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Merge pull request opencv#17325 from hunter-college-ossd-spr-2020:nav-links
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doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown

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Camera calibration With OpenCV {#tutorial_camera_calibration}
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==============================
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Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole*
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cameras in the late 20th century, they became a common occurrence in our everyday life.
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Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are

doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown

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Create calibration pattern {#tutorial_camera_calibration_pattern}
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=========================================
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The goal of this tutorial is to learn how to create calibration pattern.
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You can find a chessboard pattern in https://github.com/opencv/opencv/blob/3.4/doc/pattern.png

doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.markdown

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Camera calibration with square chessboard {#tutorial_camera_calibration_square_chess}
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=========================================
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The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
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*Test data*: use images in your data/chess folder.

doc/tutorials/calib3d/interactive_calibration/interactive_calibration.markdown

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Interactive camera calibration application {#tutorial_interactive_calibration}
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==============================
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According to classical calibration technique user must collect all data first and when run @ref cv::calibrateCamera function
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to obtain camera parameters. If average re-projection error is huge or if estimated parameters seems to be wrong, process of
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selection or collecting data and starting of @ref cv::calibrateCamera repeats.

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

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Real Time pose estimation of a textured object {#tutorial_real_time_pose}
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==============================================
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Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields.
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The most elemental problem in augmented reality is the estimation of the camera pose respect of an
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object in the case of computer vision area to do later some 3D rendering or in the case of robotics

doc/tutorials/dnn/dnn_android/dnn_android.markdown

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# How to run deep networks on Android device {#tutorial_dnn_android}
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## Introduction
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In this tutorial you'll know how to run deep learning networks on Android device
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using OpenCV deep learning module.

doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md

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# Custom deep learning layers support {#tutorial_dnn_custom_layers}
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## Introduction
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Deep learning is a fast growing area. The new approaches to build neural networks
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usually introduce new types of layers. They could be modifications of existing

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

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Load Caffe framework models {#tutorial_dnn_googlenet}
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===========================
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Introduction
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------------
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doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

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# How to enable Halide backend for improve efficiency {#tutorial_dnn_halide}
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## Introduction
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This tutorial guidelines how to run your models in OpenCV deep learning module
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using Halide language backend. Halide is an open-source project that let us

doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown

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# How to schedule your network for Halide backend {#tutorial_dnn_halide_scheduling}
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## Introduction
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Halide code is the same for every device we use. But for achieving the satisfied
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efficiency we should schedule computations properly. In this tutorial we describe

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