900字范文 > YOLOv7姿态估计pose estimation(姿态估计+目标检测+跟踪)

YOLOv7姿态估计pose estimation(姿态估计+目标检测+跟踪)

时间：2022-06-15 05:23:30

概述

YOLOv7姿态估计：一种快速准确的人体姿态估计模型

人体姿态估计是计算机视觉中的一项重要任务，具有各种应用，例如动作识别、人机交互和监控。近年来，基于深度学习的方法在人体姿态估计方面取得了显著的性能。其中最流行的深度学习方法之一是YOLOv7姿态估计模型。

算法

YOLOv7姿态估计模型是YOLOv7目标检测模型的扩展，使用单个神经网络同时预测图像中多个物体的边界框和类别概率。在YOLOv7姿态估计模型中，网络预测每个人的关键点位置，从而可以用于估计人的姿态。

网络

YOLOv7姿态估计模型基于深度卷积神经网络架构，由多个卷积层、最大池化和全连接层组成。网络接受输入图像并产生特征图，然后用于预测每个人的关键点位置。

数据集

YOLOv7姿态估计模型使用大型数据集进行训练，例如COCO（通用对象上下文）和MPII（马克斯·普朗克计算机科学研究所），这些数据集包含成千上万的人在各种姿势和环境中的注释图像。该模型使用监督学习和数据增强技术进行训练，例如随机缩放、旋转和平移输入图像。

优势

YOLOv7姿态估计模型的一个关键优势是其速度和准确性。该模型能够实时估计多个人的姿态，使其适用于人机交互和监控等应用。此外，该模型在COCO和MPII等基准数据集上实现了最先进的性能，展示了其准确性和鲁棒性。

结论

总之，YOLOv7姿态估计模型是一种快速准确的基于深度学习的人体姿态估计模型。其能够实时估计多个人的姿态，使其适用于各种应用，而其在基准数据集上的最先进性能证明了其有效性。随着深度学习的不断发展，我们可以预期在人体姿态估计方面会有进一步的改进，而YOLOv7姿态估计模型很可能在这些发展中发挥重要作用。

代码

#全部代码可私信或者qq1309399183def run(poseweights="yolov7-w6-pose.pt",source="football1.mp4",device='cpu',view_img=False,save_conf=False,line_thickness = 3,hide_labels=False, hide_conf=True):frame_count = 0 #count no of framestotal_fps = 0 #count total fpstime_list = [] #list to store timefps_list = [] #list to store fpsdevice = select_device(opt.device) #select devicehalf = device.type != 'cpu'model = attempt_load(poseweights, map_location=device) #Load model_ = model.eval()names = model.module.names if hasattr(model, 'module') else model.names # get class namesif source.isnumeric() : cap = cv2.VideoCapture(int(source)) #pass video to videocapture objectelse :cap = cv2.VideoCapture(source) #pass video to videocapture objectif (cap.isOpened() == False): #check if videocapture not openedprint('Error while trying to read video. Please check path again')raise SystemExit()else:frame_width = int(cap.get(3)) #get video frame widthframe_height = int(cap.get(4)) #get video frame heightvid_write_image = letterbox(cap.read()[1], (frame_width), stride=64, auto=True)[0] #init videowriterresize_height, resize_width = vid_write_image.shape[:2]out_video_name = f"{source.split('/')[-1].split('.')[0]}"out = cv2.VideoWriter(f"{source}_keypoint.mp4",cv2.VideoWriter_fourcc(*'mp4v'), 30,(resize_width, resize_height))while(cap.isOpened): #loop until cap opened or video not completeprint("Frame {} Processing".format(frame_count+1))ret, frame = cap.read() #get frame and success from video captureif ret: #if success is true, means frame existorig_image = frame #store frameimage = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB) #convert frame to RGBimage = letterbox(image, (frame_width), stride=64, auto=True)[0]image_ = image.copy()image = transforms.ToTensor()(image)image = torch.tensor(np.array([image.numpy()]))image = image.to(device) #convert image data to deviceimage = image.float() #convert image to float precision (cpu)start_time = time.time() #start time for fps calculationwith torch.no_grad(): #get predictionsoutput_data, _ = model(image)output_data = non_max_suppression_kpt(output_data, #Apply non max suppression0.25, # Conf. Threshold.0.65, # IoU Threshold.nc=model.yaml['nc'], # Number of classes.nkpt=model.yaml['nkpt'], # Number of keypoints.kpt_label=True)output = output_to_keypoint(output_data)im0 = image[0].permute(1, 2, 0) * 255 # Change format [b, c, h, w] to [h, w, c] for displaying the image.im0 = im0.cpu().numpy().astype(np.uint8)im0 = cv2.cvtColor(im0, cv2.COLOR_RGB2BGR) #reshape image format to (BGR)gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwhfor i, pose in enumerate(output_data): # detections per imageif len(output_data): #check if no posefor c in pose[:, 5].unique(): # Print resultsn = (pose[:, 5] == c).sum() # detections per classprint("No of Objects in Current Frame : {}".format(n))for det_index, (*xyxy, conf, cls) in enumerate(reversed(pose[:,:6])): #loop over poses for drawing on framec = int(cls) # integer classkpts = pose[det_index, 6:]label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')plot_one_box_kpt(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness,kpt_label=True, kpts=kpts, steps=3, orig_shape=im0.shape[:2])