Fix `yoloPostProcessing`` to handle variable number of classes (nc)

Previously, the yoloPostProcessing function assumed that the number of classes (nc) was fixed at 80. This caused incorrect behavior when a different number of classes was specified, leading to mismatched output shapes.

This update modifies the code to use the provided `nc` value dynamically, ensuring that the output shapes are correctly calculated based on the specified number of classes. This prevents issues when `nc` is not equal to 80 and allows for greater flexibility in model configurations.

modules/dnn/test/test_onnx_importer.cpp

@@ -2691,7 +2691,7 @@ void yoloPostProcessing(
     }
 
     if (model_name == "yolonas"){
-        // outs contains 2 elemets of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
+        // outs contains 2 elemets of shape [1, 8400, nc] and [1, 8400, 4]. Concat them to get [1, 8400, nc+4]
         Mat concat_out;
         // squeeze the first dimension
         outs[0] = outs[0].reshape(1, outs[0].size[1]);
@@ -2701,12 +2701,12 @@ void yoloPostProcessing(
         // remove the second element
         outs.pop_back();
         // unsqueeze the first dimension
-        outs[0] = outs[0].reshape(0, std::vector<int>{1, 8400, 84});
+        outs[0] = outs[0].reshape(0, std::vector<int>{1, outs[0].size[0], outs[0].size[1]});
     }
 
-    // assert if last dim is 85 or 84
-    CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, 85 or 84]");
-    CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == 80 + 4), true, "Invalid output shape: ");
+    // assert if last dim is nc+5 or nc+4
+    CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, nc+5 or nc+4]");
+    CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == nc + 4), true, "Invalid output shape: ");
 
     for (auto preds : outs){
 

samples/dnn/yolo_detector.cpp

@@ -125,7 +125,7 @@ void yoloPostProcessing(
 
     if (model_name == "yolonas")
     {
-        // outs contains 2 elemets of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
+        // outs contains 2 elemets of shape [1, 8400, nc] and [1, 8400, 4]. Concat them to get [1, 8400, nc+4]
         Mat concat_out;
         // squeeze the first dimension
         outs[0] = outs[0].reshape(1, outs[0].size[1]);
@@ -135,12 +135,12 @@ void yoloPostProcessing(
         // remove the second element
         outs.pop_back();
         // unsqueeze the first dimension
-        outs[0] = outs[0].reshape(0, std::vector<int>{1, 8400, nc + 4});
+        outs[0] = outs[0].reshape(0, std::vector<int>{1, outs[0].size[0], outs[0].size[1]});
     }
 
-    // assert if last dim is 85 or 84
-    CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, 85 or 84]");
-    CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == 80 + 4), true, "Invalid output shape: ");
+    // assert if last dim is nc+5 or nc+4
+    CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, nc+5 or nc+4]");
+    CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == nc + 4), true, "Invalid output shape: ");
 
     for (auto preds : outs)
     {