add scale factor to DB demo.

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -1422,7 +1422,7 @@ CV__DNN_INLINE_NS_BEGIN
          /** @brief Set scalefactor value for frame.
           *  @param[in] scale Multiplier for frame values.
          */
-         CV_WRAP Model& setInputScale(double scale);
+         CV_WRAP Model& setInputScale(const Scalar& scale);
 
          /** @brief Set flag crop for frame.
           *  @param[in] crop Flag which indicates whether image will be cropped after resize or not.

modules/dnn/src/dnn_common.hpp

@@ -154,6 +154,21 @@ static inline std::string toString(const Mat& blob, const std::string& name = st
     return ss.str();
 }
 
+// Scalefactor is a common parameter used for data scaling. In OpenCV, we often use Scalar to represent it.
+// Because 0 is meaningless in scalefactor.
+// If the scalefactor is (x, 0, 0, 0), we convert it to (x, x, x, x). The following func will do this hack.
+static inline Scalar_<double> broadcastRealScalar(const Scalar_<double>& _scale)
+{
+    Scalar_<double> scale = _scale;
+    if (scale[1] == 0 && scale[2] == 0 && scale[3] == 0)
+    {
+        CV_Assert(scale[0] != 0 && "Scalefactor of 0 is meaningless.");
+        scale = Scalar_<double>::all(scale[0]);
+    }
+
+    return scale;
+}
+
 
 CV__DNN_INLINE_NS_END
 

modules/dnn/src/model.cpp

@@ -21,7 +21,7 @@ struct Model::Impl
 
     Size   size;
     Scalar mean;
-    double  scale = 1.0;
+    Scalar scale = Scalar::all(1.0);
     bool   swapRB = false;
     bool   crop = false;
     Mat    blob;
@@ -60,7 +60,7 @@ public:
     {
         size = size_;
         mean = mean_;
-        scale = scale_;
+        scale = Scalar::all(scale_);
         crop = crop_;
         swapRB = swapRB_;
     }
@@ -75,7 +75,7 @@ public:
         mean = mean_;
     }
     /*virtual*/
-    void setInputScale(double scale_)
+    void setInputScale(const Scalar& scale_)
     {
         scale = scale_;
     }
@@ -97,7 +97,17 @@ public:
         if (size.empty())
             CV_Error(Error::StsBadSize, "Input size not specified");
 
-        blob = blobFromImage(frame, scale, size, mean, swapRB, crop);
+        Image2BlobParams param;
+        param.scalefactor = scale;
+        param.size = size;
+        param.mean = mean;
+        param.swapRB = swapRB;
+        if (crop)
+        {
+            param.paddingmode = DNN_PMODE_CROP_CENTER;
+        }
+        Mat blob = dnn::blobFromImageWithParams(frame, param); // [1, 10, 10, 4]
+
         net.setInput(blob);
 
         // Faster-RCNN or R-FCN
@@ -162,9 +172,11 @@ Model& Model::setInputMean(const Scalar& mean)
     return *this;
 }
 
-Model& Model::setInputScale(double scale)
+Model& Model::setInputScale(const Scalar& scale_)
 {
     CV_DbgAssert(impl);
+
+    Scalar scale = broadcastRealScalar(scale_);
     impl->setInputScale(scale);
     return *this;
 }
@@ -1358,7 +1370,7 @@ struct TextDetectionModel_DB_Impl : public TextDetectionModel_Impl
     {
         CV_TRACE_FUNCTION();
         std::vector< std::vector<Point2f> > results;
-
+        confidences.clear();
         std::vector<Mat> outs;
         processFrame(frame, outs);
         CV_Assert(outs.size() == 1);
@@ -1385,7 +1397,8 @@ struct TextDetectionModel_DB_Impl : public TextDetectionModel_Impl
             std::vector<Point>& contour = contours[i];
 
             // Calculate text contour score
-            if (contourScore(binary, contour) < polygonThreshold)
+            float score = contourScore(binary, contour);
+            if (score < polygonThreshold)
                 continue;
 
             // Rescale
@@ -1398,6 +1411,11 @@ struct TextDetectionModel_DB_Impl : public TextDetectionModel_Impl
 
             // Unclip
             RotatedRect box = minAreaRect(contourScaled);
+            float minLen = std::min(box.size.height/scaleWidth, box.size.width/scaleHeight);
+
+            // Filter very small boxes
+            if (minLen < 3)
+                continue;
 
             // minArea() rect is not normalized, it may return rectangles with angle=-90 or height < width
             const float angle_threshold = 60;  // do not expect vertical text, TODO detection algo property
@@ -1422,10 +1440,12 @@ struct TextDetectionModel_DB_Impl : public TextDetectionModel_Impl
                 approx.emplace_back(vertex[j]);
             std::vector<Point2f> polygon;
             unclip(approx, polygon, unclipRatio);
+            if (polygon.empty())
+                continue;
             results.push_back(polygon);
+            confidences.push_back(score);
         }
 
-        confidences = std::vector<float>(contours.size(), 1.0f);
         return results;
     }
 
@@ -1458,7 +1478,10 @@ struct TextDetectionModel_DB_Impl : public TextDetectionModel_Impl
     {
         double area = contourArea(inPoly);
         double length = arcLength(inPoly, true);
-        CV_Assert(length > FLT_EPSILON);
+
+        if(length == 0.)
+            return;
+
         double distance = area * unclipRatio / length;
 
         size_t numPoints = inPoly.size();

modules/dnn/test/test_model.cpp

@@ -153,8 +153,8 @@ public:
                                     const std::string& imgPath, const std::vector<std::vector<Point>>& gt,
                                     float binThresh, float polyThresh,
                                     uint maxCandidates, double unclipRatio,
-                                    const Size& size = {-1, -1}, Scalar mean = Scalar(),
-                                    double scale = 1.0, bool swapRB = false, bool crop = false)
+                                    const Size& size = {-1, -1}, Scalar mean = Scalar(), Scalar scale = Scalar::all(1.0),
+                                    double boxes_iou_diff = 0.05, bool swapRB = false, bool crop = false)
     {
         checkBackend();
 
@@ -197,7 +197,7 @@ public:
         imshow("result", result); // imwrite("result.png", result);
         waitKey(0);
 #endif
-        normAssertTextDetections(gt, contours, "", 0.05f);
+        normAssertTextDetections(gt, contours, "", boxes_iou_diff);
 
         // 2. Check quadrangle-based API
         // std::vector< std::vector<Point> > contours;
@@ -209,7 +209,7 @@ public:
         imshow("result_contours", result); // imwrite("result_contours.png", result);
         waitKey(0);
 #endif
-        normAssertTextDetections(gt, contours, "", 0.05f);
+        normAssertTextDetections(gt, contours, "", boxes_iou_diff);
     }
 
     void testTextDetectionModelByEAST(
@@ -743,7 +743,8 @@ TEST_P(Test_Model, TextDetectionByDB)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
 
     std::string imgPath = _tf("text_det_test1.png");
-    std::string weightPath = _tf("onnx/models/DB_TD500_resnet50.onnx", false);
+    std::string weightPathDB = _tf("onnx/models/DB_TD500_resnet50.onnx", false);
+    std::string weightPathPPDB = _tf("onnx/models/PP_OCRv3_DB_text_det.onnx", false);
 
     // GroundTruth
     std::vector<std::vector<Point>> gt = {
@@ -752,15 +753,28 @@ TEST_P(Test_Model, TextDetectionByDB)
     };
 
     Size size{736, 736};
-    double scale = 1.0 / 255.0;
-    Scalar mean = Scalar(122.67891434, 116.66876762, 104.00698793);
+    Scalar scaleDB = Scalar::all(1.0 / 255.0);
+    Scalar meanDB = Scalar(122.67891434, 116.66876762, 104.00698793);
+
+    // new mean and stddev
+    Scalar meanPPDB = Scalar(123.675, 116.28, 103.53);
+    Scalar stddevPPDB = Scalar(0.229, 0.224, 0.225);
+    Scalar scalePPDB = scaleDB / stddevPPDB;
 
     float binThresh = 0.3;
     float polyThresh = 0.5;
     uint maxCandidates = 200;
     double unclipRatio = 2.0;
 
-    testTextDetectionModelByDB(weightPath, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, mean, scale);
+    {
+    SCOPED_TRACE("Original DB");
+    testTextDetectionModelByDB(weightPathDB, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, meanDB, scaleDB, 0.05f);
+    }
+
+    {
+    SCOPED_TRACE("PP-OCRDBv3");
+    testTextDetectionModelByDB(weightPathPPDB, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, meanPPDB, scalePPDB, 0.21f);
+    }
 }
 
 TEST_P(Test_Model, TextDetectionByEAST)