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254 lines
12 KiB
254 lines
12 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 mode=predict model=yolov8n.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 mode=predict model=yolov8n.pt # PyTorch |
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yolov8n.torchscript # TorchScript |
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yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True |
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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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import torch |
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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 import load_inference_source |
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from ultralytics.yolo.data.augment import classify_transforms |
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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_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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if self.args.show: |
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self.args.show = check_imshow(warn=True) |
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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.imgsz = None |
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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.annotator = None |
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self.data_path = None |
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self.source_type = None |
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self.batch = None |
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self.callbacks = defaultdict(list, callbacks.default_callbacks) # 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): |
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return preds |
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@smart_inference_mode() |
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def __call__(self, source=None, model=None, stream=False): |
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if stream: |
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return self.stream_inference(source, model) |
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else: |
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return list(self.stream_inference(source, model)) # merge list of Result into one |
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def predict_cli(self, source=None, model=None): |
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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(source, model) |
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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 setup_source(self, source): |
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self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size |
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if self.args.task == 'classify': |
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transforms = getattr(self.model.model, 'transforms', classify_transforms(self.imgsz[0])) |
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else: # predict, segment |
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transforms = None |
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self.dataset = load_inference_source(source=source, |
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transforms=transforms, |
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imgsz=self.imgsz, |
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vid_stride=self.args.vid_stride, |
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stride=self.model.stride, |
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auto=self.model.pt) |
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self.source_type = self.dataset.source_type |
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self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs |
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def stream_inference(self, source=None, model=None): |
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if self.args.verbose: |
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LOGGER.info('') |
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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 every time predict is called |
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self.setup_source(source if source is not None else self.args.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.dataset.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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self.run_callbacks('on_predict_start') |
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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) |
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self.run_callbacks('on_predict_postprocess_end') |
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# visualize, save, write results |
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for i in range(len(im)): |
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p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \ |
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else (path, im0s.copy()) |
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p = Path(p) |
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if self.args.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 self.args.verbose: |
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LOGGER.info(f'{s}{self.dt[1].dt * 1E3:.1f}ms') |
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# Release assets |
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if isinstance(self.vid_writer[-1], cv2.VideoWriter): |
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self.vid_writer[-1].release() # release final video writer |
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# Print results |
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if self.args.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 preprocess, %.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 or self.args.save_txt or self.args.save_crop: |
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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, data=self.args.data, fp16=self.args.half) |
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self.device = device |
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self.model.eval() |
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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(500 if self.batch[4].startswith('image') else 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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