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English | Türkçe

Örnekler

Bu repository altında aşağıdaki yöntemlere ilişkin örnekler bulunmaktadır.

  1. Haar Cascade - Yüz, göz, burun vb. nesne tanıma.
  2. Renk Tespiti - Nesne rengi tespiti ve renk ile nesne takibi.
  3. Template Matching - Template matching / Şablon eşleştirme yöntemi kullanarak nesne tespiti.
  4. Derin öğrenme - Derin sinir ağı (DNN) ile nesne tespiti.

Örnek 1: Yüz ve Göz Tespiti

Kaynak kod dizini: src/FaceAndEyeDetection/

Object detection Örneks with haar cascade classifier algorithm (Face, eyes, mouth, other objects etc.). Cascade Classifier Training http://docs.opencv.org/3.1.0/dc/d88/tutorial_traincascade.html

What is Haar cascade? Haar cascade classifier Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images.

Gereksinimler

  • OpenCV 3.x Versiyon
  • Java > 6 Versiyon

Face and eye detection by the camera using haar cascade algorithm.

Video:

Örnek 2: Renk Tespiti ve Nesne Takibi

Kaynak kod dizini: src/ColorBasedObjectTracker/

An Örnek of an application where OpenCV is used to detect objects based on color differences.

Gereksinimler

  • OpenCV >2.x Versiyon
  • Java >6 Versiyon

Örnek 3: Template Matching ile Nesne Tespiti

Kaynak kod dizini: src/TemplateMatchingObjectDetection/

Template matching is a technique for finding areas of an image that match (are similar) to a template image (patch).

Gereksinimler

  • OpenCV 3.x Versiyon
  • Java >6 Versiyon

My blog post for template matching.

Örnek 4: Derin Sinir Ağı DNN ile Nesne Tespiti

Kaynak kod dizini: src/DeepNeuralNetwork/

  • OpenCV > 3.3 Versiyon

In this tutorial you will learn how to use opencv dnn module for image classification by using MobileNetSSD_deploy trained network. My blog post for deep neural network.