add csrc code

csrc/fastdeploy/vision/utils/utils.h

@@ -0,0 +1,140 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <set>
+#include <vector>
+#include "fastdeploy/core/fd_tensor.h"
+#include "fastdeploy/utils/utils.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace utils {
+// topk sometimes is a very small value
+// so this implementation is simple but I don't think it will
+// cost too much time
+// Also there may be cause problem since we suppose the minimum value is
+// -99999999
+// Do not use this function on array which topk contains value less than
+// -99999999
+template <typename T>
+std::vector<int32_t> TopKIndices(const T* array, int array_size, int topk) {
+  topk = std::min(array_size, topk);
+  std::vector<int32_t> res(topk);
+  std::set<int32_t> searched;
+  for (int32_t i = 0; i < topk; ++i) {
+    T min = -99999999;
+    for (int32_t j = 0; j < array_size; ++j) {
+      if (searched.find(j) != searched.end()) {
+        continue;
+      }
+      if (*(array + j) > min) {
+        res[i] = j;
+        min = *(array + j);
+      }
+    }
+    searched.insert(res[i]);
+  }
+  return res;
+}
+
+template <typename T>
+void ArgmaxScoreMap(T infer_result_buffer, SegmentationResult* result,
+                    bool with_softmax) {
+  int64_t height = result->shape[0];
+  int64_t width = result->shape[1];
+  int64_t num_classes = result->shape[2];
+  int index = 0;
+  for (size_t i = 0; i < height; ++i) {
+    for (size_t j = 0; j < width; ++j) {
+      int64_t s = (i * width + j) * num_classes;
+      T max_class_score = std::max_element(
+          infer_result_buffer + s, infer_result_buffer + s + num_classes);
+      int label_id = std::distance(infer_result_buffer + s, max_class_score);
+      if (label_id >= 255) {
+        FDWARNING << "label_id is stored by uint8_t, now the value is bigger "
+                     "than 255, it's "
+                  << static_cast<int>(label_id) << "." << std::endl;
+      }
+      result->label_map[index] = static_cast<uint8_t>(label_id);
+
+      if (with_softmax) {
+        double_t total = 0;
+        for (int k = 0; k < num_classes; k++) {
+          total += exp(*(infer_result_buffer + s + k) - *max_class_score);
+        }
+        double_t softmax_class_score = 1 / total;
+        result->score_map[index] = static_cast<float>(softmax_class_score);
+
+      } else {
+        result->score_map[index] = static_cast<float>(*max_class_score);
+      }
+      index++;
+    }
+  }
+}
+
+template <typename T>
+void NCHW2NHWC(FDTensor& infer_result) {
+  T* infer_result_buffer = reinterpret_cast<T*>(infer_result.MutableData());
+  int num = infer_result.shape[0];
+  int channel = infer_result.shape[1];
+  int height = infer_result.shape[2];
+  int width = infer_result.shape[3];
+  int chw = channel * height * width;
+  int wc = width * channel;
+  int wh = width * height;
+  std::vector<T> hwc_data(chw);
+  int index = 0;
+  for (int n = 0; n < num; n++) {
+    for (int c = 0; c < channel; c++) {
+      for (int h = 0; h < height; h++) {
+        for (int w = 0; w < width; w++) {
+          hwc_data[n * chw + h * wc + w * channel + c] =
+              *(infer_result_buffer + index);
+          index++;
+        }
+      }
+    }
+  }
+  std::memcpy(infer_result.MutableData(), hwc_data.data(),
+              num * chw * sizeof(T));
+  infer_result.shape = {num, height, width, channel};
+}
+
+void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
+                        bool contain_score_map);
+
+void NMS(DetectionResult* output, float iou_threshold = 0.5);
+
+void NMS(FaceDetectionResult* result, float iou_threshold = 0.5);
+
+// MergeSort
+void SortDetectionResult(DetectionResult* output);
+
+void SortDetectionResult(FaceDetectionResult* result);
+
+// L2 Norm / cosine similarity  (for face recognition, ...)
+FASTDEPLOY_DECL std::vector<float> L2Normalize(
+    const std::vector<float>& values);
+
+FASTDEPLOY_DECL float CosineSimilarity(const std::vector<float>& a,
+                                       const std::vector<float>& b,
+                                       bool normalized = true);
+
+}  // namespace utils
+}  // namespace vision
+}  // namespace fastdeploy