doc/tutorials/features2d/feature_detection/feature_detection.markdown

   1 Feature Detection {#tutorial_feature_detection}
   2 =================
   3
   4 @prev_tutorial{tutorial_corner_subpixels}
   5 @next_tutorial{tutorial_feature_description}
   6
   7 Goal
   8 ----
   9
  10 In this tutorial you will learn how to:
  11
  12 -   Use the @ref cv::FeatureDetector interface in order to find interest points. Specifically:
  13     -   Use the cv::xfeatures2d::SURF and its function cv::xfeatures2d::SURF::detect to perform the
  14         detection process
  15     -   Use the function @ref cv::drawKeypoints to draw the detected keypoints
  16
  17 \warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
  18 (alternatives are ORB, KAZE, ... features).
  19
  20 Theory
  21 ------
  22
  23 Code
  24 ----
  25
  26 @add_toggle_cpp
  27 This tutorial code's is shown lines below. You can also download it from
  28 [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/features2D/feature_detection/SURF_detection_Demo.cpp)
  29 @include samples/cpp/tutorial_code/features2D/feature_detection/SURF_detection_Demo.cpp
  30 @end_toggle
  31
  32 @add_toggle_java
  33 This tutorial code's is shown lines below. You can also download it from
  34 [here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/features2D/feature_detection/SURFDetectionDemo.java)
  35 @include samples/java/tutorial_code/features2D/feature_detection/SURFDetectionDemo.java
  36 @end_toggle
  37
  38 @add_toggle_python
  39 This tutorial code's is shown lines below. You can also download it from
  40 [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/features2D/feature_detection/SURF_detection_Demo.py)
  41 @include samples/python/tutorial_code/features2D/feature_detection/SURF_detection_Demo.py
  42 @end_toggle
  43
  44 Explanation
  45 -----------
  46
  47 Result
  48 ------
  49
  50 -#  Here is the result of the feature detection applied to the `box.png` image:
  51
  52     ![](images/Feature_Detection_Result_a.jpg)
  53
  54 -#  And here is the result for the `box_in_scene.png` image:
  55
  56     ![](images/Feature_Detection_Result_b.jpg)