Python+Yolov5跌倒检测摔倒检测人物目标行为人体特征识别

作者：羊村懒王 | 2024-02-23 04:28:29

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yolov5跌倒检测

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 random
 
from 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_size
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://'))
 
    # Directories
    save_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# Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'# half precision only supported on CUDA# Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_sizeif half:
        model.half()  # to FP16# Second-stage classifier
    classify = Falseif classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
 
    # Set Dataloader
    vid_path, vid_writer = None, Noneif webcam:
        view_img = check_imshow()
        cudnn.benchmark = True# set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
 
    # Get names and colors
    names = model.module.names ifhasattr(model, 'module') else model.names
    colors = [[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 once
    t0 = 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 fp16/32
        img /= 255.0# 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:
            img = img.unsqueeze(0)
 
        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]
 
        # Apply NMS
        pred = 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 >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
 
            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + (''if dataset.mode == 'image'elsef'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhiflen(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
 
 
                # Write resultsfor *xyxy, conf, cls inreversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (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 image
                        label = 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 video
                        vid_path = save_path
                        ifisinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
 
                        fourcc = 'mp4v'# output video codec
                        fps = 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()