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287 lines
13 KiB
287 lines
13 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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"""
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Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ yolo task=... mode=predict model=s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ yolo task=... mode=predict --weights yolov8n.pt # PyTorch
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yolov8n.torchscript # TorchScript
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yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov8n_openvino_model # OpenVINO
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yolov8n.engine # TensorRT
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yolov8n.mlmodel # CoreML (macOS-only)
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yolov8n_saved_model # TensorFlow SavedModel
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yolov8n.pb # TensorFlow GraphDef
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yolov8n.tflite # TensorFlow Lite
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yolov8n_edgetpu.tflite # TensorFlow Edge TPU
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yolov8n_paddle_model # PaddlePaddle
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"""
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import platform
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from collections import defaultdict
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from pathlib import Path
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import cv2
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams
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from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
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from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
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class BasePredictor:
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"""
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BasePredictor
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A base class for creating predictors.
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Attributes:
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args (SimpleNamespace): Configuration for the predictor.
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save_dir (Path): Directory to save results.
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done_setup (bool): Whether the predictor has finished setup.
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model (nn.Module): Model used for prediction.
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data (dict): Data configuration.
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device (torch.device): Device used for prediction.
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dataset (Dataset): Dataset used for prediction.
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vid_path (str): Path to video file.
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vid_writer (cv2.VideoWriter): Video writer for saving video output.
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annotator (Annotator): Annotator used for prediction.
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data_path (str): Path to data.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None):
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"""
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Initializes the BasePredictor class.
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Args:
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cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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self.args = get_cfg(cfg, overrides)
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project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
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name = self.args.name or f"{self.args.mode}"
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self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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if self.args.conf is None:
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self.args.conf = 0.25 # default conf=0.25
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self.done_warmup = False
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# Usable if setup is done
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self.model = None
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self.data = self.args.data # data_dict
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self.bs = None
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self.imgsz = None
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self.device = None
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self.classes = self.args.classes
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.annotator = None
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self.data_path = None
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self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()}) # add callbacks
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callbacks.add_integration_callbacks(self)
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def preprocess(self, img):
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pass
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def get_annotator(self, img):
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raise NotImplementedError("get_annotator function needs to be implemented")
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def write_results(self, results, batch, print_string):
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raise NotImplementedError("print_results function needs to be implemented")
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def postprocess(self, preds, img, orig_img, classes=None):
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return preds
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def setup_source(self, source=None):
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if not self.model:
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raise Exception("setup model before setting up source!")
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# source
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source, webcam, screenshot, from_img = self.check_source(source)
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# model
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stride, pt = self.model.stride, self.model.pt
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imgsz = check_imgsz(self.args.imgsz, stride=stride, min_dim=2) # check image size
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# Dataloader
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bs = 1 # batch_size
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if webcam:
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self.args.show = check_imshow(warn=True)
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self.dataset = LoadStreams(source,
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imgsz=imgsz,
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stride=stride,
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auto=pt,
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transforms=getattr(self.model.model, 'transforms', None),
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vid_stride=self.args.vid_stride)
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bs = len(self.dataset)
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elif screenshot:
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self.dataset = LoadScreenshots(source,
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imgsz=imgsz,
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stride=stride,
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auto=pt,
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transforms=getattr(self.model.model, 'transforms', None))
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elif from_img:
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self.dataset = LoadPilAndNumpy(source,
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imgsz=imgsz,
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stride=stride,
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auto=pt,
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transforms=getattr(self.model.model, 'transforms', None))
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else:
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self.dataset = LoadImages(source,
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imgsz=imgsz,
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stride=stride,
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auto=pt,
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transforms=getattr(self.model.model, 'transforms', None),
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vid_stride=self.args.vid_stride)
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self.vid_path, self.vid_writer = [None] * bs, [None] * bs
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self.webcam = webcam
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self.screenshot = screenshot
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self.from_img = from_img
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self.imgsz = imgsz
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self.bs = bs
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@smart_inference_mode()
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def __call__(self, source=None, model=None, verbose=False, stream=False):
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if stream:
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return self.stream_inference(source, model, verbose)
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else:
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return list(self.stream_inference(source, model, verbose)) # merge list of Result into one
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def predict_cli(self):
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# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
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gen = self.stream_inference(verbose=True)
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for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
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pass
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def stream_inference(self, source=None, model=None, verbose=False):
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self.run_callbacks("on_predict_start")
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# setup model
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if not self.model:
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self.setup_model(model)
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# setup source. Run every time predict is called
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self.setup_source(source)
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# check if save_dir/ label file exists
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if self.args.save or self.args.save_txt:
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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# warmup model
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if not self.done_warmup:
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self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.bs, 3, *self.imgsz))
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self.done_warmup = True
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self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None
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for batch in self.dataset:
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self.run_callbacks("on_predict_batch_start")
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self.batch = batch
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path, im, im0s, vid_cap, s = batch
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visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
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with self.dt[0]:
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im = self.preprocess(im)
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with self.dt[1]:
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preds = self.model(im, augment=self.args.augment, visualize=visualize)
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# postprocess
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with self.dt[2]:
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self.results = self.postprocess(preds, im, im0s, self.classes)
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for i in range(len(im)):
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p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
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p = Path(p)
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if verbose or self.args.save or self.args.save_txt or self.args.show:
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s += self.write_results(i, self.results, (p, im, im0))
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if self.args.show:
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self.show(p)
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if self.args.save:
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self.save_preds(vid_cap, i, str(self.save_dir / p.name))
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self.run_callbacks("on_predict_batch_end")
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yield from self.results
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# Print time (inference-only)
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if verbose:
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LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
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# Print results
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if verbose and self.seen:
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t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape '
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f'{(1, 3, *self.imgsz)}' % t)
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if self.args.save_txt or self.args.save:
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nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels
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s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
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self.run_callbacks("on_predict_end")
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def setup_model(self, model):
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device = select_device(self.args.device)
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model = model or self.args.model
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self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
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self.model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
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self.device = device
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self.model.eval()
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def check_source(self, source):
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source = source if source is not None else self.args.source
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webcam, screenshot, from_img = False, False, False
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if isinstance(source, (str, int, Path)): # int for local usb carame
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source = str(source)
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
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screenshot = source.lower().startswith('screen')
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if is_url and is_file:
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source = check_file(source) # download
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else:
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from_img = True
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return source, webcam, screenshot, from_img
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def show(self, p):
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im0 = self.annotator.result()
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if platform.system() == 'Linux' and p not in self.windows:
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self.windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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def save_preds(self, vid_cap, idx, save_path):
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im0 = self.annotator.result()
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# save imgs
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if self.dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if self.vid_path[idx] != save_path: # new video
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self.vid_path[idx] = save_path
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if isinstance(self.vid_writer[idx], cv2.VideoWriter):
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self.vid_writer[idx].release() # release previous video writer
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if vid_cap: # video
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fps = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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self.vid_writer[idx].write(im0)
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def run_callbacks(self, event: str):
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for callback in self.callbacks.get(event, []):
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callback(self)
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