关键点检测项目代码开源了！

Datawhale干货

作者：闫永强，算法工程师，Datawhale成员

本文通过自建手势数据集，利用YOLOv5s检测，然后通过开源数据集训练squeezenet进行手部关键点预测，最后通过指间的夹角算法来判断具体的手势，并显示出来。文章第四部分为用C++实现整体的ncnn推理（代码较长，可先马后看）

一、YOLOV5训练手部检测

训练及部署思路类似表情识别，需要将handpose数据集标签改成一类，只检测手部，简化流程，更易上手。

此部分数据集来源格物钛 https://gas.graviti.cn/dataset/datawhale/HandPose，具体的效果如图：

本教程所用训练环境：

系统环境：Ubuntu16.04

cuda版本：10.2

cudnn版本：7.6.5

pytorch版本：1.6.0

python版本：3.8

部署环境：

编译器：vs2015

依赖库：opencv ncnn

外设：普通USB摄像头

二、手部关节点检测

1、依赖环境

和YOLOV5训练手部检测一致。

2、检测数据集准备

该数据集包括网络图片以及数据集<Large-scale Multiview 3D Hand Pose Dataset> 筛选动作重复度低的图片，进行制作大概有5w张数据样本。其中<Large-scale Multiview 3D Hand Pose Dataset>数据集的官网地址：http://www.rovit.ua.es/dataset/mhpdataset/，其中标注文件示例如图2所示

制作好可以直接训练的数据集放在了开源数据平台格物钛：https://gas.graviti.com/dataset/datawhale/HandPoseKeyPoints

3、数据集在线使用

步骤1：安装格物钛平台SDK

pip install tensorbay

步骤2: 数据预处理

要使用已经处理好可以直接训练的数据集，步骤如下：

a. 打开本文对应数据集链接 https://gas.graviti.cn/dataset/datawhale/HandPose，在数据集页面，fork数据集到自己账户下；

b. 点击网页上方开发者工具 --> AccessKey --> 新建一个AccessKey --> 复制这个Key：KEY = 'Acces...........'

我们可以在不下载数据集的情况下，通过格物钛进行数据预处理，并将结果保存在本地。下面以使用HandPose数据集为例，使用HandPoseKeyPoints数据集操作同HandPose操作一样。

数据集开源地址：

https://gas.graviti.com/dataset/datawhale/HandPoseKeyPoints

完整项目代码：

https://github.com/datawhalechina/HandPoseKeyPoints

import numpy as np
from PIL import Image
from tensorbay import GAS
from tensorbay.dataset import Datasetdef read_gas_image(data):with data.open() as fp:image = Image.open(fp)image.load()return np.array(image)
# Authorize a GAS client.
gas = GAS('填入你的AccessKey')
# Get a dataset.
dataset = Dataset("HandPose", gas)dataset.enable_cache("data")
# List dataset segments.
segments = dataset.keys()# Get a segment by name
segment = dataset["train"]
for data in segment:# 图片数据image = read_gas_image(data)# 标签数据# Use the data as you like.for label_box2d in data.label.box2d:xmin = label_box2d.xminymin = label_box2d.yminxmax = label_box2d.xmaxymax = label_box2d.ymaxbox2d_category = label_box2d.categorybreak

数据集页面可视化效果：

#数据集划分
print(segments)
#  ("train",'val')print(len(dataset["train"]), "images in train dataset")
print(len(dataset["val"]), "images in valid dataset")# 1306 images in train dataset
# 14 images in valid dataset

4、关节点检测原理

关节点检测pipeline流程是：

1）输入图片对应手部的42个关节点坐标，

2）整个网络的backbone可以是任何分类网络，我这里采用的是squeezenet，然后损失函数是wingloss。

3）整个过程就是输入原图经过squeezenet网路计算出42个坐标值，然后通过wingloss进行回归计算更新权重，最后达到指定阈值，得出最终模型。

5、手部关节点训练

手部关节点算法采用开源代码参考地址：https://gitcode.net/EricLee/handpose_x

1）预训练模型

预训练模型在上述链接中有相应的网盘链接，可以直接下载。如果不想用预训练模型，可以直接从原始分类网络的原始权重开始训练。

2）模型的训练

以下是训练网络指定参数解释，其意义直接看图中注释就可以了。

训练只需要运行训练命令，指定自己想要指定的参数就可以跑起来了，如下图：

python train.py

6、手部关节点模型转换

1）安装依赖库

pip install onnx coremltools onnx-simplifier

2）导出onnx模型

python model2onnx.py --model_path squeezenet1_1-size-256-loss-wing_loss-model_epoch-2999.pth --model squeezenet1_1

会出现如下图所示

其中model2onnx.py文件是在上述链接工程目录下的。此时当前文件夹下会出现一个相应的onnx模型export。

3）用onnx-simplifer简化模型

为什么要简化？

因为在训练完深度学习的pytorch或者tensorflow模型后，有时候需要把模型转成onnx，但是很多时候，很多节点比如cast节点，Identity这些节点可能都不需要，需要进行简化，这样会方便把模型转成ncnn mnn等端侧部署模型格式。

python -m onnxsim squeezenet1_1_size-256.onnx squeezenet1_1_sim.onnx

会出现下图：

上述过程完成后就生成了简化版本的模型squeezenet1_1_sim.onnx。

4）把检测模型转换成ncnn模型

可以直接利用网页在线版本转换模型，地址：https://convertmodel.com/ 页面如图：

选择目标格式ncnn，选择输入格式onnx，点击选择，选择本地的简化版本的模型，然后选择转换，可以看到转换成功，下面两个就是转换成功的模型文件，如图。

三、利用关节点手势识别算法

通过对检测到的手部关节点之间的角度计算，可以实现简单的手势识别。例如：计算大拇指向量0-2和3-4之间的角度，它们之间的角度大于某一个角度阈值（经验值）定义为弯曲，小于某一个阈值（经验值）为伸直。具体效果如下面三张图。

四、工程推理部署整体实现

此关节点手势识别的整体过程总结：首先是利用目标检测模型检测到手的位置，然后利用手部关节点检测模型，检测手部关节点具体位置，绘制关节点，以及关节点之间的连线。再利用简单的向量之间角度进行手势识别。

整体的ncnn推理C++ 代码实现：

#include <string>
#include <vector>
#include "iostream"
#include<cmath>// ncnn
#include "ncnn/layer.h"
#include "ncnn/net.h"
#include "ncnn/benchmark.h"#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/imgproc.hpp>
#include "opencv2/opencv.hpp"  using namespace std;
using namespace cv;static ncnn::UnlockedPoolAllocator g_blob_pool_allocator;
static ncnn::PoolAllocator g_workspace_pool_allocator;static ncnn::Net yolov5;
static ncnn::Net hand_keyPoints;class YoloV5Focus : public ncnn::Layer
{
public:YoloV5Focus(){one_blob_only = true;}virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const{int w = bottom_blob.w;int h = bottom_blob.h;int channels = bottom_blob.c;int outw = w / 2;int outh = h / 2;int outc = channels * 4;top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);if (top_blob.empty())return -100;
#pragma omp parallel for num_threads(opt.num_threads)for (int p = 0; p < outc; p++){const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2);float* outptr = top_blob.channel(p);for (int i = 0; i < outh; i++){for (int j = 0; j < outw; j++){*outptr = *ptr;outptr += 1;ptr += 2;}ptr += w;}}return 0;}
};DEFINE_LAYER_CREATOR(YoloV5Focus)
struct Object
{float x;float y;float w;float h;int label;float prob;
};static inline float intersection_area(const Object& a, const Object& b)
{if (a.x > b.x + b.w || a.x + a.w < b.x || a.y > b.y + b.h || a.y + a.h < b.y){// no intersectionreturn 0.f;}float inter_width = std::min(a.x + a.w, b.x + b.w) - std::max(a.x, b.x);float inter_height = std::min(a.y + a.h, b.y + b.h) - std::max(a.y, b.y);return inter_width * inter_height;
}static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{int i = left;int j = right;float p = faceobjects[(left + right) / 2].prob;while (i <= j){while (faceobjects[i].prob > p)i++;while (faceobjects[j].prob < p)j--;if (i <= j){std::swap(faceobjects[i], faceobjects[j]);i++;j--;}}#pragma omp parallel sections{
#pragma omp section{if (left < j) qsort_descent_inplace(faceobjects, left, j);}
#pragma omp section{if (i < right) qsort_descent_inplace(faceobjects, i, right);}}
}static void qsort_descent_inplace(std::vector<Object>& faceobjects)
{if (faceobjects.empty())return;qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{picked.clear();const int n = faceobjects.size();std::vector<float> areas(n);for (int i = 0; i < n; i++){areas[i] = faceobjects[i].w * faceobjects[i].h;}for (int i = 0; i < n; i++){const Object& a = faceobjects[i];int keep = 1;for (int j = 0; j < (int)picked.size(); j++){const Object& b = faceobjects[picked[j]];float inter_area = intersection_area(a, b);float union_area = areas[i] + areas[picked[j]] - inter_area;// float IoU = inter_area / union_areaif (inter_area / union_area > nms_threshold)keep = 0;}if (keep)picked.push_back(i);}
}static inline float sigmoid(float x)
{return static_cast<float>(1.f / (1.f + exp(-x)));
}static void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)
{const int num_grid = feat_blob.h;int num_grid_x;int num_grid_y;if (in_pad.w > in_pad.h){num_grid_x = in_pad.w / stride;num_grid_y = num_grid / num_grid_x;}else{num_grid_y = in_pad.h / stride;num_grid_x = num_grid / num_grid_y;}const int num_class = feat_blob.w - 5;const int num_anchors = anchors.w / 2;for (int q = 0; q < num_anchors; q++){const float anchor_w = anchors[q * 2];const float anchor_h = anchors[q * 2 + 1];const ncnn::Mat feat = feat_blob.channel(q);for (int i = 0; i < num_grid_y; i++){for (int j = 0; j < num_grid_x; j++){const float* featptr = feat.row(i * num_grid_x + j);// find class index with max class scoreint class_index = 0;float class_score = -FLT_MAX;for (int k = 0; k < num_class; k++){float score = featptr[5 + k];if (score > class_score){class_index = k;class_score = score;}}float box_score = featptr[4];float confidence = sigmoid(box_score) * sigmoid(class_score);if (confidence >= prob_threshold){float dx = sigmoid(featptr[0]);float dy = sigmoid(featptr[1]);float dw = sigmoid(featptr[2]);float dh = sigmoid(featptr[3]);float pb_cx = (dx * 2.f - 0.5f + j) * stride;float pb_cy = (dy * 2.f - 0.5f + i) * stride;float pb_w = pow(dw * 2.f, 2) * anchor_w;float pb_h = pow(dh * 2.f, 2) * anchor_h;float x0 = pb_cx - pb_w * 0.5f;float y0 = pb_cy - pb_h * 0.5f;float x1 = pb_cx + pb_w * 0.5f;float y1 = pb_cy + pb_h * 0.5f;Object obj;obj.x = x0;obj.y = y0;obj.w = x1 - x0;obj.h = y1 - y0;obj.label = class_index;obj.prob = confidence;objects.push_back(obj);}}}}
}extern "C" {void release(){fprintf(stderr, "YoloV5Ncnn finished!");}int init_handKeyPoint() {ncnn::Option opt;opt.lightmode = true;opt.num_threads = 4;opt.blob_allocator = &g_blob_pool_allocator;opt.workspace_allocator = &g_workspace_pool_allocator;opt.use_packing_layout = true;fprintf(stderr, "handKeyPoint init!\n");hand_keyPoints.opt = opt;int ret_hand = hand_keyPoints.load_param("squeezenet1_1.param");  //squeezenet1_1   resnet_50if (ret_hand != 0) {std::cout << "ret_hand:" << ret_hand << std::endl;}ret_hand = hand_keyPoints.load_model("squeezenet1_1.bin");  //squeezenet1_1   resnet_50if (ret_hand != 0) {std::cout << "ret_hand:" << ret_hand << std::endl;}return 0;}int init(){fprintf(stderr, "YoloV5sNcnn init!\n");ncnn::Option opt;opt.lightmode = true;opt.num_threads = 4;opt.blob_allocator = &g_blob_pool_allocator;opt.workspace_allocator = &g_workspace_pool_allocator;opt.use_packing_layout = true;yolov5.opt = opt;yolov5.register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator);// init param{int ret = yolov5.load_param("yolov5s.param");  if (ret != 0){std::cout << "ret= " << ret << std::endl;fprintf(stderr, "YoloV5Ncnn, load_param failed");return -301;}}// init bin{int ret = yolov5.load_model("yolov5s.bin");  if (ret != 0){fprintf(stderr, "YoloV5Ncnn, load_model failed");return -301;}}return 0;}int detect(cv::Mat img, std::vector<Object> &objects){double start_time = ncnn::get_current_time();const int target_size = 320;const int width = img.cols;const int height = img.rows;int w = img.cols;int h = img.rows;float scale = 1.f;if (w > h){scale = (float)target_size / w;w = target_size;h = h * scale;}else{scale = (float)target_size / h;h = target_size;w = w * scale;}cv::resize(img, img, cv::Size(w, h));ncnn::Mat in = ncnn::Mat::from_pixels(img.data, ncnn::Mat::PIXEL_BGR2RGB, w, h);int wpad = (w + 31) / 32 * 32 - w;int hpad = (h + 31) / 32 * 32 - h;ncnn::Mat in_pad;ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);{const float prob_threshold = 0.4f;const float nms_threshold = 0.51f;const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f };in_pad.substract_mean_normalize(0, norm_vals);ncnn::Extractor ex = yolov5.create_extractor();ex.input("images", in_pad);std::vector<Object> proposals;{ncnn::Mat out;ex.extract("output", out);ncnn::Mat anchors(6);anchors[0] = 10.f;anchors[1] = 13.f;anchors[2] = 16.f;anchors[3] = 30.f;anchors[4] = 33.f;anchors[5] = 23.f;std::vector<Object> objects8;generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);proposals.insert(proposals.end(), objects8.begin(), objects8.end());}{ncnn::Mat out;ex.extract("771", out);ncnn::Mat anchors(6);anchors[0] = 30.f;anchors[1] = 61.f;anchors[2] = 62.f;anchors[3] = 45.f;anchors[4] = 59.f;anchors[5] = 119.f;std::vector<Object> objects16;generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16);proposals.insert(proposals.end(), objects16.begin(), objects16.end());}{ncnn::Mat out;ex.extract("791", out);ncnn::Mat anchors(6);anchors[0] = 116.f;anchors[1] = 90.f;anchors[2] = 156.f;anchors[3] = 198.f;anchors[4] = 373.f;anchors[5] = 326.f;std::vector<Object> objects32;generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32);proposals.insert(proposals.end(), objects32.begin(), objects32.end());}// sort all proposals by score from highest to lowestqsort_descent_inplace(proposals);std::vector<int> picked;nms_sorted_bboxes(proposals, picked, nms_threshold);int count = picked.size();objects.resize(count);for (int i = 0; i < count; i++){objects[i] = proposals[picked[i]];float x0 = (objects[i].x - (wpad / 2)) / scale;float y0 = (objects[i].y - (hpad / 2)) / scale;float x1 = (objects[i].x + objects[i].w - (wpad / 2)) / scale;float y1 = (objects[i].y + objects[i].h - (hpad / 2)) / scale;// clipx0 = std::max(std::min(x0, (float)(width - 1)), 0.f);y0 = std::max(std::min(y0, (float)(height - 1)), 0.f);x1 = std::max(std::min(x1, (float)(width - 1)), 0.f);y1 = std::max(std::min(y1, (float)(height - 1)), 0.f);objects[i].x = x0;objects[i].y = y0;objects[i].w = x1;objects[i].h = y1;}}return 0;}
}static const char* class_names[] = {"hand"};void draw_face_box(cv::Mat& bgr, std::vector<Object> object)
{for (int i = 0; i < object.size(); i++){const auto obj = object[i];cv::rectangle(bgr, cv::Point(obj.x, obj.y), cv::Point(obj.w, obj.h), cv::Scalar(0, 255, 0), 3, 8, 0);std::cout << "label:" << class_names[obj.label] << std::endl;string emoji_path = "emoji\\" + string(class_names[obj.label]) + ".png";cv::Mat logo = cv::imread(emoji_path);if (logo.empty()) {std::cout << "imread logo failed!!!" << std::endl;return;}resize(logo, logo, cv::Size(80, 80));cv::Mat imageROI = bgr(cv::Range(obj.x, obj.x + logo.rows), cv::Range(obj.y, obj.y + logo.cols));logo.copyTo(imageROI);}}static int detect_resnet(const cv::Mat& bgr,std::vector<float>& output) {ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data,ncnn::Mat::PIXEL_RGB,bgr.cols,bgr.rows,256,256);const float mean_vals[3] = { 104.f,117.f,123.f };//const float norm_vals[3] = { 1/255.f, 1/255.f, 1/255.f };//1/255.fin.substract_mean_normalize(mean_vals, norm_vals);  //0  mean_vals, norm_valsncnn::Extractor ex = hand_keyPoints.create_extractor();ex.input("input", in);ncnn::Mat out;ex.extract("output",out);std::cout << "out.w:" << out.w <<" out.h: "<< out.h <<std::endl;output.resize(out.w);for (int i = 0; i < out.w; i++) {output[i] = out[i];}return 0;
}
float vector_2d_angle(cv::Point p1,cv::Point p2) {//求解二维向量的角度float angle = 0.0;try {float radian_value = acos((p1.x*p2.x+p1.y*p2.y)/(sqrt(p1.x*p1.x+p1.y*p1.y)*sqrt(p2.x*p2.x+p2.y*p2.y)));angle = 180*radian_value/3.1415;}catch(...){angle = 65535.;}if (angle > 180.) {angle = 65535.;}return angle;
}std::vector<float> hand_angle(std::vector<int>& hand_x,std::vector<int>& hand_y) {//获取对应手相关向量的二维角度，根据角度确定手势float angle = 0.0;std::vector<float> angle_list;//------------------- thumb 大拇指角度angle = vector_2d_angle(cv::Point((hand_x[0]-hand_x[2]),(hand_y[0]-hand_y[2])),cv::Point((hand_x[3]-hand_x[4]),(hand_y[3]-hand_y[4])));angle_list.push_back(angle);//--------------------index 食指角度angle = vector_2d_angle(cv::Point((hand_x[0] - hand_x[6]), (hand_y[0] - hand_y[6])), cv::Point((hand_x[7] - hand_x[8]), (hand_y[7] - hand_y[8])));angle_list.push_back(angle);//---------------------middle  中指角度angle = vector_2d_angle(cv::Point((hand_x[0] - hand_x[10]), (hand_y[0] - hand_y[10])), cv::Point((hand_x[11] - hand_x[12]), (hand_y[11] - hand_y[12])));angle_list.push_back(angle);//----------------------ring 无名指角度angle = vector_2d_angle(cv::Point((hand_x[0] - hand_x[14]), (hand_y[0] - hand_y[14])), cv::Point((hand_x[15] - hand_x[16]), (hand_y[15] - hand_y[16])));angle_list.push_back(angle);//-----------------------pink 小拇指角度angle = vector_2d_angle(cv::Point((hand_x[0] - hand_x[18]), (hand_y[0] - hand_y[18])), cv::Point((hand_x[19] - hand_x[20]), (hand_y[19] - hand_y[20])));angle_list.push_back(angle);return angle_list;
}string h_gestrue(std::vector<float>& angle_lists) {//二维约束的方式定义手势//fist five gun love one six three thumbup yeahfloat thr_angle = 65.;float thr_angle_thumb = 53.;float thr_angle_s = 49.;string gesture_str;bool flag = false;for (int i = 0; i < angle_lists.size(); i++) {if (abs(65535 - int(angle_lists[i])) > 0) {flag = true;   //进入手势判断标识}}std::cout << "flag:" << flag << std::endl;if (flag) {if (angle_lists[0] > thr_angle_thumb && angle_lists[1] > thr_angle && angle_lists[2] > thr_angle && angle_lists[3] > thr_angle && angle_lists[4] > thr_angle) {gesture_str = "fist";}else if (angle_lists[0] < thr_angle_s && angle_lists[1] < thr_angle_s&& angle_lists[2] < thr_angle_s && angle_lists[3] < thr_angle_s&& angle_lists[4] < thr_angle_s) {gesture_str = "five";}else if(angle_lists[0] < thr_angle_s && angle_lists[1] < thr_angle_s&& angle_lists[2] > thr_angle && angle_lists[3] > thr_angle&& angle_lists[4] > thr_angle){gesture_str = "gun";}else if (angle_lists[0] < thr_angle_s && angle_lists[1] < thr_angle_s&& angle_lists[2] > thr_angle && angle_lists[3] > thr_angle&& angle_lists[4] < thr_angle_s) {gesture_str = "love";}else if (angle_lists[0] < 5 && angle_lists[1] < thr_angle_s&& angle_lists[2] > thr_angle && angle_lists[3] > thr_angle&& angle_lists[4] > thr_angle) {gesture_str = "one";}else if (angle_lists[0] < thr_angle_s && angle_lists[1] > thr_angle&& angle_lists[2] > thr_angle && angle_lists[3] > thr_angle&& angle_lists[4] < thr_angle_s) {gesture_str = "six";}else if (angle_lists[0] > thr_angle_thumb && angle_lists[1] < thr_angle_s&& angle_lists[2] < thr_angle_s && angle_lists[3] < thr_angle_s&& angle_lists[4] > thr_angle) {gesture_str = "three";}else if (angle_lists[0] < thr_angle_s && angle_lists[1] > thr_angle&& angle_lists[2] > thr_angle && angle_lists[3] > thr_angle&& angle_lists[4] > thr_angle) {gesture_str = "thumbUp";}else if (angle_lists[0] > thr_angle_thumb && angle_lists[1] < thr_angle_s&& angle_lists[2] < thr_angle_s && angle_lists[3] > thr_angle&& angle_lists[4] > thr_angle) {gesture_str = "two";}}return gesture_str;
}int main()
{Mat frame;VideoCapture capture(0);init();init_handKeyPoint();while (true){capture >> frame;            if (!frame.empty()) {          std::vector<Object> objects;detect(frame, objects);std::vector<float> hand_output;for (int j = 0; j < objects.size(); ++j) {cv::Mat handRoi;int x, y, w, h;try {x = (int)objects[j].x < 0 ? 0 : (int)objects[j].x;y = (int)objects[j].y < 0 ? 0 : (int)objects[j].y;w = (int)objects[j].w < 0 ? 0 : (int)objects[j].w;h = (int)objects[j].h < 0 ? 0 : (int)objects[j].h;if (w > frame.cols){w = frame.cols;}if (h > frame.rows) {h = frame.rows;}}catch (cv::Exception e) {}//把手区域向外扩30个像素x = max(0, x - 30);y = max(0, y - 30);int w_ = min(w - x + 30, 640);int h_ = min(h - y + 30, 480);cv::Rect roi(x,y,w_,h_);handRoi = frame(roi);cv::resize(handRoi,handRoi,cv::Size(256,256));//detect_resnet(handRoi, hand_output);detect_resnet(handRoi, hand_output);std::vector<float> angle_lists;string gesture_string;std::vector<int> hand_points_x;  //std::vector<int> hand_points_y;for (int k = 0; k < hand_output.size()/2; k++) {int x = int(hand_output[k * 2 + 0] * handRoi.cols);//+int(roi.x)-1;int y = int(hand_output[k * 2 + 1] * handRoi.rows);// +int(roi.y) - 1;//x1 = x1 < 0 ? abs(x1) : x1;//x2 = x2 < 0 ? abs(x2) : x2;hand_points_x.push_back(x);hand_points_y.push_back(y);std::cout << "x1: " << x << " x2: " << y << std::endl;cv::circle(handRoi, cv::Point(x,y), 3, (0, 255, 0), 3);cv::circle(handRoi, cv::Point(x,y), 3, (0, 255, 0), 3);}cv::line(handRoi, cv::Point(hand_points_x[0], hand_points_y[0]), cv::Point(hand_points_x[1], hand_points_y[1]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[1], hand_points_y[1]), cv::Point(hand_points_x[2], hand_points_y[2]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[2], hand_points_y[2]), cv::Point(hand_points_x[3], hand_points_y[3]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[3], hand_points_y[3]), cv::Point(hand_points_x[4], hand_points_y[4]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[0], hand_points_y[0]), cv::Point(hand_points_x[5], hand_points_y[5]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[5], hand_points_y[5]), cv::Point(hand_points_x[6], hand_points_y[6]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[6], hand_points_y[6]), cv::Point(hand_points_x[7], hand_points_y[7]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[7], hand_points_y[7]), cv::Point(hand_points_x[8], hand_points_y[8]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[0], hand_points_y[0]), cv::Point(hand_points_x[9], hand_points_y[9]), cv::Scalar(0, 0, 255), 3);cv::line(handRoi, cv::Point(hand_points_x[9], hand_points_y[9]), cv::Point(hand_points_x[10], hand_points_y[10]), cv::Scalar(0, 0, 255), 3);cv::line(handRoi, cv::Point(hand_points_x[10], hand_points_y[10]), cv::Point(hand_points_x[11], hand_points_y[11]), cv::Scalar(0, 0, 255), 3);cv::line(handRoi, cv::Point(hand_points_x[11], hand_points_y[11]), cv::Point(hand_points_x[12], hand_points_y[12]), cv::Scalar(0, 0, 255), 3);cv::line(handRoi, cv::Point(hand_points_x[0], hand_points_y[0]), cv::Point(hand_points_x[13], hand_points_y[13]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[13], hand_points_y[13]), cv::Point(hand_points_x[14], hand_points_y[14]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[14], hand_points_y[14]), cv::Point(hand_points_x[15], hand_points_y[15]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[15], hand_points_y[15]), cv::Point(hand_points_x[16], hand_points_y[16]), cv::Scalar(255, 0, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[0], hand_points_y[0]), cv::Point(hand_points_x[17], hand_points_y[17]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[17], hand_points_y[17]), cv::Point(hand_points_x[18], hand_points_y[18]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[18], hand_points_y[18]), cv::Point(hand_points_x[19], hand_points_y[19]), cv::Scalar(0, 255, 0), 3);cv::line(handRoi, cv::Point(hand_points_x[19], hand_points_y[19]), cv::Point(hand_points_x[20], hand_points_y[20]), cv::Scalar(0, 255, 0), 3);angle_lists =  hand_angle(hand_points_x, hand_points_y);gesture_string = h_gestrue(angle_lists);std::cout << "getsture_string:" << gesture_string << std::endl;cv::putText(handRoi,gesture_string,cv::Point(30,30),cv::FONT_HERSHEY_COMPLEX,1, cv::Scalar(0, 255, 255), 1, 1, 0);cv::imshow("handRoi", handRoi);cv::waitKey(10);angle_lists.clear();hand_points_x.clear();hand_points_y.clear();}}if (cv::waitKey(20) == 'q')    break;}capture.release();     return 0;
}

五、视频展示

整理不易，点赞三连↓