OpenCV Python For Beginners | Hands on Computer Vision

Introduce NumPy as the Python library for array oriented computing, featuring a powerful n-dimensional array with slicing and reshaping, and provides pip-based installation across Windows, Mac, and Linux.

Explore how Python lists compare to NumPy arrays for basic operations and vectorized math, including concatenation versus vector addition, scalar multiplication, and element-wise functions like sqrt and log.

Master basic operations on NumPy arrays, including 1D and 2D arrays. Index elements, inspect shape, dtype, and transpose, and compute min, max, and sum along axes.

Learn to create arrays with zeros, ones, and empty; specify dtype, use arange and linspace, and generate random data. Reshape, stack, and split arrays along axes for flexible data handling.

Explore OpenCV, the open source computer vision library for real-time image processing with Python, and learn how grayscale and RGB images are stored as pixel matrices using NumPy.

Install Python 3.7 on Windows 10 with a customized setup and add Python to path. Then install OpenCV for Python via pip and verify by importing cv2 and printing cv2.__version__.

Learn to install opencv for python on ubuntu 18.04 through terminal, update packages, install python3 opencv, and verify by importing cv2 and checking the version.

Learn to read, display, and write images with OpenCV in Python using cv2.imread, cv2.imshow, cv2.waitKey, and cv2.imwrite, exploring color, grayscale, and unchanged options.

Learn to read, display, and save video from the default camera in Python with OpenCV, using video capture, frames, grayscale conversion, and a video writer.

Learn to draw lines, rectangles, circles, and text on images with OpenCV in Python, adjusting color in BGR, thickness, and fill options, and create black images with numpy.

Set and read video frame properties in OpenCV Python with cap.get for width and height, and cap.set or numeric ids to adjust the frame size, noting cameras use available resolutions.

Learn to display current date and time on live video streams using OpenCV Python, by drawing text on frames with cv2.putText and handling video capture.

Learn to handle mouse events in OpenCV with Python by using a mouse callback to print x,y on left-click and display BGR channel values on right-click.

Learn to handle mouse click events in OpenCV Python by placing circles on clicks, linking points with lines, and displaying the clicked color in a separate window.

Master OpenCV image operations in Python, including split and merge, resize, ROI, and add and addWeighted to blend images.

Explore bitwise operations on images with Python and OpenCV, including and, or, not, and xor, using masks to modify and combine binary regions.

Bind trackbars to an OpenCV window to dynamically adjust B, G, and R values in real time. Use a switch trackbar to toggle between color and grayscale processing.

Learn color-based object detection and tracking in Python with OpenCV using the HSV color space, including thresholding, masking, and live video tracking with trackbars.

Explore simple image thresholding with OpenCV to separate an object from its background, using binary, binary inverse, trunc, to-zero, and to-zero inverse thresholding on grayscale images.

Learn how adaptive thresholding computes region-based thresholds in OpenCV using mean or Gaussian methods, with block size and a constant C to improve readability under varying illumination.

Learn to display OpenCV images with matplotlib, convert BGR to RGB for correct colors, and use subplots, hover coordinates, and image saves for multi-image visualization.

Explore morphological transformations in OpenCV Python for beginners, including erosion, dilation, opening, and closing, with kernels and binary masks to shape images.

Explore smoothing and blurring techniques in OpenCV Python, applying kernels and 2D convolution to reduce noise with homogeneous, Gaussian, median, and bilateral filters.

Learn image gradients and edge detection in Python using OpenCV, applying Laplacian, Sobel X, and Sobel Y methods to detect edges and analyze directional intensity changes.

Learn how the canny edge detector in OpenCV powers python image processing with a multi-stage approach, including gaussian smoothing, gradients, non-maximum suppression, double threshold, and hysteresis, with adjustable thresholds.

Learn how image pyramids in OpenCV and Python handle multi-scale images by using Gaussian and Laplacian pyramids, with pare down and pair up operations for downsampling, upsampling, and reconstruction.

Explore image blending with OpenCV Python by building the gossip pyramid and La Plaza and pyramid for two images, blend their halves at each level, and reconstruct a seamless result.

Find contours in a binary image and draw them on the original. Learn thresholding, contour retrieval mode, and drawing with OpenCV in Python.

Implement a Python and OpenCV motion detection and tracking system that draws rectangles around moving people using contour analysis and a movement status display.

Detect simple geometric shapes with OpenCV in Python by grayscale conversion, thresholding, contours, and polygon approximation, then annotate shapes like triangle, square, circle, pentagon, and star.

Explore how to analyze image intensity with histograms in OpenCV Python, from grayscale to BGR channels, using matplotlib and calcHist to visualize pixel distributions and assess exposure.

Learn template matching in OpenCV with Python by applying matchTemplate, identifying the brightest match via a threshold, and drawing a rectangle around the detected area.

Explore the probabilistic hough transform with houghlinesp in OpenCV, returning line endpoints from edge points for efficient line detection. Learn key parameters and apply to road lane lines.

Study the Hough transform theory for detecting lines from edge pixels by mapping to the m-s space and rho-theta space, using an accumulator to identify line intersections.

Demonstrates the standard Hough line transform in OpenCV Python, using canny edge detection and polar-to-cartesian conversion to detect lines, noting infinite lines and upcoming probabilistic HoughLinesP coverage.

Detect circles in images with OpenCV's Hough circle transform in Python, using grayscale conversion and median blur, then draw the detected circles and their centers on the output image.

Develop a basic road lane line detector in Python with OpenCV, starting from a road image, converting color, and applying a triangular region of interest to isolate lanes.

Learn road lane line detection with OpenCV by applying grayscale conversion, canny edge detection, region of interest filtering, and probabilistic Hough line transform to draw lane lines.

Apply lane line detection to video frames with OpenCV in Python by processing each frame, drawing lines, and tuning thresholds for clearer road lane detection.

Detect faces with cascade classifiers in OpenCV Python; train with positive and negative images, load the classifier, convert to grayscale, and apply detectMultiScale to images and video.

Extend your OpenCV Python project to detect eyes inside faces using a pre-trained haar cascade classifier and draw green rectangles around each detected eye.