232 lines
8.6 KiB
C++
232 lines
8.6 KiB
C++
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//
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// Created by DefTruth on 2021/10/2.
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//
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#include "nanodet.h"
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#include "lite/ort/core/ort_utils.h"
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#include "lite/utils.h"
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using ortcv::NanoDet;
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void NanoDet::resize_unscale(const cv::Mat &mat, cv::Mat &mat_rs,
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int target_height, int target_width,
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NanoScaleParams &scale_params)
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{
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if (mat.empty()) return;
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int img_height = static_cast<int>(mat.rows);
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int img_width = static_cast<int>(mat.cols);
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mat_rs = cv::Mat(target_height, target_width, CV_8UC3,
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cv::Scalar(0, 0, 0));
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// scale ratio (new / old) new_shape(h,w)
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float w_r = (float) target_width / (float) img_width;
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float h_r = (float) target_height / (float) img_height;
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float r = std::min(w_r, h_r);
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// compute padding
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int new_unpad_w = static_cast<int>((float) img_width * r); // floor
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int new_unpad_h = static_cast<int>((float) img_height * r); // floor
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int pad_w = target_width - new_unpad_w; // >=0
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int pad_h = target_height - new_unpad_h; // >=0
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int dw = pad_w / 2;
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int dh = pad_h / 2;
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// resize with unscaling
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cv::Mat new_unpad_mat = mat.clone();
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cv::resize(new_unpad_mat, new_unpad_mat, cv::Size(new_unpad_w, new_unpad_h));
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new_unpad_mat.copyTo(mat_rs(cv::Rect(dw, dh, new_unpad_w, new_unpad_h)));
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// record scale params.
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scale_params.ratio = r;
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scale_params.dw = dw;
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scale_params.dh = dh;
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scale_params.flag = true;
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}
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Ort::Value NanoDet::transform(const cv::Mat &mat_rs)
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{
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cv::Mat canvas = mat_rs.clone();
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// e.g (1,3,320,320) 1xCXHXW
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ortcv::utils::transform::normalize_inplace(canvas, mean_vals, scale_vals); // float32
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return ortcv::utils::transform::create_tensor(
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canvas, input_node_dims, memory_info_handler,
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input_values_handler, ortcv::utils::transform::CHW);
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}
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void NanoDet::detect(const cv::Mat &mat, std::vector<types::Boxf> &detected_boxes,
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float score_threshold, float iou_threshold,
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unsigned int topk, unsigned int nms_type)
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{
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if (mat.empty()) return;
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auto img_height = static_cast<float>(mat.rows);
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auto img_width = static_cast<float>(mat.cols);
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const int target_height = (int) input_node_dims.at(2);
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const int target_width = (int) input_node_dims.at(3);
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// resize & unscale
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cv::Mat mat_rs;
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NanoScaleParams scale_params;
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this->resize_unscale(mat, mat_rs, target_height, target_width, scale_params);
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// 1. make input tensor
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Ort::Value input_tensor = this->transform(mat_rs);
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// 2. inference scores & boxes.
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auto output_tensors = ort_session->Run(
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Ort::RunOptions{nullptr}, input_node_names.data(),
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&input_tensor, 1, output_node_names.data(), num_outputs
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);
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// 3. rescale & exclude.
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std::vector<types::Boxf> bbox_collection;
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this->generate_bboxes(scale_params, bbox_collection, output_tensors, score_threshold, img_height, img_width);
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// 4. hard|blend|offset nms with topk.
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this->nms(bbox_collection, detected_boxes, iou_threshold, topk, nms_type);
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}
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void NanoDet::generate_points(unsigned int target_height, unsigned int target_width)
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{
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if (center_points_is_update) return;
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for (auto stride : strides)
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{
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unsigned int num_grid_w = target_width / stride;
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unsigned int num_grid_h = target_height / stride;
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std::vector<NanoCenterPoint> points;
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for (unsigned int g1 = 0; g1 < num_grid_h; ++g1)
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{
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for (unsigned int g0 = 0; g0 < num_grid_w; ++g0)
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{
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float grid0 = (float) g0 + 0.5f;
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float grid1 = (float) g1 + 0.5f;
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#ifdef LITE_WIN32
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NanoCenterPoint point;
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point.grid0 = grid0;
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point.grid1 = grid1;
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point.stride = (float) stride;
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points.push_back(point);
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#else
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points.push_back((NanoCenterPoint) {grid0, grid1, (float) stride});
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#endif
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}
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}
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center_points[stride] = points;
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}
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center_points_is_update = true;
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}
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void NanoDet::generate_bboxes(const NanoScaleParams &scale_params,
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std::vector<types::Boxf> &bbox_collection,
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std::vector<Ort::Value> &output_tensors,
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float score_threshold, float img_height,
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float img_width)
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{
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Ort::Value &cls_pred_stride_8 = output_tensors.at(0); // e.g (1,1600,80)
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Ort::Value &cls_pred_stride_16 = output_tensors.at(1); // e.g (1,400,80)
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Ort::Value &cls_pred_stride_32 = output_tensors.at(2); // e.g (1,100,80)
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Ort::Value &dis_pred_stride_8 = output_tensors.at(3); // e.g (1,1600,4) xyxy (l,t,r,b)
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Ort::Value &dis_pred_stride_16 = output_tensors.at(4); // e.g (1,400,4) xyxy (l,t,r,b)
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Ort::Value &dis_pred_stride_32 = output_tensors.at(5); // e.g (1,100,4) xyxy (l,t,r,b)
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auto input_height = static_cast<unsigned int>(input_node_dims.at(2)); // e.g 320
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auto input_width = static_cast<unsigned int>(input_node_dims.at(3)); // e.g 320
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this->generate_points(input_height, input_width);
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bbox_collection.clear();
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// level 8 & 16 & 32
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this->generate_bboxes_single_stride(scale_params, cls_pred_stride_8, dis_pred_stride_8, 8,
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score_threshold, img_height, img_width, bbox_collection);
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this->generate_bboxes_single_stride(scale_params, cls_pred_stride_16, dis_pred_stride_16, 16,
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score_threshold, img_height, img_width, bbox_collection);
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this->generate_bboxes_single_stride(scale_params, cls_pred_stride_32, dis_pred_stride_32, 32,
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score_threshold, img_height, img_width, bbox_collection);
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#if LITEORT_DEBUG
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std::cout << "generate_bboxes num: " << bbox_collection.size() << "\n";
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#endif
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}
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void NanoDet::generate_bboxes_single_stride(const NanoScaleParams &scale_params,
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Ort::Value &cls_pred,
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Ort::Value &dis_pred,
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unsigned int stride,
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float score_threshold,
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float img_height,
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float img_width,
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std::vector<types::Boxf> &bbox_collection)
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{
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unsigned int nms_pre_ = (stride / 8) * nms_pre; // 1 * 1000,2*1000,...
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nms_pre_ = nms_pre_ >= nms_pre ? nms_pre_ : nms_pre;
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auto cls_pred_dims = cls_pred.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
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const unsigned int num_points = cls_pred_dims.at(1); // e.g 1600
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const unsigned int num_classes = cls_pred_dims.at(2); // e.g 80
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float ratio = scale_params.ratio;
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int dw = scale_params.dw;
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int dh = scale_params.dh;
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unsigned int count = 0;
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auto &stride_points = center_points[stride];
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for (unsigned int i = 0; i < num_points; ++i)
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{
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float cls_conf = cls_pred.At<float>({0, i, 0});
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unsigned int label = 0;
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for (unsigned int j = 0; j < num_classes; ++j)
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{
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float tmp_conf = cls_pred.At<float>({0, i, j});
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if (tmp_conf > cls_conf)
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{
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cls_conf = tmp_conf;
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label = j;
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}
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} // argmax
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if (cls_conf < score_threshold) continue; // filter
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auto &point = stride_points.at(i);
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const float cx = point.grid0; // cx
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const float cy = point.grid1; // cy
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const float s = point.stride; // stride
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float l = dis_pred.At<float>({0, i, 0}); // left
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float t = dis_pred.At<float>({0, i, 1}); // top
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float r = dis_pred.At<float>({0, i, 2}); // right
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float b = dis_pred.At<float>({0, i, 3}); // bottom
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types::Boxf box;
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float x1 = ((cx - l) * s - (float) dw) / ratio; // cx - l x1
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float y1 = ((cy - t) * s - (float) dh) / ratio; // cy - t y1
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float x2 = ((cx + r) * s - (float) dw) / ratio; // cx + r x2
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float y2 = ((cy + b) * s - (float) dh) / ratio; // cy + b y2
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box.x1 = std::max(0.f, x1);
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box.y1 = std::max(0.f, y1);
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box.x2 = std::min(img_width, x2);
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box.y2 = std::min(img_height, y2);
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box.score = cls_conf;
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box.label = label;
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box.label_text = class_names[label];
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box.flag = true;
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bbox_collection.push_back(box);
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count += 1; // limit boxes for nms.
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if (count > max_nms)
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break;
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}
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if (bbox_collection.size() > nms_pre_)
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{
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std::sort(bbox_collection.begin(), bbox_collection.end(),
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[](const types::Boxf &a, const types::Boxf &b)
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{ return a.score > b.score; }); // sort inplace
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// trunc
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bbox_collection.resize(nms_pre_);
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}
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}
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void NanoDet::nms(std::vector<types::Boxf> &input, std::vector<types::Boxf> &output,
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float iou_threshold, unsigned int topk, unsigned int nms_type)
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{
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if (nms_type == NMS::BLEND) lite::utils::blending_nms(input, output, iou_threshold, topk);
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else if (nms_type == NMS::OFFSET) lite::utils::offset_nms(input, output, iou_threshold, topk);
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else lite::utils::hard_nms(input, output, iou_threshold, topk);
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}
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