| /external/llvm/lib/Analysis/ |
| D | StratifiedSets.h | 63 StratifiedIndex Below; member
68 StratifiedLink() : Above(SetSentinel), Below(SetSentinel) {} in StratifiedLink()
70 bool hasBelow() const { return Below != SetSentinel; } in hasBelow()
73 void clearBelow() { Below = SetSentinel; } in clearBelow()
236 Link.Below = I; in setBelow()
257 return Link.Below; in getBelow()
335 auto &Below = linksAt(Link.Below); in finalizeSets() local
336 auto Iter = Remaps.find(Below.Number); in finalizeSets()
338 Link.Below = Iter->second; in finalizeSets()
372 auto NextIndex = Links[CurrentIndex].Below; in propagateAttrs()
[all …]
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| D | CFLAliasAnalysis.cpp | 111 enum class Level { Same, Above, Below }; enumerator
266 if (Current->Below == Index2) in getIndexRelation()
267 return Level::Below; in getIndexRelation()
268 Current = &Sets.getLink(Current->Below); in getIndexRelation()
775 return Level::Below; in directionOfEdgeType()
954 case Level::Below: in buildSetsFrom()
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| /external/opencv3/doc/py_tutorials/py_calib3d/py_depthmap/ |
| D | py_depthmap.markdown | 15 way. Below is an image and some simple mathematical formulas which proves that intuition. (Image
38 Below code snippet shows a simple procedure to create disparity map.
52 Below image contains the original image (left) and its disparity map (right). As you can see, result
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| /external/icu/icu4c/source/samples/ucnv/ |
| D | data06.txt | 82 // /**************** Info Below is needed ****************/
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_pyramids/ |
| D | py_pyramids.markdown | 36 Below is the 4 levels in an image pyramid.
45 loose the information. Below image is 3 level down the pyramid created from smallest image in
79 Below is the full code. (For sake of simplicity, each step is done separately which may take more
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| /external/opencv3/doc/py_tutorials/py_feature2d/py_features_harris/ |
| D | py_features_harris.markdown | 97 Below are the three results:
105 **cv2.cornerSubPix()** which further refines the corners detected with sub-pixel accuracy. Below is
142 Below is the result, where some important locations are shown in zoomed window to visualize:
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_gradients/ |
| D | py_gradients.markdown | 38 Below code shows all operators in a single diagram. All kernels are of 5x5 size. Depth of output
76 Below code demonstrates this procedure for a horizontal Sobel filter and difference in results.
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| /external/valgrind/exp-bbv/tests/amd64-linux/ |
| D | clone_test.S | 36 # Below required for Valgrind
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| /external/valgrind/exp-bbv/tests/x86-linux/ |
| D | clone_test.S | 36 # Below required for Valgrind
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_colorspaces/ |
| D | py_colorspaces.markdown | 45 Below is the code which are commented in detail :
79 Below image shows tracking of the blue object:
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| /external/opencv3/doc/py_tutorials/py_ml/py_kmeans/py_kmeans_opencv/ |
| D | py_kmeans_opencv.markdown | 96 Below is the output we got:
145 Below is the output we get:
161 Below is the code:
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| /external/skia/site/dev/testing/ |
| D | ios.md | 8 Below are instructions on how to install a set of tools that make this possible.
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| /external/opencv3/doc/py_tutorials/py_ml/py_svm/py_svm_opencv/ |
| D | py_svm_opencv.markdown | 19 a function **deskew()** which takes a digit image and deskew it. Below is the deskew() function:
30 Below image shows above deskew function applied to an image of zero. Left image is the original
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| /external/protobuf/src/solaris/ |
| D | libstdc++.la | 10 # /usr/sfw/lib/libstdc++.la is empty. Below is the correct content,
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/ |
| D | py_histogram_backprojection.markdown | 83 kernel and apply threshold. Below is my code and output :
113 Below is one example I worked with. I used the region inside blue rectangle as sample object and I
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/ |
| D | py_2d_histogram.markdown | 99 Below is the input image and its color histogram plot. X axis shows S values and Y axis shows Hue.
118 I leave it to the readers to run the code, analyze it and have your own hack arounds. Below is the
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| /external/clang/include/clang/StaticAnalyzer/Core/BugReporter/ |
| D | PathDiagnostic.h | 340 enum DisplayHint { Above, Below }; enumerator
364 PathDiagnosticPiece(StringRef s, Kind k, DisplayHint hint = Below);
366 PathDiagnosticPiece(Kind k, DisplayHint hint = Below);
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_contours/py_contours_hierarchy/ |
| D | py_contours_hierarchy.markdown | 100 Below is the result I got, and each row is hierarchy details of corresponding contour. For eg, first
124 element is same as above. Compare it with above result. Below is what I got :
200 And remaining, try yourself. Below is the full answer:
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| /external/opencv3/doc/py_tutorials/py_setup/py_setup_in_windows/ |
| D | py_setup_in_windows.markdown | 10 Below steps are tested in a Windows 7-64 bit machine with Visual Studio 2010 and Visual Studio 2012.
16 -# Below Python packages are to be downloaded and installed to their default locations.
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/ |
| D | py_histogram_equalization.markdown | 82 Below is a simple code snippet showing its usage for same image we used :
122 Below code snippet shows how to apply CLAHE in OpenCV:
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| /external/opencv3/doc/py_tutorials/py_photo/py_non_local_means/ |
| D | py_non_local_means.markdown | 89 Below is a zoomed version of result. My input image has a gaussian noise of \f$\sigma = 25\f$. See …
136 Below image shows a zoomed version of the result we got:
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| /external/vulkan-validation-layers/layers/ |
| D | vk_layer_settings.txt | 12 # Below is a general description of three common settings, followed by
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| /external/icu/icu4c/source/samples/ |
| D | readme.txt | 5 Below is a short description of the contents of this directory.
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| /external/opencv3/doc/py_tutorials/py_imgproc/py_contours/py_contour_features/ |
| D | py_contour_features.markdown | 74 Below, in second image, green line shows the approximated curve for epsilon = 10% of arc length.
188 Similarly we can fit a line to a set of points. Below image contains a set of white points. We can
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| /external/opencv3/doc/tutorials/ios/image_manipulation/ |
| D | image_manipulation.markdown | 14 convert an *OpenCV Mat* to an *UIImage* we use the *Core Graphics* framework available in iOS. Below
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