、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qq1309399183 def run(poseweightsyolov7-w6-pose.pt,sourcefootball1.mp4,devicecpu,view_imgFalse, save_confFalse,line_thickness 3,hide_labelsFalse, hide_confTrue): frame_count 0 #count no of frames total_fps 0 #count total fps time_list [] #list to store time fps_list [] #list to store fps device select_device(opt.device) #select device half device.type ! cpu model attempt_load(poseweights, map_locationdevice) #Load model _ model.eval() names model.module.names if hasattr(model, module) else model.names # get class names if source.isnumeric() : cap cv2.VideoCapture(int(source)) #pass video to videocapture object else : cap cv2.VideoCapture(source) #pass video to videocapture object if (cap.isOpened() False): #check if videocapture not opened print(Error while trying to read video. Please check path again) raise SystemExit() else: frame_width int(cap.get(3)) #get video frame width frame_height int(cap.get(4)) #get video frame height vid_write_image letterbox(cap.read()[1], (frame_width), stride64, autoTrue)[0] #init videowriter resize_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 complete print(Frame {} Processing.format(frame_count1)) ret, frame cap.read() #get frame and success from video capture if ret: #if success is true, means frame exist orig_image frame #store frame image cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB) #convert frame to RGB image letterbox(image, (frame_width), stride64, autoTrue)[0] image_ image.copy() image transforms.ToTensor()(image) image torch.tensor(np.array([image.numpy()])) image image.to(device) #convert image data to device image image.float() #convert image to float precision (cpu) start_time time.time() #start time for fps calculation with torch.no_grad(): #get predictions output_data, _ model(image) output_data non_max_suppression_kpt(output_data, #Apply non max suppression 0.25, # Conf. Threshold. 0.65, # IoU Threshold. ncmodel.yaml[nc], # Number of classes. nkptmodel.yaml[nkpt], # Number of keypoints. kpt_labelTrue) 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 whwh for i, pose in enumerate(output_data): # detections per image if len(output_data): #check if no pose for c in pose[:, 5].unique(): # Print results n (pose[:, 5] c).sum() # detections per class print(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 frame c int(cls) # integer class kpts 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, labellabel, colorcolors(c, True), line_thicknessopt.line_thickness,kpt_labelTrue, kptskpts, steps3, orig_shapeim0.shape[:2])环境安装教程#1.克隆项目并进入#联系我然后git clone my_projcet2.linux创建虚拟环境python3 -m venv psestenv source psestenv/bin/activate3.如果windows用户请用这个python3 -m venv psestenv cd psestenv cd Scripts activate cd .. cd .. pip install --upgrade pippip installpip install -r requirements.txt结果展示更多视觉相关项目见专栏。如果对你有用欢迎私聊点赞交流​