зеркало из https://github.com/microsoft/opencv.git
Brian Wignall
Alexander Alekhin
Родитель
5e2bcc9149
Коммит
f9c514b391
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@ -1078,8 +1078,8 @@ void cvCreateTrainingSamples( const char* filename,
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icvPlaceDistortedSample( sample, inverse, maxintensitydev,
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maxxangle, maxyangle, maxzangle,
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0 /* nonzero means placing image without cut offs */,
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0.0 /* nozero adds random shifting */,
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0.0 /* nozero adds random scaling */,
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0.0 /* nonzero adds random shifting */,
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0.0 /* nonzero adds random scaling */,
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&data );
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if( showsamples )
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@ -45,7 +45,7 @@ protected:
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};
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std::vector<Feature> features;
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cv::Mat normSum; //for nomalization calculation (L1 or L2)
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cv::Mat normSum; //for normalization calculation (L1 or L2)
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std::vector<cv::Mat> hist;
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};
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@ -70,7 +70,7 @@ inline float CvHOGEvaluator::Feature::calc( const std::vector<cv::Mat>& _hists,
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const float *pnormSum = _normSum.ptr<float>((int)y);
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normFactor = (float)(pnormSum[fastRect[0].p0] - pnormSum[fastRect[1].p1] - pnormSum[fastRect[2].p2] + pnormSum[fastRect[3].p3]);
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res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which apper due to floating precision
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res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which appear due to floating precision
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return res;
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}
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@ -145,7 +145,7 @@ no child, parent is contour-3. So array is [-1,-1,-1,3].
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And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
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hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
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For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
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For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
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result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
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give the hierarchy order.
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@ -17,7 +17,7 @@ In short, we found locations of some parts of an object in another cluttered ima
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is sufficient to find the object exactly on the trainImage.
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For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
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of points from both the images, it will find the perpective transformation of that object. Then we
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of points from both the images, it will find the perspective transformation of that object. Then we
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can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
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find the transformation.
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@ -68,7 +68,7 @@ Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are
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find the object. Otherwise simply show a message saying not enough matches are present.
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If enough matches are found, we extract the locations of matched keypoints in both the images. They
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are passed to find the perpective transformation. Once we get this 3x3 transformation matrix, we use
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are passed to find the perspective transformation. Once we get this 3x3 transformation matrix, we use
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it to transform the corners of queryImage to corresponding points in trainImage. Then we draw it.
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@code{.py}
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if len(good)>MIN_MATCH_COUNT:
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@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
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From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
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\f$\lambda_{min}\f$, it is conidered as a corner(green region).
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\f$\lambda_{min}\f$, it is considered as a corner(green region).
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Code
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----
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@ -144,7 +144,7 @@ cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
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### 7.b. Rotated Rectangle
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Here, bounding rectangle is drawn with minimum area, so it considers the rotation also. The function
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used is **cv.minAreaRect()**. It returns a Box2D structure which contains following detals - (
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used is **cv.minAreaRect()**. It returns a Box2D structure which contains following details - (
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center (x,y), (width, height), angle of rotation ). But to draw this rectangle, we need 4 corners of
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the rectangle. It is obtained by the function **cv.boxPoints()**
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@code{.py}
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@ -185,7 +185,7 @@ array([[[ 3, -1, 1, -1],
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And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
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hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.
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For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
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For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
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result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
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give the hierarchy order.
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@ -381,7 +381,7 @@ Here is explained in detail the code for the real time application:
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as not, there are false correspondences or also called *outliers*. The [Random Sample
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Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
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method which estimate parameters of a mathematical model from observed data producing an
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approximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
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approximate result as the number of iterations increase. After applying *Ransac* all the *outliers*
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will be eliminated to then estimate the camera pose with a certain probability to obtain a good
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solution.
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@ -499,7 +499,7 @@ using the following OpenCV methods:
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- the imwrite static method from the Highgui class to write an image to a file
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- the GaussianBlur static method from the Imgproc class to apply to blur the original image
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We're also going to use the Mat class which is returned from the imread method and accpeted as the
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We're also going to use the Mat class which is returned from the imread method and accepted as the
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main argument to both the GaussianBlur and the imwrite methods.
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### Add an image to the project
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@ -10,7 +10,7 @@ In this tutorial,
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- We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
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- We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
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will use the functions:
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- @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
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- @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifier
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- @ref cv::CascadeClassifier::detectMultiScale to perform the detection.
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Theory
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@ -168,7 +168,7 @@ Command line arguments of opencv_traincascade application grouped by purposes:
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- `-w <sampleWidth>` : Width of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
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- `-h <sampleHeight>` : Height of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
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- Boosted classifer parameters:
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- Boosted classifier parameters:
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- `-bt <{DAB, RAB, LB, GAB(default)}>` : Type of boosted classifiers: DAB - Discrete AdaBoost, RAB - Real AdaBoost, LB - LogitBoost, GAB - Gentle AdaBoost.
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- `-minHitRate <min_hit_rate>` : Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
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- `-maxFalseAlarmRate <max_false_alarm_rate>` : Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.
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@ -43,7 +43,7 @@ VideoCapture can retrieve the following data:
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- CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
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- CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
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- CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
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- CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.)
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- CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded etc.)
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(CV_8UC1)
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-# data given from BGR image generator:
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@ -1218,7 +1218,7 @@ struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFind
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CV_WRAP CirclesGridFinderParameters2();
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CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
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CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING.
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CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
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};
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/** @brief Finds centers in the grid of circles.
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@ -48,7 +48,7 @@
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#include <iterator>
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/*
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This is stright-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
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This is straight-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
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that is (in a large extent) based on the paper:
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Z. Zhang. "A flexible new technique for camera calibration".
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.
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@ -115,7 +115,7 @@ void CV_ChessboardDetectorBadArgTest::run( int /*start_from */)
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img = cb.clone();
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pattern_size = Size(2,2);
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errors += run_test_case( CV_StsOutOfRange, "Invlid pattern size" );
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errors += run_test_case( CV_StsOutOfRange, "Invalid pattern size" );
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pattern_size = cbg.cornersSize();
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cb.convertTo(img, CV_32F);
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@ -1309,7 +1309,7 @@ CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order,
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const CvArr* delta CV_DEFAULT(NULL),
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double scale CV_DEFAULT(1.) );
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/** Tranposes matrix. Square matrices can be transposed in-place */
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/** Transposes matrix. Square matrices can be transposed in-place */
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CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst );
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#define cvT cvTranspose
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@ -569,7 +569,7 @@ inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b)
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{ return v_int64x4(v256_blend<m>(v_uint64x4(a.val), v_uint64x4(b.val)).val); }
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// shuffle
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// todo: emluate 64bit
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// todo: emulate 64bit
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#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \
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template<int m> \
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inline _Tpvec v256_shuffle(const _Tpvec& a) \
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@ -73,7 +73,7 @@ implemented as a structure based on a one SIMD register.
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- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char
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- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short
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- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int
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- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsigned/signed) - int
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- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
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- cv::v_float32x4: four 32-bit floating point values (signed) - float
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- cv::v_float64x2: two 64-bit floating point values (signed) - double
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@ -1805,7 +1805,7 @@ inline v_float32x4 v_broadcast_element(const v_float32x4& a)
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return v_setall_f32(v_extract_n<i>(a));
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}
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////// FP16 suport ///////
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////// FP16 support ///////
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#if CV_FP16
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inline v_float32x4 v_load_expand(const float16_t* ptr)
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{
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@ -94,7 +94,7 @@ struct v_uint16x8
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}
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ushort get0() const
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{
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return (ushort)wasm_i16x8_extract_lane(val, 0); // wasm_u16x8_extract_lane() unimplemeted yet
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return (ushort)wasm_i16x8_extract_lane(val, 0); // wasm_u16x8_extract_lane() unimplemented yet
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}
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v128_t val;
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@ -50,7 +50,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
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#define msa_ld1q_f32(__a) ((v4f32)__builtin_msa_ld_w(__a, 0))
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#define msa_ld1q_f64(__a) ((v2f64)__builtin_msa_ld_d(__a, 0))
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/* Store 64bits vector elments values to the given memory address. */
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/* Store 64bits vector elements values to the given memory address. */
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#define msa_st1_s8(__a, __b) (*((v8i8*)(__a)) = __b)
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#define msa_st1_s16(__a, __b) (*((v4i16*)(__a)) = __b)
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#define msa_st1_s32(__a, __b) (*((v2i32*)(__a)) = __b)
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@ -377,7 +377,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
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})
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/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector.
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Input is signed and outpus is unsigned. */
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Input is signed and output is unsigned. */
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#define msa_qrshrun_n_s16(__a, __b) \
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({ \
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v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__b)); \
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@ -62,7 +62,7 @@ static String getDeviceTypeString(const cv::ocl::Device& device)
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}
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}
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return "unkown";
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return "unknown";
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}
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} // namespace
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@ -165,7 +165,7 @@ public:
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/** @brief Sets the initial step that will be used in downhill simplex algorithm.
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Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional
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Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional
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vectors that are used to determine the shape of initial simplex. Roughly said, initial point
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determines the position of a simplex (it will become simplex's centroid), while step determines the
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spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are
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@ -317,7 +317,7 @@ VSX_IMPL_1RG(vec_udword2, wi, vec_float4, wf, xvcvspuxds, vec_ctulo)
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* Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837
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*
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* So we're not able to use inline asm and only use built-in functions that CLANG supports
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* and use __builtin_convertvector if clang missng any of vector conversions built-in functions
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* and use __builtin_convertvector if clang missing any of vector conversions built-in functions
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*
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* todo: clang asm template bug is fixed, need to reconsider the current workarounds.
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*/
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@ -491,7 +491,7 @@ VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo)
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// Only for Eigen!
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/*
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* changing behavior of conversion intrinsics for gcc has effect on Eigen
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* so we redfine old behavior again only on gcc, clang
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* so we redefine old behavior again only on gcc, clang
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*/
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#if !defined(__clang__) || __clang_major__ > 4
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// ignoring second arg since Eigen only truncates toward zero
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@ -250,7 +250,7 @@ cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes,
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for( int i = dims - 1; i >= 0; i-- )
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{
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if( sizes[i] < 0 )
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CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
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CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
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mat->dim[i].size = sizes[i];
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if( step > INT_MAX )
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CV_Error( CV_StsOutOfRange, "The array is too big" );
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@ -545,7 +545,7 @@ cvCreateSparseMat( int dims, const int* sizes, int type )
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for( i = 0; i < dims; i++ )
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{
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if( sizes[i] <= 0 )
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CV_Error( CV_StsBadSize, "one of dimesion sizes is non-positive" );
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CV_Error( CV_StsBadSize, "one of dimension sizes is non-positive" );
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}
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CvSparseMat* arr = (CvSparseMat*)cvAlloc(sizeof(*arr)+MAX(0,dims-CV_MAX_DIM)*sizeof(arr->size[0]));
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@ -53,7 +53,7 @@ cvtabs_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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}
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}
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// variant for convrsions 16f <-> ... w/o unrolling
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// variant for conversions 16f <-> ... w/o unrolling
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template<typename _Ts, typename _Td> inline void
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cvtabs1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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@ -123,7 +123,7 @@ cvt_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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}
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}
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// variant for convrsions 16f <-> ... w/o unrolling
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// variant for conversions 16f <-> ... w/o unrolling
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template<typename _Ts, typename _Td> inline void
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cvt1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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@ -77,7 +77,7 @@ Replaced y(1,ndim,0.0) ------> y(1,ndim+1,0.0)
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***********************************************************************************************************************************
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The code below was used in tesing the source code.
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The code below was used in testing the source code.
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Created by @SareeAlnaghy
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#include <iostream>
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@ -1592,7 +1592,7 @@ public:
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{
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TlsAbstraction* tls = getTlsAbstraction();
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if (NULL == tls)
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return; // TLS signleton is not available (terminated)
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return; // TLS singleton is not available (terminated)
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ThreadData *pTD = tlsValue == NULL ? (ThreadData*)tls->getData() : (ThreadData*)tlsValue;
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if (pTD == NULL)
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return; // no OpenCV TLS data for this thread
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@ -1683,7 +1683,7 @@ public:
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TlsAbstraction* tls = getTlsAbstraction();
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if (NULL == tls)
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return NULL; // TLS signleton is not available (terminated)
|
||||
return NULL; // TLS singleton is not available (terminated)
|
||||
|
||||
ThreadData* threadData = (ThreadData*)tls->getData();
|
||||
if(threadData && threadData->slots.size() > slotIdx)
|
||||
|
|
@ -1719,7 +1719,7 @@ public:
|
|||
|
||||
TlsAbstraction* tls = getTlsAbstraction();
|
||||
if (NULL == tls)
|
||||
return; // TLS signleton is not available (terminated)
|
||||
return; // TLS singleton is not available (terminated)
|
||||
|
||||
ThreadData* threadData = (ThreadData*)tls->getData();
|
||||
if(!threadData)
|
||||
|
|
|
|||
|
|
@ -134,7 +134,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
|
|||
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
|
||||
|
||||
/** @deprecated Use flag `produce_cell_output` in LayerParams.
|
||||
* @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
|
||||
* @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
|
||||
*
|
||||
* If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
|
||||
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
|
||||
|
|
|
|||
|
|
@ -622,7 +622,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
|
|||
try {
|
||||
wrapper->outProms[processedOutputs].setException(std::current_exception());
|
||||
} catch(...) {
|
||||
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
|
||||
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -635,7 +635,7 @@ void InfEngineNgraphNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlo
|
|||
try {
|
||||
wrapper->outProms[processedOutputs].setException(e);
|
||||
} catch(...) {
|
||||
CV_LOG_ERROR(NULL, "DNN: Exception occured during async inference exception propagation");
|
||||
CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -116,7 +116,7 @@ message AttributeProto {
|
|||
|
||||
// The type field MUST be present for this version of the IR.
|
||||
// For 0.0.1 versions of the IR, this field was not defined, and
|
||||
// implementations needed to use has_field hueristics to determine
|
||||
// implementations needed to use has_field heuristics to determine
|
||||
// which value field was in use. For IR_VERSION 0.0.2 or later, this
|
||||
// field MUST be set and match the f|i|s|t|... field in use. This
|
||||
// change was made to accommodate proto3 implementations.
|
||||
|
|
@ -323,7 +323,7 @@ message TensorProto {
|
|||
// For float and complex64 values
|
||||
// Complex64 tensors are encoded as a single array of floats,
|
||||
// with the real components appearing in odd numbered positions,
|
||||
// and the corresponding imaginary component apparing in the
|
||||
// and the corresponding imaginary component appearing in the
|
||||
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
|
||||
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
|
||||
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
|
||||
|
|
@ -373,7 +373,7 @@ message TensorProto {
|
|||
// For double
|
||||
// Complex64 tensors are encoded as a single array of doubles,
|
||||
// with the real components appearing in odd numbered positions,
|
||||
// and the corresponding imaginary component apparing in the
|
||||
// and the corresponding imaginary component appearing in the
|
||||
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
|
||||
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
|
||||
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
|
||||
|
|
|
|||
|
|
@ -385,7 +385,7 @@ code which is distributed under GPL.
|
|||
class CV_EXPORTS_W MSER : public Feature2D
|
||||
{
|
||||
public:
|
||||
/** @brief Full consturctor for %MSER detector
|
||||
/** @brief Full constructor for %MSER detector
|
||||
|
||||
@param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
|
||||
@param _min_area prune the area which smaller than minArea
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int
|
|||
// Nonlinear diffusion filtering scalar step
|
||||
void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
|
||||
|
||||
// For non-maxima suppresion
|
||||
// For non-maxima suppression
|
||||
bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
|
||||
|
||||
// Image downsampling
|
||||
|
|
|
|||
|
|
@ -983,7 +983,7 @@ extractMSER_8uC3( const Mat& src,
|
|||
double s = (double)(lr->size-lr->sizei)/(lr->dt-lr->di);
|
||||
if ( s < lr->s )
|
||||
{
|
||||
// skip the first one and check stablity
|
||||
// skip the first one and check stability
|
||||
if ( i > lr->reinit+1 && MSCRStableCheck( lr, params ) )
|
||||
{
|
||||
if ( lr->tmsr == NULL )
|
||||
|
|
|
|||
|
|
@ -131,7 +131,7 @@ float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
|
|||
}
|
||||
|
||||
if (val_r<vals[0]) {
|
||||
// value is smaller than smalest in simplex
|
||||
// value is smaller than smallest in simplex
|
||||
|
||||
// expand some more to see if it drops further
|
||||
for (int i=0; i<n; ++i) {
|
||||
|
|
|
|||
|
|
@ -1184,7 +1184,7 @@ CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype );
|
|||
/** @brief Returns the polygon points which make up the given ellipse.
|
||||
|
||||
The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
|
||||
sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than
|
||||
sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than
|
||||
0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
|
||||
number of points stored into 'pts' is returned by this function.
|
||||
@see cv::ellipse2Poly
|
||||
|
|
|
|||
|
|
@ -630,7 +630,7 @@ approxPolyDP_( const Point_<T>* src_contour, int count0, Point_<T>* dst_contour,
|
|||
WRITE_PT( src_contour[count-1] );
|
||||
|
||||
// last stage: do final clean-up of the approximated contour -
|
||||
// remove extra points on the [almost] stright lines.
|
||||
// remove extra points on the [almost] straight lines.
|
||||
is_closed = is_closed0;
|
||||
count = new_count;
|
||||
pos = is_closed ? count - 1 : 0;
|
||||
|
|
|
|||
|
|
@ -776,7 +776,7 @@ cv::RotatedRect cv::fitEllipseDirect( InputArray _points )
|
|||
namespace cv
|
||||
{
|
||||
|
||||
// Calculates bounding rectagnle of a point set or retrieves already calculated
|
||||
// Calculates bounding rectangle of a point set or retrieves already calculated
|
||||
static Rect pointSetBoundingRect( const Mat& points )
|
||||
{
|
||||
int npoints = points.checkVector(2);
|
||||
|
|
@ -1392,7 +1392,7 @@ cvFitEllipse2( const CvArr* array )
|
|||
return cvBox2D(cv::fitEllipse(points));
|
||||
}
|
||||
|
||||
/* Calculates bounding rectagnle of a point set or retrieves already calculated */
|
||||
/* Calculates bounding rectangle of a point set or retrieves already calculated */
|
||||
CV_IMPL CvRect
|
||||
cvBoundingRect( CvArr* array, int update )
|
||||
{
|
||||
|
|
|
|||
|
|
@ -325,7 +325,7 @@ void CV_ApproxPolyTest::run( int /*start_from*/ )
|
|||
if( DstSeq == NULL )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG,
|
||||
"cvApproxPoly returned NULL for contour #%d, espilon = %g\n", i, Eps );
|
||||
"cvApproxPoly returned NULL for contour #%d, epsilon = %g\n", i, Eps );
|
||||
code = cvtest::TS::FAIL_INVALID_OUTPUT;
|
||||
goto _exit_;
|
||||
} // if( DstSeq == NULL )
|
||||
|
|
|
|||
|
|
@ -60,7 +60,7 @@ namespace opencv_test { namespace {
|
|||
// 6 - partial intersection, rectangle on top of different size
|
||||
// 7 - full intersection, rectangle fully enclosed in the other
|
||||
// 8 - partial intersection, rectangle corner just touching. point contact
|
||||
// 9 - partial intersetion. rectangle side by side, line contact
|
||||
// 9 - partial intersection. rectangle side by side, line contact
|
||||
|
||||
static void compare(const std::vector<Point2f>& test, const std::vector<Point2f>& target)
|
||||
{
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ foreach(file ${seed_project_files_rel})
|
|||
endforeach()
|
||||
|
||||
list(APPEND depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
|
||||
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Andoid SDK) source files" ${depends})
|
||||
ocv_copyfiles_add_target(${the_module}_android_source_copy JAVA_SRC_COPY "Copy Java(Android SDK) source files" ${depends})
|
||||
file(REMOVE "${OPENCV_DEPHELPER}/${the_module}_android_source_copy") # force rebuild after CMake run
|
||||
|
||||
set(depends ${the_module}_android_source_copy "${OPENCV_DEPHELPER}/${the_module}_android_source_copy")
|
||||
|
|
|
|||
|
|
@ -232,7 +232,7 @@ public abstract class CameraBridgeViewBase extends SurfaceView implements Surfac
|
|||
|
||||
/**
|
||||
* This method is provided for clients, so they can disable camera connection and stop
|
||||
* the delivery of frames even though the surface view itself is not destroyed and still stays on the scren
|
||||
* the delivery of frames even though the surface view itself is not destroyed and still stays on the screen
|
||||
*/
|
||||
public void disableView() {
|
||||
synchronized(mSyncObject) {
|
||||
|
|
|
|||
|
|
@ -32,4 +32,4 @@ To run performance tests, please launch a local web server in <build_dir>/bin fo
|
|||
|
||||
Navigate the web browser to the kernel page you want to test, like http://localhost:8080/perf/imgproc/cvtcolor.html.
|
||||
|
||||
You can input the paramater, and then click the `Run` button to run the specific case, or it will run all the cases.
|
||||
You can input the parameter, and then click the `Run` button to run the specific case, or it will run all the cases.
|
||||
|
|
|
|||
|
|
@ -1679,7 +1679,7 @@ public:
|
|||
|
||||
/** @brief This function returns the trained parameters arranged across rows.
|
||||
|
||||
For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of
|
||||
For a two class classification problem, it returns a row matrix. It returns learnt parameters of
|
||||
the Logistic Regression as a matrix of type CV_32F.
|
||||
*/
|
||||
CV_WRAP virtual Mat get_learnt_thetas() const = 0;
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
#!/usr/bin/env python
|
||||
"""Algorithm serializaion test."""
|
||||
"""Algorithm serialization test."""
|
||||
import tempfile
|
||||
import os
|
||||
import cv2 as cv
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
#!/usr/bin/env python
|
||||
""""Core serializaion tests."""
|
||||
""""Core serialization tests."""
|
||||
import tempfile
|
||||
import os
|
||||
import cv2 as cv
|
||||
|
|
|
|||
|
|
@ -332,14 +332,14 @@ finds two best matches for each feature and leaves the best one only if the
|
|||
ratio between descriptor distances is greater than the threshold match_conf.
|
||||
|
||||
Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
|
||||
transformation (affine trasformation estimate will be placed in matches_info).
|
||||
transformation (affine transformation estimate will be placed in matches_info).
|
||||
|
||||
@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
|
||||
*/
|
||||
class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
|
||||
{
|
||||
public:
|
||||
/** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
|
||||
/** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
|
||||
between images
|
||||
|
||||
@param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
|
||||
|
|
|
|||
|
|
@ -11367,7 +11367,7 @@ void UniversalTersePrint(const T& value, ::std::ostream* os) {
|
|||
// NUL-terminated string.
|
||||
template <typename T>
|
||||
void UniversalPrint(const T& value, ::std::ostream* os) {
|
||||
// A workarond for the bug in VC++ 7.1 that prevents us from instantiating
|
||||
// A workaround for the bug in VC++ 7.1 that prevents us from instantiating
|
||||
// UniversalPrinter with T directly.
|
||||
typedef T T1;
|
||||
UniversalPrinter<T1>::Print(value, os);
|
||||
|
|
|
|||
|
|
@ -94,11 +94,11 @@ class Aapt(Tool):
|
|||
# get test instrumentation info
|
||||
instrumentation_tag = [t for t in tags if t.startswith("instrumentation ")]
|
||||
if not instrumentation_tag:
|
||||
raise Err("Can not find instrumentation detials in: %s", exe)
|
||||
raise Err("Can not find instrumentation details in: %s", exe)
|
||||
res.pkg_runner = re.search(r"^[ ]+A: android:name\(0x[0-9a-f]{8}\)=\"(?P<runner>.*?)\" \(Raw: \"(?P=runner)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("runner")
|
||||
res.pkg_target = re.search(r"^[ ]+A: android:targetPackage\(0x[0-9a-f]{8}\)=\"(?P<pkg>.*?)\" \(Raw: \"(?P=pkg)\"\)\r?$", instrumentation_tag[0], flags=re.MULTILINE).group("pkg")
|
||||
if not res.pkg_name or not res.pkg_runner or not res.pkg_target:
|
||||
raise Err("Can not find instrumentation detials in: %s", exe)
|
||||
raise Err("Can not find instrumentation details in: %s", exe)
|
||||
return res
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -452,7 +452,7 @@ int BadArgTest::run_test_case( int expected_code, const string& _descr )
|
|||
{
|
||||
thrown = true;
|
||||
if (e.code != expected_code &&
|
||||
e.code != cv::Error::StsError && e.code != cv::Error::StsAssert // Exact error codes support will be dropped. Checks should provide proper text messages intead.
|
||||
e.code != cv::Error::StsError && e.code != cv::Error::StsAssert // Exact error codes support will be dropped. Checks should provide proper text messages instead.
|
||||
)
|
||||
{
|
||||
ts->printf(TS::LOG, "%s (test case #%d): the error code %d is different from the expected %d\n",
|
||||
|
|
|
|||
|
|
@ -110,7 +110,7 @@ public:
|
|||
//set parameters
|
||||
// N - the number of samples stored in memory per model
|
||||
nN = defaultNsamples;
|
||||
//kNN - k nearest neighbour - number on NN for detcting background - default K=[0.1*nN]
|
||||
//kNN - k nearest neighbour - number on NN for detecting background - default K=[0.1*nN]
|
||||
nkNN=MAX(1,cvRound(0.1*nN*3+0.40));
|
||||
|
||||
//Tb - Threshold Tb*kernelwidth
|
||||
|
|
@ -292,7 +292,7 @@ protected:
|
|||
//less important parameters - things you might change but be careful
|
||||
////////////////////////
|
||||
int nN;//totlal number of samples
|
||||
int nkNN;//number on NN for detcting background - default K=[0.1*nN]
|
||||
int nkNN;//number on NN for detecting background - default K=[0.1*nN]
|
||||
|
||||
//shadow detection parameters
|
||||
bool bShadowDetection;//default 1 - do shadow detection
|
||||
|
|
|
|||
|
|
@ -181,7 +181,7 @@ public:
|
|||
//! computes a background image which are the mean of all background gaussians
|
||||
virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
|
||||
|
||||
//! re-initiaization method
|
||||
//! re-initialization method
|
||||
void initialize(Size _frameSize, int _frameType)
|
||||
{
|
||||
frameSize = _frameSize;
|
||||
|
|
|
|||
|
|
@ -319,8 +319,8 @@ enum
|
|||
CV_CAP_PROP_XI_COOLING = 466, // Start camera cooling.
|
||||
CV_CAP_PROP_XI_TARGET_TEMP = 467, // Set sensor target temperature for cooling.
|
||||
CV_CAP_PROP_XI_CHIP_TEMP = 468, // Camera sensor temperature
|
||||
CV_CAP_PROP_XI_HOUS_TEMP = 469, // Camera housing tepmerature
|
||||
CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, // Camera housing back side tepmerature
|
||||
CV_CAP_PROP_XI_HOUS_TEMP = 469, // Camera housing temperature
|
||||
CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, // Camera housing back side temperature
|
||||
CV_CAP_PROP_XI_SENSOR_BOARD_TEMP = 596, // Camera sensor board temperature
|
||||
CV_CAP_PROP_XI_CMS = 470, // Mode of color management system.
|
||||
CV_CAP_PROP_XI_APPLY_CMS = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
|
||||
|
|
|
|||
|
|
@ -299,7 +299,7 @@ bool CvCaptureCAM_Aravis::grabFrame()
|
|||
size_t buffer_size;
|
||||
framebuffer = (void*)arv_buffer_get_data (arv_buffer, &buffer_size);
|
||||
|
||||
// retrieve image size properites
|
||||
// retrieve image size properties
|
||||
arv_buffer_get_image_region (arv_buffer, &xoffset, &yoffset, &width, &height);
|
||||
|
||||
// retrieve image ID set by camera
|
||||
|
|
|
|||
|
|
@ -1293,7 +1293,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
|
|||
colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
|
||||
provider, NULL, false, kCGRenderingIntentDefault);
|
||||
|
||||
//CGImage -> CVPixelBufferRef coversion
|
||||
//CGImage -> CVPixelBufferRef conversion
|
||||
CVPixelBufferRef pixelBuffer = NULL;
|
||||
CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
|
||||
int status = CVPixelBufferCreateWithBytes(NULL,
|
||||
|
|
|
|||
|
|
@ -805,7 +805,7 @@ bool CvCaptureFile::setupReadingAt(CMTime position) {
|
|||
if (mMode == CV_CAP_MODE_BGR || mMode == CV_CAP_MODE_RGB) {
|
||||
// For CV_CAP_MODE_BGR, read frames as BGRA (AV Foundation's YUV->RGB conversion is slightly faster than OpenCV's CV_YUV2BGR_YV12)
|
||||
// kCVPixelFormatType_32ABGR is reportedly faster on OS X, but OpenCV doesn't have a CV_ABGR2BGR conversion.
|
||||
// kCVPixelFormatType_24RGB is significanly slower than kCVPixelFormatType_32BGRA.
|
||||
// kCVPixelFormatType_24RGB is significantly slower than kCVPixelFormatType_32BGRA.
|
||||
pixelFormat = kCVPixelFormatType_32BGRA;
|
||||
mFormat = CV_8UC3;
|
||||
} else if (mMode == CV_CAP_MODE_GRAY) {
|
||||
|
|
@ -1323,7 +1323,7 @@ bool CvVideoWriter_AVFoundation::writeFrame(const IplImage* iplimage) {
|
|||
colorSpace, kCGImageAlphaLast|kCGBitmapByteOrderDefault,
|
||||
provider, NULL, false, kCGRenderingIntentDefault);
|
||||
|
||||
//CGImage -> CVPixelBufferRef coversion
|
||||
//CGImage -> CVPixelBufferRef conversion
|
||||
CVPixelBufferRef pixelBuffer = NULL;
|
||||
CFDataRef cfData = CGDataProviderCopyData(CGImageGetDataProvider(cgImage));
|
||||
int status = CVPixelBufferCreateWithBytes(NULL,
|
||||
|
|
|
|||
|
|
@ -1045,7 +1045,7 @@ bool GStreamerCapture::open(const String &filename_)
|
|||
* \return property value
|
||||
*
|
||||
* There are two ways the properties can be retrieved. For seek-based properties we can query the pipeline.
|
||||
* For frame-based properties, we use the caps of the lasst receivef sample. This means that some properties
|
||||
* For frame-based properties, we use the caps of the last receivef sample. This means that some properties
|
||||
* are not available until a first frame was received
|
||||
*/
|
||||
double GStreamerCapture::getProperty(int propId) const
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ if (APPLE_FRAMEWORK AND BUILD_SHARED_LIBS)
|
|||
set (CMAKE_INSTALL_NAME_DIR "@rpath")
|
||||
endif()
|
||||
|
||||
# Hidden visibilty is required for cxx on iOS
|
||||
# Hidden visibility is required for cxx on iOS
|
||||
set (no_warn "-Wno-unused-function -Wno-overloaded-virtual")
|
||||
set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${no_warn}")
|
||||
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libc++ -fvisibility=hidden -fvisibility-inlines-hidden ${no_warn}")
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
# Toolchains with 'img' in the name are for MIPS R6 instruction sets.
|
||||
# It is recommended to use cmake-gui application for build scripts configuration and generation:
|
||||
# 1. Run cmake-gui
|
||||
# 2. Specifiy toolchain file for cross-compiling, mips32r5el-gnu.toolchian.cmake or mips64r6el-gnu.toolchain.cmake
|
||||
# 2. Specify toolchain file for cross-compiling, mips32r5el-gnu.toolchian.cmake or mips64r6el-gnu.toolchain.cmake
|
||||
# can be selected.
|
||||
# 3. Configure and Generate makefiles.
|
||||
# 4. make -j4 & make install
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
# Toolchains with 'img' in the name are for MIPS R6 instruction sets.
|
||||
# It is recommended to use cmake-gui for build scripts configuration and generation:
|
||||
# 1. Run cmake-gui
|
||||
# 2. Specifiy toolchain file mips32r5el-gnu.toolchian.cmake for cross-compiling.
|
||||
# 2. Specify toolchain file mips32r5el-gnu.toolchian.cmake for cross-compiling.
|
||||
# 3. Configure and Generate makefiles.
|
||||
# 4. make -j4 & make install
|
||||
# ----------------------------------------------------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
# Toolchains with 'img' in the name are for MIPS R6 instruction sets.
|
||||
# It is recommended to use cmake-gui for build scripts configuration and generation:
|
||||
# 1. Run cmake-gui
|
||||
# 2. Specifiy toolchain file mips64r6el-gnu.toolchain.cmake for cross-compiling.
|
||||
# 2. Specify toolchain file mips64r6el-gnu.toolchain.cmake for cross-compiling.
|
||||
# 3. Configure and Generate makefiles.
|
||||
# 4. make -j4 & make install
|
||||
# ----------------------------------------------------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ foreach(sample_filename ${cpp_samples})
|
|||
target_compile_definitions(${tgt} PRIVATE HAVE_OPENGL)
|
||||
endif()
|
||||
if(sample_filename MATCHES "simd_")
|
||||
# disabled intentionally - demonstation purposes only
|
||||
# disabled intentionally - demonstration purposes only
|
||||
#target_include_directories(${tgt} PRIVATE "${CMAKE_CURRENT_LIST_DIR}")
|
||||
#target_compile_definitions(${tgt} PRIVATE OPENCV_SIMD_CONFIG_HEADER=opencv_simd_config_custom.hpp)
|
||||
#target_compile_definitions(${tgt} PRIVATE OPENCV_SIMD_CONFIG_INCLUDE_DIR=1)
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ static void help()
|
|||
"It draws a random set of points in an image and then delaunay triangulates them.\n"
|
||||
"Usage: \n"
|
||||
"./delaunay \n"
|
||||
"\nThis program builds the traingulation interactively, you may stop this process by\n"
|
||||
"\nThis program builds the triangulation interactively, you may stop this process by\n"
|
||||
"hitting any key.\n";
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -157,7 +157,7 @@ int main()
|
|||
cout << responses.t() << endl;
|
||||
cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl;
|
||||
|
||||
// save the classfier
|
||||
// save the classifier
|
||||
const String saveFilename = "NewLR_Trained.xml";
|
||||
cout << "saving the classifier to " << saveFilename << endl;
|
||||
lr1->save(saveFilename);
|
||||
|
|
@ -167,7 +167,7 @@ int main()
|
|||
Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
|
||||
|
||||
// predict using loaded classifier
|
||||
cout << "predicting the dataset using the loaded classfier...";
|
||||
cout << "predicting the dataset using the loaded classifier...";
|
||||
Mat responses2;
|
||||
lr2->predict(data_test, responses2);
|
||||
cout << "done!" << endl;
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@
|
|||
* This program demonstrates how to use OpenCV PCA with a
|
||||
* specified amount of variance to retain. The effect
|
||||
* is illustrated further by using a trackbar to
|
||||
* change the value for retained varaince.
|
||||
* change the value for retained variance.
|
||||
*
|
||||
* The program takes as input a text file with each line
|
||||
* begin the full path to an image. PCA will be performed
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ public:
|
|||
void load(const std::string &path);
|
||||
|
||||
private:
|
||||
/** The current number of correspondecnes */
|
||||
/** The current number of correspondences */
|
||||
int n_correspondences_;
|
||||
/** The list of 2D points on the model surface */
|
||||
std::vector<cv::KeyPoint> list_keypoints_;
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ static void help()
|
|||
" CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)\n"
|
||||
" CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)\n"
|
||||
" CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)\n"
|
||||
" CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.) (CV_8UC1)\n"
|
||||
" CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded etc.) (CV_8UC1)\n"
|
||||
"2.) Data given from RGB image generator\n"
|
||||
" CAP_OPENNI_BGR_IMAGE - color image (CV_8UC3)\n"
|
||||
" CAP_OPENNI_GRAY_IMAGE - gray image (CV_8UC1)\n"
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
// This will loop through frames of video either from input media file
|
||||
// or camera device and do processing of these data in OpenCL and then
|
||||
// in OpenCV. In OpenCL it does inversion of pixels in left half of frame and
|
||||
// in OpenCV it does bluring in the right half of frame.
|
||||
// in OpenCV it does blurring in the right half of frame.
|
||||
*/
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ Usage:
|
|||
|
||||
Use sliders to adjust PSF paramitiers.
|
||||
Keys:
|
||||
SPACE - switch btw linear/cirular PSF
|
||||
SPACE - switch btw linear/circular PSF
|
||||
ESC - exit
|
||||
|
||||
Examples:
|
||||
|
|
|
|||
|
|
@ -17,6 +17,6 @@ using namespace SDKSample;
|
|||
Platform::Array<Scenario>^ MainPage::scenariosInner = ref new Platform::Array<Scenario>
|
||||
{
|
||||
// The format here is the following:
|
||||
// { "Description for the sample", "Fully quaified name for the class that implements the scenario" }
|
||||
// { "Description for the sample", "Fully qualified name for the class that implements the scenario" }
|
||||
{ "Enumerate cameras and add a video effect", "SDKSample.MediaCapture.AdvancedCapture" },
|
||||
};
|
||||
|
|
|
|||
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