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| # 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 | |
| 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 | |
| STREAM_WARNING = """ | |
| WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed, | |
| causing potential out-of-memory errors for large sources or long-running streams/videos. | |
| Usage: | |
| results = model(source=..., stream=True) # generator of Results objects | |
| for r in results: | |
| boxes = r.boxes # Boxes object for bbox outputs | |
| masks = r.masks # Masks object for segment masks outputs | |
| probs = r.probs # Class probabilities for classification outputs | |
| """ | |
| 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 | |
| def __call__(self, source=None, model=None, stream=False): | |
| self.stream = stream | |
| 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 | |
| if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or # streams | |
| len(self.dataset) > 1000 or # images | |
| any(getattr(self.dataset, 'video_flag', [False]))): # videos | |
| LOGGER.warning(STREAM_WARNING) | |
| 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 | |
| # preprocess | |
| 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 | |
| n = len(im) | |
| for i in range(n): | |
| self.results[i].speed = { | |
| 'preprocess': self.dt[0].dt * 1E3 / n, | |
| 'inference': self.dt[1].dt * 1E3 / n, | |
| 'postprocess': self.dt[2].dt * 1E3 / n} | |
| if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor | |
| continue | |
| 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, verbose=True): | |
| device = select_device(self.args.device, verbose=verbose) | |
| 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, | |
| verbose=verbose) | |
| 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) | |