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Python+Yolov5跌倒检测摔倒检测人物目标行为人体特征识别

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前言

这篇博客针对<<Python+Yolov5跌倒摔倒人体特征识别>>编写代码，代码整洁，规则，易读。学习与应用推荐首选。

文章目录

一、所需工具软件

二、使用步骤

1. 引入库

2. 识别图像特征

3. 参数设置

4. 运行结果

三、在线协助

一、所需工具软件

1. Pycharm, Python

2. Qt, OpenCV

二、使用步骤

1.引入库

代码如下（示例）：

import cv2
import torch
from numpy import randomfrom models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized

2.识别图像特征

代码如下（示例）：

defdetect(save_img=False):source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_sizewebcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(('rtsp://', 'rtmp://', 'http://'))# Directoriessave_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run(save_dir / 'labels'if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# Initializeset_logging()device = select_device(opt.device)half = device.type != 'cpu'# half precision only supported on CUDA# Load modelmodel = attempt_load(weights, map_location=device)  # load FP32 modelstride = int(model.stride.max())  # model strideimgsz = check_img_size(imgsz, s=stride)  # check img_sizeif half:model.half()  # to FP16# Second-stage classifierclassify = Falseif classify:modelc = load_classifier(name='resnet101', n=2)  # initializemodelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()# Set Dataloadervid_path, vid_writer = None, Noneif webcam:view_img = check_imshow()cudnn.benchmark = True# set True to speed up constant image size inferencedataset = LoadStreams(source, img_size=imgsz, stride=stride)else:save_img = Truedataset = LoadImages(source, img_size=imgsz, stride=stride)# Get names and colorsnames = model.module.names ifhasattr(model, 'module') else model.namescolors = [[random.randint(0, 255) for _ inrange(3)] for _ in names]# Run inferenceif device.type != 'cpu':model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run oncet0 = time.time()for path, img, im0s, vid_cap in dataset:img = torch.from_numpy(img).to(device)img = img.half() if half else img.float()  # uint8 to fp16/32img /= 255.0# 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)# Inferencet1 = time_synchronized()pred = model(img, augment=opt.augment)[0]# Apply NMSpred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)t2 = time_synchronized()# Apply Classifierif classify:pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det inenumerate(pred):  # detections per imageif webcam:  # batch_size >= 1p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.countelse:p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)p = Path(p)  # to Pathsave_path = str(save_dir / p.name)  # img.jpgtxt_path = str(save_dir / 'labels' / p.stem) + (''if dataset.mode == 'image'elsef'_{frame}')  # img.txts += '%gx%g ' % img.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhiflen(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Write resultsfor *xyxy, conf, cls inreversed(det):if save_txt:  # Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label formatwithopen(txt_path + '.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or view_img:  # Add bbox to imagelabel = f'{names[int(cls)]}{conf:.2f}'plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)# Print time (inference + NMS)print(f'{s}Done. ({t2 - t1:.3f}s)')# Save results (image with detections)if save_img:if dataset.mode == 'image':cv2.imwrite(save_path, im0)else:  # 'video'if vid_path != save_path:  # new videovid_path = save_pathifisinstance(vid_writer, cv2.VideoWriter):vid_writer.release()  # release previous video writerfourcc = 'mp4v'# output video codecfps = vid_cap.get(cv2.CAP_PROP_FPS)w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))vid_writer.write(im0)if save_txt or save_img:s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"if save_txt else''print(f"Results saved to {save_dir}{s}")print(f'Done. ({time.time() - t0:.3f}s)')print(opt)check_requirements()with torch.no_grad():if opt.update:  # update all models (to fix SourceChangeWarning)for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:detect()strip_optimizer(opt.weights)else:detect()

3.参数定义

代码如下（示例）：

if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default='yolov5_best_road_crack_recog.pt', help='model.pt path(s)')parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')parser.add_argument('--view-img', action='store_true', help='display results')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--classes', nargs='+', type=int, default='0', help='filter by class: --class 0, or --class 0 2 3')parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')parser.add_argument('--augment', action='store_true', help='augmented inference')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default='runs/detect', help='save results to project/name')parser.add_argument('--name', default='exp', help='save results to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')opt = parser.parse_args()print(opt)check_requirements()with torch.no_grad():if opt.update:  # update all models (to fix SourceChangeWarning)for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:detect()strip_optimizer(opt.weights)else:detect()