# Ultralytics YOLO 🚀, GPL-3.0 license """ Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ yolo task=... mode=predict model=s.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: $ yolo task=... mode=predict --weights yolov8n.pt # PyTorch yolov8n.torchscript # TorchScript yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov8n_openvino_model # OpenVINO yolov8n.engine # TensorRT yolov8n.mlmodel # CoreML (macOS-only) yolov8n_saved_model # TensorFlow SavedModel yolov8n.pb # TensorFlow GraphDef yolov8n.tflite # TensorFlow Lite yolov8n_edgetpu.tflite # TensorFlow Edge TPU yolov8n_paddle_model # PaddlePaddle """ import platform from collections import defaultdict from pathlib import Path import cv2 from ultralytics.nn.autobackend import AutoBackend from ultralytics.yolo.configs import get_config from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, SETTINGS, callbacks, colorstr, ops from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow from ultralytics.yolo.utils.files import increment_path from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode class BasePredictor: """ BasePredictor A base class for creating predictors. Attributes: args (OmegaConf): Configuration for the predictor. save_dir (Path): Directory to save results. done_setup (bool): Whether the predictor has finished setup. model (nn.Module): Model used for prediction. data (dict): Data configuration. device (torch.device): Device used for prediction. dataset (Dataset): Dataset used for prediction. vid_path (str): Path to video file. vid_writer (cv2.VideoWriter): Video writer for saving video output. annotator (Annotator): Annotator used for prediction. data_path (str): Path to data. """ def __init__(self, config=DEFAULT_CONFIG, overrides=None): """ Initializes the BasePredictor class. Args: config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG. overrides (dict, optional): Configuration overrides. Defaults to None. """ if overrides is None: overrides = {} self.args = get_config(config, overrides) project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task name = self.args.name or f"{self.args.mode}" self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok) if self.args.save: (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) if self.args.conf is None: self.args.conf = 0.25 # default conf=0.25 self.done_setup = False # Usable if setup is done self.model = None self.data = self.args.data # data_dict self.device = None self.dataset = None self.vid_path, self.vid_writer = None, None self.annotator = None self.data_path = None self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks callbacks.add_integration_callbacks(self) def preprocess(self, img): pass def get_annotator(self, img): raise NotImplementedError("get_annotator function needs to be implemented") def write_results(self, pred, batch, print_string): raise NotImplementedError("print_results function needs to be implemented") def postprocess(self, preds, img, orig_img): return preds def setup(self, source=None, model=None): # source source = str(source if source is not None else self.args.source) 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 # model device = select_device(self.args.device) model = model or self.args.model self.args.half &= device.type != 'cpu' # half precision only supported on CUDA model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half) stride, pt = model.stride, model.pt imgsz = check_imgsz(self.args.imgsz, stride=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: self.args.show = check_imshow(warn=True) self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, transforms=getattr(model.model, 'transforms', None), vid_stride=self.args.vid_stride) bs = len(self.dataset) elif screenshot: self.dataset = LoadScreenshots(source, imgsz=imgsz, stride=stride, auto=pt, transforms=getattr(model.model, 'transforms', None)) else: self.dataset = LoadImages(source, imgsz=imgsz, stride=stride, auto=pt, transforms=getattr(model.model, 'transforms', None), vid_stride=self.args.vid_stride) self.vid_path, self.vid_writer = [None] * bs, [None] * bs model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup self.model = model self.webcam = webcam self.screenshot = screenshot self.imgsz = imgsz self.done_setup = True self.device = device return model @smart_inference_mode() def __call__(self, source=None, model=None): self.run_callbacks("on_predict_start") model = self.model if self.done_setup else self.setup(source, model) model.eval() self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()) self.all_outputs = [] for batch in self.dataset: self.run_callbacks("on_predict_batch_start") path, im, im0s, vid_cap, s = batch visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False with self.dt[0]: im = self.preprocess(im) if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with self.dt[1]: preds = model(im, augment=self.args.augment, visualize=visualize) # postprocess with self.dt[2]: preds = self.postprocess(preds, im, im0s) for i in range(len(im)): if self.webcam: path, im0s = path[i], im0s[i] p = Path(path) res = self.write_results(i, preds, (p, im, im0s)) s += res[0] return res[1] if self.args.show: self.show(p) if self.args.save: self.save_preds(vid_cap, i, str(self.save_dir / p.name)) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms") self.run_callbacks("on_predict_batch_end") # Print results t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image LOGGER.info( f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}' % t) if self.args.save_txt or self.args.save: s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" if self.args.save_txt else '' LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}") self.run_callbacks("on_predict_end") return self.all_outputs def show(self, p): im0 = self.annotator.result() if platform.system() == 'Linux' and p not in self.windows: self.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 def save_preds(self, vid_cap, idx, save_path): im0 = self.annotator.result() # save imgs if self.dataset.mode == 'image': cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if self.vid_path[idx] != save_path: # new video self.vid_path[idx] = save_path if isinstance(self.vid_writer[idx], cv2.VideoWriter): self.vid_writer[idx].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 self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) self.vid_writer[idx].write(im0) def run_callbacks(self, event: str): for callback in self.callbacks.get(event, []): callback(self)