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353 lines
16 KiB
353 lines
16 KiB
# 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/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 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.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 LetterBox, 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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STREAM_WARNING = """
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WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
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causing potential out-of-memory errors for large sources or long-running streams/videos.
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Usage:
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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 = self.get_save_dir()
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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.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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callbacks.add_integration_callbacks(self)
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def get_save_dir(self):
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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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return increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
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def preprocess(self, im):
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"""Prepares input image before inference.
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Args:
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im (torch.Tensor | List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
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"""
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if not isinstance(im, torch.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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# NOTE: assuming im with (b, 3, h, w) if it's a tensor
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img = im.to(self.device)
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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return img
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def pre_transform(self, im):
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"""Pre-tranform 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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Return: A list of transformed imgs.
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"""
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same_shapes = all(x.shape == im[0].shape for x in im)
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auto = same_shapes and self.model.pt
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return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(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: # 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 = dict(line_width=self.args.line_width,
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boxes=self.args.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', file_name=self.data_path.stem)
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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):
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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)
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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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"""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, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
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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, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs
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@smart_inference_mode()
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def stream_inference(self, source=None, model=None):
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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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# 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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visualize = increment_path(self.save_dir / Path(path[0]).stem,
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mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
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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.model(im, augment=self.args.augment, visualize=visualize)
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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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if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
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if self.args.save or self.args.save_txt or self.args.show:
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LOGGER.warning('WARNING ⚠️ save, save_txt and show argument not enabled for tensor inference.')
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continue
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p, im0 = path[i], 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, *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, verbose=True):
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"""Initialize YOLO model with given parameters and set it to evaluation mode."""
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device = select_device(self.args.device, verbose=verbose)
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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,
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device=device,
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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 = device
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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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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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"""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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"""
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Add callback
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"""
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self.callbacks[event].append(func)
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