# Ultralytics YOLO 🚀, GPL-3.0 license
"""
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 itertools import chain
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, LoadPilAndNumpy, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, SETTINGS, 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 (SimpleNamespace): 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.
annotator (Annotator): Annotator used for prediction.
data_path (str): Path to data.
"""
def __init__(self, config=DEFAULT_CFG_PATH, 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.
"""
self.args = get_config(config, overrides)
project = self.args.project or Path(SETTINGS['runs_dir']) / 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)
if self.args.conf is None:
self.args.conf = 0.25 # default conf=0.25
self.done_warmup = False
# Usable if setup is done
self.model = None
self.data = self.args.data # data_dict
self.bs = None
self.imgsz = None
self.device = None
self.dataset = None
self.vid_path, self.vid_writer = None, 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, results, batch, print_string):
raise NotImplementedError("print_results function needs to be implemented")
def postprocess(self, preds, img, orig_img):
return preds
def setup_source(self, source=None):
if not self.model:
raise Exception("setup model before setting up source!")
# source
source, webcam, screenshot, from_img = self.check_source(source)
# model
stride, pt = self.model.stride, self.model.pt
imgsz = check_imgsz(self.args.imgsz, stride=stride, min_dim=2) # check image size
# Dataloader
bs = 1 # batch_size
if webcam:
self.args.show = check_imshow(warn=True)
self.dataset = LoadStreams(source,
imgsz=imgsz,
stride=stride,
auto=pt,
transforms=getattr(self.model.model, 'transforms', None),
vid_stride=self.args.vid_stride)
bs = len(self.dataset)
elif screenshot:
self.dataset = LoadScreenshots(source,
imgsz=imgsz,
stride=stride,
auto=pt,
transforms=getattr(self.model.model, 'transforms', None))
elif from_img:
self.dataset = LoadPilAndNumpy(source,
imgsz=imgsz,
stride=stride,
auto=pt,
transforms=getattr(self.model.model, 'transforms', None))
else:
self.dataset = LoadImages(source,
imgsz=imgsz,
stride=stride,
auto=pt,
transforms=getattr(self.model.model, 'transforms', None),
vid_stride=self.args.vid_stride)
self.vid_path, self.vid_writer = [None] * bs, [None] * bs
self.webcam = webcam
self.screenshot = screenshot
self.from_img = from_img
self.imgsz = imgsz
self.bs = bs
@smart_inference_mode()
def __call__(self, source=None, model=None, verbose=False, stream=False):
if stream:
return self.stream_inference(source, model, verbose)
else:
return list(self.stream_inference(source, model, verbose)) # merge list of Result into one
def predict_cli(self):
# Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode
gen = self.stream_inference(verbose=True)
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
pass
def stream_inference(self, source=None, model=None, verbose=False):
self.run_callbacks("on_predict_start")
# setup model
if not self.model:
self.setup_model(model)
# setup source. Run every time predict is called
self.setup_source(source)
# check if save_dir/ label file exists
if self.args.save:
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
# warmup model
if not self.done_warmup:
self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.bs, 3, *self.imgsz))
self.done_warmup = True
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 = self.model(im, augment=self.args.augment, visualize=visualize)
# postprocess
with self.dt[2]:
results = self.postprocess(preds, im, im0s)
for i in range(len(im)):
p, im0 = (path[i], im0s[i]) if self.webcam or self.from_img else (path, im0s)
p = Path(p)
if verbose or self.args.save or self.args.save_txt or self.args.show:
s += self.write_results(i, results, (p, im, im0))
if self.args.show:
self.show(p)
if self.args.save:
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
yield from results
# Print time (inference-only)
if verbose:
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
if verbose:
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 '
f'{(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 setup_model(self, 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
self.model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half)
self.device = device
self.model.eval()
def check_source(self, source):
source = source if source is not None else self.args.source
webcam, screenshot, from_img = False, False, False
if isinstance(source, (str, int, Path)): # int for local usb carame
source = str(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
else:
from_img = True
return source, webcam, screenshot, from_img
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 = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
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)