mirror of
https://github.com/Tencent/ncnn.git
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nihui
622 lines
24 KiB
C++
622 lines
24 KiB
C++
// Copyright 2024 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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// 1. install
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// pip3 install -U ultralytics pnnx ncnn
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// 2. export yolov8 torchscript
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// yolo export model=yolov8n.pt format=torchscript
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// 3. convert torchscript with static shape
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// pnnx yolov8n.torchscript
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// 4. modify yolov8n_pnnx.py for dynamic shape inference
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// A. modify reshape to support dynamic image sizes
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// B. permute tensor before concat and adjust concat axis
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// C. drop post-process part
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// before:
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// v_165 = v_142.view(1, 144, 6400)
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// v_166 = v_153.view(1, 144, 1600)
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// v_167 = v_164.view(1, 144, 400)
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// v_168 = torch.cat((v_165, v_166, v_167), dim=2)
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// ...
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// after:
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// v_165 = v_142.view(1, 144, -1).transpose(1, 2)
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// v_166 = v_153.view(1, 144, -1).transpose(1, 2)
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// v_167 = v_164.view(1, 144, -1).transpose(1, 2)
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// v_168 = torch.cat((v_165, v_166, v_167), dim=1)
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// return v_168
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// 5. re-export yolov8 torchscript
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// python3 -c 'import yolov8n_pnnx; yolov8n_pnnx.export_torchscript()'
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// 6. convert new torchscript with dynamic shape
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// pnnx yolov8n_pnnx.py.pt inputshape=[1,3,640,640] inputshape2=[1,3,320,320]
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// 7. now you get ncnn model files
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// mv yolov8n_pnnx.py.ncnn.param yolov8n.ncnn.param
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// mv yolov8n_pnnx.py.ncnn.bin yolov8n.ncnn.bin
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// the out blob would be a 2-dim tensor with w=144 h=8400
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//
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// | bbox-reg 16 x 4 | per-class scores(80) |
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// +-----+-----+-----+-----+----------------------+
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// | dx0 | dy0 | dx1 | dy1 |0.1 0.0 0.0 0.5 ......|
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// all /| | | | | . |
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// boxes | .. | .. | .. | .. |0.0 0.9 0.0 0.0 ......|
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// (8400)| | | | | . |
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// \| | | | | . |
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// +-----+-----+-----+-----+----------------------+
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//
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#include "layer.h"
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#include "net.h"
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#if defined(USE_NCNN_SIMPLEOCV)
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#include "simpleocv.h"
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#else
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#endif
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#include <float.h>
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#include <stdio.h>
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#include <vector>
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struct Object
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{
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cv::Rect_<float> rect;
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int label;
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float prob;
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};
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static inline float intersection_area(const Object& a, const Object& b)
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{
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cv::Rect_<float> inter = a.rect & b.rect;
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return inter.area();
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}
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static void qsort_descent_inplace(std::vector<Object>& objects, int left, int right)
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{
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int i = left;
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int j = right;
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float p = objects[(left + right) / 2].prob;
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while (i <= j)
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{
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while (objects[i].prob > p)
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i++;
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while (objects[j].prob < p)
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j--;
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if (i <= j)
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{
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// swap
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std::swap(objects[i], objects[j]);
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i++;
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j--;
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}
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}
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// #pragma omp parallel sections
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{
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// #pragma omp section
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{
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if (left < j) qsort_descent_inplace(objects, left, j);
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}
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// #pragma omp section
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{
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if (i < right) qsort_descent_inplace(objects, i, right);
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}
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}
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}
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static void qsort_descent_inplace(std::vector<Object>& objects)
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{
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if (objects.empty())
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return;
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qsort_descent_inplace(objects, 0, objects.size() - 1);
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}
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static void nms_sorted_bboxes(const std::vector<Object>& objects, std::vector<int>& picked, float nms_threshold, bool agnostic = false)
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{
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picked.clear();
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const int n = objects.size();
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std::vector<float> areas(n);
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for (int i = 0; i < n; i++)
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{
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areas[i] = objects[i].rect.area();
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}
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for (int i = 0; i < n; i++)
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{
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const Object& a = objects[i];
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int keep = 1;
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for (int j = 0; j < (int)picked.size(); j++)
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{
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const Object& b = objects[picked[j]];
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if (!agnostic && a.label != b.label)
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continue;
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// intersection over union
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float inter_area = intersection_area(a, b);
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float union_area = areas[i] + areas[picked[j]] - inter_area;
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// float IoU = inter_area / union_area
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if (inter_area / union_area > nms_threshold)
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keep = 0;
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}
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if (keep)
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picked.push_back(i);
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}
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}
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static inline float sigmoid(float x)
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{
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return 1.0f / (1.0f + expf(-x));
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}
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static void generate_proposals(const ncnn::Mat& pred, int stride, const ncnn::Mat& in_pad, float prob_threshold, std::vector<Object>& objects)
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{
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const int w = in_pad.w;
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const int h = in_pad.h;
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const int num_grid_x = w / stride;
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const int num_grid_y = h / stride;
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const int reg_max_1 = 16;
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const int num_class = pred.w - reg_max_1 * 4; // number of classes. 80 for COCO
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for (int y = 0; y < num_grid_y; y++)
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{
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for (int x = 0; x < num_grid_x; x++)
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{
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const ncnn::Mat pred_grid = pred.row_range(y * num_grid_x + x, 1);
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// find label with max score
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int label = -1;
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float score = -FLT_MAX;
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{
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const ncnn::Mat pred_score = pred_grid.range(reg_max_1 * 4, num_class);
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for (int k = 0; k < num_class; k++)
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{
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float s = pred_score[k];
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if (s > score)
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{
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label = k;
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score = s;
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}
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}
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score = sigmoid(score);
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}
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if (score >= prob_threshold)
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{
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ncnn::Mat pred_bbox = pred_grid.range(0, reg_max_1 * 4).reshape(reg_max_1, 4);
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{
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ncnn::Layer* softmax = ncnn::create_layer("Softmax");
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ncnn::ParamDict pd;
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pd.set(0, 1); // axis
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pd.set(1, 1);
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softmax->load_param(pd);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = false;
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softmax->create_pipeline(opt);
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softmax->forward_inplace(pred_bbox, opt);
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softmax->destroy_pipeline(opt);
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delete softmax;
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}
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float pred_ltrb[4];
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for (int k = 0; k < 4; k++)
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{
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float dis = 0.f;
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const float* dis_after_sm = pred_bbox.row(k);
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for (int l = 0; l < reg_max_1; l++)
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{
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dis += l * dis_after_sm[l];
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}
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pred_ltrb[k] = dis * stride;
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}
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float pb_cx = (x + 0.5f) * stride;
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float pb_cy = (y + 0.5f) * stride;
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float x0 = pb_cx - pred_ltrb[0];
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float y0 = pb_cy - pred_ltrb[1];
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float x1 = pb_cx + pred_ltrb[2];
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float y1 = pb_cy + pred_ltrb[3];
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Object obj;
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obj.rect.x = x0;
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obj.rect.y = y0;
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obj.rect.width = x1 - x0;
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obj.rect.height = y1 - y0;
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obj.label = label;
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obj.prob = score;
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objects.push_back(obj);
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}
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}
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}
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}
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static void generate_proposals(const ncnn::Mat& pred, const std::vector<int>& strides, const ncnn::Mat& in_pad, float prob_threshold, std::vector<Object>& objects)
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{
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const int w = in_pad.w;
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const int h = in_pad.h;
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int pred_row_offset = 0;
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for (size_t i = 0; i < strides.size(); i++)
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{
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const int stride = strides[i];
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const int num_grid_x = w / stride;
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const int num_grid_y = h / stride;
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const int num_grid = num_grid_x * num_grid_y;
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generate_proposals(pred.row_range(pred_row_offset, num_grid), stride, in_pad, prob_threshold, objects);
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pred_row_offset += num_grid;
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}
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}
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static int detect_yolov8(const cv::Mat& bgr, std::vector<Object>& objects)
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{
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ncnn::Net yolov8;
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yolov8.opt.use_vulkan_compute = true;
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// yolov8.opt.use_bf16_storage = true;
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// https://github.com/nihui/ncnn-android-yolov8/tree/master/app/src/main/assets
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yolov8.load_param("yolov8n.ncnn.param");
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yolov8.load_model("yolov8n.ncnn.bin");
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// yolov8.load_param("yolov8s.ncnn.param");
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// yolov8.load_model("yolov8s.ncnn.bin");
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// yolov8.load_param("yolov8m.ncnn.param");
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// yolov8.load_model("yolov8m.ncnn.bin");
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// if you use oiv7 models, you shall call draw_objects_oiv() instead
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// yolov8.load_param("yolov8n_oiv7.ncnn.param");
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// yolov8.load_model("yolov8n_oiv7.ncnn.bin");
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// yolov8.load_param("yolov8s_oiv7.ncnn.param");
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// yolov8.load_model("yolov8s_oiv7.ncnn.bin");
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// yolov8.load_param("yolov8m_oiv7.ncnn.param");
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// yolov8.load_model("yolov8m_oiv7.ncnn.bin");
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const int target_size = 640;
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const float prob_threshold = 0.25f;
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const float nms_threshold = 0.45f;
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int img_w = bgr.cols;
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int img_h = bgr.rows;
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// ultralytics/cfg/models/v8/yolov8.yaml
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std::vector<int> strides(3);
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strides[0] = 8;
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strides[1] = 16;
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strides[2] = 32;
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const int max_stride = 32;
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// letterbox pad to multiple of max_stride
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int w = img_w;
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int h = img_h;
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float scale = 1.f;
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if (w > h)
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{
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scale = (float)target_size / w;
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w = target_size;
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h = h * scale;
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}
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else
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{
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scale = (float)target_size / h;
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h = target_size;
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w = w * scale;
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}
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ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);
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// letterbox pad to target_size rectangle
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int wpad = (w + max_stride - 1) / max_stride * max_stride - w;
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int hpad = (h + max_stride - 1) / max_stride * max_stride - h;
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ncnn::Mat in_pad;
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ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);
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const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
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in_pad.substract_mean_normalize(0, norm_vals);
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ncnn::Extractor ex = yolov8.create_extractor();
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ex.input("in0", in_pad);
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ncnn::Mat out;
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ex.extract("out0", out);
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std::vector<Object> proposals;
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generate_proposals(out, strides, in_pad, prob_threshold, proposals);
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// sort all proposals by score from highest to lowest
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qsort_descent_inplace(proposals);
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// apply nms with nms_threshold
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std::vector<int> picked;
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nms_sorted_bboxes(proposals, picked, nms_threshold);
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int count = picked.size();
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objects.resize(count);
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for (int i = 0; i < count; i++)
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{
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objects[i] = proposals[picked[i]];
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// adjust offset to original unpadded
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float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
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float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
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float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
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float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;
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// clip
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x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
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y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
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x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
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y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);
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objects[i].rect.x = x0;
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objects[i].rect.y = y0;
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objects[i].rect.width = x1 - x0;
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objects[i].rect.height = y1 - y0;
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}
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return 0;
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}
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static void draw_objects_coco(const cv::Mat& bgr, const std::vector<Object>& objects)
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{
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static const char* class_names[] = {
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"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
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"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
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"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
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"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
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"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
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"hair drier", "toothbrush"
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};
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static cv::Scalar colors[] = {
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cv::Scalar(244, 67, 54),
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cv::Scalar(233, 30, 99),
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cv::Scalar(156, 39, 176),
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cv::Scalar(103, 58, 183),
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cv::Scalar(63, 81, 181),
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cv::Scalar(33, 150, 243),
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cv::Scalar(3, 169, 244),
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cv::Scalar(0, 188, 212),
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cv::Scalar(0, 150, 136),
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cv::Scalar(76, 175, 80),
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cv::Scalar(139, 195, 74),
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cv::Scalar(205, 220, 57),
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cv::Scalar(255, 235, 59),
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cv::Scalar(255, 193, 7),
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cv::Scalar(255, 152, 0),
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cv::Scalar(255, 87, 34),
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cv::Scalar(121, 85, 72),
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cv::Scalar(158, 158, 158),
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cv::Scalar(96, 125, 139)
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};
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cv::Mat image = bgr.clone();
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for (size_t i = 0; i < objects.size(); i++)
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{
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const Object& obj = objects[i];
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const cv::Scalar& color = colors[i % 19];
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fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
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obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
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cv::rectangle(image, obj.rect, color);
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char text[256];
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sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
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int baseLine = 0;
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cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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int x = obj.rect.x;
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int y = obj.rect.y - label_size.height - baseLine;
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if (y < 0)
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y = 0;
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if (x + label_size.width > image.cols)
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x = image.cols - label_size.width;
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cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
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cv::Scalar(255, 255, 255), -1);
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cv::putText(image, text, cv::Point(x, y + label_size.height),
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cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
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}
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cv::imshow("image", image);
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cv::waitKey(0);
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}
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static void draw_objects_oiv(const cv::Mat& bgr, const std::vector<Object>& objects)
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{
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static const char* class_names[] = {
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"Accordion", "Adhesive tape", "Aircraft", "Airplane", "Alarm clock", "Alpaca", "Ambulance", "Animal",
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"Ant", "Antelope", "Apple", "Armadillo", "Artichoke", "Auto part", "Axe", "Backpack", "Bagel",
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"Baked goods", "Balance beam", "Ball", "Balloon", "Banana", "Band-aid", "Banjo", "Barge", "Barrel",
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"Baseball bat", "Baseball glove", "Bat (Animal)", "Bathroom accessory", "Bathroom cabinet", "Bathtub",
|
|
"Beaker", "Bear", "Bed", "Bee", "Beehive", "Beer", "Beetle", "Bell pepper", "Belt", "Bench", "Bicycle",
|
|
"Bicycle helmet", "Bicycle wheel", "Bidet", "Billboard", "Billiard table", "Binoculars", "Bird",
|
|
"Blender", "Blue jay", "Boat", "Bomb", "Book", "Bookcase", "Boot", "Bottle", "Bottle opener",
|
|
"Bow and arrow", "Bowl", "Bowling equipment", "Box", "Boy", "Brassiere", "Bread", "Briefcase",
|
|
"Broccoli", "Bronze sculpture", "Brown bear", "Building", "Bull", "Burrito", "Bus", "Bust", "Butterfly",
|
|
"Cabbage", "Cabinetry", "Cake", "Cake stand", "Calculator", "Camel", "Camera", "Can opener", "Canary",
|
|
"Candle", "Candy", "Cannon", "Canoe", "Cantaloupe", "Car", "Carnivore", "Carrot", "Cart", "Cassette deck",
|
|
"Castle", "Cat", "Cat furniture", "Caterpillar", "Cattle", "Ceiling fan", "Cello", "Centipede",
|
|
"Chainsaw", "Chair", "Cheese", "Cheetah", "Chest of drawers", "Chicken", "Chime", "Chisel", "Chopsticks",
|
|
"Christmas tree", "Clock", "Closet", "Clothing", "Coat", "Cocktail", "Cocktail shaker", "Coconut",
|
|
"Coffee", "Coffee cup", "Coffee table", "Coffeemaker", "Coin", "Common fig", "Common sunflower",
|
|
"Computer keyboard", "Computer monitor", "Computer mouse", "Container", "Convenience store", "Cookie",
|
|
"Cooking spray", "Corded phone", "Cosmetics", "Couch", "Countertop", "Cowboy hat", "Crab", "Cream",
|
|
"Cricket ball", "Crocodile", "Croissant", "Crown", "Crutch", "Cucumber", "Cupboard", "Curtain",
|
|
"Cutting board", "Dagger", "Dairy Product", "Deer", "Desk", "Dessert", "Diaper", "Dice", "Digital clock",
|
|
"Dinosaur", "Dishwasher", "Dog", "Dog bed", "Doll", "Dolphin", "Door", "Door handle", "Doughnut",
|
|
"Dragonfly", "Drawer", "Dress", "Drill (Tool)", "Drink", "Drinking straw", "Drum", "Duck", "Dumbbell",
|
|
"Eagle", "Earrings", "Egg (Food)", "Elephant", "Envelope", "Eraser", "Face powder", "Facial tissue holder",
|
|
"Falcon", "Fashion accessory", "Fast food", "Fax", "Fedora", "Filing cabinet", "Fire hydrant",
|
|
"Fireplace", "Fish", "Flag", "Flashlight", "Flower", "Flowerpot", "Flute", "Flying disc", "Food",
|
|
"Food processor", "Football", "Football helmet", "Footwear", "Fork", "Fountain", "Fox", "French fries",
|
|
"French horn", "Frog", "Fruit", "Frying pan", "Furniture", "Garden Asparagus", "Gas stove", "Giraffe",
|
|
"Girl", "Glasses", "Glove", "Goat", "Goggles", "Goldfish", "Golf ball", "Golf cart", "Gondola",
|
|
"Goose", "Grape", "Grapefruit", "Grinder", "Guacamole", "Guitar", "Hair dryer", "Hair spray", "Hamburger",
|
|
"Hammer", "Hamster", "Hand dryer", "Handbag", "Handgun", "Harbor seal", "Harmonica", "Harp",
|
|
"Harpsichord", "Hat", "Headphones", "Heater", "Hedgehog", "Helicopter", "Helmet", "High heels",
|
|
"Hiking equipment", "Hippopotamus", "Home appliance", "Honeycomb", "Horizontal bar", "Horse", "Hot dog",
|
|
"House", "Houseplant", "Human arm", "Human beard", "Human body", "Human ear", "Human eye", "Human face",
|
|
"Human foot", "Human hair", "Human hand", "Human head", "Human leg", "Human mouth", "Human nose",
|
|
"Humidifier", "Ice cream", "Indoor rower", "Infant bed", "Insect", "Invertebrate", "Ipod", "Isopod",
|
|
"Jacket", "Jacuzzi", "Jaguar (Animal)", "Jeans", "Jellyfish", "Jet ski", "Jug", "Juice", "Kangaroo",
|
|
"Kettle", "Kitchen & dining room table", "Kitchen appliance", "Kitchen knife", "Kitchen utensil",
|
|
"Kitchenware", "Kite", "Knife", "Koala", "Ladder", "Ladle", "Ladybug", "Lamp", "Land vehicle",
|
|
"Lantern", "Laptop", "Lavender (Plant)", "Lemon", "Leopard", "Light bulb", "Light switch", "Lighthouse",
|
|
"Lily", "Limousine", "Lion", "Lipstick", "Lizard", "Lobster", "Loveseat", "Luggage and bags", "Lynx",
|
|
"Magpie", "Mammal", "Man", "Mango", "Maple", "Maracas", "Marine invertebrates", "Marine mammal",
|
|
"Measuring cup", "Mechanical fan", "Medical equipment", "Microphone", "Microwave oven", "Milk",
|
|
"Miniskirt", "Mirror", "Missile", "Mixer", "Mixing bowl", "Mobile phone", "Monkey", "Moths and butterflies",
|
|
"Motorcycle", "Mouse", "Muffin", "Mug", "Mule", "Mushroom", "Musical instrument", "Musical keyboard",
|
|
"Nail (Construction)", "Necklace", "Nightstand", "Oboe", "Office building", "Office supplies", "Orange",
|
|
"Organ (Musical Instrument)", "Ostrich", "Otter", "Oven", "Owl", "Oyster", "Paddle", "Palm tree",
|
|
"Pancake", "Panda", "Paper cutter", "Paper towel", "Parachute", "Parking meter", "Parrot", "Pasta",
|
|
"Pastry", "Peach", "Pear", "Pen", "Pencil case", "Pencil sharpener", "Penguin", "Perfume", "Person",
|
|
"Personal care", "Personal flotation device", "Piano", "Picnic basket", "Picture frame", "Pig",
|
|
"Pillow", "Pineapple", "Pitcher (Container)", "Pizza", "Pizza cutter", "Plant", "Plastic bag", "Plate",
|
|
"Platter", "Plumbing fixture", "Polar bear", "Pomegranate", "Popcorn", "Porch", "Porcupine", "Poster",
|
|
"Potato", "Power plugs and sockets", "Pressure cooker", "Pretzel", "Printer", "Pumpkin", "Punching bag",
|
|
"Rabbit", "Raccoon", "Racket", "Radish", "Ratchet (Device)", "Raven", "Rays and skates", "Red panda",
|
|
"Refrigerator", "Remote control", "Reptile", "Rhinoceros", "Rifle", "Ring binder", "Rocket",
|
|
"Roller skates", "Rose", "Rugby ball", "Ruler", "Salad", "Salt and pepper shakers", "Sandal",
|
|
"Sandwich", "Saucer", "Saxophone", "Scale", "Scarf", "Scissors", "Scoreboard", "Scorpion",
|
|
"Screwdriver", "Sculpture", "Sea lion", "Sea turtle", "Seafood", "Seahorse", "Seat belt", "Segway",
|
|
"Serving tray", "Sewing machine", "Shark", "Sheep", "Shelf", "Shellfish", "Shirt", "Shorts",
|
|
"Shotgun", "Shower", "Shrimp", "Sink", "Skateboard", "Ski", "Skirt", "Skull", "Skunk", "Skyscraper",
|
|
"Slow cooker", "Snack", "Snail", "Snake", "Snowboard", "Snowman", "Snowmobile", "Snowplow",
|
|
"Soap dispenser", "Sock", "Sofa bed", "Sombrero", "Sparrow", "Spatula", "Spice rack", "Spider",
|
|
"Spoon", "Sports equipment", "Sports uniform", "Squash (Plant)", "Squid", "Squirrel", "Stairs",
|
|
"Stapler", "Starfish", "Stationary bicycle", "Stethoscope", "Stool", "Stop sign", "Strawberry",
|
|
"Street light", "Stretcher", "Studio couch", "Submarine", "Submarine sandwich", "Suit", "Suitcase",
|
|
"Sun hat", "Sunglasses", "Surfboard", "Sushi", "Swan", "Swim cap", "Swimming pool", "Swimwear",
|
|
"Sword", "Syringe", "Table", "Table tennis racket", "Tablet computer", "Tableware", "Taco", "Tank",
|
|
"Tap", "Tart", "Taxi", "Tea", "Teapot", "Teddy bear", "Telephone", "Television", "Tennis ball",
|
|
"Tennis racket", "Tent", "Tiara", "Tick", "Tie", "Tiger", "Tin can", "Tire", "Toaster", "Toilet",
|
|
"Toilet paper", "Tomato", "Tool", "Toothbrush", "Torch", "Tortoise", "Towel", "Tower", "Toy",
|
|
"Traffic light", "Traffic sign", "Train", "Training bench", "Treadmill", "Tree", "Tree house",
|
|
"Tripod", "Trombone", "Trousers", "Truck", "Trumpet", "Turkey", "Turtle", "Umbrella", "Unicycle",
|
|
"Van", "Vase", "Vegetable", "Vehicle", "Vehicle registration plate", "Violin", "Volleyball (Ball)",
|
|
"Waffle", "Waffle iron", "Wall clock", "Wardrobe", "Washing machine", "Waste container", "Watch",
|
|
"Watercraft", "Watermelon", "Weapon", "Whale", "Wheel", "Wheelchair", "Whisk", "Whiteboard", "Willow",
|
|
"Window", "Window blind", "Wine", "Wine glass", "Wine rack", "Winter melon", "Wok", "Woman",
|
|
"Wood-burning stove", "Woodpecker", "Worm", "Wrench", "Zebra", "Zucchini"
|
|
};
|
|
|
|
static cv::Scalar colors[] = {
|
|
cv::Scalar(244, 67, 54),
|
|
cv::Scalar(233, 30, 99),
|
|
cv::Scalar(156, 39, 176),
|
|
cv::Scalar(103, 58, 183),
|
|
cv::Scalar(63, 81, 181),
|
|
cv::Scalar(33, 150, 243),
|
|
cv::Scalar(3, 169, 244),
|
|
cv::Scalar(0, 188, 212),
|
|
cv::Scalar(0, 150, 136),
|
|
cv::Scalar(76, 175, 80),
|
|
cv::Scalar(139, 195, 74),
|
|
cv::Scalar(205, 220, 57),
|
|
cv::Scalar(255, 235, 59),
|
|
cv::Scalar(255, 193, 7),
|
|
cv::Scalar(255, 152, 0),
|
|
cv::Scalar(255, 87, 34),
|
|
cv::Scalar(121, 85, 72),
|
|
cv::Scalar(158, 158, 158),
|
|
cv::Scalar(96, 125, 139)
|
|
};
|
|
|
|
cv::Mat image = bgr.clone();
|
|
|
|
for (size_t i = 0; i < objects.size(); i++)
|
|
{
|
|
const Object& obj = objects[i];
|
|
|
|
const cv::Scalar& color = colors[i % 19];
|
|
|
|
fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
|
|
obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
|
|
|
|
cv::rectangle(image, obj.rect, color);
|
|
|
|
char text[256];
|
|
sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
|
|
|
|
int baseLine = 0;
|
|
cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
|
|
int x = obj.rect.x;
|
|
int y = obj.rect.y - label_size.height - baseLine;
|
|
if (y < 0)
|
|
y = 0;
|
|
if (x + label_size.width > image.cols)
|
|
x = image.cols - label_size.width;
|
|
|
|
cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
|
|
cv::Scalar(255, 255, 255), -1);
|
|
|
|
cv::putText(image, text, cv::Point(x, y + label_size.height),
|
|
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
|
|
}
|
|
|
|
cv::imshow("image", image);
|
|
cv::waitKey(0);
|
|
}
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
if (argc != 2)
|
|
{
|
|
fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
|
|
return -1;
|
|
}
|
|
|
|
const char* imagepath = argv[1];
|
|
|
|
cv::Mat m = cv::imread(imagepath, 1);
|
|
if (m.empty())
|
|
{
|
|
fprintf(stderr, "cv::imread %s failed\n", imagepath);
|
|
return -1;
|
|
}
|
|
|
|
std::vector<Object> objects;
|
|
detect_yolov8(m, objects);
|
|
|
|
draw_objects_coco(m, objects);
|
|
// draw_objects_oiv(m, objects);
|
|
|
|
return 0;
|
|
}
|