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Object detection and counting involve identifying objects in an image or video and determining their total number. This can be achieved using deep learning models like YOLO (You Only Look Once) for detection and OpenCV for image processing and counting.
Using YOLO with OpenCV YOLO models (e.g., YOLOv3, YOLO11) can detect multiple object classes in real-time. Once objects are detected, OpenCV can be used to draw bounding boxes and count detections.
Example with Ultralytics YOLO11 for counting in a defined region:
import cv2from ultralytics import solutionsdef count_objects(video_path, output_path, model_path):cap = cv2.VideoCapture(video_path)assert cap.isOpened(), "Error reading video file"w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)]counter = solutions.ObjectCounter(show=True, region=region_points, model=model_path)while cap.isOpened():success, frame = cap.read()if not success:breakresults = counter(frame)writer.write(results.plot_im)cap.release()writer.release()cv2.destroyAllWindows()count_objects("input.mp4", "output.avi", "yolo11n.pt")Copied!âś•Copy YOLOv8 Object Tracking and Counting - LearnOpenCV
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A comprehensive Python-based object detection system that automatically detects and counts objects in video feeds using YOLO (You Only Look Once) deep learning model.
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For more information on counting objects with Ultralytics, you can explore the comprehensive Ultralytics Object Counting Docs. This guide covers everything …
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