Merge pull request #18510 from OrestChura:oc/boundingRect

[G-API]: findContours() and boundingRect() Standard Kernels Implementation * Add findContours() standard kernel - API and documentation provided: - as OpenCV provides two overloads whether to calculate hierarchy or not, but they differ by only the output in sight of G-API, two different G-API functions and kernels implemented - G-API Imgproc documentation divided into more parts according to imgproc module parts - some typos connected with division into parts corrected - `GArray<GArray<U>>` overload for `get_out` function provided to coonvert correctly into `vector<vector<U>>` - OCV backend supported - accuracy tests provided * Add boundingRect() standard kernel - API and documentation provided: - GOpaque<Rect> used as an output - as OpenCV provides two possibilities whether to take a gray-scale image or a set of 2D points (`Point2i` or `Point2f` supported), three different overloads of a single G-API function and three kernels implemented - for a gray-scale image the overload via `GMat` - for a set of `Point2i` - the one via GArray<`Point2i`> - set of `Point2f` -> GArray<`Point2f`> - OCV backend supported - accuracy tests provided - comparison function for Rects provided - some typos in `gapi_tests_common` corrected * Fix precommit windows warnings * - Addressing comments: - split tests - Fix Windows warnings * Static_cast for warnings * - Remove randomness - Fix unnecessary precision losses * - Forgot reference for RNG * addressing comments * equalizeHist -> no group * `const` addedin new functions * Address suggestions: - Hierarchical -> H - added cv::GMatDesc::isVectorPoins() - added support of giving a set of points to boundingRect() * Addressing comments - IoU comparison function added for Rects - isPointsVector moved from a GMatDesc method to a separate function in imgproc.hpp - enums instead of int - typos corrected * Addressing comments - findContours: Point offset -> GOpaque<Point> - removed "straight" comparison for Rects, IoU available only - changed vectors initialization -> fix Debug test run - Some typos * added comment for later upgrades * Fix not to corrupt docs by FIXME * Addressing commens - overload without offset added (as a temporary workaround) - checkMetaForFindingContours -> validateFindingContoursMeta - added ostream overload for enums used in tests
2020-11-11 15:13:10 +03:00
parent ef32d7fd16
commit 3fc1c73064
8 changed files with 821 additions and 14 deletions
@@ -271,6 +271,11 @@ template<> struct get_out<cv::GArray<cv::GMat> >: public get_out<cv::GArray<cv::
 {
 };

+// FIXME(dm): GArray<vector<U>>/GArray<GArray<U>> conversion should be done more gracefully in the system
+template<typename U> struct get_out<cv::GArray<cv::GArray<U>> >: public get_out<cv::GArray<std::vector<U>> >
+{
+};
+
 template<typename U> struct get_out<cv::GOpaque<U>>
 {
    static U& get(GCPUContext &ctx, int idx)
@@ -21,14 +21,45 @@
@{
    @defgroup gapi_filters Graph API: Image filters
    @defgroup gapi_colorconvert Graph API: Converting image from one color space to another
+    @defgroup gapi_feature Graph API: Image Feature Detection
+    @defgroup gapi_shape Graph API: Image Structural Analysis and Shape Descriptors
@}
 */

+namespace {
+void validateFindingContoursMeta(const int depth, const int chan, const int mode)
+{
+    GAPI_Assert(chan == 1);
+    switch (mode)
+    {
+    case cv::RETR_CCOMP:
+        GAPI_Assert(depth == CV_8U || depth == CV_32S);
+        break;
+    case cv::RETR_FLOODFILL:
+        GAPI_Assert(depth == CV_32S);
+        break;
+    default:
+        GAPI_Assert(depth == CV_8U);
+        break;
+    }
+}
+
+// Checks if the passed mat is a set of n-dimentional points of the given depth
+bool isPointsVector(const int chan, const cv::Size &size, const int depth,
+                    const int n, const int ddepth)
+{
+    return (ddepth == depth || ddepth < 0) &&
+           ((chan == n && (size.height == 1 || size.width == 1)) ||
+            (chan == 1 && size.width == n));
+}
+} // anonymous namespace
+
 namespace cv { namespace gapi {

 namespace imgproc {
    using GMat2 = std::tuple<GMat,GMat>;
    using GMat3 = std::tuple<GMat,GMat,GMat>; // FIXME: how to avoid this?
+    using GFindContoursOutput = std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>;

    G_TYPED_KERNEL(GFilter2D, <GMat(GMat,int,Mat,Point,Scalar,int,Scalar)>,"org.opencv.imgproc.filters.filter2D") {
        static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Point, Scalar, int, Scalar) {
@@ -118,7 +149,7 @@ namespace imgproc {
        }
    };

-    G_TYPED_KERNEL(GCanny, <GMat(GMat,double,double,int,bool)>, "org.opencv.imgproc.canny"){
+    G_TYPED_KERNEL(GCanny, <GMat(GMat,double,double,int,bool)>, "org.opencv.imgproc.feature.canny"){
        static GMatDesc outMeta(GMatDesc in, double, double, int, bool) {
            return in.withType(CV_8U, 1);
        }
@@ -126,12 +157,83 @@ namespace imgproc {

    G_TYPED_KERNEL(GGoodFeatures,
                   <cv::GArray<cv::Point2f>(GMat,int,double,double,Mat,int,bool,double)>,
-                   "org.opencv.imgproc.goodFeaturesToTrack") {
+                   "org.opencv.imgproc.feature.goodFeaturesToTrack") {
        static GArrayDesc outMeta(GMatDesc, int, double, double, const Mat&, int, bool, double) {
            return empty_array_desc();
        }
    };

+    using RetrMode = RetrievalModes;
+    using ContMethod = ContourApproximationModes;
+    G_TYPED_KERNEL(GFindContours, <GArray<GArray<Point>>(GMat,RetrMode,ContMethod,GOpaque<Point>)>,
+                   "org.opencv.imgproc.shape.findContours")
+    {
+        static GArrayDesc outMeta(GMatDesc in, RetrMode mode, ContMethod, GOpaqueDesc)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return empty_array_desc();
+        }
+    };
+
+    // FIXME oc: make default value offset = Point()
+    G_TYPED_KERNEL(GFindContoursNoOffset, <GArray<GArray<Point>>(GMat,RetrMode,ContMethod)>,
+                   "org.opencv.imgproc.shape.findContoursNoOffset")
+    {
+        static GArrayDesc outMeta(GMatDesc in, RetrMode mode, ContMethod)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return empty_array_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFindContoursH,<GFindContoursOutput(GMat,RetrMode,ContMethod,GOpaque<Point>)>,
+                   "org.opencv.imgproc.shape.findContoursH")
+    {
+        static std::tuple<GArrayDesc,GArrayDesc>
+        outMeta(GMatDesc in, RetrMode mode, ContMethod, GOpaqueDesc)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    // FIXME oc: make default value offset = Point()
+    G_TYPED_KERNEL(GFindContoursHNoOffset,<GFindContoursOutput(GMat,RetrMode,ContMethod)>,
+                   "org.opencv.imgproc.shape.findContoursHNoOffset")
+    {
+        static std::tuple<GArrayDesc,GArrayDesc>
+        outMeta(GMatDesc in, RetrMode mode, ContMethod)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectMat, <GOpaque<Rect>(GMat)>,
+                   "org.opencv.imgproc.shape.boundingRectMat") {
+        static GOpaqueDesc outMeta(GMatDesc in) {
+            GAPI_Assert((in.depth == CV_8U && in.chan == 1) ||
+                        (isPointsVector(in.chan, in.size, in.depth, 2, CV_32S) ||
+                         isPointsVector(in.chan, in.size, in.depth, 2, CV_32F)));
+
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectVector32S, <GOpaque<Rect>(GArray<Point2i>)>,
+                   "org.opencv.imgproc.shape.boundingRectVector32S") {
+        static GOpaqueDesc outMeta(GArrayDesc) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectVector32F, <GOpaque<Rect>(GArray<Point2f>)>,
+                   "org.opencv.imgproc.shape.boundingRectVector32F") {
+        static GOpaqueDesc outMeta(GArrayDesc) {
+            return empty_gopaque_desc();
+        }
+    };
+
    G_TYPED_KERNEL(GBGR2RGB, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2rgb") {
        static GMatDesc outMeta(GMatDesc in) {
            return in; // type still remains CV_8UC3;
@@ -280,7 +382,7 @@ namespace imgproc {
        }
    };

-    G_TYPED_KERNEL(GNV12toRGBp, <GMatP(GMat,GMat)>, "org.opencv.colorconvert.imgproc.nv12torgbp") {
+    G_TYPED_KERNEL(GNV12toRGBp, <GMatP(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12torgbp") {
        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
            GAPI_Assert(inY.depth == CV_8U);
            GAPI_Assert(inUV.depth == CV_8U);
@@ -294,7 +396,7 @@ namespace imgproc {
        }
    };

-    G_TYPED_KERNEL(GNV12toGray, <GMat(GMat,GMat)>, "org.opencv.colorconvert.imgproc.nv12togray") {
+    G_TYPED_KERNEL(GNV12toGray, <GMat(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12togray") {
        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
            GAPI_Assert(inY.depth   == CV_8U);
            GAPI_Assert(inUV.depth  == CV_8U);
@@ -309,7 +411,7 @@ namespace imgproc {
        }
    };

-    G_TYPED_KERNEL(GNV12toBGRp, <GMatP(GMat,GMat)>, "org.opencv.colorconvert.imgproc.nv12tobgrp") {
+    G_TYPED_KERNEL(GNV12toBGRp, <GMatP(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12tobgrp") {
        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
            GAPI_Assert(inY.depth == CV_8U);
            GAPI_Assert(inUV.depth == CV_8U);
@@ -800,6 +902,10 @@ proportional to sigmaSpace.
 GAPI_EXPORTS GMat bilateralFilter(const GMat& src, int d, double sigmaColor, double sigmaSpace,
                                  int borderType = BORDER_DEFAULT);

+//! @} gapi_filters
+
+//! @addtogroup gapi_feature
+//! @{
 /** @brief Finds edges in an image using the Canny algorithm.

 The function finds edges in the input image and marks them in the output map edges using the
@@ -807,7 +913,7 @@ Canny algorithm. The smallest value between threshold1 and threshold2 is used fo
 largest value is used to find initial segments of strong edges. See
 <http://en.wikipedia.org/wiki/Canny_edge_detector>

-@note Function textual ID is "org.opencv.imgproc.filters.canny"
+@note Function textual ID is "org.opencv.imgproc.feature.canny"

@param image 8-bit input image.
@param threshold1 first threshold for the hysteresis procedure.
@@ -842,7 +948,7 @@ The function can be used to initialize a point-based tracker of an object.
 A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
 with qualityLevel=B .

-@note Function textual ID is "org.opencv.imgproc.goodFeaturesToTrack"
+@note Function textual ID is "org.opencv.imgproc.feature.goodFeaturesToTrack"

@param image Input 8-bit or floating-point 32-bit, single-channel image.
@param maxCorners Maximum number of corners to return. If there are more corners than are found,
@@ -876,6 +982,8 @@ GAPI_EXPORTS GArray<Point2f> goodFeaturesToTrack(const GMat  &image,

 /** @brief Equalizes the histogram of a grayscale image.

+//! @} gapi_feature
+
 The function equalizes the histogram of the input image using the following algorithm:

 - Calculate the histogram \f$H\f$ for src .
@@ -893,6 +1001,120 @@ The algorithm normalizes the brightness and increases the contrast of the image.
 */
 GAPI_EXPORTS GMat equalizeHist(const GMat& src);

+//! @addtogroup gapi_shape
+//! @{
+/** @brief Finds contours in a binary image.
+
+The function retrieves contours from the binary image using the algorithm @cite Suzuki85 .
+The contours are a useful tool for shape analysis and object detection and recognition.
+See squares.cpp in the OpenCV sample directory.
+
+@note Function textual ID is "org.opencv.imgproc.shape.findContours"
+
+@param src Input gray-scale image @ref CV_8UC1. Non-zero pixels are treated as 1's. Zero
+pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
+#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
+If mode equals to #RETR_CCOMP, the input can also be a 32-bit integer
+image of labels ( @ref CV_32SC1 ). If #RETR_FLOODFILL then @ref CV_32SC1 is supported only.
+@param mode Contour retrieval mode, see #RetrievalModes
+@param method Contour approximation method, see #ContourApproximationModes
+@param offset Optional offset by which every contour point is shifted. This is useful if the
+contours are extracted from the image ROI and then they should be analyzed in the whole image
+context.
+
+@return GArray of detected contours. Each contour is stored as a GArray of points.
+ */
+GAPI_EXPORTS GArray<GArray<Point>>
+findContours(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method,
+             const GOpaque<Point> &offset);
+
+// FIXME oc: make default value offset = Point()
+/** @overload
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursNoOffset"
+ */
+GAPI_EXPORTS GArray<GArray<Point>>
+findContours(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method);
+
+/** @brief Finds contours and their hierarchy in a binary image.
+
+The function retrieves contours from the binary image using the algorithm @cite Suzuki85
+and calculates their hierarchy.
+The contours are a useful tool for shape analysis and object detection and recognition.
+See squares.cpp in the OpenCV sample directory.
+
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursH"
+
+@param src Input gray-scale image @ref CV_8UC1. Non-zero pixels are treated as 1's. Zero
+pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
+#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
+If mode equals to #RETR_CCOMP, the input can also be a 32-bit integer
+image of labels ( @ref CV_32SC1 ). If #RETR_FLOODFILL -- @ref CV_32SC1 supports only.
+@param mode Contour retrieval mode, see #RetrievalModes
+@param method Contour approximation method, see #ContourApproximationModes
+@param offset Optional offset by which every contour point is shifted. This is useful if the
+contours are extracted from the image ROI and then they should be analyzed in the whole image
+context.
+
+@return GArray of detected contours. Each contour is stored as a GArray of points.
+@return Optional output GArray of cv::Vec4i, containing information about the image topology.
+It has as many elements as the number of contours. For each i-th contour contours[i], the elements
+hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based
+indices in contours of the next and previous contours at the same hierarchical level, the first
+child contour and the parent contour, respectively. If for the contour i there are no next,
+previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
+ */
+GAPI_EXPORTS std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>
+findContoursH(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method,
+              const GOpaque<Point> &offset);
+
+// FIXME oc: make default value offset = Point()
+/** @overload
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursHNoOffset"
+ */
+GAPI_EXPORTS std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>
+findContoursH(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method);
+
+/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels
+of gray-scale image.
+
+The function calculates and returns the minimal up-right bounding rectangle for the specified
+point set or non-zero pixels of gray-scale image.
+
+@note Function textual ID is "org.opencv.imgproc.shape.boundingRectMat"
+
+@param src Input gray-scale image @ref CV_8UC1; or input set of @ref CV_32S or @ref CV_32F
+2D points stored in Mat.
+
+@note In case of a 2D points' set given, Mat should be 2-dimensional, have a single row or column
+if there are 2 channels, or have 2 columns if there is a single channel. Mat should have either
+@ref CV_32S or @ref CV_32F depth
+ */
+GAPI_EXPORTS GOpaque<Rect> boundingRect(const GMat& src);
+
+/** @overload
+
+Calculates the up-right bounding rectangle of a point set.
+
+@note Function textual ID is "org.opencv.imgproc.shape.boundingRectVector32S"
+
+@param src Input 2D point set, stored in std::vector<cv::Point2i>.
+ */
+GAPI_EXPORTS GOpaque<Rect> boundingRect(const GArray<Point2i>& src);
+
+/** @overload
+
+Calculates the up-right bounding rectangle of a point set.
+
+@note Function textual ID is "org.opencv.imgproc.shape.boundingRectVector32F"
+
+@param src Input 2D point set, stored in std::vector<cv::Point2f>.
+ */
+GAPI_EXPORTS GOpaque<Rect> boundingRect(const GArray<Point2f>& src);
+
+//! @} gapi_shape
+
+//! @addtogroup gapi_colorconvert
+//! @{
 /** @brief Converts an image from BGR color space to RGB color space.

 The function converts an input image from BGR color space to RGB.
@@ -907,10 +1129,6 @@ Output image is 8-bit unsigned 3-channel image @ref CV_8UC3.
 */
 GAPI_EXPORTS GMat BGR2RGB(const GMat& src);

-//! @} gapi_filters
-
-//! @addtogroup gapi_colorconvert
-//! @{
 /** @brief Converts an image from RGB color space to gray-scaled.
 The conventional ranges for R, G, and B channel values are 0 to 255.
 Resulting gray color value computed as