Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/dnn/src/darknet/darknet_io.cpp

@@ -376,7 +376,7 @@ namespace cv {
                     int begin[] = {0, split_size * group_id, 0, 0};
                     cv::dnn::DictValue paramBegin = cv::dnn::DictValue::arrayInt(begin, 4);
 
-                    int end[] = {-1, begin[1] + split_size, -1, -1};
+                    int end[] = {INT_MAX, begin[1] + split_size, INT_MAX, INT_MAX};
                     cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end, 4);
 
                     darknet::LayerParameter lp;

modules/dnn/src/layers/slice_layer.cpp

@@ -64,12 +64,32 @@ namespace cv
 namespace dnn
 {
 
-void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector<std::vector<cv::Range> >& sliceRanges)
+Range normalizeRange(const Range& input_range, int n)
 {
+    Range range = input_range;
+
+    range.start = std::min(std::max(range.start, -n), n - 1);
+    if (range.start < 0)
+    {
+        range.start += n;
+    }
+
+    range.end = std::min(std::max(range.end, -n), n);
+    if (range.end < 0)
+    {
+        range.end += n;
+    }
+
+    return range;
+}
+
+std::vector<std::vector<cv::Range> > finalizeSliceRange(const MatShape& inpShape, int& axis,
+                                                        const std::vector<std::vector<cv::Range> >& inputSliceRanges)
+{
+    std::vector<std::vector<cv::Range> > sliceRanges = inputSliceRanges;
     CV_Assert(inpShape.size() > 0);
     bool axisNeg = (axis < 0);
     axis = (axis + static_cast<int>(inpShape.size())) % inpShape.size();
-    int n = inpShape[axis];
 
     for (size_t i = 0; i < sliceRanges.size(); ++i){
         std::vector<Range>& ranges = sliceRanges[i];
@@ -77,16 +97,20 @@ void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector<std::
         {
             ranges.insert(ranges.begin(), axis, Range::all());
         }
-        Range& range = ranges.back();
 
-        if (range.start >= 0)
+        for (size_t j = 0; j < ranges.size(); ++j)
         {
-            continue;
+            int n = inpShape[j];
-        }
+            if (n <= 0)
+            {
+                continue;
+            }
 
-        CV_Assert(n != 0);
+            ranges[j] = normalizeRange(ranges[j], n);
-        range.start = (n + range.start) % n;
+        }
     }
+
+    return sliceRanges;
 }
 
 class SliceLayerImpl : public SliceLayer
@@ -136,7 +160,7 @@ public:
                 {
                     int size = sizeOrEnd;
                     CV_Assert(size == -1 || size > 0);  // -1 value means range [start, axis_size).
-                    sliceRanges[0][i].end = size > 0 ? (start + size) : -1;  // We'll finalize a negative value later.
+                    sliceRanges[0][i].end = size > 0 ? (start + size) : INT_MAX;  // We'll finalize a negative value later.
                 }
                 else
                 {
@@ -186,8 +210,7 @@ public:
         MatShape inpShape = inputs[0];
 
         int axis_rw = axis;
-        std::vector<std::vector<cv::Range> > sliceRanges_rw = sliceRanges;
+        std::vector<std::vector<cv::Range> > sliceRanges_rw = finalizeSliceRange(inpShape, axis_rw, sliceRanges);
-        sliceRangesFromShape(inpShape, axis_rw, sliceRanges_rw);
 
         if (!sliceRanges_rw.empty())
         {
@@ -198,7 +221,7 @@ public:
                 for (int j = 0; j < sliceRanges_rw[i].size(); ++j)
                 {
                     if (shapesInitialized || inpShape[j] > 0)
-                        outputs[i][j] = normalize_axis_range(sliceRanges_rw[i][j], inpShape[j]).size();
+                        outputs[i][j] = normalizeRange(sliceRanges_rw[i][j], inpShape[j]).size();
 
                     if (!sliceSteps.empty() && (i < sliceSteps.size()) && (j < sliceSteps[i].size()) && (sliceSteps[i][j] > 1))
                         outputs[i][j] = (outputs[i][j] + sliceSteps[i][j] - 1) / sliceSteps[i][j];
@@ -235,8 +258,7 @@ public:
         CV_Assert(inputs.size() == 1);
         const MatSize& inpShape = inputs[0].size;
 
-        sliceRangesFromShape(shape(inputs[0]), axis, sliceRanges);
+        finalSliceRanges = finalizeSliceRange(shape(inputs[0]), axis, sliceRanges);
-        finalSliceRanges = sliceRanges;
 
         if (sliceRanges.empty())
         {
@@ -266,7 +288,7 @@ public:
             // Clamp.
             for (int j = 0; j < finalSliceRanges[i].size(); ++j)
             {
-                finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]);
+                finalSliceRanges[i][j] = normalizeRange(finalSliceRanges[i][j], inpShape[j]);
             }
         }
 

modules/dnn/src/onnx/onnx_importer.cpp

@@ -1275,13 +1275,12 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP
         if (axis > 0) {
             CV_CheckLE(axis, 1024, "Slice layer can't have more than 1024 axes"); // arbitrary limit
             begin.resize(axis, 0);
-            end.resize(axis, -1);
+            end.resize(axis, INT_MAX);
         }
         for (int i = 0; i < starts.size(); ++i)
         {
             begin.push_back(starts.get<int>(i));
-            int finish = ends.get<int>(i);
+            end.push_back(ends.get<int>(i));
-            end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
         }
     } else { // inp_size > 1
         CV_Assert(inp_size >= 3);
@@ -1305,14 +1304,10 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP
         const int* ends   = end_blob.ptr<int>();
         if (axis > 0) {
             begin.resize(axis, 0);
-            end.resize(axis, -1);
+            end.resize(axis, INT_MAX);
         }
         std::copy(starts, starts + start_blob.total(), std::back_inserter(begin));
-        for (int i = 0; i < end_blob.total(); ++i)
+        std::copy(ends, ends + end_blob.total(), std::back_inserter(end));
-        {
-            int finish = ends[i];
-            end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
-        }
 
         if (inp_size == 5) {
             CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
@@ -2485,9 +2480,15 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node
 
     if (!haveVariables)
     {
-        if (broadcast_axes.size() != 1)
+        if (broadcast_axes.size() > 1)
             CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input");
 
+        if (broadcast_axes.empty())
+        {
+            addConstant(output_name, getBlob(node_proto, 0));
+            return;
+        }
+
         Mat input = getBlob(node_proto, 0);
         input = input.reshape(0, total(inpShape, 0, broadcast_axes[0]));
         Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]);
@@ -2708,7 +2709,7 @@ void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::Node
         sliceLp.type = "Slice";
         sliceLp.name = inpShape.size() > 1 ? layerParams.name + "/slice" : layerParams.name;
         std::vector<int> begin(inpShape.size(), 0);
-        std::vector<int> end(inpShape.size(), -1);
+        std::vector<int> end(inpShape.size(), INT_MAX);
         begin[axis] = index;
         end[axis] = index + 1;
 

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -1681,10 +1681,8 @@ void TFImporter::parseStridedSlice(tensorflow::GraphDef& net, const tensorflow::
     int end_mask = getLayerAttr(layer, "end_mask").i();
     for (int i = 0; i < num; ++i)
     {
-        if (ends.at<int>(i) < 0)
-            ends.at<int>(i) -= 1;
         if (end_mask & (1 << i))
-            ends.at<int>(i) = -1;
+            ends.at<int>(i) = INT_MAX;
         if (strides.at<int>(i) != 1)
             CV_Error(Error::StsNotImplemented,
                      format("StridedSlice with stride %d", strides.at<int>(i)));
@@ -1982,15 +1980,16 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
     int64_t pads[8];
     bool explicit_pads = getExplicitPadding(layerParams, layer, pads);
     int64_t begs[4] = {};
-    int64_t ends[4] = {-1, -1, -1, -1};
+    int64_t ends[4] = {};
     if (explicit_pads)
     {
         name += "/deconv";
         layerParams.set("pad_mode", "VALID");
+        ends[0] = ends[1] = INT_MAX;
         for (int i = 2; i < 4; ++i) // begins=[0, 0, a, b], ends=[-1, -1, c, d]
         {
             begs[i] = pads[2*i];
-            ends[i] = -1 - pads[2*i + 1];
+            ends[i] = -pads[2*i + 1];
         }
     }
 
@@ -2010,8 +2009,8 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
     const int strideX = layerParams.get<int>("stride_w");
     Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0));
     int shift = (getDataLayout(layer) == DATA_LAYOUT_NCHW);
-    const int outH = outShape.at<int>(1 + shift) + begs[2] - 1 - ends[2];
+    const int outH = outShape.at<int>(1 + shift) + begs[2] - ends[2];
-    const int outW = outShape.at<int>(2 + shift) + begs[3] - 1 - ends[3];
+    const int outW = outShape.at<int>(2 + shift) + begs[3] - ends[3];
     if (layerParams.get<String>("pad_mode") == "SAME")
     {
         layerParams.set("adj_w", (outW - 1) % strideX);

modules/dnn/src/torch/torch_importer.cpp

@@ -954,7 +954,7 @@ struct TorchImporter
                 int size = scalarParams.get<int>("size");
 
                 int begins[] = {0, 0, size, size};
-                int ends[] = {-1, -1, -size - 1, -size - 1};
+                int ends[] = {INT_MAX, INT_MAX, -size, -size};
 
                 newModule->apiType = "Slice";
                 layerParams.set("begin", DictValue::arrayInt<int*>(&begins[0], 4));

modules/dnn/test/test_layers.cpp

@@ -2082,7 +2082,7 @@ TEST_P(Layer_Test_Slice, variable_input_shape)
     int targetId = get<1>(GetParam());
 
     int begin[] = {0, 0, 0, 0};
-    int end[] = {-1, -1, -1, -1};
+    int end[] = {INT_MAX, INT_MAX, INT_MAX, INT_MAX};
 
     Net net;
     LayerParams lp;

modules/photo/src/seamless_cloning.hpp

@@ -53,7 +53,7 @@ namespace cv
     class Cloning
     {
         public:
-            void normalClone(const cv::Mat& destination, const cv::Mat &mask, const cv::Mat &wmask, cv::Mat &cloned, int flag);
+            void normalClone(const cv::Mat& destination, const cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, int flag);
             void illuminationChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float alpha, float beta);
             void localColorChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float red_mul, float green_mul, float blue_mul);
             void textureFlatten(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, float low_threshold, float high_threhold, int kernel_size, cv::Mat &cloned);
@@ -61,10 +61,10 @@ namespace cv
         protected:
 
             void initVariables(const cv::Mat &destination, const cv::Mat &binaryMask);
-            void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, const cv::Mat &binaryMask);
+            void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, cv::Mat &binaryMask);
             void scalarProduct(cv::Mat mat, float r, float g, float b);
             void poisson(const cv::Mat &destination);
-            void evaluate(const cv::Mat &I, const cv::Mat &wmask, const cv::Mat &cloned);
+            void evaluate(const cv::Mat &I, cv::Mat &wmask, const cv::Mat &cloned);
             void dst(const Mat& src, Mat& dest, bool invert = false);
             void solve(const Mat &img, Mat& mod_diff, Mat &result);
 

modules/photo/src/seamless_cloning_impl.cpp

@@ -246,7 +246,7 @@ void Cloning::initVariables(const Mat &destination, const Mat &binaryMask)
         filter_Y[j] = 2.0f * (float)std::cos(scale * (j + 1));
 }
 
-void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, const Mat &binaryMask)
+void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, Mat &binaryMask)
 {
     initVariables(destination, binaryMask);
 
@@ -306,7 +306,7 @@ void Cloning::poisson(const Mat &destination)
     }
 }
 
-void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned)
+void Cloning::evaluate(const Mat &I, Mat &wmask, const Mat &cloned)
 {
     bitwise_not(wmask,wmask);
 
@@ -320,7 +320,7 @@ void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned)
     merge(output,cloned);
 }
 
-void Cloning::normalClone(const Mat &destination, const Mat &patch, const Mat &binaryMask, Mat &cloned, int flag)
+void Cloning::normalClone(const Mat &destination, const Mat &patch, Mat &binaryMask, Mat &cloned, int flag)
 {
     const int w = destination.cols;
     const int h = destination.rows;

modules/python/test/test_misc.py

@@ -643,6 +643,16 @@ class Arguments(NewOpenCVTests):
                          msg="Classes from submodules and global module don't refer "
                          "to the same type")
 
+    def test_class_from_submodule_has_global_alias(self):
+        self.assertTrue(hasattr(cv.ml, "Boost"),
+                        msg="Class is not registered in the submodule")
+        self.assertTrue(hasattr(cv, "ml_Boost"),
+                        msg="Class from submodule doesn't have alias in the "
+                        "global module")
+        self.assertEqual(cv.ml.Boost, cv.ml_Boost,
+                         msg="Classes from submodules and global module don't refer "
+                         "to the same type")
+
     def test_inner_class_has_global_alias(self):
         self.assertTrue(hasattr(cv.SimpleBlobDetector, "Params"),
                         msg="Class is not registered as inner class")