# Ultralytics YOLO 🚀, AGPL-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 pathlib import Path import cv2 import numpy as np 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 LetterBox, classify_transforms from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, 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_warmup (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. data_path (str): Path to data. """ def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=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) self.save_dir = self.get_save_dir() 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.plotted_img = None self.data_path = None self.source_type = None self.batch = None self.results = None self.transforms = None self.callbacks = _callbacks or callbacks.get_default_callbacks() callbacks.add_integration_callbacks(self) def get_save_dir(self): project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task name = self.args.name or f'{self.args.mode}' return increment_path(Path(project) / name, exist_ok=self.args.exist_ok) def preprocess(self, im): """Prepares input image before inference. Args: im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list. """ not_tensor = not isinstance(im, torch.Tensor) if not_tensor: im = np.stack(self.pre_transform(im)) im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW, (n, 3, h, w) im = np.ascontiguousarray(im) # contiguous im = torch.from_numpy(im) img = im.to(self.device) img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 if not_tensor: img /= 255 # 0 - 255 to 0.0 - 1.0 return img def inference(self, im, *args, **kwargs): visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem, mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False return self.model(im, augment=self.args.augment, visualize=visualize) def pre_transform(self, im): """Pre-transform input image before inference. Args: im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. Return: A list of transformed imgs. """ same_shapes = all(x.shape == im[0].shape for x in im) auto = same_shapes and self.model.pt return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im] def write_results(self, idx, results, batch): """Write inference results to a file or directory.""" p, im, _ = batch log_string = '' if len(im.shape) == 3: im = im[None] # expand for batch dim if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1 log_string += f'{idx}: ' frame = self.dataset.count else: frame = getattr(self.dataset, 'frame', 0) self.data_path = p self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') log_string += '%gx%g ' % im.shape[2:] # print string result = results[idx] log_string += result.verbose() if self.args.save or self.args.show: # Add bbox to image plot_args = { 'line_width': self.args.line_width, 'boxes': self.args.boxes, 'conf': self.args.show_conf, 'labels': self.args.show_labels} if not self.args.retina_masks: plot_args['im_gpu'] = im[idx] self.plotted_img = result.plot(**plot_args) # Write if self.args.save_txt: result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf) if self.args.save_crop: result.save_crop(save_dir=self.save_dir / 'crops', file_name=self.data_path.stem) return log_string def postprocess(self, preds, img, orig_imgs): """Post-processes predictions for an image and returns them.""" return preds def __call__(self, source=None, model=None, stream=False, *args, **kwargs): """Performs inference on an image or stream.""" self.stream = stream if stream: return self.stream_inference(source, model, *args, **kwargs) else: return list(self.stream_inference(source, model, *args, **kwargs)) # 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): """Sets up source and inference mode.""" self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size self.transforms = getattr(self.model.model, 'transforms', classify_transforms( self.imgsz[0])) if self.args.task == 'classify' else None self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride) 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 @smart_inference_mode() def stream_inference(self, source=None, model=None, *args, **kwargs): """Streams real-time inference on camera feed and saves results to file.""" 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.batch, profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile()) self.run_callbacks('on_predict_start') for batch in self.dataset: self.run_callbacks('on_predict_batch_start') self.batch = batch path, im0s, vid_cap, s = batch # Preprocess with profilers[0]: im = self.preprocess(im0s) # Inference with profilers[1]: preds = self.inference(im, *args, **kwargs) # Postprocess with profilers[2]: self.results = self.postprocess(preds, im, im0s) self.run_callbacks('on_predict_postprocess_end') # Visualize, save, write results n = len(im0s) for i in range(n): self.seen += 1 self.results[i].speed = { 'preprocess': profilers[0].dt * 1E3 / n, 'inference': profilers[1].dt * 1E3 / n, 'postprocess': profilers[2].dt * 1E3 / n} p, im0 = path[i], None if self.source_type.tensor else im0s[i].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.save or self.args.save_txt: self.results[i].save_dir = self.save_dir.__str__() if self.args.show and self.plotted_img is not None: self.show(p) if self.args.save and self.plotted_img is not None: 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}{profilers[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 profilers) # speeds per image LOGGER.info(f'Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape ' f'{(1, 3, *im.shape[2:])}' % 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): """Initialize YOLO model with given parameters and set it to evaluation mode.""" self.model = AutoBackend(model or self.args.model, device=select_device(self.args.device, verbose=verbose), dnn=self.args.dnn, data=self.args.data, fp16=self.args.half, fuse=True, verbose=verbose) self.device = self.model.device # update device self.args.half = self.model.fp16 # update half self.model.eval() def show(self, p): """Display an image in a window using OpenCV imshow().""" im0 = self.plotted_img 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[3].startswith('image') else 1) # 1 millisecond def save_preds(self, vid_cap, idx, save_path): """Save video predictions as mp4 at specified path.""" im0 = self.plotted_img # 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] suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi' fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG' save_path = str(Path(save_path).with_suffix(suffix)) self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) self.vid_writer[idx].write(im0) def run_callbacks(self, event: str): """Runs all registered callbacks for a specific event.""" for callback in self.callbacks.get(event, []): callback(self) def add_callback(self, event: str, func): """ Add callback """ self.callbacks[event].append(func)