Merge pull request #22750 from zihaomu:improve_blobFromImage

DNN: Add New API blobFromImageParam #22750

The purpose of this PR:

1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box.
2. ~~`blobFromImage` can output `CV_16F`~~

### 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
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

modules/dnn/misc/python/test/test_dnn.py

@@ -119,7 +119,7 @@ class dnn_test(NewOpenCVTests):
             inp = np.random.standard_normal([1, 2, 10, 11]).astype(np.float32)
             net.setInput(inp)
             net.forward()
-        except BaseException as e:
+        except BaseException:
             return False
         return True
 
@@ -153,6 +153,41 @@ class dnn_test(NewOpenCVTests):
         target = target.transpose(2, 0, 1).reshape(1, 3, height, width)  # to NCHW
         normAssert(self, blob, target)
 
+    def test_blobFromImageWithParams(self):
+        np.random.seed(324)
+
+        width = 6
+        height = 7
+        stddev = np.array([0.2, 0.3, 0.4])
+        scalefactor = 1.0/127.5 * stddev
+        mean = (10, 20, 30)
+
+        # Test arguments names.
+        img = np.random.randint(0, 255, [4, 5, 3]).astype(np.uint8)
+
+        param = cv.dnn.Image2BlobParams()
+        param.scalefactor = scalefactor
+        param.size = (6, 7)
+        param.mean = mean
+        param.swapRB=True
+        param.datalayout = cv.dnn.DNN_LAYOUT_NHWC
+
+        blob = cv.dnn.blobFromImageWithParams(img, param)
+        blob_args = cv.dnn.blobFromImageWithParams(img, cv.dnn.Image2BlobParams(scalefactor=scalefactor, size=(6, 7), mean=mean,
+                                                                      swapRB=True, datalayout=cv.dnn.DNN_LAYOUT_NHWC))
+        normAssert(self, blob, blob_args)
+
+        target2 = cv.resize(img, (width, height), interpolation=cv.INTER_LINEAR).astype(np.float32)
+        target2 = target2[:,:,[2, 1, 0]]  # BGR2RGB
+        target2[:,:,0] -= mean[0]
+        target2[:,:,1] -= mean[1]
+        target2[:,:,2] -= mean[2]
+
+        target2[:,:,0] *= scalefactor[0]
+        target2[:,:,1] *= scalefactor[1]
+        target2[:,:,2] *= scalefactor[2]
+        target2 = target2.reshape(1, height, width, 3)  # to NHWC
+        normAssert(self, blob, target2)
 
     def test_model(self):
         img_path = self.find_dnn_file("dnn/street.png")