# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Run YOLOv3 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 os import platform import sys from pathlib import Path import torch FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv3 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_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 ): """ 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 frame 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, 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 += "{: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: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): """ 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 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): """ 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 frame-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")) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt)