Merge pull request #28746 from omrope79:wechat-fix

WeChatQR-fix conversion Caffe to ONNX #28746

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/src/layers/nary_eltwise_layers.cpp

@@ -322,8 +322,13 @@ public:
             for (int j = 0; j < maxdims; j++) {
                 int inpsz = j < delta ? 1 : inpShape[j - delta];
                 int outsz = outShape[j];
-                CV_Assert(inpsz == outsz || inpsz == 1 || outsz == 1);
-                outShape[j] = inpsz != 1 ? inpsz : outsz;
+                if (inpsz == outsz || inpsz == 1 || outsz == 1) {
+                    outShape[j] = inpsz != 1 ? inpsz : outsz;
+                } else {
+                    // Clamp to minimum to tolerate off-by-1 spatial mismatches from Caffe→ONNX export.
+                    CV_LOG_WARNING(NULL, "NaryEltwiseLayer: Shape mismatch detected at dimension " << j << " (" << inpsz << " vs " << outsz << "). Clamping to " << std::min(inpsz, outsz) << ".");
+                    outShape[j] = std::min(inpsz, outsz);
+                }
             }
         }
 
@@ -928,6 +933,42 @@ public:
         }
 
         std::vector<Mat> used_inputs = inputs;
+
+        const Mat& out0 = outputs[0];
+        bool needsCrop = false;
+        for (const auto& inp : used_inputs) {
+            int delta = out0.dims - inp.dims;
+            for (int d = 0; d < inp.dims; d++) {
+                int od = d + delta;
+                if (od >= 0 && inp.size[d] > 1 && out0.size[od] > 1 &&
+                    inp.size[d] != out0.size[od]) {
+                    needsCrop = true; break;
+                }
+            }
+            if (needsCrop) break;
+        }
+        if (needsCrop) {
+            std::vector<Range> ranges;
+            for (size_t k = 0; k < used_inputs.size(); k++) {
+                Mat& inp = used_inputs[k];
+                int delta = out0.dims - inp.dims;
+                bool modified = false;
+                ranges.assign(inp.dims, Range::all());
+                for (int d = 0; d < inp.dims; d++) {
+                    int od = d + delta;
+                    if (od >= 0 && inp.size[d] > out0.size[od] &&
+                        inp.size[d] > 1 && out0.size[od] > 1) {
+                        ranges[d] = Range(0, out0.size[od]);
+                        modified = true;
+                    }
+                }
+                if (modified)
+                    inp = inp(ranges).clone();
+            }
+            helper.init(used_inputs, outputs);
+            CV_CheckTrue(helper.prepare_for_broadcast_op(), "NaryEltwiseLayer: Preparation for broadcasting failed");
+        }
+
         if (op == OPERATION::POW) {
             CV_Assert(used_inputs.size() == 2);
             const int out_type = outputs[0].type();

modules/dnn/src/layers/prior_box_layer.cpp

@@ -203,6 +203,14 @@ public:
         remap("min_sizes",     "min_size");
         remap("max_sizes",     "max_size");
         remap("aspect_ratios", "aspect_ratio");
+
+        if (p.has("steps") && !p.has("step_h") && !p.has("step_w")) {
+            DictValue steps = p.get("steps");
+            if (steps.size() == 2) {
+                p.set("step_h", steps.get<float>(0));
+                p.set("step_w", steps.get<float>(1));
+            }
+        }
         return p;
     }
 

modules/dnn/src/layers/reshape2_layer.cpp

@@ -171,7 +171,11 @@ public:
             int64_t autoSize = inpTotal/outTotal;
             CV_Assert(autoSize <= INT_MAX && autoSize*outTotal == inpTotal);
             outShape[m1idx] = (int)autoSize;
-        } else {
+        }
+        else if (outTotal != inpTotal && ndims == 2 && outShape[0] == 1 && outShape[1] > 0) {
+            outShape[1] = (int)(inpTotal / outShape[0]);
+        }
+        else {
             CV_Assert(outTotal == inpTotal);
         }