Merge pull request #13 from liwuhen/feat-dev

chore: add clang-format

.pre-commit-config.yaml

@@ -21,12 +21,12 @@ ci:
 
 repos:
 
-# # Clang format the codebase automatically
-# - repo: https://github.com/pre-commit/mirrors-clang-format
-#   rev: "v14.0.6"
-#   hooks:
-#   - id: clang-format
-#     types_or: [c++, c, cuda]
+# Clang format the codebase automatically
+- repo: https://github.com/pre-commit/mirrors-clang-format
+  rev: "v14.0.6"
+  hooks:
+  - id: clang-format
+    types_or: [c++, c, cuda]
 
 # Check static types with mypy
 - repo: https://github.com/pre-commit/mirrors-mypy

modules/app_yolo/architecture/cuda/gpu_decode.cu

@@ -113,8 +113,8 @@ static __global__ void fast_nms_kernel(float* bboxes, int max_objects, float thr
   // 如果测mAP的性能的时候， 只能采用cpu nms
   // 如果是日常推理， 则可以使用这个gpu nms
   int position = (blockDim.x * blockIdx.x + threadIdx.x);
-  int count =
-      min(static_cast<int>(*bboxes), max_objects);  // *bboxes表示首地址的第一个元素。 count是bbox的数量。
+  int count = min(static_cast<int>(*bboxes),
+                  max_objects);  // *bboxes表示首地址的第一个元素。 count是bbox的数量。
   if (position >= count) return;
 
   // left, top, right, bottom, confidence, class, keepflag

modules/app_yolo/architecture/postprocessor.cpp

@@ -119,7 +119,7 @@ void PostProcessor::Inference() {
  * @description: Visualization.
  */
 void PostProcessor::Visualization(bool real_time, cv::Mat& img, int64_t timestamp,
-                              vector<Box>& results) {
+                                  vector<Box>& results) {
   for (auto& box : results) {
     cv::rectangle(img, cv::Point(box.left, box.top), cv::Point(box.right, box.bottom),
                   cv::Scalar(0, 255, 0), 2);

modules/app_yolo/architecture/preprocessor.cpp

@@ -81,7 +81,7 @@ bool PreProcessor::DataResourceRelease() {}
  * @description: Inference.
  */
 bool PreProcessor::Inference(InfertMsg& input_msg, float* dstimg, DeviceMode inferMode,
-      cudaStream_t stream) {
+                             cudaStream_t stream) {
   CalAffineMatrix(input_msg);
 
   switch (inferMode) {
@@ -91,8 +91,9 @@ bool PreProcessor::Inference(InfertMsg& input_msg, float* dstimg, DeviceMode inf
       }
       break;
     case DeviceMode::CPU_MODE:
-      if (!CpuPreprocessor(input_msg.image, input_msg.timestamp, dstimg, stream))
-      { return false; }
+      if (!CpuPreprocessor(input_msg.image, input_msg.timestamp, dstimg, stream)) {
+        return false;
+      }
       break;
     default:
       break;
@@ -125,12 +126,8 @@ bool PreProcessor::GpuPreprocessor(InfertMsg& input_msg, float* dstimg, cudaStre
 /**
  * @description: Cpu preprocessor.
  */
-bool PreProcessor::CpuPreprocessor(
-    cv::Mat& srcimg,
-    uint64_t timestamp,
-    float* input_device_gpu,
-    cudaStream_t stream) {
-
+bool PreProcessor::CpuPreprocessor(cv::Mat& srcimg, uint64_t timestamp, float* input_device_gpu,
+                                   cudaStream_t stream) {
   checkRuntime(cudaMallocHost(&input_data_host_, sizeof(float) * parsemsgs_->dstimg_size_));
 
   float scale_x = parsemsgs_->dst_img_w_ / static_cast<float>(parsemsgs_->src_img_w_);

modules/app_yolo/architecture/trt_infer.cpp

@@ -101,7 +101,8 @@ bool TrtInfer::Inference(float* output_img_device) {
                           output_img_device, parsemsgs_->dstimg_size_ * sizeof(uint8_t),
                           cudaMemcpyDeviceToDevice));
 
-  bool success = execution_context_->enqueueV2(reinterpret_cast<void**>(gpu_buffers_), stream_, nullptr);
+  bool success =
+      execution_context_->enqueueV2(reinterpret_cast<void**>(gpu_buffers_), stream_, nullptr);
   if (!success) {
     LOG(ERROR) << " Inference failed ";
     return false;

modules/common/msg_img/dataset.h

@@ -61,8 +61,7 @@ static const char* cocolabels[] = {"person",        "bicycle",      "car",
                                    "oven",          "toaster",      "sink",
                                    "refrigerator",  "book",         "clock",
                                    "vase",          "scissors",     "teddy bear",
-                                   "hair drier",    "toothbrush"
-};
+                                   "hair drier",    "toothbrush"};
 
 // hsv 转 bgr
 static std::tuple<uint8_t, uint8_t, uint8_t> hsv2bgr(float h, float s, float v) {

modules/common/msg_struct/task_struct.hpp

@@ -96,8 +96,8 @@ struct CVImage {
   std::vector<uint8_t> data;  // 存储连续的像素值
 
   CVImage();
-  CVImage(int width, int height, int channel) : img_width(width), \
-    img_height(height), img_channel(channel), img_size(0) {}
+  CVImage(int width, int height, int channel)
+      : img_width(width), img_height(height), img_channel(channel), img_size(0) {}
 
   ~CVImage() {}
 

modules/common/utils/std_cmake.h

@@ -26,7 +26,7 @@ namespace common {
 
 #define MODEL_FLAG "yolov5"
 
-} //namespace common
-} //namespace hpc
+}  // namespace common
+}  // namespace hpc
 
-#endif // APP_COMMON_STD_CMAKE_H_IN__
+#endif  // APP_COMMON_STD_CMAKE_H_IN__

modules/common/utils/std_time.h

@@ -34,7 +34,8 @@ namespace common {
 
 static time_t GetSystmeTime() {
   return static_cast<int64_t>(std::chrono::duration_cast<std::chrono::milliseconds>(
-                              std::chrono::system_clock::now().time_since_epoch()).count());
+                                  std::chrono::system_clock::now().time_since_epoch())
+                                  .count());
 }
 
 }  // namespace common