mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-17 04:41:47 +08:00
nihui
582 lines
18 KiB
C++
582 lines
18 KiB
C++
// Copyright 2025 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 yolo11-pose torchscript
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// yolo export model=yolo11n-pose.pt format=torchscript
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// 3. convert torchscript with static shape
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// pnnx yolo11n-pose.torchscript
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// 4. modify yolo11n_pose_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_195 = v_194.view(1, 51, 6400)
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// v_201 = v_200.view(1, 51, 1600)
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// v_207 = v_206.view(1, 51, 400)
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// v_208 = torch.cat((v_195, v_201, v_207), dim=-1)
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// ...
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// v_254 = v_223.view(1, 65, 6400)
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// v_255 = v_238.view(1, 65, 1600)
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// v_256 = v_253.view(1, 65, 400)
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// v_257 = torch.cat((v_254, v_255, v_256), dim=2)
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// ...
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// after:
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// v_195 = v_194.view(1, 51, -1).transpose(1, 2)
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// v_201 = v_200.view(1, 51, -1).transpose(1, 2)
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// v_207 = v_206.view(1, 51, -1).transpose(1, 2)
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// v_208 = torch.cat((v_195, v_201, v_207), dim=1)
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// ...
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// v_254 = v_223.view(1, 65, -1).transpose(1, 2)
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// v_255 = v_238.view(1, 65, -1).transpose(1, 2)
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// v_256 = v_253.view(1, 65, -1).transpose(1, 2)
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// v_257 = torch.cat((v_254, v_255, v_256), dim=1)
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// return v_257, v_208
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// D. modify area attention for dynamic shape inference
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// before:
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// v_95 = self.model_10_m_0_attn_qkv_conv(v_94)
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// v_96 = v_95.view(1, 2, 128, 400)
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// v_97, v_98, v_99 = torch.split(tensor=v_96, dim=2, split_size_or_sections=(32,32,64))
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// v_100 = torch.transpose(input=v_97, dim0=-2, dim1=-1)
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// v_101 = torch.matmul(input=v_100, other=v_98)
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// v_102 = (v_101 * 0.176777)
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// v_103 = F.softmax(input=v_102, dim=-1)
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// v_104 = torch.transpose(input=v_103, dim0=-2, dim1=-1)
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// v_105 = torch.matmul(input=v_99, other=v_104)
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// v_106 = v_105.view(1, 128, 20, 20)
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// v_107 = v_99.reshape(1, 128, 20, 20)
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// v_108 = self.model_10_m_0_attn_pe_conv(v_107)
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// v_109 = (v_106 + v_108)
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// v_110 = self.model_10_m_0_attn_proj_conv(v_109)
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// after:
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// v_95 = self.model_10_m_0_attn_qkv_conv(v_94)
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// v_96 = v_95.view(1, 2, 128, -1)
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// v_97, v_98, v_99 = torch.split(tensor=v_96, dim=2, split_size_or_sections=(32,32,64))
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// v_100 = torch.transpose(input=v_97, dim0=-2, dim1=-1)
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// v_101 = torch.matmul(input=v_100, other=v_98)
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// v_102 = (v_101 * 0.176777)
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// v_103 = F.softmax(input=v_102, dim=-1)
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// v_104 = torch.transpose(input=v_103, dim0=-2, dim1=-1)
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// v_105 = torch.matmul(input=v_99, other=v_104)
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// v_106 = v_105.view(1, 128, v_95.size(2), v_95.size(3))
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// v_107 = v_99.reshape(1, 128, v_95.size(2), v_95.size(3))
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// v_108 = self.model_10_m_0_attn_pe_conv(v_107)
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// v_109 = (v_106 + v_108)
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// v_110 = self.model_10_m_0_attn_proj_conv(v_109)
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// 5. re-export yolo11-pose torchscript
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// python3 -c 'import yolo11n_pose_pnnx; yolo11n_pose_pnnx.export_torchscript()'
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// 6. convert new torchscript with dynamic shape
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// pnnx yolo11n_pose_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 yolo11n_pose_pnnx.py.ncnn.param yolo11n_pose.ncnn.param
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// mv yolo11n_pose_pnnx.py.ncnn.bin yolo11n_pose.ncnn.bin
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// the out blob would be a 2-dim tensor with w=65 h=8400
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//
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// | bbox-reg 16 x 4 |score(1)|
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// +-----+-----+-----+-----+--------+
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// | dx0 | dy0 | dx1 | dy1 | 0.1 |
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// all /| | | | | |
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// boxes | .. | .. | .. | .. | 0.0 |
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// (8400)| | | | | . |
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// \| | | | | . |
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// +-----+-----+-----+-----+--------+
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//
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//
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// | pose (51) |
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// +-----------+
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// |0.1........|
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// all /| |
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// boxes |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 KeyPoint
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{
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cv::Point2f p;
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float prob;
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};
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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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std::vector<KeyPoint> keypoints;
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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, const ncnn::Mat& pred_points, 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_points = pred_points.w / 3;
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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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const ncnn::Mat pred_points_grid = pred_points.row_range(y * num_grid_x + x, 1).reshape(3, num_points);
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// find label with max score
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int label = 0;
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float score = sigmoid(pred_grid[reg_max_1 * 4]);
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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).clone();
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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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std::vector<KeyPoint> keypoints;
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for (int k = 0; k < num_points; k++)
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{
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KeyPoint keypoint;
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keypoint.p.x = (x + pred_points_grid.row(k)[0] * 2) * stride;
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keypoint.p.y = (y + pred_points_grid.row(k)[1] * 2) * stride;
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keypoint.prob = sigmoid(pred_points_grid.row(k)[2]);
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keypoints.push_back(keypoint);
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}
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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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obj.keypoints = keypoints;
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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 ncnn::Mat& pred_points, 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), pred_points.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_yolo11_pose(const cv::Mat& bgr, std::vector<Object>& objects)
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{
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ncnn::Net yolo11;
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yolo11.opt.use_vulkan_compute = true;
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// yolo11.opt.use_bf16_storage = true;
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// https://github.com/nihui/ncnn-android-yolo11/tree/master/app/src/main/assets
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yolo11.load_param("yolo11n_pose.ncnn.param");
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yolo11.load_model("yolo11n_pose.ncnn.bin");
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// yolo11.load_param("yolo11s_pose.ncnn.param");
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// yolo11.load_model("yolo11s_pose.ncnn.bin");
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// yolo11.load_param("yolo11m_pose.ncnn.param");
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// yolo11.load_model("yolo11m_pose.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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const float mask_threshold = 0.5f;
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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/yolo11.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 = yolo11.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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ncnn::Mat out_points;
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ex.extract("out1", out_points);
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std::vector<Object> proposals;
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generate_proposals(out, out_points, 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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if (count == 0)
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return 0;
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const int num_points = out_points.w / 3;
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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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for (int j = 0; j < num_points; j++)
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{
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objects[i].keypoints[j].p.x = (objects[i].keypoints[j].p.x - (wpad / 2)) / scale;
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objects[i].keypoints[j].p.y = (objects[i].keypoints[j].p.y - (hpad / 2)) / scale;
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}
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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(const cv::Mat& bgr, const std::vector<Object>& objects)
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{
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static const char* class_names[] = {"person"};
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static const 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),
|
|
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);
|
|
|
|
// draw bone
|
|
static const int joint_pairs[16][2] = {
|
|
{0, 1}, {1, 3}, {0, 2}, {2, 4}, {5, 6}, {5, 7}, {7, 9}, {6, 8}, {8, 10}, {5, 11}, {6, 12}, {11, 12}, {11, 13}, {12, 14}, {13, 15}, {14, 16}
|
|
};
|
|
static const cv::Scalar bone_colors[] = {
|
|
cv::Scalar(0, 255, 0),
|
|
cv::Scalar(0, 255, 0),
|
|
cv::Scalar(0, 255, 0),
|
|
cv::Scalar(0, 255, 0),
|
|
cv::Scalar(255, 128, 0),
|
|
cv::Scalar(255, 128, 0),
|
|
cv::Scalar(255, 128, 0),
|
|
cv::Scalar(255, 128, 0),
|
|
cv::Scalar(255, 128, 0),
|
|
cv::Scalar(255, 51, 255),
|
|
cv::Scalar(255, 51, 255),
|
|
cv::Scalar(255, 51, 255),
|
|
cv::Scalar(51, 153, 255),
|
|
cv::Scalar(51, 153, 255),
|
|
cv::Scalar(51, 153, 255),
|
|
cv::Scalar(51, 153, 255),
|
|
};
|
|
|
|
for (int j = 0; j < 16; j++)
|
|
{
|
|
const KeyPoint& p1 = obj.keypoints[joint_pairs[j][0]];
|
|
const KeyPoint& p2 = obj.keypoints[joint_pairs[j][1]];
|
|
|
|
if (p1.prob < 0.2f || p2.prob < 0.2f)
|
|
continue;
|
|
|
|
cv::line(image, p1.p, p2.p, bone_colors[j], 2);
|
|
}
|
|
|
|
// draw joint
|
|
for (size_t j = 0; j < obj.keypoints.size(); j++)
|
|
{
|
|
const KeyPoint& keypoint = obj.keypoints[j];
|
|
|
|
fprintf(stderr, "%.2f %.2f = %.5f\n", keypoint.p.x, keypoint.p.y, keypoint.prob);
|
|
|
|
if (keypoint.prob < 0.2f)
|
|
continue;
|
|
|
|
cv::circle(image, keypoint.p, 3, color, -1);
|
|
}
|
|
|
|
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_yolo11_pose(m, objects);
|
|
|
|
draw_objects(m, objects);
|
|
|
|
return 0;
|
|
}
|