Merge pull request #24231 from fengyuentau:halide_cleanup_5.x

dnn: cleanup of halide backend for 5.x #24231

Merge with https://github.com/opencv/opencv_extra/pull/1092.

### 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

samples/dnn/human_parsing.cpp

@@ -70,26 +70,28 @@ static Mat parse_human(const Mat &image, const std::string &model, int backend=d
 
 int main(int argc, char**argv)
 {
-    CommandLineParser parser(argc,argv,
+    std::string param_keys =
         "{help    h |                 | show help screen / args}"
         "{image   i |                 | person image to process }"
-        "{model   m |lip_jppnet_384.pb| network model}"
-        "{backend b | 0               | Choose one of computation backends: "
-                                         "0: automatically (by default), "
-                                         "1: Halide language (http://halide-lang.org/), "
-                                         "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
-                                         "3: OpenCV implementation, "
-                                         "4: VKCOM, "
-                                         "5: CUDA }"
-        "{target  t | 0               | Choose one of target computation devices: "
-                                         "0: CPU target (by default), "
-                                         "1: OpenCL, "
-                                         "2: OpenCL fp16 (half-float precision), "
-                                         "3: VPU, "
-                                         "4: Vulkan, "
-                                         "6: CUDA, "
-                                         "7: CUDA fp16 (half-float preprocess) }"
-    );
+        "{model   m |lip_jppnet_384.pb| network model}";
+    std::string backend_keys = cv::format(
+        "{ backend  | 0 | Choose one of computation backends: "
+                          "%d: automatically (by default), "
+                          "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
+                          "%d: OpenCV implementation, "
+                          "%d: VKCOM, "
+                          "%d: CUDA }", cv::dnn::DNN_BACKEND_DEFAULT, cv::dnn::DNN_BACKEND_INFERENCE_ENGINE, cv::dnn::DNN_BACKEND_OPENCV, cv::dnn::DNN_BACKEND_VKCOM, cv::dnn::DNN_BACKEND_CUDA);
+    std::string target_keys = cv::format(
+    "{ target       | 0 | Choose one of target computation devices: "
+                          "%d: CPU target (by default), "
+                          "%d: OpenCL, "
+                          "%d: OpenCL fp16 (half-float precision), "
+                          "%d: VPU, "
+                          "%d: Vulkan, "
+                          "%d: CUDA, "
+                          "%d: CUDA fp16 (half-float preprocess) }", cv::dnn::DNN_TARGET_CPU, cv::dnn::DNN_TARGET_OPENCL, cv::dnn::DNN_TARGET_OPENCL_FP16, cv::dnn::DNN_TARGET_MYRIAD, cv::dnn::DNN_TARGET_VULKAN, cv::dnn::DNN_TARGET_CUDA, cv::dnn::DNN_TARGET_CUDA_FP16);
+    std::string keys = param_keys + backend_keys + target_keys;
+    CommandLineParser parser(argc, argv, keys);
     if (argc == 1 || parser.has("help"))
     {
         parser.printMessage();