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，而后通过开源数据集训练squeezenet进行手部关键点预测，最初通过指间的夹角算法来判断具体的手势，并显示进去。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
训练及部署思路相似表情辨认，须要将handpose数据集标签改成一类，只检测手部，简化流程，更易上手。
此局部数据集起源格物钛 https://gas.graviti.cn/datase...，具体的成果如图：
本教程所用训练环境：
零碎环境：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：一般USB摄像头
二、手部关节点检测
1、依赖环境
和YOLOV5训练手部检测统一。
2、检测数据集筹备
该数据集包含网络图片以及数据集<Large-scale Multiview 3D Hand Pose Dataset> 筛选动作反复度低的图片，进行制作大略有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/datase...，其中标注文件示例如图2所示
制作好能够间接训练的数据集放在了开源数据平台格物钛：https://gas.graviti.com/datas...
3、数据集在线应用
步骤1：装置格物钛平台SDK
pip install tensorbay步骤2: 数据预处理
要应用曾经解决好能够间接训练的数据集，步骤如下：
a. 关上本文对应数据集链接 https://gas.graviti.cn/datase...，在数据集页面，fork数据集到本人账户下；
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...........'
咱们能够在不下载数据集的状况下，通过格物钛进行数据预处理，并将后果保留在本地。上面以应用HandPose数据集为例，应用HandPoseKeyPoints数据集操作同HandPose操作一样。
数据集开源地址：
https://gas.graviti.com/datas...
残缺我的项目代码：
https://github.com/datawhalec...
import numpy as npfrom PIL import Imagefrom tensorbay import GASfrom 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 namesegment = 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.xmin ymin = label_box2d.ymin xmax = label_box2d.xmax ymax = label_box2d.ymax box2d_category = label_box2d.category break数据集页面可视化成果：
#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 dataset4、关节点检测原理
关节点检测pipeline流程是：
1）输出图片对应手部的42个关节点坐标，
2）整个网络的backbone能够是任何分类网络，我这里采纳的是squeezenet，而后损失函数是wingloss。
3）整个过程就是输出原图通过squeezenet网路计算出42个坐标值，而后通过wingloss进行回归计算更新权重，最初达到指定阈值，得出最终模型。
5、手部关节点训练
手部关节点算法采纳开源代码参考地址：https://gitcode.net/EricLee/h...
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，能够间接下载。如果不想用预训练模型，能够间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，其意义间接看图中正文就能够了。
训练只须要运行训练命令，指定本人想要指定的参数就能够跑起来了，如下图：
6、手部关节点模型转换
1）装置依赖库
pip install onnx coremltools onnx-simplifier2）导出onnx模型
python model2onnx.py --model_path squeezenet1_1-size-256-loss-wing_loss-model_epoch-2999.pth --model squeezenet1_1会呈现如下图所示
其中model2onnx.py文件是在上述链接工程目录下的。此时以后文件夹下会呈现一个相应的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，有时候须要把模型转成onnx，然而很多时候，很多节点比方cast节点，Identity这些节点可能都不须要，须要进行简化，这样会不便把模型转成ncnn mnn等端侧部署模型格局。
python -m onnxsim squeezenet1_1_size-256.onnx squeezenet1_1_sim.onnx会呈现下图：
上述过程实现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
能够间接利用网页在线版本转换模型，地址：https://convertmodel.com/ 页面如图：
抉择指标格局ncnn，抉择输出格局onnx，点击抉择，抉择本地的简化版本的模型，而后抉择转换，能够看到转换胜利，上面两个就是转换胜利的模型文件，如图。
三、利用关节点手势辨认算法
通过对检测到的手部关节点之间的角度计算，能够实现简略的手势辨认。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）定义为蜿蜒，小于某一个阈值（经验值）为蜷缩。具体成果如上面三张图。
四、工程推理部署整体实现
此关节点手势辨认的整体过程总结：首先是利用指标检测模型检测到手的地位，而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。再利用简略的向量之间角度进行手势辨认。
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 intersection return 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_area if (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 score int 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_50 if (ret_hand != 0) { std::cout << "ret_hand:" << ret_hand << std::endl; } ret_hand = hand_keyPoints.load_model("squeezenet1_1.bin"); //squeezenet1_1 resnet_50 if (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 lowest qsort_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; // clip x0 = 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.f in.substract_mean_normalize(mean_vals, norm_vals); //0 mean_vals, norm_vals ncnn::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) { //获取对应手相干向量的二维角度，依据角度确定手势 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) { //二维束缚的形式定义手势 //fist five gun love one six three thumbup yeah float 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;} 更多信息请拜访格物钛官网