Python - OpenCV and PyQt5 together

Read the image, display it, and save it (including changing its extension), then verify saving with a terminal printout and use Q to close.

This lecture demonstrates saving a live video with a video writer by configuring the output file and format, choosing a codec, setting frame size and fps, and writing frames.

Learn how to compute and plot an image histogram to analyze contrast and intensity distribution, load grayscale images, and visualize the cumulative distribution function to interpret trends.

Learn how thresholding in OpenCV separates foreground from background using simple thresholding and adaptive thresholding, including global, binary, and binary inverse methods, with trial-and-error value selection.

Learn contour detection using grayscale conversion, Otsu thresholding, and binary thresholding in OpenCV to extract, count, and draw contours on images.

Learn to detect circles in images using the Hough circles transform in OpenCV, by converting to grayscale, applying blur, and identifying circle centers and radii for visualization.

Learn how to implement a status bar in a PyQt5 interface, format and display the current date and time, and customize messages and icons for user feedback.

Create a PyQt5 interface with a box layout that splits the main window into a left image area and a right options panel, using image labels and geometry settings.

Learn to load an image from the computer using a file open action, convert it to the correct color format, and display it in a PyQt5 QLabel with proper sizing.

Learn to build a dropdown menu in PyQt5 by creating a function menu with a drawing shapes submenu, wiring actions like draw line, draw circle, and blur image.

The lecture demonstrates adding more functions to an OpenCV and PyQt5 project, including loading and displaying images, drawing lines, and applying blur.

Explore creating an information message box in PyQt5 for OpenCV apps, including importing the box, creating it, and configuring title and text in the parent window.

Explore the fundamentals of machine learning, including supervised, unsupervised, and semi-supervised learning, and review classification, regression, and clustering algorithms.

Demonstrates k-means clustering, showing how centroids define two data groups and assign labeled, color-coded clusters with random initialization. Illustrates data preparation, applying means, and visualizing results, including a real-image example.

Learn how to apply k-means clustering to an image by converting pixels to data points, choosing a k value, and reconstructing a color-quantized result for display.

Prepare for recognition by using image segmentation and color analysis, building a four-category dataset, applying circle detection for cropping, and using histograms to distinguish color groups.

Detect circles, create masks, and segment regions to predict classifications, then compare using a nearest-neighbor classifier and histogram validation on the output images.

Explore starting and stopping the webcam, capturing live frames, recording video, saving images, and coin detection in a PyQt5 and OpenCV setup, with hands-on demonstrations and guidance.