### Object Detection Example Setup and Usage

Source: https://docs.opencv.org/5.0.0-alpha/d4/db9/samples_2dnn_2object_detection_8cpp-example.html

This section provides instructions on how to download models, set environment variables, and run the object detection example. It also details the command-line arguments available for configuring the model, input source, and detection parameters.

```cpp
#include <fstream>
#include <sstream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <mutex>
#include <thread>
#include <queue>
#include "iostream"
#include "common.hpp"
using namespace cv;
using namespace dnn;
using namespace std;
const string about =
"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
"To run:\n"
"\t ./example_dnn_object_detection model_name --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)\n"
"Sample command:\n"
"\t ./example_dnn_object_detection yolov8 --input=$OPENCV_SAMPLES_DATA_PATH/baboon.jpg\n"
"Model path can also be specified using --model argument. ";
const string param_keys =
"{ help h | | Print help message. }"
"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ thr | .5 | Confidence threshold. }"
"{ nms | .4 | Non-maximum suppression threshold. }"
"{ async | 0 | Number of asynchronous forwards at the same time. "
"Choose 0 for synchronous mode }"
"{ padvalue | 114.0 | padding value. }"
"{ paddingmode | 2 | Choose one of padding modes: "
"0: resize to required input size without extra processing, "
"1: Image will be cropped after resize, "
"2: Resize image to the desired size while preserving the aspect ratio of original image }";
const string backend_keys = format(
"{ backend | default | Choose one of computation backends: "
"default: automatically (by default), "
"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"opencv: OpenCV implementation, "
"vkcom: VKCOM, "
"cuda: CUDA, "
"webnn: WebNN }", backend_keys);
const string target_keys = format(
"{ target | cpu | Choose one of target computation devices: "
"cpu: CPU target (by default), "
"opencl: OpenCL, "
"opencl_fp16: OpenCL fp16 (half-float precision), "
"vpu: VPU, "
"vulkan: Vulkan, "
"cuda: CUDA, "
"cuda_fp16: CUDA fp16 (half-float preprocess) }", target_keys);
string keys = param_keys + backend_keys + target_keys;
float confThreshold, nmsThreshold, scale, paddingValue;
vector<string> labels;
Scalar meanv;
bool swapRB;
int inpWidth, inpHeight;
size_t asyncNumReq = 0;
ImagePaddingMode paddingMode;
string modelName, framework;
static void preprocess(const Mat& frame, Net& net, Size inpSize);
static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing);
static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
static void callback(int pos, void* userdata);
static Scalar getColor(int classId);
static void yoloPostProcessing(
const vector<Mat>& outs,
vector<int>& keep_classIds,
vector<float>& keep_confidences,
vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const string& postprocessing);
```

--------------------------------

### FloodFill Example Setup and Help Function

Source: https://docs.opencv.org/5.0.0-alpha/d9/dae/samples_2cpp_2floodfill_8cpp-example.html

Includes necessary headers, namespace declarations, and a help function to display usage instructions and hotkeys for interacting with the flood fill demonstration.

```cpp
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
static void help(char** argv)
{
cout << "\nThis program demonstrated the floodFill() function\n"
"Call:\n"
<< argv[0]
<< " [image_name -- Default: fruits.jpg]\n" << endl;
cout << "Hot keys: \n"
"\tESC - quit the program\n"
"\tc - switch color/grayscale mode\n"
"\tm - switch mask mode\n"
"\tr - restore the original image\n"
"\ts - use null-range floodfill\n"
"\tf - use gradient floodfill with fixed(absolute) range\n"
"\tg - use gradient floodfill with floating(relative) range\n"
"\t4 - use 4-connectivity mode\n"
"\t8 - use 8-connectivity mode\n" << endl;
}
```

--------------------------------

### Main Function with PCA Setup

Source: https://docs.opencv.org/5.0.0-alpha/d3/db0/samples_2cpp_2pca_8cpp-example.html

Parses command-line arguments to get the input image list file. Initializes the PCA process and sets up the trackbar for interactive variance control.

```cpp
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, "{@input||image list}{help h||show help message}");
if (parser.has("help"))
{
parser.printMessage();
exit(0);
}

```

--------------------------------

### Hough Lines Python Example Setup

Source: https://docs.opencv.org/5.0.0-alpha/d9/db0/tutorial_hough_lines.html

Sets up the filename for the Hough lines example. Uses a default image if no filename is provided.

```python
import sys
import math
import cv2 as cv
import numpy as np
def main(argv):
    default_file = 'sudoku.png'
    filename = argv[0] if len(argv) > 0 else default_file
```

--------------------------------

### GrabCut Algorithm Setup and Help Function

Source: https://docs.opencv.org/5.0.0-alpha/d8/d34/samples_2cpp_2grabcut_8cpp-example.html

This C++ code sets up the necessary includes and a help function for the GrabCut example. It outlines the program's usage, hotkeys, and mouse interactions for segmentation.

```cpp
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
static void help(char** argv)
{
cout << "\nThis program demonstrates GrabCut segmentation -- select an object in a region\n"
"and then grabcut will attempt to segment it out.\n"
"Call:\n"
<< argv[0] << " <image_name>\n"
"\nSelect a rectangular area around the object you want to segment\n" <<
"\nHot keys: \n"
"\tESC - quit the program\n"
"\tr - restore the original image\n"
"\tn - next iteration\n"
"\n"
"\tleft mouse button - set rectangle\n"
"\n"
"\tCTRL+left mouse button - set GC_BGD pixels\n"
"\tSHIFT+left mouse button - set GC_FGD pixels\n"
"\n"
"\tCTRL+right mouse button - set GC_PR_BGD pixels\n"
"\tSHIFT+right mouse button - set GC_PR_FGD pixels\n" << endl;
}
```

--------------------------------

### Configure OpenCV with Relative Install Prefix

Source: https://docs.opencv.org/5.0.0-alpha/db/d05/tutorial_config_reference.html

Set the installation root directory relative to the current working directory. This example sets it to a 'install' subdirectory.

```bash
cmake -DCMAKE_INSTALL_PREFIX=install ../opencv
```

--------------------------------

### setup

Source: https://docs.opencv.org/5.0.0-alpha/dc/d47/classcv_1_1bioinspired_1_1Retina-members.html

Configures the retina model with various setup options.

```APIDOC
## setup (String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)

### Description
Sets up the retina model using parameters from a file. If the file is not found or invalid, a default setup can be applied.

### Method
`pure virtual`

### Parameters
* **retinaParameterFile** (String) - Path to the parameter file (default: empty string).
* **applyDefaultSetupOnFailure** (const bool) - Whether to apply default settings if setup fails (default: true).
```

```APIDOC
## setup (cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)

### Description
Sets up the retina model using parameters from a cv::FileStorage object.

### Method
`pure virtual`

### Parameters
* **fs** (cv::FileStorage&) - FileStorage object containing the parameters.
* **applyDefaultSetupOnFailure** (const bool) - Whether to apply default settings if setup fails (default: true).
```

```APIDOC
## setup (RetinaParameters newParameters)

### Description
Sets up the retina model with a provided RetinaParameters object.

### Method
`pure virtual`

### Parameters
* **newParameters** (RetinaParameters) - The new parameters to configure the retina model.
```

--------------------------------

### Print Setup Configuration (C++)

Source: https://docs.opencv.org/5.0.0-alpha/d2/d94/bioinspired_retina.html

Outputs a string representation of the current parameter setup for the Retina instance.

```cpp
const String printSetup ();
```

--------------------------------

### Build and Install OpenCV

Source: https://docs.opencv.org/5.0.0-alpha/db/d05/tutorial_config_reference.html

Build the OpenCV library and install the produced binaries to the configured installation location.

```bash
cmake --build . --target install
```

--------------------------------

### Python Meanshift Tracking Setup

Source: https://docs.opencv.org/5.0.0-alpha/d7/d00/tutorial_meanshift.html

Initializes argument parsing for a Python script demonstrating the Meanshift algorithm. Specifies the video file URL for the example.

```python
import numpy as np
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
The example file can be downloaded from: \
https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
```

--------------------------------

### Write Setup Configuration (C++)

Source: https://docs.opencv.org/5.0.0-alpha/d2/d94/bioinspired_retina.html

Writes the current parameter setup to an XML or YAML file.

```cpp
virtual void write (String fs) const;
virtual void write (FileStorage &fs) const;
```

--------------------------------

### Build and Install OpenCV

Source: https://docs.opencv.org/5.0.0-alpha/d2/de6/tutorial_py_setup_in_ubuntu.html

Compile the OpenCV library using the 'make' command and then install it using 'sudo make install'. This process builds the necessary files and installs them into the '/usr/local/' directory.

```bash
make
```

```bash
# sudo make install
```

--------------------------------

### Run Example from Command Line

Source: https://docs.opencv.org/5.0.0-alpha/d5/de7/tutorial_dnn_googlenet.html

Executes the compiled DNN example from the command line, specifying the GoogLeNet model.

```bash
./example_dnn_classification googlenet
```

--------------------------------

### Install Tesseract Script

Source: https://docs.opencv.org/5.0.0-alpha/db/d4c/tutorial_install_tesseract.html

Executes the installation script for Tesseract.

```bash
./installTesseract.sh
```

--------------------------------

### Build and Install zlib and PNG

Source: https://docs.opencv.org/5.0.0-alpha/db/d4c/tutorial_install_tesseract.html

This script builds and installs zlib and libpng libraries using CMake and Visual Studio. It configures installation paths and builds both release and debug versions.

```bash
#!/bin/bash
myRepo=$(pwd)
CMAKE_CONFIG_GENERATOR="Visual Studio 14 2015 Win64"
RepoSource=zlib
mkdir Build/$RepoSource
pushd Build/$RepoSource
cmake . -G"Visual Studio 14 2015 Win64" \
-DCMAKE_INSTALL_PREFIX:PATH="$myRepo"/install/zlib -DINSTALL_BIN_DIR:PATH="$myRepo"/install/zlib/bin \
-DINSTALL_INC_DIR:PATH="$myRepo"/install/zlib/include -DINSTALL_LIB_DIR:PATH="$myRepo"/install/zlib/lib "$myRepo"/"$RepoSource"
cmake --build . --config release
cmake --build . --target install --config release
cmake --build . --config debug
cmake --build . --target install --config debug
popd
RepoSource=lpng
mkdir Build/$RepoSource
pushd Build/$RepoSource
cp "$myRepo"/"$RepoSource"/scripts/pnglibconf.h.prebuilt "$myRepo"/"$RepoSource"/pnglibconf.h
cmake . -G"Visual Studio 14 2015 Win64" \
-DZLIB_INCLUDE_DIR:PATH="$myRepo"/install/zlib/include -DZLIB_LIBRARY_DEBUG:FILE="$myRepo"/install/zlib/lib/zlibstaticd.lib \
-Dld-version-script:BOOL=OFF -DPNG_TESTS:BOOL=OFF -DAWK:STRING= \
-DZLIB_LIBRARY_RELEASE:FILE="$myRepo"/install/zlib/lib/zlibstatic.lib -DCMAKE_INSTALL_PREFIX="$myRepo"/Install/"$RepoSource" \
"$myRepo"/"$RepoSource"
cmake --build . --config release
cmake --build . --target install --config release
cmake --build . --config debug
cmake --build . --target install --config debug
popd

```

--------------------------------

### Install with Make

Source: https://docs.opencv.org/5.0.0-alpha/d7/d9f/tutorial_linux_install.html

Installs OpenCV to the default location (/usr/local) using Make. Requires elevated privileges.

```bash
sudo make install
```

--------------------------------

### Set Installation Prefix for OpenCV

Source: https://docs.opencv.org/5.0.0-alpha/d3/d52/tutorial_windows_install.html

This CMake option specifies the directory where OpenCV will be installed after compilation. The example shows installation into a subdirectory named 'opencv' within an 'install' folder.

```bash
-DCMAKE_INSTALL_PREFIX="$myRepo/install/$RepoSource"
```

--------------------------------

### Install with Ninja

Source: https://docs.opencv.org/5.0.0-alpha/d7/d9f/tutorial_linux_install.html

Installs OpenCV to the default location (/usr/local) using Ninja. Requires elevated privileges.

```bash
sudo ninja install
```

--------------------------------

### setup()

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/bioinspired/Retina.html

Attempts to load retina parameters from an XML file. If the file is not found, default settings are applied. Exceptions are thrown if the XML file is invalid.

```APIDOC
## setup

### Description
Try to open an XML retina parameters file to adjust current retina instance setup. If the xml file does not exist, then default setup is applied. Warning, Exceptions are thrown if read XML file is not valid. You can retrieve the current parameters structure using the method Retina::getParameters and update it before running method Retina::setup.
```

--------------------------------

### Configure OpenCV with Custom Install Prefix

Source: https://docs.opencv.org/5.0.0-alpha/db/d05/tutorial_config_reference.html

Set the installation root directory for OpenCV binaries during configuration. This example sets it to '/opt/opencv'.

```bash
cmake -DCMAKE_INSTALL_PREFIX=/opt/opencv ../opencv
```

--------------------------------

### setup

Source: https://docs.opencv.org/5.0.0-alpha/dd/dc9/structcv_1_1detail_1_1OCVSetupHelper_3_01Impl_00_01std_1_1tuple_3_01Ins_8_8_8_01_4_01_4-members.html

Sets up the OpenCV environment with provided metadata and arguments.

```APIDOC
## setup

### Description
Sets up the OpenCV environment using metadata arguments, general arguments, state, and compilation arguments.

### Method
static

### Parameters
- **metaArgs** (const GMetaArgs &) - Description not provided.
- **args** (const GArgs &) - Description not provided.
- **state** (GArg &) - Description not provided.
- **compileArgs** (const GCompileArgs &) - Description not provided.

### Request Example
```json
{
  "example": "Not available"
}
```

### Response
#### Success Response (200)
- **void** - Description not provided.

#### Response Example
```json
{
  "example": "Not available"
}
```
```

--------------------------------

### Install OpenCV-Python from Fedora Repositories

Source: https://docs.opencv.org/5.0.0-alpha/dd/dd5/tutorial_py_setup_in_fedora.html

Installs necessary packages including OpenCV and Numpy using the yum package manager. After installation, verify the setup by importing cv2 and printing its version in a Python interpreter.

```bash
$ yum install numpy opencv*
```

```python
>>> import cv2 as cv
>>> print( cv.__version__ )
```

--------------------------------

### setup()

Source: https://docs.opencv.org/5.0.0-alpha/dc/d54/classcv_1_1bioinspired_1_1Retina.html

Sets up the retina module using parameters from a FileStorage object. It can optionally apply default settings on failure.

```APIDOC
## cv::bioinspired::Retina::setup()

### Description
Sets up the retina module using parameters from a FileStorage object.

### Method
virtual void

### Parameters
* **_fs** (cv::FileStorage &) - The open Filestorage which contains retina parameters.
* **_applyDefaultSetupOnFailure** (const bool) - Set to true if an error must be thrown on error. Defaults to true.

### Python Equivalent
```python
cv.bioinspired.Retina.setup([, retinaParameterFile[, applyDefaultSetupOnFailure]]) -> None
```
```

--------------------------------

### setup

Source: https://docs.opencv.org/5.0.0-alpha/d5/d0a/classcv_1_1bioinspired_1_1RetinaFastToneMapping-members.html

Sets up the parameters for the RetinaFastToneMapping algorithm.

```APIDOC
## setup

### Description
Sets up the parameters for the RetinaFastToneMapping algorithm.

### Method
`virtual void setup(const float photoreceptorsNeighborhoodRadius = 3.f, const float ganglioncellsNeighborhoodRadius = 1.f, const float meanLuminanceModulatorK = 1.f) = 0`

### Parameters
* `photoreceptorsNeighborhoodRadius` (float) - The radius of the neighborhood for photoreceptors. Defaults to 3.f.
* `ganglioncellsNeighborhoodRadius` (float) - The radius of the neighborhood for ganglion cells. Defaults to 1.f.
* `meanLuminanceModulatorK` (float) - The modulator constant for mean luminance. Defaults to 1.f.
```

--------------------------------

### Execute the OpenCV Installation Script

Source: https://docs.opencv.org/5.0.0-alpha/d3/d52/tutorial_windows_install.html

Run the previously saved 'installOCV.sh' script from your Git Bash terminal to start the OpenCV build and installation process.

```bash
./installOCV.sh
```

--------------------------------

### Install Node.js Dependencies

Source: https://docs.opencv.org/5.0.0-alpha/d4/da1/tutorial_js_setup.html

Navigate to the build directory and install necessary Node.js packages for running tests.

```bash
cd build_js/bin
npm install
```

--------------------------------

### Get OpenCV Version Information

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/core/Core.html

Retrieves the major, minor, and revision numbers of the installed OpenCV library.

```java
System.out.println(Core.VERSION);
```

--------------------------------

### Get OpenCV Version String

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/core/Core.html

Returns the full library version as a string, for example, "3.4.1-dev".

```java
public static java.lang.String getVersionString()
```

--------------------------------

### Sample Command-Line Arguments

Source: https://docs.opencv.org/5.0.0-alpha/db/da9/tutorial_aruco_board_detection.html

Provides example command-line arguments for running the `detect_board.cpp` sample. These arguments specify board configuration, input video, and camera calibration files.

```cpp
Sample video:

A full working example is included in the `detect_board.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
The samples now take input via command line via the `cv::CommandLineParser`. For this file the example parameters will look like: 
-w=5 -h=7 -l=100 -s=10
-v=/path_to_opencv/opencv/doc/tutorials/objdetect/aruco_board_detection/gboriginal.jpg
-c=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_params.yml
-cd=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_dict.yml
```

--------------------------------

### printSetup()

Source: https://docs.opencv.org/5.0.0-alpha/dc/d54/classcv_1_1bioinspired_1_1Retina.html

Outputs a string containing the current parameters setup of the retina module.

```APIDOC
## cv::bioinspired::Retina::printSetup()

### Description
Outputs a string showing the used parameters setup.

### Method
virtual String

### Returns
A string which contains formatted parameters information.

### Python Equivalent
```python
cv.bioinspired.Retina.printSetup() -> retval
```
```

--------------------------------

### Java Morphological Lines Detection Setup

Source: https://docs.opencv.org/5.0.0-alpha/dd/dd7/tutorial_morph_lines_detection.html

This Java code snippet shows the initial setup for image processing in OpenCV. It checks for command-line arguments and loads an image. This is a starting point for more complex image manipulation tasks.

```java
import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
class Morphology_3Run {
public void run(String[] args) {
// Check number of arguments
if (args.length == 0){
System.out.println("Not enough parameters!");
System.out.println("Program Arguments: [image_path]");
System.exit(-1);
}

```

--------------------------------

### ORB Create Method

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/features/ORB.html

Creates an ORB detector instance with default parameters. This is a convenient way to get started with ORB.

```java
public static ORB create()
```

--------------------------------

### Get OpenCV Version String

Source: https://docs.opencv.org/5.0.0-alpha/db/de0/group__core__utils.html

Retrieves the full version string of the OpenCV library, for example, "3.4.1-dev".

```cpp
cv::String versionString = cv::getVersionString();
```

--------------------------------

### help_init

Source: https://docs.opencv.org/5.0.0-alpha/d0/d83/structcv_1_1detail_1_1scratch__helper_3_01true_00_01Impl_00_01Ins_8_8_8_01_4-members.html

Initializes the scratch buffer with metadata and input arguments.

```APIDOC
## help_init

### Description
Initializes the scratch buffer with metadata and input arguments.

### Method
static

### Parameters
#### Path Parameters
- **metas** (const cv::GMetaArgs &) - Description
- **in_args** (const cv::GArgs &) - Description
- **b** (gapi::fluid::Buffer &) - Description
```

--------------------------------

### TickMeter Counter and FPS Methods

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/core/TickMeter.html

Gets the number of times the timer has been started and stopped, and calculates the frames per second (FPS).

```java
public long getCounter()
```

```java
public double getFPS()
```

--------------------------------

### setup_impl

Source: https://docs.opencv.org/5.0.0-alpha/dd/dc9/structcv_1_1detail_1_1OCVSetupHelper_3_01Impl_00_01std_1_1tuple_3_01Ins_8_8_8_01_4_01_4-members.html

Internal implementation for setting up the OpenCV environment.

```APIDOC
## setup_impl

### Description
Internal implementation function for setting up the OpenCV environment. It handles the details of extracting metadata and state for the setup process.

### Method
static

### Parameters
- **metaArgs** (const GMetaArgs &) - Metadata arguments for the setup.
- **args** (const GArgs &) - General arguments for the setup.
- **state** (GArg &) - The current state object.
- **compileArgs** (const GCompileArgs &) - Compilation arguments.
- **detail::Seq< IIs... >** - Sequence of indices for template parameter deduction.

### Request Example
```json
{
  "example": "Not available"
}
```

### Response
#### Success Response (200)
- **decltype(...)** - The return type is deduced based on the Impl::setup call, typically void.

#### Response Example
```json
{
  "example": "Not available"
}
```
```

--------------------------------

### Python SVM Setup

Source: https://docs.opencv.org/5.0.0-alpha/d1/d73/tutorial_introduction_to_svm.html

Imports necessary libraries for OpenCV and NumPy operations in Python. This is a common starting point for computer vision tasks.

```Python
import cv2 as cv
import numpy as np
```

--------------------------------

### setup(String retinaParameterFile, boolean applyDefaultSetupOnFailure)

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/bioinspired/Retina.html

Attempts to load retina parameters from an XML file. If the file is not found, default settings are applied. Exceptions are thrown if the XML file is invalid.

```APIDOC
## setup

### Description
Try to open an XML retina parameters file to adjust current retina instance setup. If the xml file does not exist, then default setup is applied. Warning, Exceptions are thrown if read XML file is not valid. You can retrieve the current parameters structure using the method Retina::getParameters and update it before running method Retina::setup.

### Parameters
- `retinaParameterFile` (String) - the parameters filename
- `applyDefaultSetupOnFailure` (boolean) - set to true if an error must be thrown on error
```

--------------------------------

### Get Tick Count for Performance Timing

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/core/Core.html

Returns the number of clock ticks since the program started. This can be used in conjunction with `getTickFrequency()` for performance measurements.

```java
public static long getTickCount()
```

--------------------------------

### setup(String retinaParameterFile)

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/bioinspired/Retina.html

Attempts to load retina parameters from an XML file. If the file is not found, default settings are applied. Exceptions are thrown if the XML file is invalid.

```APIDOC
## setup

### Description
Try to open an XML retina parameters file to adjust current retina instance setup. If the xml file does not exist, then default setup is applied. Warning, Exceptions are thrown if read XML file is not valid. You can retrieve the current parameters structure using the method Retina::getParameters and update it before running method Retina::setup.

### Parameters
- `retinaParameterFile` (String) - the parameters filename
```

--------------------------------

### Stitching Images (Python)

Source: https://docs.opencv.org/5.0.0-alpha/examples.html

Python example for image stitching using OpenCV. This snippet shows the basic setup for creating a panorama from a list of images.

```python
import cv2
import numpy as np

# Load images
images = []
# ... load images into the 'images' list ...

# Create a Stitcher object
stitcher = cv2.Stitcher_create(cv::Stitcher::PANORAMA)

# Stitch images
(status, pano) = stitcher.stitch(images)

if status == cv2.Stitcher_OK:
    # ... process the stitched panorama 'pano' ...
    cv2.imshow("Stitched Image", pano)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
else:
    print("Stitching failed")
```

--------------------------------

### Initialize GrabCut Application

Source: https://docs.opencv.org/5.0.0-alpha/d8/d34/samples_2cpp_2grabcut_8cpp-example.html

Sets up the GrabCut application with an image and window name, then displays the initial image.

```cpp
GCApplication gcapp;
static void on_mouse( int event, int x, int y, int flags, void* param )
{
    gcapp.mouseClick( event, x, y, flags, param );
}
int main( int argc, char** argv )
{
    cv::CommandLineParser parser(argc, argv, "{@input| messi5.jpg |}");
    help(argv);
    string filename = parser.get<string>("@input");
    if( filename.empty() )
    {
        cout << "\nDurn, empty filename" << endl;
        return 1;
    }
    Mat image = imread(samples::findFile(filename), IMREAD_COLOR);
    if( image.empty() )
    {
        cout << "\n Durn, couldn't read image filename " << filename << endl;
        return 1;
    }
    const string winName = "image";
    namedWindow( winName, WINDOW_AUTOSIZE );
    setMouseCallback( winName, on_mouse, 0 );
    gcapp.setImageAndWinName( image, winName );
    gcapp.showImage();
    for(;;)
    {
        char c = (char)waitKey(0);
        switch( c )
        {
            case '\x1b':
                cout << "Exiting ..." << endl;
                goto exit_main;
            case 'r':
                cout << endl;
                gcapp.reset();
                gcapp.showImage();
                break;
            case 'n':
                int iterCount = gcapp.getIterCount();
                cout << "<" << iterCount << "... ";
                int newIterCount = gcapp.nextIter();
                if( newIterCount > iterCount )
                {
                    gcapp.showImage();
                    cout << iterCount << ">" << endl;
                }
                else
                    cout << "rect must be determined>" << endl;
                break;
        }
    }
exit_main:
    destroyWindow( winName );
    return 0;
}
```

--------------------------------

### OpenCV Hello World C++ Example

Source: https://docs.opencv.org/5.0.0-alpha/d7/d16/tutorial_linux_eclipse.html

A basic C++ program that uses OpenCV to display 'Hello World!' in a window. Ensure OpenCV is installed and configured.

```cpp
#include <opencv2/opencv.hpp>
using namespace cv;
int main ( int argc, char **argv )
{
Mat img(480, 640, CV_8U);
putText(img, "Hello World!", Point( 200, 400 ), FONT_HERSHEY_SIMPLEX | FONT_ITALIC, 1.0, Scalar( 255, 255, 0 ));
imshow("My Window", img);
waitKey();
return 0;
}
```

--------------------------------

### printSetup

Source: https://docs.opencv.org/5.0.0-alpha/javadoc/org/opencv/bioinspired/Retina.html

Outputs a string containing the currently used parameters setup in a formatted manner.

```APIDOC
## printSetup

### Description
Outputs a string showing the used parameters setup.

### Returns
- String: a string which contains formatted parameters information
```

--------------------------------

### Command Line Parser Setup

Source: https://docs.opencv.org/5.0.0-alpha/db/db6/samples_2cpp_2tutorial_code_2features_2Homography_2homography_from_camera_displacement_8cpp-example.html

Defines command-line parameters for the homography tutorial, including image paths, intrinsics file, and chessboard dimensions.

```cpp
const char* params
= "{ help h | | print usage }"
"{ image1 | left02.jpg | path to the source chessboard image }"
"{ image2 | left01.jpg | path to the desired chessboard image }"
"{ intrinsics | left_intrinsics.yml | path to camera intrinsics }"
"{ width bw | 9 | chessboard width }"
"{ height bh | 6 | chessboard height }"
"{ square_size | 0.025 | chessboard square size }";
```

--------------------------------

### C++ Geometric Drawing Example

Source: https://docs.opencv.org/5.0.0-alpha/d3/d96/tutorial_basic_geometric_drawing.html

Demonstrates drawing various geometric shapes using OpenCV's C++ API. Includes setup for image display and window management.

```cpp
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#define w 400
using namespace cv;
void MyEllipse( Mat img, double angle );
void MyFilledCircle( Mat img, Point center );
void MyPolygon( Mat img );
void MyLine( Mat img, Point start, Point end );
int main( void ){
char atom_window[] = "Drawing 1: Atom";
char rook_window[] = "Drawing 2: Rook";
Mat atom_image = Mat::zeros( w, w, CV_8UC3 );
Mat rook_image = Mat::zeros( w, w, CV_8UC3 );
MyEllipse( atom_image, 90 );
MyEllipse( atom_image, 0 );
MyEllipse( atom_image, 45 );
MyEllipse( atom_image, -45 );
MyFilledCircle( atom_image, Point( w/2, w/2) );
MyPolygon( rook_image );
rectangle( rook_image,
Point( 0, 7*w/8 ),
Point( w, w),
Scalar( 0, 255, 255 ),
FILLED,
LINE_8 );
MyLine( rook_image, Point( 0, 15*w/16 ), Point( w, 15*w/16 ) );
MyLine( rook_image, Point( w/4, 7*w/8 ), Point( w/4, w ) );
MyLine( rook_image, Point( w/2, 7*w/8 ), Point( w/2, w ) );
MyLine( rook_image, Point( 3*w/4, 7*w/8 ), Point( 3*w/4, w ) );
imshow( atom_window, atom_image );
moveWindow( atom_window, 0, 200 );
imshow( rook_window, rook_image );
moveWindow( rook_window, w, 200 );
waitKey( 0 );
return(0);
}
void MyEllipse( Mat img, double angle )
{
int thickness = 2;
int lineType = 8;
ellipse( img,
Point( w/2, w/2 ),
Size( w/4, w/16 ),
angle, 
0, 
360, 
Scalar( 255, 0, 0 ),
thickness,
lineType );
}
void MyFilledCircle( Mat img, Point center )
{
circle( img,
center,
w/32,
Scalar( 0, 0, 255 ),
FILLED,
LINE_8 );
}
void MyPolygon( Mat img )
{
int lineType = LINE_8;
Point rook_points[1][20];
rook_points[0][0] = Point( w/4, 7*w/8 );
rook_points[0][1] = Point( 3*w/4, 7*w/8 );
rook_points[0][2] = Point( 3*w/4, 13*w/16 );
rook_points[0][3] = Point( 11*w/16, 13*w/16 );
rook_points[0][4] = Point( 19*w/32, 3*w/8 );
rook_points[0][5] = Point( 3*w/4, 3*w/8 );
rook_points[0][6] = Point( 3*w/4, w/8 );
rook_points[0][7] = Point( 26*w/40, w/8 );
rook_points[0][8] = Point( 26*w/40, w/4 );
rook_points[0][9] = Point( 22*w/40, w/4 );
rook_points[0][10] = Point( 22*w/40, w/8 );
rook_points[0][11] = Point( 18*w/40, w/8 );
rook_points[0][12] = Point( 18*w/40, w/4 );
rook_points[0][13] = Point( 14*w/40, w/4 );
rook_points[0][14] = Point( 14*w/40, w/8 );
rook_points[0][15] = Point( w/4, w/8 );
rook_points[0][16] = Point( w/4, 3*w/8 );
rook_points[0][17] = Point( 13*w/32, 3*w/8 );
rook_points[0][18] = Point( 5*w/16, 13*w/16 );
rook_points[0][19] = Point( w/4, 13*w/16 );
const Point* ppt[1] = { rook_points[0] };
int npt[] = { 20 };
fillPoly( img,
ppt,
npt,
1,
Scalar( 255, 255, 255 ),
lineType );
}
void MyLine( Mat img, Point start, Point end )
{
int thickness = 2;
int lineType = LINE_8;
line( img,
start,
end,
Scalar( 0, 0, 0 ),
thickness,
lineType );
}
```

--------------------------------

### Command Line Execution Example

Source: https://docs.opencv.org/5.0.0-alpha/dd/d6e/tutorial_windows_visual_studio_opencv.html

This example shows how to run an executable with command-line arguments from the Windows command prompt. Navigate to the executable's directory and provide the argument, such as an image file name.

```bash
D:
CD OpenCV\MySolutionName\Release
MySolutionName.exe exampleImage.jpg
```

--------------------------------

### Minimal OpenCV.js Node.js Example

Source: https://docs.opencv.org/5.0.0-alpha/dc/de6/tutorial_js_nodejs.html

This snippet shows the basic setup for running OpenCV.js in Node.js. It defines the `Module.onRuntimeInitialized` callback and loads the `opencv.js` library using Node.js's `require` function.

```javascript
Module = {
  onRuntimeInitialized() {
    // this is our application:
    console.log(cv.getBuildInformation())
  }
}
// Load 'opencv.js' assigning the value to the global variable 'cv'
cv = require('./opencv.js')
```

--------------------------------

### Prepare Build Directory

Source: https://docs.opencv.org/5.0.0-alpha/d2/de6/tutorial_py_setup_in_ubuntu.html

Create a 'build' directory within the downloaded 'opencv' folder and navigate into it. This directory will be used for the build process.

```bash
mkdir build
```

```bash
cd build
```

--------------------------------

### Java Dense Optical Flow Initialization

Source: https://docs.opencv.org/5.0.0-alpha/d4/dee/tutorial_optical_flow.html

Initializes video capture and converts the first frame to grayscale for dense optical flow computation. This is the setup part of the Java optical flow example.

```java
import java.util.ArrayList;
import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgproc.Imgproc;
import org.opencv.video.Video;
import org.opencv.videoio.VideoCapture;
class OptFlowDense {
public void run(String[] args) {
String filename = args[0];
VideoCapture capture = new VideoCapture(filename);
if (!capture.isOpened()) {
//error in opening the video input
System.out.println("Unable to open file!");
System.exit(-1);
}
Mat frame1 = new Mat() , prvs = new Mat();
capture.read(frame1);
Imgproc.cvtColor(frame1, prvs, Imgproc.COLOR_BGR2GRAY);
while (true) {

```

--------------------------------

### Run Retina Tutorial with Webcam

Source: https://docs.opencv.org/5.0.0-alpha/d3/d86/tutorial_bioinspired_retina_model.html

Execute the compiled retina tutorial program to process live video from a webcam. Add 'log' as the last parameter for spatial log sampling.

```bash
./Retina_tuto -video
```

--------------------------------

### Serve Build Directory for Browser Tests

Source: https://docs.opencv.org/5.0.0-alpha/d4/da1/tutorial_js_setup.html

Launch a local web server in the build output directory to serve test files. This command uses `npx http-server` and assumes Node.js is installed.

```bash
npx http-server build_js/bin
```

--------------------------------

### Read Specific Number of KeyPoints from HDF5

Source: https://docs.opencv.org/5.0.0-alpha/d3/d23/classcv_1_1hdf_1_1HDF5.html

This example shows how to read a specific count of KeyPoints from an HDF5 dataset, starting at a given offset. It's useful for retrieving subsets of data efficiently.

```cpp
// open hdf5 file
cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );
// blank KeyPoint container
std::vector<cv::KeyPoint> keypoints;
// read three keypoints starting second one
h5io->kpread( keypoints, "keypoints", 1, 3 );
// release
h5io->close();
```

--------------------------------

### Intelligent Scissors Example

Source: https://docs.opencv.org/5.0.0-alpha/d9/df5/tutorial_js_intelligent_scissors.html

Launches an interactive Intelligent Scissors tool. Click on the image to pick a source point, then hover to specify a target point candidate. The code can be modified in the textarea for further investigation. Requires 'Start' button click to launch.

```javascript
let canvas = new cv.Mat();
let ctx = canvas.getContext('2d');
let img = cv.imread('canvasInput');
ctx.drawImage(img.data, img.cols, img.rows);
img.delete();
let scissors = new cv.segmentation.IntelligentScissorsMB(img, 10, 10);
let dst = new cv.Mat();
let mask = new cv.Mat();
let point = new cv.Point(x, y);
scissors.set(point, mask, dst);
scissors.advance(point, mask, dst);
scissors.advanceAndGrow(point, mask, dst);
scissors.advanceAndShrink(point, mask, dst);
scissors.forward(mask, dst);
scissors.backward(mask, dst);
scissors.grow(mask, dst);
scissors.shrink(mask, dst);
scissors.getContour(dst);
scissors.getMask(mask);
scissors.clearPath();
scissors.clear();
scissors.delete();
```

--------------------------------

### Retrieve Depth and BGR Images using grab/retrieve

Source: https://docs.opencv.org/5.0.0-alpha/d7/d6f/tutorial_kinect_openni.html

This example shows how to use VideoCapture::grab and VideoCapture::retrieve to get both the depth map and the BGR image from a depth sensor. It initializes the capture object and then continuously grabs and retrieves the specified data maps.

```cpp
VideoCapture capture(0); // or CAP_OPENNI2
for(;;)
{
Mat depthMap;
Mat bgrImage;
capture.grab();
capture.retrieve( depthMap, CAP_OPENNI_DEPTH_MAP );
capture.retrieve( bgrImage, CAP_OPENNI_BGR_IMAGE );
if( waitKey( 30 ) >= 0 )
break;
}
```

--------------------------------

### cv::detail::OCVSetupHelper::setup_impl

Source: https://docs.opencv.org/5.0.0-alpha/d3/d2a/structcv_1_1detail_1_1OCVSetupHelper_3_01Impl_00_01std_1_1tuple_3_01Ins_8_8_8_01_4_01_4.html

Internal implementation helper for the setup method. This is typically not called directly by users.

```APIDOC
## template<int... IIs> static auto setup_impl(const GMetaArgs &metaArgs, const GArgs &args, GArg &state, const GCompileArgs &, detail::Seq< IIs... >) -> decltype(Impl::setup(detail::get_in_meta< Ins >(metaArgs, args, IIs)..., std::declval< typename std::add_lvalue_reference< std::shared_ptr< typename Impl::State > >::type >()), void())

### Description
This is the first overload of the internal setup implementation. It is responsible for invoking the `Impl::setup` method with specific arguments derived from the input metadata and arguments, and a reference to the state object.

### Parameters
- **metaArgs** (const GMetaArgs &) - Metadata for the operation.
- **args** (const GArgs &) - Arguments for the operation.
- **state** (GArg &) - The state object to be configured.
- **compileArgs** (const GCompileArgs &) - Compilation arguments (unused in this overload).
- **detail::Seq< IIs... >** - A sequence of integers used for parameter expansion.
```

```APIDOC
## template<int... IIs> static auto setup_impl(const GMetaArgs &metaArgs, const GArgs &args, GArg &state, const GCompileArgs &compileArgs, detail::Seq< IIs... >) -> decltype(Impl::setup(detail::get_in_meta< Ins >(metaArgs, args, IIs)..., std::declval< typename std::add_lvalue_reference< std::shared_ptr< typename Impl::State > >::type >(), compileArgs), void())

### Description
This is the second overload of the internal setup implementation. It is similar to the first overload but also passes the `compileArgs` to the `Impl::setup` method, allowing for more detailed compilation-time configuration.

### Parameters
- **metaArgs** (const GMetaArgs &) - Metadata for the operation.
- **args** (const GArgs &) - Arguments for the operation.
- **state** (GArg &) - The state object to be configured.
- **compileArgs** (const GCompileArgs &) - Compilation arguments for the kernel.
- **detail::Seq< IIs... >** - A sequence of integers used for parameter expansion.
```

--------------------------------

### Set Image Generator Output Mode and Get FPS

Source: https://docs.opencv.org/5.0.0-alpha/d7/d6f/tutorial_kinect_openni.html

This example demonstrates how to set the output mode for the image generator of a depth sensor and retrieve its current frames per second (FPS). It uses VideoCapture::set and VideoCapture::get with specific OpenNI flags.

```cpp
VideoCapture capture( CAP_OPENNI2 );
capture.set( CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE, CAP_OPENNI_VGA_30HZ );
cout << "FPS " << capture.get( CAP_OPENNI_IMAGE_GENERATOR+CAP_PROP_FPS ) << endl;
```

--------------------------------

### printSetup

Source: https://docs.opencv.org/5.0.0-alpha/dc/d47/classcv_1_1bioinspired_1_1Retina-members.html

Prints the current setup and parameters of the retina model.

```APIDOC
## printSetup

### Description
Prints the current setup and parameters of the retina model to the console or log.

### Method
`pure virtual`
```

--------------------------------

### Main Function for Mask Creation Example

Source: https://docs.opencv.org/5.0.0-alpha/d3/d10/samples_2cpp_2snippets_2create_mask_8cpp-example.html

Initializes the image, window, and mouse callback. It parses command-line arguments for the input image and displays instructions for using the mouse.

```cpp
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace std;
using namespace cv;
Mat src, img1, mask, final;
Point point;
vector<Point> pts;
int drag = 0;
int var = 0;
int flag = 0;
void mouseHandler(int, int, int, int, void*);
int main(int argc, char **argv)
{
CommandLineParser parser(argc, argv, "{@input | lena.jpg | input image}");
parser.about("This program demonstrates using mouse events\n");
parser.printMessage();
cout << "\n\tleft mouse button - set a point to create mask shape\n"
"\tright mouse button - create mask from points\n"
"\tmiddle mouse button - reset\n";
String input_image = parser.get<String>("@input");
src = imread(samples::findFile(input_image));
if (src.empty())
{
printf("Error opening image: %s\n", input_image.c_str());
return 0;
}
namedWindow("Source", WINDOW_AUTOSIZE);
setMouseCallback("Source", mouseHandler, NULL);
imshow("Source", src);
waitKey(0);
return 0;
}
```

--------------------------------

### Setup Retina Parameters (C++)

Source: https://docs.opencv.org/5.0.0-alpha/d2/d94/bioinspired_retina.html

Configures the Retina instance using parameters loaded from an XML file or a FileStorage object. A default setup is applied on failure if specified.

```cpp
void setup (String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true);
void setup (FileStorage &fs, const bool applyDefaultSetupOnFailure=true);
void setup (RetinaParameters newParameters);
```

--------------------------------

### start()

Source: https://docs.opencv.org/5.0.0-alpha/d2/d77/classcv_1_1GStreamingCompiled-members.html

Starts the stream processing.

```APIDOC
## start()

### Description
Starts the execution of the GStreamingCompiled stream.

### Method
member function

### Endpoint
N/A

### Parameters
None

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Initialize Video Capture and Image List

Source: https://docs.opencv.org/5.0.0-alpha/d5/de7/tutorial_dnn_googlenet.html

Sets up video capture from a file, camera, or a list of images. Handles errors if the input cannot be opened.

```cpp
VideoCapture cap;
vector<string> imageList;
size_t currentImageIndex = 0;
if (parser.has("input")) {
string input = findFile(parser.get<String>("input"));
if (isImgList) {
bool check = readStringList(samples::findFile(input), imageList);
if (imageList.empty() || !check) {
cout << "Error: No images found or the provided file is not a valid .yaml or .xml file." << endl;
return -1;
}
} else {
// Input is not a directory, try to open as video or image
cap.open(input);
if (!cap.isOpened()) {
cout << "Failed to open the input." << endl;
return -1;
}
}
} else {
cap.open(0); // Open default camera
}
```

--------------------------------

### Display Help Message

Source: https://docs.opencv.org/5.0.0-alpha/dc/da9/tutorial_decode_graycode_pattern.html

Provides a help message explaining how to run the example and its required arguments.

```cpp
static void help()
{
cout << "\nThis example shows how to use the \"Structured Light module\" to decode a previously acquired gray code pattern, generating a pointcloud"
<< "\nCall:\n"
<< "./example_structured_light_pointcloud <images_list> <calib_param_path> <proj_width> <proj_height> <white_thresh> <black_thresh>\n"
<< endl;
}
```

--------------------------------

### Install OpenCV

Source: https://docs.opencv.org/5.0.0-alpha/d6/d15/tutorial_building_tegra_cuda.html

Install the built OpenCV library to the system. Root privileges may be required depending on the installation path.

```bash
$ make install

```

--------------------------------

### cv::detail::OCVSetupHelper::setup

Source: https://docs.opencv.org/5.0.0-alpha/d3/d2a/structcv_1_1detail_1_1OCVSetupHelper_3_01Impl_00_01std_1_1tuple_3_01Ins_8_8_8_01_4_01_4.html

Static method to set up the G-API kernel state using provided metadata, arguments, and compilation arguments. This is the primary interface for users to call.

```APIDOC
## static void setup(const GMetaArgs &metaArgs, const GArgs &args, GArg &state, const GCompileArgs &compileArgs)

### Description
Sets up the kernel state for G-API operations using OpenCV's CPU backend. It takes metadata, arguments, the state object, and compilation arguments to configure the kernel.

### Parameters
- **metaArgs** (const GMetaArgs &) - Metadata for the operation.
- **args** (const GArgs &) - Arguments for the operation.
- **state** (GArg &) - The state object to be configured.
- **compileArgs** (const GCompileArgs &) - Compilation arguments for the kernel.
```

--------------------------------

### Install CMake for Building OpenCV

Source: https://docs.opencv.org/5.0.0-alpha/d2/de6/tutorial_py_setup_in_ubuntu.html

CMake is required to configure the installation process when compiling OpenCV from source. Install it using apt-get.

```bash
sudo apt-get install cmake
```

--------------------------------

### start

Source: https://docs.opencv.org/5.0.0-alpha/d1/d9b/classcv_1_1GStreamingCompiled.html

Starts the execution of the streaming pipeline.

```APIDOC
## start ()

### Description
Start the pipeline execution.

### Method
`GAPI_WRAP void start()`

### Parameters
None

### Response
None

### Response Example
```cpp
streaming_compiled_graph.start();
```
```

--------------------------------

### Enable Documentation and Disable Tests/Samples

Source: https://docs.opencv.org/5.0.0-alpha/dd/dd5/tutorial_py_setup_in_fedora.html

Configure the build to include documentation while excluding tests and sample applications.

```bash
cmake -D BUILD_DOCS=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF ..
```

--------------------------------

### Verify OpenCV-Python Installation

Source: https://docs.opencv.org/5.0.0-alpha/d2/de6/tutorial_py_setup_in_ubuntu.html

After installation, run these commands in a Python interpreter to confirm OpenCV is working and to check its version. This is useful for verifying a successful installation.

```python
import cv2 as cv
print(cv.__version__)
```

--------------------------------

### Install CMake using Homebrew

Source: https://docs.opencv.org/5.0.0-alpha/d0/db2/tutorial_macos_install.html

A convenient way to install CMake on macOS using the Homebrew package manager. Ensure Homebrew is installed first.

```bash
brew install cmake
```