Add YOLOv5Face model support (#38)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* Add YOLOv5Face Model support

* fixed examples/vision typos

* fixed runtime_option print func bugs

fastdeploy/vision/utils/nms.cc

@@ -66,6 +66,62 @@ void NMS(DetectionResult* result, float iou_threshold) {
   }
 }
 
+void NMS(FaceDetectionResult* result, float iou_threshold) {
+  utils::SortDetectionResult(result);
+
+  std::vector<float> area_of_boxes(result->boxes.size());
+  std::vector<int> suppressed(result->boxes.size(), 0);
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    area_of_boxes[i] = (result->boxes[i][2] - result->boxes[i][0]) *
+                       (result->boxes[i][3] - result->boxes[i][1]);
+  }
+
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    if (suppressed[i] == 1) {
+      continue;
+    }
+    for (size_t j = i + 1; j < result->boxes.size(); ++j) {
+      if (suppressed[j] == 1) {
+        continue;
+      }
+      float xmin = std::max(result->boxes[i][0], result->boxes[j][0]);
+      float ymin = std::max(result->boxes[i][1], result->boxes[j][1]);
+      float xmax = std::min(result->boxes[i][2], result->boxes[j][2]);
+      float ymax = std::min(result->boxes[i][3], result->boxes[j][3]);
+      float overlap_w = std::max(0.0f, xmax - xmin);
+      float overlap_h = std::max(0.0f, ymax - ymin);
+      float overlap_area = overlap_w * overlap_h;
+      float overlap_ratio =
+          overlap_area / (area_of_boxes[i] + area_of_boxes[j] - overlap_area);
+      if (overlap_ratio > iou_threshold) {
+        suppressed[j] = 1;
+      }
+    }
+  }
+  FaceDetectionResult backup(*result);
+  int landmarks_per_face = result->landmarks_per_face;
+
+  result->Clear();
+  // don't forget to reset the landmarks_per_face
+  // before apply Reserve method.
+  result->landmarks_per_face = landmarks_per_face;
+  result->Reserve(suppressed.size());
+  for (size_t i = 0; i < suppressed.size(); ++i) {
+    if (suppressed[i] == 1) {
+      continue;
+    }
+    result->boxes.emplace_back(backup.boxes[i]);
+    result->scores.push_back(backup.scores[i]);
+    // landmarks (if have)
+    if (result->landmarks_per_face > 0) {
+      for (size_t j = 0; j < result->landmarks_per_face; ++j) {
+        result->landmarks.emplace_back(
+            backup.landmarks[i * result->landmarks_per_face + j]);
+      }
+    }
+  }
+}
+
 }  // namespace utils
 }  // namespace vision
 }  // namespace fastdeploy