基于YOLOv5的视频检测的打架模型

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.  Usage - sources:  $ python detect.py --weights yolov5s.pt --source 0                               # webcam  img.jpg                         # image  vid.mp4                         # video  screen                          # screenshot  path/                           # directory  list.txt                        # list of images  list.streams                    # list of streams  'path/*.jpg'                    # glob  'https://youtu.be/LNwODJXcvt4'  # YouTube  'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream  Usage - formats:  $ python detect.py --weights yolov5s.pt                 # PyTorch  yolov5s.torchscript        # TorchScript  yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn  yolov5s_openvino_model     # OpenVINO  yolov5s.engine             # TensorRT  yolov5s.mlmodel            # CoreML (macOS-only)  yolov5s_saved_model        # TensorFlow SavedModel  yolov5s.pb                 # TensorFlow GraphDef  yolov5s.tflite             # TensorFlow Lite  yolov5s_edgetpu.tflite     # TensorFlow Edge TPU  yolov5s_paddle_model       # PaddlePaddle """  import argparse import csv import os import platform import sys from pathlib import Path import torch
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative  from ultralytics.utils.plotting import Annotator, colors, save_one_box from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import (
    LOGGER,
    Profile,
    check_file,
    check_img_size,
    check_imshow,
    check_requirements,
    colorstr,
    cv2,
    increment_path,
    non_max_suppression,
    print_args,
    scale_boxes,
    strip_optimizer,
    xyxy2xywh,
) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run(
    weights=ROOT / "yolov5s.pt", # model path or triton URL  source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)  data=ROOT / "data/coco128.yaml", # dataset.yaml path  imgsz=(640, 640), # inference size (height, width)  conf_thres=0.25, # confidence threshold  iou_thres=0.45, # NMS IOU threshold  max_det=1000, # maximum detections per image  device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu  view_img=False, # show results  save_txt=False, # save results to *.txt  save_csv=False, # save results in CSV format  save_conf=False, # save confidences in --save-txt labels  save_crop=False, # save cropped prediction boxes  nosave=False, # do not save images/videos  classes=None, # filter by class: --class 0, or --class 0 2 3  agnostic_nms=False, # class-agnostic NMS  augment=False, # augmented inference  visualize=False, # visualize features  update=False, # update all models  project=ROOT / "runs/detect", # save results to project/name  name="exp", # save results to project/name  exist_ok=False, # existing project/name ok, do not increment  line_thickness=3, # bounding box thickness (pixels)  hide_labels=False, # hide labels  hide_conf=False, # hide confidences  half=False, # use FP16 half-precision inference  dnn=False, # use OpenCV DNN for ONNX inference  vid_stride=1, # video frame-rate stride ):
    source = str(source)
    save_img = not nosave and not source.endswith(".txt") # save inference images  is_file = Path(source).suffix[1:] in (IMG_FORMATS   VID_FORMATS)
    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
    screenshot = source.lower().startswith("screen") if is_url and is_file:
        source = check_file(source) # download   # Directories  save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run  (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir   # Load model  device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride) # check image size   # Dataloader  bs = 1 # batch_size  if webcam:
        view_img = check_imshow(warn=True)
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset) elif screenshot:
        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
    vid_path, vid_writer = [None] * bs, [None] * bs # Run inference  model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup  seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) for path, im, im0s, vid_cap, s in dataset: with dt[0]:
            im = torch.from_numpy(im).to(model.device)
            im = im.half() if model.fp16 else im.float() # uint8 to fp16/32  im /= 255 # 0 - 255 to 0.0 - 1.0  if len(im.shape) == 3:
                im = im[None] # expand for batch dim  if model.xml and im.shape[0] > 1:
                ims = torch.chunk(im, im.shape[0], 0) # Inference  with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False  if model.xml and im.shape[0] > 1:
                pred = None  for image in ims: if pred is None:
                        pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) else:
                        pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
                pred = [pred, None] else:
                pred = model(im, augment=augment, visualize=visualize) # NMS  with dt[2]:
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Second-stage classifier (optional)  # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)   # Define the path for the CSV file  csv_path = save_dir / "predictions.csv"   # Create or append to the CSV file  def write_to_csv(image_name, prediction, confidence): """Writes prediction data for an image to a CSV file, appending if the file exists."""  data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence} with open(csv_path, mode="a", newline="") as f:
                writer = csv.DictWriter(f, fieldnames=data.keys()) if not csv_path.is_file():
                    writer.writeheader()
                writer.writerow(data) # Process predictions  for i, det in enumerate(pred): # per image  seen  = 1  if webcam: # batch_size >= 1  p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s  = f"{i}: "  else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
            p = Path(p) # to Path  save_path = str(save_dir / p.name) # im.jpg  txt_path = str(save_dir / "labels" / p.stem)   ("" if dataset.mode == "image" else f"_{frame}") # im.txt  s  = "%gx%g " % im.shape[2:] # print string  gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh  imc = im0.copy() if save_crop else im0 # for save_crop  annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size  det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # Print results  for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum() # detections per class  s  = f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string   # Write results  for *xyxy, conf, cls in reversed(det):
                    c = int(cls) # integer class  label = names[c] if hide_conf else f"{names[c]}"  confidence = float(conf)
                    confidence_str = f"{confidence:.2f}"   if save_csv:
                        write_to_csv(p.name, label, confidence_str) 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 save_conf else (cls, *xywh) # label format  with open(f"{txt_path}.txt", "a") as f:
                            f.write(("%g " * len(line)).rstrip() % line "\n") if save_img or save_crop or view_img: # Add bbox to image  c = int(cls) # integer class  label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
                        annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # Stream results  im0 = annotator.result() if view_img: if platform.system() == "Linux" and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)  cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.waitKey(1) # 1 millisecond   # Save results (image with detections)  if save_img: if dataset.mode == "image":
                    cv2.imwrite(save_path, im0) else: # 'video' or 'stream'  if vid_path[i] != save_path: # new video  vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release() # release previous video writer  if vid_cap: # video  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)) else: # stream  fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos  vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
                    vid_writer[i].write(im0) # Print time (inference-only)  LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") # Print results  t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image  LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) 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 ""  LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update:
        strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)   def parse_opt(): """Parses command-line arguments for YOLOv5 detection, setting inference options and model configurations."""  parser = argparse.ArgumentParser()
    parser.add_argument("--weights", nargs=" ", type=str, default=ROOT /'runs/train/exp2/weights/best.pt', help='model path(s)')#修改处，权重文件  #parser.add_argument("--source", type=str, default=ROOT / 'data/mydata/image/test/frame0194.jpg', help='file/dir/URL/glob, 0 for webcam')#修改处，图像、视频、或摄像头  #parser.add_argument("--source", type=str, default='0', help='source')  # 修改处，图像、视频、或摄像头  parser.add_argument('--source', type=str, default='0', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument("--data", type=str, default=ROOT / 'mydata/mydata_parameter.yaml', help='(optional) dataset.yaml path')#修改处，参数文件  parser.add_argument("--imgsz", "--img", "--img-size", nargs=" ", type=int, default=[640], help='inference size h,w')#修改处，高、宽  parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")#非极大抑制  parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
    parser.add_argument("--device", default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')#修改处，  parser.add_argument("--view-img", action="store_true", help="show results")
    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
    parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")
    parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
    parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
    parser.add_argument("--classes", nargs=" ", type=int, help="filter by class: --classes 0, or --classes 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("--visualize", action="store_true", help="visualize features")
    parser.add_argument("--update", action="store_true", help="update all models")
    parser.add_argument("--project", default=ROOT / "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")
    parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
    parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
    parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand  print_args(vars(opt)) return opt def main(opt): """Executes YOLOv5 model inference with given options, checking requirements before running the model."""  check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
    run(**vars(opt)) if __name__ == "__main__":
    opt = parse_opt()
    main(opt)