GoCV comes with various useful command line utilities, that are also examples of how to use the package.
Capture video from a connected webcam, then use the Caffe deep learning framework to classify whatever is in front of the camera.
Tests to verify you can capture video from a connected webcam.
Capture video from a connected webcam and display the video in a Window.
Capture video from a pre-recorded file, and then count the number of detected objects that cross a user-definable vertical or horizontal line.
Use a Deep Neural Network to detect and track objects or faces.
Use a Deep Neural Network trained using OpenPose to detect and track human body poses.
Use a Deep Neural Network to perform real-time style transfer.
Captures video from a connected camera, then uses the CascadeClassifier to detect faces, blurs them using a Gaussian blur, then displays the blurred video in a window.
Captures video from a connected camera, then uses the CascadeClassifier to detect faces, and draw a rectangle around each of them, before displaying them within a Window.
Find circles in an image using the Hough transform.
Count the number of fingers being held up in front of the camera by looking for convexity defects.
Compute and compare perceptual hashes for a pair of images, with a variety of algorithms.
Opens a video capture device, then streams MJPEG from it that you can view in any browser.
Opens a video capture device, then processes it looking for motion, human or otherwise.
Advanced Deep Neural Network example does pose detection on an image.
Capture a single frame from a connected webcam, then save it to an image file on disk.
Capture video from a connected camera, and save 100 frames worth to a video file on disk.
Open an image file from disk, then display it in a window.
Advanced Deep Neural Network example that uses SSD classifier to detect faces from a connected camera.
Capture video from a connected webcam, then use the Tensorflow machine learning framework to classify whatever is in front of the camera.
Example of using Tracker from OpenCV Contrib to track any region of interest selected by the user using the TrackerMOSSE algorithm using the connected camera.
Displays the current version of OpenCV that is being used by GoCV.