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# Ultralytics YOLO 🚀, AGPL-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/LNwODJXcvt4' # YouTube |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP, TCP 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.mlpackage # 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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import threading |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import torch |
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from ultralytics.cfg import get_cfg, get_save_dir |
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from ultralytics.data import load_inference_source |
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from ultralytics.data.augment import LetterBox, classify_transforms |
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from ultralytics.nn.autobackend import AutoBackend |
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from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, WINDOWS, callbacks, colorstr, ops |
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from ultralytics.utils.checks import check_imgsz, check_imshow |
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from ultralytics.utils.files import increment_path |
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from ultralytics.utils.torch_utils import select_device, smart_inference_mode |
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STREAM_WARNING = """ |
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WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory |
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errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help. |
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Example: |
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results = model(source=..., stream=True) # generator of Results objects |
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for r in results: |
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boxes = r.boxes # Boxes object for bbox outputs |
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masks = r.masks # Masks object for segment masks outputs |
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probs = r.probs # Class probabilities for classification outputs |
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""" |
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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_warmup (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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data_path (str): Path to data. |
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""" |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=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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self.save_dir = get_save_dir(self.args) |
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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, self.vid_frame = None, None, None |
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self.plotted_img = 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.results = None |
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self.transforms = None |
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self.callbacks = _callbacks or callbacks.get_default_callbacks() |
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self.txt_path = None |
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self._lock = threading.Lock() # for automatic thread-safe inference |
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callbacks.add_integration_callbacks(self) |
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def preprocess(self, im): |
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""" |
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Prepares input image before inference. |
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Args: |
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im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list. |
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""" |
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not_tensor = not isinstance(im, torch.Tensor) |
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if not_tensor: |
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im = np.stack(self.pre_transform(im)) |
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im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW, (n, 3, h, w) |
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im = np.ascontiguousarray(im) # contiguous |
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im = torch.from_numpy(im) |
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im = im.to(self.device) |
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im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32 |
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if not_tensor: |
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im /= 255 # 0 - 255 to 0.0 - 1.0 |
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return im |
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def inference(self, im, *args, **kwargs): |
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"""Runs inference on a given image using the specified model and arguments.""" |
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visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem, |
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mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False |
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return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs) |
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def pre_transform(self, im): |
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""" |
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Pre-transform input image before inference. |
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Args: |
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im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. |
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Returns: |
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(list): A list of transformed images. |
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""" |
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same_shapes = all(x.shape == im[0].shape for x in im) |
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letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride) |
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return [letterbox(image=x) for x in im] |
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def write_results(self, idx, results, batch): |
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"""Write inference results to a file or directory.""" |
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p, im, _ = batch |
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log_string = '' |
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if len(im.shape) == 3: |
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im = im[None] # expand for batch dim |
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if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1 |
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log_string += f'{idx}: ' |
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frame = self.dataset.count |
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else: |
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frame = getattr(self.dataset, 'frame', 0) |
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self.data_path = p |
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') |
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log_string += '%gx%g ' % im.shape[2:] # print string |
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result = results[idx] |
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log_string += result.verbose() |
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if self.args.save or self.args.show: # Add bbox to image |
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plot_args = { |
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'line_width': self.args.line_width, |
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'boxes': self.args.show_boxes, |
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'conf': self.args.show_conf, |
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'labels': self.args.show_labels} |
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if not self.args.retina_masks: |
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plot_args['im_gpu'] = im[idx] |
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self.plotted_img = result.plot(**plot_args) |
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# Write |
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if self.args.save_txt: |
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result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf) |
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if self.args.save_crop: |
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result.save_crop(save_dir=self.save_dir / 'crops', |
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file_name=self.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}')) |
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return log_string |
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def postprocess(self, preds, img, orig_imgs): |
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"""Post-processes predictions for an image and returns them.""" |
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return preds |
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def __call__(self, source=None, model=None, stream=False, *args, **kwargs): |
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"""Performs inference on an image or stream.""" |
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self.stream = stream |
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if stream: |
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return self.stream_inference(source, model, *args, **kwargs) |
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else: |
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return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one |
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def predict_cli(self, source=None, model=None): |
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""" |
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Method used for CLI prediction. |
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It uses always generator as outputs as not required by CLI mode. |
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""" |
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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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"""Sets up source and inference mode.""" |
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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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self.transforms = getattr(self.model.model, 'transforms', classify_transforms( |
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self.imgsz[0])) if self.args.task == 'classify' else None |
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self.dataset = load_inference_source(source=source, |
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imgsz=self.imgsz, |
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vid_stride=self.args.vid_stride, |
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buffer=self.args.stream_buffer) |
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self.source_type = self.dataset.source_type |
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if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or # streams |
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len(self.dataset) > 1000 or # images |
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any(getattr(self.dataset, 'video_flag', [False]))): # videos |
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LOGGER.warning(STREAM_WARNING) |
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self.vid_path = [None] * self.dataset.bs |
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self.vid_writer = [None] * self.dataset.bs |
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self.vid_frame = [None] * self.dataset.bs |
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@smart_inference_mode() |
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def stream_inference(self, source=None, model=None, *args, **kwargs): |
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"""Streams real-time inference on camera feed and saves results to file.""" |
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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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with self._lock: # for thread-safe inference |
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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.batch, profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile()) |
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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, im0s, vid_cap, s = batch |
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# Preprocess |
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with profilers[0]: |
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im = self.preprocess(im0s) |
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# Inference |
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with profilers[1]: |
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preds = self.inference(im, *args, **kwargs) |
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if self.args.embed: |
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yield from [preds] if isinstance(preds, torch.Tensor) else preds # yield embedding tensors |
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continue |
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# Postprocess |
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with profilers[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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n = len(im0s) |
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for i in range(n): |
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self.seen += 1 |
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self.results[i].speed = { |
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'preprocess': profilers[0].dt * 1E3 / n, |
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'inference': profilers[1].dt * 1E3 / n, |
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'postprocess': profilers[2].dt * 1E3 / n} |
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p, im0 = path[i], None if self.source_type.tensor else im0s[i].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.save or self.args.save_txt: |
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self.results[i].save_dir = self.save_dir.__str__() |
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if self.args.show and self.plotted_img is not None: |
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self.show(p) |
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if self.args.save and self.plotted_img is not None: |
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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}{profilers[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 profilers) # 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, *im.shape[2:])}' % 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, verbose=True): |
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"""Initialize YOLO model with given parameters and set it to evaluation mode.""" |
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self.model = AutoBackend(model or self.args.model, |
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device=select_device(self.args.device, verbose=verbose), |
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dnn=self.args.dnn, |
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data=self.args.data, |
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fp16=self.args.half, |
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fuse=True, |
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verbose=verbose) |
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self.device = self.model.device # update device |
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self.args.half = self.model.fp16 # update half |
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self.model.eval() |
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def show(self, p): |
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"""Display an image in a window using OpenCV imshow().""" |
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im0 = self.plotted_img |
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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[3].startswith('image') else 1) # 1 millisecond |
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def save_preds(self, vid_cap, idx, save_path): |
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"""Save video predictions as mp4 at specified path.""" |
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im0 = self.plotted_img |
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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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frames_path = f'{save_path.split(".", 1)[0]}_frames/' |
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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 self.args.save_frames: |
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Path(frames_path).mkdir(parents=True, exist_ok=True) |
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self.vid_frame[idx] = 0 |
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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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suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG') |
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self.vid_writer[idx] = cv2.VideoWriter(str(Path(save_path).with_suffix(suffix)), |
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cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) |
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# Write video |
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self.vid_writer[idx].write(im0) |
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# Write frame |
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if self.args.save_frames: |
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cv2.imwrite(f'{frames_path}{self.vid_frame[idx]}.jpg', im0) |
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self.vid_frame[idx] += 1 |
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def run_callbacks(self, event: str): |
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"""Runs all registered callbacks for a specific event.""" |
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for callback in self.callbacks.get(event, []): |
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callback(self) |
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def add_callback(self, event: str, func): |
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"""Add callback.""" |
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self.callbacks[event].append(func)
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