OpenCV Practical with Python - 3 Complete Projects + CODE

Compare MATLAB, Python, and C++ for OpenCV projects, highlighting MATLAB's matrix prowess, toolboxes, visualization, and strong documentation, alongside its high cost, steep learning curve, and slower runtime.

Compare OpenCV for C++ in computer vision, highlighting free licensing, a large optimized library, cross-platform support, and a strong community, with beginner difficulty and documentation and debugging challenges.

Compare Matlab, C++, and Python for OpenCV development, highlighting Python’s ease of use, powerful scientific computing ecosystem, rapid prototyping, and the plan to shift to C++ for production.

Explore OpenCV, a cross-platform, open-source library for real-time computer vision and image and video processing, with Python bindings and a BSD license.

Set up a robust OpenCV with Python development environment on Ubuntu by installing and using pyenv to manage multiple Python versions, keeping the system Python separate.

install and configure pyenv to manage multiple python versions, set global per-user and per-project versions, override with environment variables, and switch between them for testing and development.

Learn to set up a Python virtual environment with venv for OpenCV projects, install OpenCV and dependencies, activate environments, and manage module versions to avoid conflicts.

OpenCV practical with Python guides building a motion detector that streams from webcam or video file. Use argparse and a 500-pixel minimum area to detect motion.

Explore the theory of motion detection using background subtraction and foreground segmentation in OpenCV, including first-frame background modeling, min-area filtering, and handling shadows and lighting changes.

Learn to process video frames for motion detection with OpenCV in Python: resize to 500, grayscale, and Gaussian blur; model the first frame as the background for subtraction.

Detect motion with OpenCV and Python by comparing first frame to frames, threshold at 25, dilate, detect contours, filter by min area, and draw bounding boxes while updating motion status.

Display the motion status on the frame's top-left and a timestamp, using the font and color, while showing frame, threshold, and delta, then stop with a key and release resources.

Run and verify the motion detection project in Python using OpenCV with webcam input, viewing frame, delta, and threshold windows, with contour lines indicating motion in this final video.

Delve into hand detection in video with OpenCV and Python by applying background modeling and thresholding to recognize hand gestures, building on prior motion detection.

learn to build a real-time hand gesture recognition app with OpenCV and Python by discovering background subtraction, thresholding, and contours to segment the hand and count fingers.

Build a hand detector by segmenting the hand via background subtraction with a running average, then apply motion detection, thresholding, and contour extraction to draw a bounding box.

Learn hand gesture recognition using motion detection and background subtraction, then extract contours through thresholding and grayscale processing to identify hands in OpenCV.

Detect a hand in real time with OpenCV and Python by capturing webcam video, converting to grayscale, thresholding, and locating contours and convex hulls to count fingers.

Practice a hand detector project in OpenCV with Python by extracting fingers from convex hull defects, counting fingertips, and visualizing results with interactive controls.

Explore a practical OpenCV with Python hand detector project, running the gesture recognition script to detect a hand, analyze contours and convex defects, and count fingers.

Wrap up the hand detection project and preview the next project in the next video.

Build a facial recognition application with OpenCV in Python, learning how to detect faces and identify who they belong to, using code taught in the project.

Build a facial recognition application by chaining face detection, facial feature analysis, and comparison against known faces in a step-by-step OpenCV Python pipeline.

Build a facial recognition app with Python face_recognition to detect faces in images, use PIL for visualization, and explore command-line usage and accuracy on labeled faces in the wild.

Set up a Python OpenCV face recognition environment, install Pillow and face_recognition, and run code to detect, print coordinates, and crop faces for review.

Explore the face detection stage in a Python OpenCV project, explaining how Viola-Jones and HOG gradients locate faces and draw bounding boxes before posing and projecting faces.

Advance the facial recognition project by detecting faces, drawing rectangles in the camera feed, and using key facial points to identify the same person despite bad lighting.

Estimate 68 facial landmarks and warp each face with an affine transformation so eyes and mouth align, centering features for more accurate face comparison in future steps.

This lecture shows standardizing face images for recognition by extracting 68 landmarks and aligning eyes and nose with a fine transformation, then simulating makeup using Python and PIL.

Run the face pose estimation script to visualize landmarks, and color the left eyebrow, right eyebrow, left eye, right eye, upper lip, and lower lip.

Learn how to advance from direct face comparison to face encoding in OpenCV with Python, addressing the limitations of naive recognition and preparing for a robust solution.

Encode faces into 128-dimensional embeddings with a convolutional neural network and triplet loss, then recognize faces by Euclidean distances between embeddings, using OpenFace models.

Encode a face using hog, detect landmarks, align, and extract a 128-dimension embedding; then compare to a database using euclidean distance to identify the closest match with a trained classifier.