# YOLOv5 🚀 by Ultralytics, GPL-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/Zgi9g1ksQHc' # 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] # 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 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.plots import Annotator, colors, save_one_box 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 ): 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 += "%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) ) results = [] if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes( im.shape[2:], det[:, :4], im0.shape ).round() results.append((path, det)) return results