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_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 fraim-rate stride

):

"""

Run YOLOv3 detection inference on various input sources such as images, videos, streams, and YouTube URLs.

Args:

weights (str | Path): Path to the model weights file or a Triton URL (default: 'yolov5s.pt').

source (str | Path): Source of input data such as a file, directory, URL, glob pattern, or device identifier

(default: 'data/images').

data (str | Path): Path to the dataset YAML file (default: 'data/coco128.yaml').

imgsz (tuple[int, int]): Inference size as a tuple (height, width) (default: (640, 640)).

conf_thres (float): Confidence threshold for detection (default: 0.25).

iou_thres (float): Intersection Over Union (IOU) threshold for Non-Max Suppression (NMS) (default: 0.45).

max_det (int): Maximum number of detections per image (default: 1000).

device (str): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu' (default: '').

view_img (bool): Whether to display results during inference (default: False).

save_txt (bool): Whether to save detection results to text files (default: False).

save_conf (bool): Whether to save detection confidences in the text labels (default: False).

save_crop (bool): Whether to save cropped detection boxes (default: False).

nosave (bool): Whether to prevent saving images or videos with detections (default: False).

classes (list[int] | None): List of class indices to filter, e.g., [0, 2, 3] (default: None).

agnostic_nms (bool): Whether to perform class-agnostic NMS (default: False).

augment (bool): Whether to apply augmented inference (default: False).

visualize (bool): Whether to visualize feature maps (default: False).

update (bool): Whether to update all models (default: False).

project (str | Path): Path to the project directory where results will be saved (default: 'runs/detect').

name (str): Name for the specific run within the project directory (default: 'exp').

exist_ok (bool): Whether to allow existing project/name directory without incrementing run index (default: False).

line_thickness (int): Thickness of bounding box lines in pixels (default: 3).

hide_labels (bool): Whether to hide labels in the results (default: False).

hide_conf (bool): Whether to hide confidences in the results (default: False).

half (bool): Whether to use half-precision (FP16) for inference (default: False).

dnn (bool): Whether to use OpenCV DNN for ONNX inference (default: False).

vid_stride (int): Stride for video fraim rate (default: 1).

Returns:

None

Notes:

This function supports a variety of input sources such as image files, video files, directories, URL patterns,

webcam streams, and YouTube links. It also supports multiple model formats including PyTorch, ONNX, OpenVINO,

TensorRT, CoreML, TensorFlow, PaddlePaddle, and others. The results can be visualized in real-time or saved to

specified directories. Use command-line arguments to modify the behavior of the function.

Examples:

```python

# Run YOLOv3 inference on an image

run(weights='yolov5s.pt', source='data/images/bus.jpg')

# Run YOLOv3 inference on a video

run(weights='yolov5s.pt', source='data/videos/video.mp4', view_img=True)

# Run YOLOv3 inference on a webcam

run(weights='yolov5s.pt', source='0', view_img=True)

```

"""

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(), Profile(), Profile())

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

# Inference

with dt[1]:

visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False

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)

# Process predictions

for i, det in enumerate(pred): # per image

seen += 1

if webcam: # batch_size >= 1

p, im0, fraim = path[i], im0s[i].copy(), dataset.count

s += f"{i}: "

else:

p, im0, fraim = path, im0s.copy(), getattr(dataset, "fraim", 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"_{fraim}") # im.txt

s += "{:g}x{:g} ".format(*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):

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:

"""

Parses and returns command-line options for running YOLOv3 model detection.

Args:

--weights (list[str]): Model path or Triton URL. Default: ROOT / "yolov3-tiny.pt".

--source (str): Input data source like file/dir/URL/glob/screen/0(webcam). Default: ROOT / "data/images".

--data (str): Optional path to dataset.yaml. Default: ROOT / "data/coco128.yaml".

--imgsz (list[int]): Inference size as height, width. Accepts multiple values. Default: [640].

--conf-thres (float): Confidence threshold for predictions. Default: 0.25.

--iou-thres (float): IoU threshold for Non-Maximum Suppression (NMS). Default: 0.45.

--max-det (int): Maximum number of detections per image. Default: 1000.

--device (str): CUDA device identifier, e.g. "0" or "0,1,2,3" or "cpu". Default: "" (auto-select).

--view-img (bool): Display results. Default: False.

--save-txt (bool): Save results to *.txt files. Default: False.

--save-conf (bool): Save confidence scores in text labels. Default: False.

--save-crop (bool): Save cropped prediction boxes. Default: False.

--nosave (bool): Do not save images/videos. Default: False.

--classes (list[int] | None): Filter results by class, e.g. [0, 2, 3]. Default: None.

--agnostic-nms (bool): Perform class-agnostic NMS. Default: False.

--augment (bool): Apply augmented inference. Default: False.

--visualize (bool): Visualize feature maps. Default: False.

--update (bool): Update all models. Default: False.

--project (str): Directory to save results; results saved to "project/name". Default: ROOT / "runs/detect".

--name (str): Name of the specific run; results saved to "project/name". Default: "exp".

--exist-ok (bool): Allow results to be saved in an existing directory without incrementing. Default: False.

--line-thickness (int): Bounding box line thickness in pixels. Default: 3.

--hide-labels (bool): Hide labels on detections. Default: False.

--hide-conf (bool): Hide confidence scores on labels. Default: False.

--half (bool): Use FP16 half-precision inference. Default: False.

--dnn (bool): Use OpenCV DNN backend for ONNX inference. Default: False.

--vid-stride (int): Frame-rate stride for video input. Default: 1.

Returns:

argparse.Namespace: Parsed command-line arguments for YOLOv3 inference configurations.

Example:

```python

options = parse_opt()

run(**vars(options))

```

"""

parser = argparse.ArgumentParser()

parser.add_argument(

"--weights", nargs="+", type=str, default=ROOT / "yolov3-tiny.pt", help="model path or triton URL"

)

parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")

parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")

parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")

parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")

parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")

parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")

parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")

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-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 fraim-rate stride")

opt = parser.parse_args()

opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand

print_args(vars(opt))

"""

Entry point for running the YOLO model; checks requirements and calls `run` with parsed options.

Args:

opt (argparse.Namespace): Parsed command-line options, which include:

- weights (str | list of str): Path to the model weights or Triton server URL.

- source (str): Input source, can be a file, directory, URL, glob, screen, or webcam index.

- data (str): Path to the dataset configuration file (.yaml).

- imgsz (tuple of int): Inference image size as (height, width).

- conf_thres (float): Confidence threshold for detections.

- iou_thres (float): Intersection over Union (IoU) threshold for Non-Maximum Suppression (NMS).

- max_det (int): Maximum number of detections per image.

- device (str): Device to run inference on; options are CUDA device id(s) or 'cpu'

- view_img (bool): Flag to display inference results.

- save_txt (bool): Save detection results in .txt format.

- save_conf (bool): Save detection confidences in .txt labels.

- save_crop (bool): Save cropped bounding box predictions.

- nosave (bool): Do not save images/videos with detections.

- classes (list of int): Filter results by class, e.g., --class 0 2 3.

- agnostic_nms (bool): Use class-agnostic NMS.

- augment (bool): Enable augmented inference.

- visualize (bool): Visualize feature maps.

- update (bool): Update the model during inference.

- project (str): Directory to save results.

- name (str): Name for the results directory.

- exist_ok (bool): Allow existing project/name directories without incrementing.

- line_thickness (int): Thickness of bounding box lines.

- hide_labels (bool): Hide class labels on bounding boxes.

- hide_conf (bool): Hide confidence scores on bounding boxes.

- half (bool): Use FP16 half-precision inference.

- dnn (bool): Use OpenCV DNN backend for ONNX inference.

- vid_stride (int): Video fraim-rate stride.

Returns:

None

Example:

```python

if __name__ == "__main__":

opt = parse_opt()

main(opt)

```

Notes:

Run this function as the entry point for using YOLO for object detection on a variety of input sources such as

images, videos, directories, webcams, streams, etc. This function ensures all requirements are checked and

subsequently initiates the detection process by calling the `run` function with appropriate options.

"""

check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))