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245 lines
11 KiB
245 lines
11 KiB
# predictor engine by Ultralytics
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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.configs import get_config
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, 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_CONFIG, LOGGER, colorstr, ops
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from ultralytics.yolo.utils.callbacks import default_callbacks
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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 (OmegaConf): 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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view_img (bool): Whether to view image 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, config=DEFAULT_CONFIG, overrides=None):
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"""
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Initializes the BasePredictor class.
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Args:
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config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
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overrides (dict, optional): Configuration overrides. Defaults to None.
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"""
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if overrides is None:
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overrides = {}
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self.args = get_config(config, overrides)
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project = self.args.project or f"runs/{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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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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self.done_setup = 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.device = None
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.view_img = None
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self.annotator = None
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self.data_path = None
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# callbacks
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self.callbacks = defaultdict([])
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for callback, func in default_callbacks.items():
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self.add_callback(callback, func)
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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, pred, 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):
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return preds
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def setup(self, source=None, model=None):
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# source
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source = str(source or self.args.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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# 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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model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
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stride, pt = model.stride, model.pt
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imgsz = check_imgsz(self.args.imgsz, stride=stride) # 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.view_img = check_imshow(warn=True)
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self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, 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, imgsz=imgsz, stride=stride, auto=pt)
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else:
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self.dataset = LoadImages(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
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self.vid_path, self.vid_writer = [None] * bs, [None] * bs
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model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
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self.model = model
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self.webcam = webcam
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self.screenshot = screenshot
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self.imgsz = imgsz
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self.done_setup = True
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self.device = device
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return model
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@smart_inference_mode()
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def __call__(self, source=None, model=None):
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self.run_callbacks("on_predict_start")
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model = self.model if self.done_setup else self.setup(source, model)
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self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
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for batch in self.dataset:
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self.run_callbacks("on_predict_batch_start")
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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 = 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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preds = self.postprocess(preds, im, im0s)
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for i in range(len(im)):
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if self.webcam:
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path, im0s = path[i], im0s[i]
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p = Path(path)
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s += self.write_results(i, preds, (p, im, im0s))
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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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# Print time (inference-only)
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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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self.run_callbacks("on_predict_batch_end")
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# Print results
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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(
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f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}'
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% t)
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if self.args.save_txt or self.args.save:
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s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels 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 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 = vid_cap.get(cv2.CAP_PROP_FPS)
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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 add_callback(self, event: str, callback):
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"""
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appends the given callback
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"""
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self.callbacks[event].append(callback)
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def set_callback(self, event: str, callback):
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"""
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overrides the existing callbacks with the given callback
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"""
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self.callbacks[event] = [callback]
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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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