def __init__(self, parent=None):
super().__init__(parent) #父类的构造函数
self.model, flag = net.load_model(use_gpu=False) #加载模型
self.timer_camera = QtCore.QTimer() #定义定时器，用于控制显示视频的帧率
self.cap = cv2.VideoCapture() #视频流
self.CAM_NUM = 0 #为0时表示视频流来自笔记本内置摄像头
self.danceMatching = DanceMatching() #实例化一个姿态检测类，也是线程类
self.setupUi()
self.slot_init()
def startGame(self):
use_gpu = False#设置是否使用GPU
model , flag = net.load_model(use_gpu = use_gpu)#加载模型
gl.GL_Model = model#将模型放在全局变量中
def load_model(self):
'''
加载模型 并弹出提示
'''
QMessageBox.information(self, "提示", "模型即将加载,请等待...初次使用软件会自动后台下载模型...")
self.model, flag = net.load_model(use_gpu=False)
涉及到的文件有的地方写的还不够完善
后期需要改进
'''
import os
import cv2 as cv
from score import tools
from model import net
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = str(0)
#展示图片和玩家图片地址
showerUrl = "./image/test.jpeg"
playerUrl = "./image/test3.jpeg"
showerImage = cv.imread(showerUrl)
playerImage = cv.imread(playerUrl)
#加载模型并获得预测结果
use_gpu = False
model = net.load_model(use_gpu=use_gpu)
showerPre, showerImage = net.detection(model, showerImage, use_gpu)
playerPre, playerImage = net.detection(model, playerImage, use_gpu)
#归一化
showerDis, showerSkeleton = tools.normalization(showerPre['pred_coords'][0])
playerDis, playerSkeleton = tools.normalization(playerPre['pred_coords'][0])
#匹配计算分数并输出结果
score = tools.matching(playerSkeleton, showerSkeleton, use_gpu=use_gpu)
print("匹配得分{}".format(score))