# Ultralytics YOLO 🚀, GPL-3.0 license """ Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ yolo mode=predict model=yolov8n.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 mode=predict model=yolov8n.pt # PyTorch yolov8n.torchscript # TorchScript yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True 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 import torch from ultralytics.nn.autobackend import AutoBackend from ultralytics.yolo.cfg import get_cfg from ultralytics.yolo.data import load_inference_source from ultralytics.yolo.data.augment import classify_transforms from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops from ultralytics.yolo.utils.checks import 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 (SimpleNamespace): 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, cfg=DEFAULT_CFG, overrides=None): """ Initializes the BasePredictor class. Args: cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG. overrides (dict, optional): Configuration overrides. Defaults to None. """ self.args = get_cfg(cfg, 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.conf is None: self.args.conf = 0.25 # default conf=0.25 self.done_warmup = False if self.args.show: self.args.show = check_imshow(warn=True) # Usable if setup is done self.model = None self.data = self.args.data # data_dict self.imgsz = None self.device = None self.dataset = None self.vid_path, self.vid_writer = None, None self.annotator = None self.data_path = None self.source_type = None self.batch = None self.callbacks = defaultdict(list, callbacks.default_callbacks) # 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, results, batch, print_string): raise NotImplementedError('print_results function needs to be implemented') def postprocess(self, preds, img, orig_img): return preds @smart_inference_mode() def __call__(self, source=None, model=None, stream=False): if stream: return self.stream_inference(source, model) else: return list(self.stream_inference(source, model)) # merge list of Result into one def predict_cli(self, source=None, model=None): # Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode gen = self.stream_inference(source, model) for _ in gen: # running CLI inference without accumulating any outputs (do not modify) pass def setup_source(self, source): self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size if self.args.task == 'classify': transforms = getattr(self.model.model, 'transforms', classify_transforms(self.imgsz[0])) else: # predict, segment transforms = None self.dataset = load_inference_source(source=source, transforms=transforms, imgsz=self.imgsz, vid_stride=self.args.vid_stride, stride=self.model.stride, auto=self.model.pt) self.source_type = self.dataset.source_type self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs def stream_inference(self, source=None, model=None): if self.args.verbose: LOGGER.info('') # setup model if not self.model: self.setup_model(model) # setup source every time predict is called self.setup_source(source if source is not None else self.args.source) # check if save_dir/ label file exists if self.args.save or self.args.save_txt: (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) # warmup model if not self.done_warmup: self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz)) self.done_warmup = True self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None self.run_callbacks('on_predict_start') for batch in self.dataset: self.run_callbacks('on_predict_batch_start') self.batch = batch 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 = self.model(im, augment=self.args.augment, visualize=visualize) # postprocess with self.dt[2]: self.results = self.postprocess(preds, im, im0s) self.run_callbacks('on_predict_postprocess_end') # visualize, save, write results for i in range(len(im)): p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \ else (path, im0s.copy()) p = Path(p) if self.args.verbose or self.args.save or self.args.save_txt or self.args.show: s += self.write_results(i, self.results, (p, im, im0)) if self.args.show: self.show(p) if self.args.save: self.save_preds(vid_cap, i, str(self.save_dir / p.name)) self.run_callbacks('on_predict_batch_end') yield from self.results # Print time (inference-only) if self.args.verbose: LOGGER.info(f'{s}{self.dt[1].dt * 1E3:.1f}ms') # Release assets if isinstance(self.vid_writer[-1], cv2.VideoWriter): self.vid_writer[-1].release() # release final video writer # Print results if self.args.verbose and self.seen: t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image LOGGER.info(f'Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape ' f'{(1, 3, *self.imgsz)}' % t) if self.args.save or self.args.save_txt or self.args.save_crop: nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels s = f"\n{nl} label{'s' * (nl > 1)} 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') def setup_model(self, 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 self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half) self.device = device self.model.eval() 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(500 if self.batch[4].startswith('image') else 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 = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec 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)