valid values.

Explore basic image enhancement with arithmetic operations, where addition brightens and multiplication increases grayscale contrast. Learn to clip pixel values to 0–255 and preview thresholding and bitwise operations later.

Explore morphological operations in OpenCV, including erosion, dilation, opening, and closing, to remove noise and refine image borders using kernels.

Explore image thresholding in OpenCV, converting grayscale or color images to binary (0 or 255) to separate foreground from background and enable object detection, segmentation, and feature extraction.

Apply OpenCV thresholding to grayscale images using binary, inverse binary, and adaptive methods. Explore mean and Gaussian adaptive thresholds and how illumination affects results.

Explore contours as curves joining points with the same color intensity, and learn how to use binary images, thresholding, and OpenCV's findContours to detect and draw object borders.

Align a scanned image to a reference using homography, feature detection and matching, and robust warping with RANSAC.

Master panorama creation by stitching multiple overlapping images into a seamless wide view. Align images using key points, pairwise correspondences, and refined homographies, then blend for a single composite.

Explore object detection by loading a pre-trained model from the TensorFlow model zoo, reading and preprocessing an image, running the model, and displaying detected objects with labels.

Implement object detection with a pre-trained TensorFlow model in OpenCV, downloading the model, loading config, preprocessing the image into a blob, and drawing boxes with class labels and confidence thresholds.