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""" |
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Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. |
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Usage - sources: |
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$ python detect.py --weights yolov5s.pt --source 0 # webcam |
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img.jpg # image |
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vid.mp4 # video |
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screen # screenshot |
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path/ # directory |
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list.txt # list of images |
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list.streams # list of streams |
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'path/*.jpg' # glob |
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'https://youtu.be/Zgi9g1ksQHc' # YouTube |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
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Usage - formats: |
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$ python detect.py --weights yolov5s.pt # PyTorch |
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yolov5s.torchscript # TorchScript |
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn |
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yolov5s_openvino_model # OpenVINO |
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yolov5s.engine # TensorRT |
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yolov5s.mlmodel # CoreML (macOS-only) |
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yolov5s_saved_model # TensorFlow SavedModel |
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yolov5s.pb # TensorFlow GraphDef |
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yolov5s.tflite # TensorFlow Lite |
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU |
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yolov5s_paddle_model # PaddlePaddle |
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""" |
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import argparse |
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import os |
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import platform |
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import sys |
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from pathlib import Path |
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import torch |
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FILE = Path(__file__).resolve() |
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ROOT = FILE.parents[0] |
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if str(ROOT) not in sys.path: |
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sys.path.append(str(ROOT)) |
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) |
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from models.common import DetectMultiBackend |
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from utils.dataloaders import ( |
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IMG_FORMATS, |
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VID_FORMATS, |
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LoadImages, |
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LoadScreenshots, |
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LoadStreams, |
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) |
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from utils.general import ( |
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LOGGER, |
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Profile, |
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check_file, |
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check_img_size, |
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check_imshow, |
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check_requirements, |
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colorstr, |
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cv2, |
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increment_path, |
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non_max_suppression, |
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print_args, |
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scale_boxes, |
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strip_optimizer, |
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xyxy2xywh, |
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) |
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from utils.plots import Annotator, colors, save_one_box |
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from utils.torch_utils import select_device, smart_inference_mode |
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@smart_inference_mode() |
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def run( |
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weights=ROOT / "yolov5s.pt", |
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source=ROOT / "data/images", |
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data=ROOT / "data/coco128.yaml", |
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imgsz=(640, 640), |
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conf_thres=0.25, |
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iou_thres=0.45, |
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max_det=1000, |
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device="", |
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view_img=False, |
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save_txt=False, |
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save_conf=False, |
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save_crop=False, |
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nosave=False, |
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classes=None, |
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agnostic_nms=False, |
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augment=False, |
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visualize=False, |
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update=False, |
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project=ROOT / "runs/detect", |
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name="exp", |
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exist_ok=False, |
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line_thickness=3, |
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hide_labels=False, |
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hide_conf=False, |
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half=False, |
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dnn=False, |
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vid_stride=1, |
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): |
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source = str(source) |
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save_img = not nosave and not source.endswith( |
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".txt" |
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) |
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) |
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is_url = source.lower().startswith( |
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("rtsp://", "rtmp://", "http://", "https://") |
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) |
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webcam = ( |
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source.isnumeric() |
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or source.endswith(".streams") |
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or (is_url and not is_file) |
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) |
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screenshot = source.lower().startswith("screen") |
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if is_url and is_file: |
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source = check_file(source) |
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save_dir = increment_path( |
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Path(project) / name, exist_ok=exist_ok |
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) |
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(save_dir / "labels" if save_txt else save_dir).mkdir( |
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parents=True, exist_ok=True |
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) |
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device = select_device(device) |
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model = DetectMultiBackend( |
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weights, device=device, dnn=dnn, data=data, fp16=half |
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) |
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stride, names, pt = model.stride, model.names, model.pt |
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imgsz = check_img_size(imgsz, s=stride) |
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bs = 1 |
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if webcam: |
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view_img = check_imshow(warn=True) |
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dataset = LoadStreams( |
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source, |
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img_size=imgsz, |
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stride=stride, |
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auto=pt, |
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vid_stride=vid_stride, |
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) |
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bs = len(dataset) |
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elif screenshot: |
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dataset = LoadScreenshots( |
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source, img_size=imgsz, stride=stride, auto=pt |
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) |
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else: |
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dataset = LoadImages( |
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source, |
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img_size=imgsz, |
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stride=stride, |
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auto=pt, |
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vid_stride=vid_stride, |
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) |
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vid_path, vid_writer = [None] * bs, [None] * bs |
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) |
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seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) |
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for path, im, im0s, vid_cap, s in dataset: |
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with dt[0]: |
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im = torch.from_numpy(im).to(model.device) |
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im = im.half() if model.fp16 else im.float() |
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im /= 255 |
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if len(im.shape) == 3: |
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im = im[None] |
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with dt[1]: |
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visualize = ( |
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increment_path(save_dir / Path(path).stem, mkdir=True) |
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if visualize |
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else False |
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) |
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pred = model(im, augment=augment, visualize=visualize) |
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with dt[2]: |
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pred = non_max_suppression( |
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pred, |
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conf_thres, |
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iou_thres, |
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classes, |
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agnostic_nms, |
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max_det=max_det, |
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) |
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for i, det in enumerate(pred): |
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seen += 1 |
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if webcam: |
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p, im0, frame = path[i], im0s[i].copy(), dataset.count |
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s += f"{i}: " |
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else: |
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p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) |
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p = Path(p) |
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save_path = str(save_dir / p.name) |
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txt_path = str(save_dir / "labels" / p.stem) + ( |
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"" if dataset.mode == "image" else f"_{frame}" |
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) |
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s += "%gx%g " % im.shape[2:] |
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gn = torch.tensor(im0.shape)[ |
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[1, 0, 1, 0] |
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] |
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imc = im0.copy() if save_crop else im0 |
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annotator = Annotator( |
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im0, line_width=line_thickness, example=str(names) |
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) |
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if len(det): |
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det[:, :4] = scale_boxes( |
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im.shape[2:], det[:, :4], im0.shape |
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).round() |
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for c in det[:, 5].unique(): |
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n = (det[:, 5] == c).sum() |
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " |
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for *xyxy, conf, cls in reversed(det): |
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if save_txt: |
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xywh = ( |
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(xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn) |
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.view(-1) |
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.tolist() |
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) |
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line = ( |
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(cls, *xywh, conf) if save_conf else (cls, *xywh) |
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) |
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with open(f"{txt_path}.txt", "a") as f: |
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f.write(("%g " * len(line)).rstrip() % line + "\n") |
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if save_img or save_crop or view_img: |
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c = int(cls) |
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label = ( |
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None |
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if hide_labels |
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else ( |
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names[c] |
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if hide_conf |
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else f"{names[c]} {conf:.2f}" |
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) |
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) |
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annotator.box_label(xyxy, label, color=colors(c, True)) |
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if save_crop: |
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save_one_box( |
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xyxy, |
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imc, |
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file=save_dir |
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/ "crops" |
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/ names[c] |
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/ f"{p.stem}.jpg", |
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BGR=True, |
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) |
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im0 = annotator.result() |
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if view_img: |
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if platform.system() == "Linux" and p not in windows: |
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windows.append(p) |
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cv2.namedWindow( |
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str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO |
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) |
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) |
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cv2.imshow(str(p), im0) |
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cv2.waitKey(1) |
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if save_img: |
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if dataset.mode == "image": |
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cv2.imwrite(save_path, im0) |
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else: |
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if vid_path[i] != save_path: |
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vid_path[i] = save_path |
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if isinstance(vid_writer[i], cv2.VideoWriter): |
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vid_writer[ |
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i |
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].release() |
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if vid_cap: |
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fps = vid_cap.get(cv2.CAP_PROP_FPS) |
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
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else: |
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fps, w, h = 30, im0.shape[1], im0.shape[0] |
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save_path = str( |
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Path(save_path).with_suffix(".mp4") |
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) |
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vid_writer[i] = cv2.VideoWriter( |
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save_path, |
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cv2.VideoWriter_fourcc(*"mp4v"), |
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fps, |
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(w, h), |
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) |
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vid_writer[i].write(im0) |
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LOGGER.info( |
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f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms" |
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) |
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t = tuple(x.t / seen * 1e3 for x in dt) |
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LOGGER.info( |
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f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" |
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% t |
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) |
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if save_txt or save_img: |
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s = ( |
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f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" |
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if save_txt |
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else "" |
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) |
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") |
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if update: |
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strip_optimizer( |
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weights[0] |
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) |
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def parse_opt(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--weights", |
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nargs="+", |
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type=str, |
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default=ROOT / "yolov5s.pt", |
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help="model path or triton URL", |
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) |
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parser.add_argument( |
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"--source", |
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type=str, |
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default=ROOT / "data/images", |
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help="file/dir/URL/glob/screen/0(webcam)", |
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) |
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parser.add_argument( |
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"--data", |
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type=str, |
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default=ROOT / "data/coco128.yaml", |
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help="(optional) dataset.yaml path", |
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) |
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parser.add_argument( |
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"--imgsz", |
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"--img", |
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"--img-size", |
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nargs="+", |
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type=int, |
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default=[640], |
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help="inference size h,w", |
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) |
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parser.add_argument( |
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"--conf-thres", type=float, default=0.25, help="confidence threshold" |
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) |
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parser.add_argument( |
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"--iou-thres", type=float, default=0.45, help="NMS IoU threshold" |
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) |
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parser.add_argument( |
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"--max-det", |
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type=int, |
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default=1000, |
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help="maximum detections per image", |
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) |
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parser.add_argument( |
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"--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu" |
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) |
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parser.add_argument("--view-img", action="store_true", help="show results") |
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parser.add_argument( |
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"--save-txt", action="store_true", help="save results to *.txt" |
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) |
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parser.add_argument( |
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"--save-conf", |
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action="store_true", |
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help="save confidences in --save-txt labels", |
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) |
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parser.add_argument( |
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"--save-crop", |
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action="store_true", |
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help="save cropped prediction boxes", |
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) |
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parser.add_argument( |
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"--nosave", action="store_true", help="do not save images/videos" |
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) |
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parser.add_argument( |
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"--classes", |
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nargs="+", |
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type=int, |
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help="filter by class: --classes 0, or --classes 0 2 3", |
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) |
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parser.add_argument( |
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"--agnostic-nms", action="store_true", help="class-agnostic NMS" |
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) |
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parser.add_argument( |
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"--augment", action="store_true", help="augmented inference" |
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) |
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parser.add_argument( |
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"--visualize", action="store_true", help="visualize features" |
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) |
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parser.add_argument( |
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"--update", action="store_true", help="update all models" |
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) |
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parser.add_argument( |
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"--project", |
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default=ROOT / "runs/detect", |
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help="save results to project/name", |
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) |
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parser.add_argument( |
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"--name", default="exp", help="save results to project/name" |
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) |
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parser.add_argument( |
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"--exist-ok", |
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action="store_true", |
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help="existing project/name ok, do not increment", |
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) |
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parser.add_argument( |
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"--line-thickness", |
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default=3, |
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type=int, |
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help="bounding box thickness (pixels)", |
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) |
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parser.add_argument( |
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"--hide-labels", default=False, action="store_true", help="hide labels" |
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) |
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parser.add_argument( |
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"--hide-conf", |
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default=False, |
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action="store_true", |
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help="hide confidences", |
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) |
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parser.add_argument( |
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"--half", action="store_true", help="use FP16 half-precision inference" |
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) |
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parser.add_argument( |
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"--dnn", action="store_true", help="use OpenCV DNN for ONNX inference" |
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) |
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parser.add_argument( |
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"--vid-stride", type=int, default=1, help="video frame-rate stride" |
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) |
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opt = parser.parse_args() |
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 |
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print_args(vars(opt)) |
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return opt |
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def main(opt): |
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check_requirements(exclude=("tensorboard", "thop")) |
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run(**vars(opt)) |
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if __name__ == "__main__": |
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opt = parse_opt() |
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main(opt) |
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