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Image Component Library (ICL)
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Image Component Library (ICL)
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undocument this line if you encounter any issues! More...
Namespaces | |
| core | |
| cv | |
| filter | |
| geom | |
| io | |
| markers | |
| math | |
| physics | |
| qt | |
| utils | |
Classes | |
| class | FoldMap |
| discretized paper space representing folds More... | |
| class | InverseUndistortionProcessor |
| Utility class that performs gradient-descent based inverse undistortion mapping. More... | |
Typedefs | |
| typedef Ipp64f | icl64f |
| 64Bit floating point type for the ICL More... | |
| typedef Ipp32f | icl32f |
| 32Bit floating point type for the ICL More... | |
| typedef Ipp32s | icl32s |
| 32bit signed integer type for the ICL More... | |
| typedef Ipp16s | icl16s |
| 16bit signed integer type for the ICL (range [-32767, 32768 ]) More... | |
| typedef Ipp8u | icl8u |
| 8Bit unsigned integer type for the ICL More... | |
| typedef int8_t | icl8s |
| 8bit signed integer More... | |
| typedef uint32_t | icl32u |
| 32bit unsigned integer type for the ICL More... | |
| typedef uint16_t | icl16u |
| 16bit unsigned integer type for the ICL More... | |
| typedef int64_t | icl64s |
| 64bit signed integer type for the ICL More... | |
| typedef uint64_t | icl64u |
| 64bit unsigned integer type for the ICL More... | |
| typedef std::complex< icl32f > | icl32c |
| float comples type More... | |
| typedef std::complex< icl64f > | icl64c |
| float comples type More... | |
undocument this line if you encounter any issues!
all ICLQuick functions are placed here
The icl namespace.
icl namespace
The ICL-namespace.
This namespace is dedicated for ICLCore- and all additional Computer-Vision packages, that are based on the ICLCore classes.
\defgroup G_RD Region Detection Package \defgroup G_CBS Color Blob Searcher API (template based) \defgroup G_PT Position Tracker template class \defgroup G_UTILS Utility classes and Functions TODO!!! The ICLCV package contains functions and classes for region and blob detection and for tracking. However, the most common component is the icl::RegionDetector class, which performs a parameterized connected component analysis on images. Here are some sample application screenshots: \image html region-inspector.png "icl-region-inspector GUI" \section BLOB_DEF Blobs and Regions At first, we have to define the terms "Blob" and "Region": <em> A "Region" is a set of connected pixels. The set of pixels belonging to a single Region has to fulfill a certain criterion of homogeneity (e.g. "all pixels" have exactly the same value"). The term of "connection" must also be defined more precisely in order to avoid misunderstandings. A pixel is connected to all pixels in it's neighborhood, which in turn is given by the 4 pixels (to the left, to the right, above and below) next to the reference pixel. The also very common neighborhood that contains the 8 nearest neighbours is currently not supported. E.g. a connected component analysis yields a list of Regions.\n\n A Blob is a more general local spot in an image. Blobs don't have to be connected completely. </em> \section SEC_DETECTION_VS_TRACKING Detection vs. Tracking We differentiate explicitly between <em>detection</em> and <em>tracking</em>. When regions or blobs are <em>detected</em> in an image, no prior knowledge for the supposed image location is used, i.e. the region/blob is detected in the whole image.\n In contrast, blob <em>tracking</em> of blob means, that blobs are tracked in general from one time step to another, i.e the former blob location is used as prior guess for it's location in the current frame (tracking over time). \section BLOB_APPROACHES Different Blob Detection Approaches The ICLCV package provides some different blob detection and tracking approaches, that are introduced shorty in the following section. For a more detailed insight look at the corresponding class descriptions. \ref G_CBS \n The Color Blob Searcher API is a template based framework which provides a mechanism for a color based blob detection. In this approach each blob is determined by a special color, so blobs with identical colors are mixed together. Main class is the icl::ColorBlobSearcher template. \ref G_RD \n The icl::RegionDetector performs a connected component analysis on images. Regions that can be found must be connected and must show identical gray values (color images can not be processed yet). Commonly the input image of the RegionDetector's <em>detect(...)-method</em> is a kind of feature map that shows only a small number of different gray values (see the class documentation for more detail). The set of detected image regions can be restricted by: (i) a minimal and maximal gray value and (ii) a minimal and maxmial pixel count <b>Note:</b> The algorithm is highly speed-optimized, by using a special kind of self developed memory handling, which avoids memory allocation and deallocation at runtime if possible. Given large images with O(pixel count) regions (e.g. ordinary gray images instead of a feature map) the algorithm may need more physical memory than available. A very common pre-processing function may be <b>ICLFilter/LUT::reduceBits(..)</b>. In section \ref REGION_DETECTOR_EXAMPLE an example is presented. \ref G_PT \n These approaches above all perform Blob or region detection. The icl::PositionTracker or it's generalized version icl::VectorTracker can be used to tracking the resulting regions or blobs through time. \ref G_UTILS \n In this group some additional support classes and functions are provided \section CSS_CORNERS The Curvature Scale Space The curvature scale space can be used to extract 2D geometry models from regions. <b>Erik:</b> Please add some information here! \image html css-demo.jpg "icl-corner-detection-css-demo GUI" \section REGION_DETECTOR_EXAMPLE Region Detection Example <table border=0><tr><td> \code
#include <ICLQt/Common.h> #include <ICLCV/RegionDetector.h> #include <ICLFilter/ColorDistanceOp.h>
icl::qt::GUI gui; GenericGrabber grabber; RegionDetector rd(100,1E9,255,255); ColorDistanceOp cd(Color(0,120,240),100);
void mouse(const MouseEvent &e){ if(e.isLeft()){ cd.setReferenceColor(e.getColor()); } }
void init(){ grabber.init(pa("-i")); gui << Draw().handle("image") << Show(); gui["image"].install(mouse); }
void run(){ DrawHandle draw = gui["image"]; const core::ImgBase *I = grabber.grab();
draw = I;
std::vector<ImageRegion> rs = rd.detect(cd.apply(I)); for(size_t i=0;i<rs.size();++i){ draw->linestrip(rs[i].getBoundary()); } draw->render(); } int main(int n,char **v){ return ICLApp(n,v,"-input|-i(2)",init,run).exec(); }
\endcode </td><td valign=top> \image html icl-online-region-detection-demo-screenshot.png "icl-online-region-detection-demo screenshot" </td></tr></table>
| typedef Ipp16s icl::icl16s |
16bit signed integer type for the ICL (range [-32767, 32768 ])
| typedef uint16_t icl::icl16u |
16bit unsigned integer type for the ICL
| typedef std::complex<icl32f> icl::icl32c |
float comples type
| typedef Ipp32f icl::icl32f |
32Bit floating point type for the ICL
| typedef Ipp32s icl::icl32s |
32bit signed integer type for the ICL
| typedef uint32_t icl::icl32u |
32bit unsigned integer type for the ICL
| typedef std::complex<icl64f> icl::icl64c |
float comples type
| typedef Ipp64f icl::icl64f |
64Bit floating point type for the ICL
| typedef int64_t icl::icl64s |
64bit signed integer type for the ICL
| typedef uint64_t icl::icl64u |
64bit unsigned integer type for the ICL
| typedef int8_t icl::icl8s |
8bit signed integer
| typedef Ipp8u icl::icl8u |
8Bit unsigned integer type for the ICL
1.8.15