# predictor engine by Ultralytics
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ yolo task=... mode=predict model=s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ yolo task=... mode=predict --weights yolov8n.pt # PyTorch
yolov8n.torchscript # TorchScript
yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlmodel # CoreML (macOS-only)
yolov8n_saved_model # TensorFlow SavedModel
yolov8n.pb # TensorFlow GraphDef
yolov8n.tflite # TensorFlow Lite
yolov8n_edgetpu.tflite # TensorFlow Edge TPU
yolov8n_paddle_model # PaddlePaddle
"""
import platform
from collections import defaultdict
from pathlib import Path
import cv2
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.configs import get_config
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, callbacks, colorstr, ops
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
class BasePredictor:
"""
BasePredictor
A base class for creating predictors.
Attributes:
args (OmegaConf): Configuration for the predictor.
save_dir (Path): Directory to save results.
done_setup (bool): Whether the predictor has finished setup.
model (nn.Module): Model used for prediction.
data (dict): Data configuration.
device (torch.device): Device used for prediction.
dataset (Dataset): Dataset used for prediction.
vid_path (str): Path to video file.
vid_writer (cv2.VideoWriter): Video writer for saving video output.
view_img (bool): Whether to view image output.
annotator (Annotator): Annotator used for prediction.
data_path (str): Path to data.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
"""
Initializes the BasePredictor class.
Args:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
project = self.args.project or f"runs/{self.args.task}"
name = self.args.name or f"{self.args.mode}"
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
self.done_setup = False
# Usable if setup is done
self.model = None
self.data = self.args.data # data_dict
self.device = None
self.dataset = None
self.vid_path, self.vid_writer = None, None
self.view_img = None
self.annotator = None
self.data_path = None
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
def preprocess(self, img):
pass
def get_annotator(self, img):
raise NotImplementedError("get_annotator function needs to be implemented")
def write_results(self, pred, batch, print_string):
raise NotImplementedError("print_results function needs to be implemented")
def postprocess(self, preds, img, orig_img):
return preds
def setup(self, source=None, model=None):
# source
source = str(source or self.args.source)
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
screenshot = source.lower().startswith('screen')
if is_url and is_file:
source = check_file(source) # download
# model
device = select_device(self.args.device)
model = model or self.args.model
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
stride, pt = model.stride, model.pt
imgsz = check_imgsz(self.args.imgsz, stride=stride) # check image size
# Dataloader
bs = 1 # batch_size
if webcam:
self.view_img = check_imshow(warn=True)
self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
bs = len(self.dataset)
elif screenshot:
self.dataset = LoadScreenshots(source, imgsz=imgsz, stride=stride, auto=pt)
else:
self.dataset = LoadImages(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
self.vid_path, self.vid_writer = [None] * bs, [None] * bs
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
self.model = model
self.webcam = webcam
self.screenshot = screenshot
self.imgsz = imgsz
self.done_setup = True
self.device = device
return model
@smart_inference_mode()
def __call__(self, source=None, model=None):
self.run_callbacks("on_predict_start")
model = self.model if self.done_setup else self.setup(source, model)
self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
for batch in self.dataset:
self.run_callbacks("on_predict_batch_start")
path, im, im0s, vid_cap, s = batch
visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
with self.dt[0]:
im = self.preprocess(im)
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with self.dt[1]:
preds = model(im, augment=self.args.augment, visualize=visualize)
# postprocess
with self.dt[2]:
preds = self.postprocess(preds, im, im0s)
for i in range(len(im)):
if self.webcam:
path, im0s = path[i], im0s[i]
p = Path(path)
s += self.write_results(i, preds, (p, im, im0s))
if self.args.show:
self.show(p)
if self.args.save:
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
self.run_callbacks("on_predict_batch_end")
# Print results
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
LOGGER.info(
f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}'
% t)
if self.args.save_txt or self.args.save:
s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
self.run_callbacks("on_predict_end")
def show(self, p):
im0 = self.annotator.result()
if platform.system() == 'Linux' and p not in self.windows:
self.windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
def save_preds(self, vid_cap, idx, save_path):
im0 = self.annotator.result()
# save imgs
if self.dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if self.vid_path[idx] != save_path: # new video
self.vid_path[idx] = save_path
if isinstance(self.vid_writer[idx], cv2.VideoWriter):
self.vid_writer[idx].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
self.vid_writer[idx].write(im0)
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)