
Learn YOLO concepts with OpenCV to detect, track, and count vehicles in video using YOLO v8 and Deepsort, train a custom YOLO model with Roboflow, and generate a heat map.
Learn to use OpenCV and numpy to draw a line, rectangle, circle, and polygon on an image with practical examples and function calls.
Explore OpenCV basics for image manipulation, including reading and writing images, converting color channels to gray, resizing, and applying filters such as Gaussian and median blur.
Load a video and apply a pre-trained YOLO model with OpenCV to detect objects frame by frame, drawing bounding boxes and labels for football players and person.
Track and count highway vehicles by integrating YOLO detection with Deepsort tracking in OpenCV, assigning unique IDs and counting crossings of a line.
Learn to generate heat maps of people movement by tracking objects in a video using Yolo from Ultralytics and heat map generation with OpenCV.
Annotate video frames in Roboflow to build a custom YOLOv8/yolov5 dataset with bounding boxes for cars, trucks, and bikes, then export formats and set train, test, and validation splits.
Train a custom YOLO model on Google Colab using Ultralytics, preparing train/valid/test data and a data.yml to achieve strong map scores.
Detect and track moving vehicles on a highway using a custom YOLO model, OpenCV and Deepsort, draw bounding boxes and IDs, and count vehicles as they cross lane lines.
contains codes used in this tutorial.
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