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Tutorials: moved imgcodecs and videoio tutorials to separate pages

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Maksim Shabunin
2016-02-12 13:35:46 +03:00
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doc/tutorials/highgui/intelperc.markdown
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Using Creative Senz3D and other Intel Perceptual Computing SDK compatible depth sensors {#tutorial_intelperc}
=======================================================================================
Depth sensors compatible with Intel Perceptual Computing SDK are supported through VideoCapture
class. Depth map, RGB image and some other formats of output can be retrieved by using familiar
interface of VideoCapture.
In order to use depth sensor with OpenCV you should do the following preliminary steps:
-# Install Intel Perceptual Computing SDK (from here <http://www.intel.com/software/perceptual>).
-# Configure OpenCV with Intel Perceptual Computing SDK support by setting WITH_INTELPERC flag in
CMake. If Intel Perceptual Computing SDK is found in install folders OpenCV will be built with
Intel Perceptual Computing SDK library (see a status INTELPERC in CMake log). If CMake process
doesn't find Intel Perceptual Computing SDK installation folder automatically, the user should
change corresponding CMake variables INTELPERC_LIB_DIR and INTELPERC_INCLUDE_DIR to the
proper value.
-# Build OpenCV.
VideoCapture can retrieve the following data:
-# data given from depth generator:
- CAP_INTELPERC_DEPTH_MAP - each pixel is a 16-bit integer. The value indicates the
distance from an object to the camera's XY plane or the Cartesian depth. (CV_16UC1)
- CAP_INTELPERC_UVDEPTH_MAP - each pixel contains two 32-bit floating point values in
the range of 0-1, representing the mapping of depth coordinates to the color
coordinates. (CV_32FC2)
- CAP_INTELPERC_IR_MAP - each pixel is a 16-bit integer. The value indicates the
intensity of the reflected laser beam. (CV_16UC1)
-# data given from RGB image generator:
- CAP_INTELPERC_IMAGE - color image. (CV_8UC3)
In order to get depth map from depth sensor use VideoCapture::operator \>\>, e. g. :
@code{.cpp}
VideoCapture capture( CAP_INTELPERC );
for(;;)
{
Mat depthMap;
capture >> depthMap;
if( waitKey( 30 ) >= 0 )
break;
}
@endcode
For getting several data maps use VideoCapture::grab and VideoCapture::retrieve, e.g. :
@code{.cpp}
VideoCapture capture(CAP_INTELPERC);
for(;;)
{
Mat depthMap;
Mat image;
Mat irImage;
capture.grab();
capture.retrieve( depthMap, CAP_INTELPERC_DEPTH_MAP );
capture.retrieve( image, CAP_INTELPERC_IMAGE );
capture.retrieve( irImage, CAP_INTELPERC_IR_MAP);
if( waitKey( 30 ) >= 0 )
break;
}
@endcode
For setting and getting some property of sensor\` data generators use VideoCapture::set and
VideoCapture::get methods respectively, e.g. :
@code{.cpp}
VideoCapture capture( CAP_INTELPERC );
capture.set( CAP_INTELPERC_DEPTH_GENERATOR | CAP_PROP_INTELPERC_PROFILE_IDX, 0 );
cout << "FPS " << capture.get( CAP_INTELPERC_DEPTH_GENERATOR+CAP_PROP_FPS ) << endl;
@endcode
Since two types of sensor's data generators are supported (image generator and depth generator),
there are two flags that should be used to set/get property of the needed generator:
- CAP_INTELPERC_IMAGE_GENERATOR -- a flag for access to the image generator properties.
- CAP_INTELPERC_DEPTH_GENERATOR -- a flag for access to the depth generator properties. This
flag value is assumed by default if neither of the two possible values of the property is set.
For more information please refer to the example of usage
[intelperc_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/intelperc_capture.cpp)
in opencv/samples/cpp folder.

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Using Kinect and other OpenNI compatible depth sensors {#tutorial_kinect_openni}
======================================================
Depth sensors compatible with OpenNI (Kinect, XtionPRO, ...) are supported through VideoCapture
class. Depth map, BGR image and some other formats of output can be retrieved by using familiar
interface of VideoCapture.
In order to use depth sensor with OpenCV you should do the following preliminary steps:
-# Install OpenNI library (from here <http://www.openni.org/downloadfiles>) and PrimeSensor Module
for OpenNI (from here <https://github.com/avin2/SensorKinect>). The installation should be done
to default folders listed in the instructions of these products, e.g.:
@code{.text}
OpenNI:
Linux & MacOSX:
Libs into: /usr/lib
Includes into: /usr/include/ni
Windows:
Libs into: c:/Program Files/OpenNI/Lib
Includes into: c:/Program Files/OpenNI/Include
PrimeSensor Module:
Linux & MacOSX:
Bins into: /usr/bin
Windows:
Bins into: c:/Program Files/Prime Sense/Sensor/Bin
@endcode
If one or both products were installed to the other folders, the user should change
corresponding CMake variables OPENNI_LIB_DIR, OPENNI_INCLUDE_DIR or/and
OPENNI_PRIME_SENSOR_MODULE_BIN_DIR.
-# Configure OpenCV with OpenNI support by setting WITH_OPENNI flag in CMake. If OpenNI is found
in install folders OpenCV will be built with OpenNI library (see a status OpenNI in CMake log)
whereas PrimeSensor Modules can not be found (see a status OpenNI PrimeSensor Modules in CMake
log). Without PrimeSensor module OpenCV will be successfully compiled with OpenNI library, but
VideoCapture object will not grab data from Kinect sensor.
-# Build OpenCV.
VideoCapture can retrieve the following data:
-# data given from depth generator:
- CAP_OPENNI_DEPTH_MAP - depth values in mm (CV_16UC1)
- CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
- CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
- CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
- CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.)
(CV_8UC1)
-# data given from BGR image generator:
- CAP_OPENNI_BGR_IMAGE - color image (CV_8UC3)
- CAP_OPENNI_GRAY_IMAGE - gray image (CV_8UC1)
In order to get depth map from depth sensor use VideoCapture::operator \>\>, e. g. :
@code{.cpp}
VideoCapture capture( CAP_OPENNI );
for(;;)
{
Mat depthMap;
capture >> depthMap;
if( waitKey( 30 ) >= 0 )
break;
}
@endcode
For getting several data maps use VideoCapture::grab and VideoCapture::retrieve, e.g. :
@code{.cpp}
VideoCapture capture(0); // or CAP_OPENNI
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;
}
@endcode
For setting and getting some property of sensor\` data generators use VideoCapture::set and
VideoCapture::get methods respectively, e.g. :
@code{.cpp}
VideoCapture capture( CAP_OPENNI );
capture.set( CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE, CAP_OPENNI_VGA_30HZ );
cout << "FPS " << capture.get( CAP_OPENNI_IMAGE_GENERATOR+CAP_PROP_FPS ) << endl;
@endcode
Since two types of sensor's data generators are supported (image generator and depth generator),
there are two flags that should be used to set/get property of the needed generator:
- CAP_OPENNI_IMAGE_GENERATOR -- A flag for access to the image generator properties.
- CAP_OPENNI_DEPTH_GENERATOR -- A flag for access to the depth generator properties. This flag
value is assumed by default if neither of the two possible values of the property is not set.
Some depth sensors (for example XtionPRO) do not have image generator. In order to check it you can
get CAP_OPENNI_IMAGE_GENERATOR_PRESENT property.
@code{.cpp}
bool isImageGeneratorPresent = capture.get( CAP_PROP_OPENNI_IMAGE_GENERATOR_PRESENT ) != 0; // or == 1
@endcode
Flags specifing the needed generator type must be used in combination with particular generator
property. The following properties of cameras available through OpenNI interfaces are supported:
- For image generator:
- CAP_PROP_OPENNI_OUTPUT_MODE -- Three output modes are supported: CAP_OPENNI_VGA_30HZ
used by default (image generator returns images in VGA resolution with 30 FPS),
CAP_OPENNI_SXGA_15HZ (image generator returns images in SXGA resolution with 15 FPS) and
CAP_OPENNI_SXGA_30HZ (image generator returns images in SXGA resolution with 30 FPS, the
mode is supported by XtionPRO Live); depth generator's maps are always in VGA resolution.
- For depth generator:
- CAP_PROP_OPENNI_REGISTRATION -- Flag that registers the remapping depth map to image map
by changing depth generator's view point (if the flag is "on") or sets this view point to
its normal one (if the flag is "off"). The registration process’s resulting images are
pixel-aligned,which means that every pixel in the image is aligned to a pixel in the depth
image.
Next properties are available for getting only:
- CAP_PROP_OPENNI_FRAME_MAX_DEPTH -- A maximum supported depth of Kinect in mm.
- CAP_PROP_OPENNI_BASELINE -- Baseline value in mm.
- CAP_PROP_OPENNI_FOCAL_LENGTH -- A focal length in pixels.
- CAP_PROP_FRAME_WIDTH -- Frame width in pixels.
- CAP_PROP_FRAME_HEIGHT -- Frame height in pixels.
- CAP_PROP_FPS -- Frame rate in FPS.
- Some typical flags combinations "generator type + property" are defined as single flags:
- CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE
- CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE
- CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH
- CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION
For more information please refer to the example of usage
[openni_capture.cpp](https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp) in
opencv/samples/cpp folder.

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Reading Geospatial Raster files with GDAL {#tutorial_raster_io_gdal}
=========================================
Geospatial raster data is a heavily used product in Geographic Information Systems and
Photogrammetry. Raster data typically can represent imagery and Digital Elevation Models (DEM). The
standard library for loading GIS imagery is the Geographic Data Abstraction Library [(GDAL)](http://www.gdal.org). In this
example, we will show techniques for loading GIS raster formats using native OpenCV functions. In
addition, we will show some an example of how OpenCV can use this data for novel and interesting
purposes.
Goals
-----
The primary objectives for this tutorial:
- How to use OpenCV [imread](@ref imread) to load satellite imagery.
- How to use OpenCV [imread](@ref imread) to load SRTM Digital Elevation Models
- Given the corner coordinates of both the image and DEM, correllate the elevation data to the
image to find elevations for each pixel.
- Show a basic, easy-to-implement example of a terrain heat map.
- Show a basic use of DEM data coupled with ortho-rectified imagery.
To implement these goals, the following code takes a Digital Elevation Model as well as a GeoTiff
image of San Francisco as input. The image and DEM data is processed and generates a terrain heat
map of the image as well as labels areas of the city which would be affected should the water level
of the bay rise 10, 50, and 100 meters.
Code
----
@include cpp/tutorial_code/HighGUI/GDAL_IO/gdal-image.cpp
How to Read Raster Data using GDAL
----------------------------------
This demonstration uses the default OpenCV imread function. The primary difference is that in order
to force GDAL to load the image, you must use the appropriate flag.
@code{.cpp}
cv::Mat image = cv::imread( argv[1], cv::IMREAD_LOAD_GDAL );
@endcode
When loading digital elevation models, the actual numeric value of each pixel is essential and
cannot be scaled or truncated. For example, with image data a pixel represented as a double with a
value of 1 has an equal appearance to a pixel which is represented as an unsigned character with a
value of 255. With terrain data, the pixel value represents the elevation in meters. In order to
ensure that OpenCV preserves the native value, use the GDAL flag in imread with the ANYDEPTH flag.
@code{.cpp}
cv::Mat dem = cv::imread( argv[2], cv::IMREAD_LOAD_GDAL | cv::IMREAD_ANYDEPTH );
@endcode
If you know beforehand the type of DEM model you are loading, then it may be a safe bet to test the
Mat::type() or Mat::depth() using an assert or other mechanism. NASA or DOD specification documents
can provide the input types for various elevation models. The major types, SRTM and DTED, are both
signed shorts.
Notes
-----
### Lat/Lon (Geographic) Coordinates should normally be avoided
The Geographic Coordinate System is a spherical coordinate system, meaning that using them with
Cartesian mathematics is technically incorrect. This demo uses them to increase the readability and
is accurate enough to make the point. A better coordinate system would be Universal Transverse
Mercator.
### Finding the corner coordinates
One easy method to find the corner coordinates of an image is to use the command-line tool gdalinfo.
For imagery which is ortho-rectified and contains the projection information, you can use the [USGS
EarthExplorer](http://http://earthexplorer.usgs.gov).
@code{.bash}
\f$> gdalinfo N37W123.hgt
Driver: SRTMHGT/SRTMHGT File Format
Files: N37W123.hgt
Size is 3601, 3601
Coordinate System is:
GEOGCS["WGS 84",
DATUM["WGS_1984",
... more output ...
Corner Coordinates:
Upper Left (-123.0001389, 38.0001389) (123d 0' 0.50"W, 38d 0' 0.50"N)
Lower Left (-123.0001389, 36.9998611) (123d 0' 0.50"W, 36d59'59.50"N)
Upper Right (-121.9998611, 38.0001389) (121d59'59.50"W, 38d 0' 0.50"N)
Lower Right (-121.9998611, 36.9998611) (121d59'59.50"W, 36d59'59.50"N)
Center (-122.5000000, 37.5000000) (122d30' 0.00"W, 37d30' 0.00"N)
... more output ...
@endcode
Results
-------
Below is the output of the program. Use the first image as the input. For the DEM model, download
the SRTM file located at the USGS here.
[<http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip>](http://dds.cr.usgs.gov/srtm/version2_1/SRTM1/Region_04/N37W123.hgt.zip)
![Input Image](images/gdal_output.jpg)
![Heat Map](images/gdal_heat-map.jpg)
![Heat Map Overlay](images/gdal_flood-zone.jpg)

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High Level GUI and Media (highgui module) {#tutorial_table_of_content_highgui}
=========================================
This section contains valuable tutorials about how to read/save your image/video files and how to
use the built-in graphical user interface of the library.
This section contains tutorials about how to use the built-in graphical user interface of the library.
- @subpage tutorial_trackbar
@@ -11,15 +10,3 @@ use the built-in graphical user interface of the library.
*Author:* Ana Huamán
We will learn how to add a Trackbar to our applications
- @subpage tutorial_raster_io_gdal
*Compatibility:* \> OpenCV 2.0
*Author:* Marvin Smith
Read common GIS Raster and DEM files to display and manipulate geographic data.
- @subpage tutorial_kinect_openni
- @subpage tutorial_intelperc