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Saeed Khosravi
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ yolo mode=predict model=yolo11n.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/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP, TCP stream
Usage - formats:
$ yolo mode=predict model=yolo11n.pt # PyTorch
yolo11n.torchscript # TorchScript
yolo11n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolo11n_openvino_model # OpenVINO
yolo11n.engine # TensorRT
yolo11n.mlpackage # CoreML (macOS-only)
yolo11n_saved_model # TensorFlow SavedModel
yolo11n.pb # TensorFlow GraphDef
yolo11n.tflite # TensorFlow Lite
yolo11n_edgetpu.tflite # TensorFlow Edge TPU
yolo11n_paddle_model # PaddlePaddle
yolo11n.mnn # MNN
yolo11n_ncnn_model # NCNN
yolo11n_imx_model # Sony IMX
yolo11n_rknn_model # Rockchip RKNN
"""
from __future__ import annotations
import platform
import re
import threading
from pathlib import Path
from typing import Any
import cv2
import numpy as np
import torch
from ultralytics.cfg import get_cfg, get_save_dir
from ultralytics.data import load_inference_source
from ultralytics.data.augment import LetterBox
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, WINDOWS, callbacks, colorstr, ops
from ultralytics.utils.checks import check_imgsz, check_imshow
from ultralytics.utils.files import increment_path
from ultralytics.utils.torch_utils import attempt_compile, select_device, smart_inference_mode
STREAM_WARNING = """
inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory
errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.
Example:
results = model(source=..., stream=True) # generator of Results objects
for r in results:
boxes = r.boxes # Boxes object for bbox outputs
masks = r.masks # Masks object for segment masks outputs
probs = r.probs # Class probabilities for classification outputs
"""
class BasePredictor:
"""
A base class for creating predictors.
This class provides the foundation for prediction functionality, handling model setup, inference,
and result processing across various input sources.
Attributes:
args (SimpleNamespace): Configuration for the predictor.
save_dir (Path): Directory to save results.
done_warmup (bool): Whether the predictor has finished setup.
model (torch.nn.Module): Model used for prediction.
data (dict): Data configuration.
device (torch.device): Device used for prediction.
dataset (Dataset): Dataset used for prediction.
vid_writer (dict[str, cv2.VideoWriter]): Dictionary of {save_path: video_writer} for saving video output.
plotted_img (np.ndarray): Last plotted image.
source_type (SimpleNamespace): Type of input source.
seen (int): Number of images processed.
windows (list[str]): List of window names for visualization.
batch (tuple): Current batch data.
results (list[Any]): Current batch results.
transforms (callable): Image transforms for classification.
callbacks (dict[str, list[callable]]): Callback functions for different events.
txt_path (Path): Path to save text results.
_lock (threading.Lock): Lock for thread-safe inference.
Methods:
preprocess: Prepare input image before inference.
inference: Run inference on a given image.
postprocess: Process raw predictions into structured results.
predict_cli: Run prediction for command line interface.
setup_source: Set up input source and inference mode.
stream_inference: Stream inference on input source.
setup_model: Initialize and configure the model.
write_results: Write inference results to files.
save_predicted_images: Save prediction visualizations.
show: Display results in a window.
run_callbacks: Execute registered callbacks for an event.
add_callback: Register a new callback function.
"""
def __init__(
self,
cfg=DEFAULT_CFG,
overrides: dict[str, Any] | None = None,
_callbacks: dict[str, list[callable]] | None = None,
):
"""
Initialize the BasePredictor class.
Args:
cfg (str | dict): Path to a configuration file or a configuration dictionary.
overrides (dict, optional): Configuration overrides.
_callbacks (dict, optional): Dictionary of callback functions.
"""
self.args = get_cfg(cfg, overrides)
self.save_dir = get_save_dir(self.args)
if self.args.conf is None:
self.args.conf = 0.25 # default conf=0.25
self.done_warmup = False
if self.args.show:
self.args.show = check_imshow(warn=True)
# Usable if setup is done
self.model = None
self.data = self.args.data # data_dict
self.imgsz = None
self.device = None
self.dataset = None
self.vid_writer = {} # dict of {save_path: video_writer, ...}
self.plotted_img = None
self.source_type = None
self.seen = 0
self.windows = []
self.batch = None
self.results = None
self.transforms = None
self.callbacks = _callbacks or callbacks.get_default_callbacks()
self.txt_path = None
self._lock = threading.Lock() # for automatic thread-safe inference
callbacks.add_integration_callbacks(self)
def preprocess(self, im: torch.Tensor | list[np.ndarray]) -> torch.Tensor:
"""
Prepare input image before inference.
Args:
im (torch.Tensor | list[np.ndarray]): Images of shape (N, 3, H, W) for tensor, [(H, W, 3) x N] for list.
Returns:
(torch.Tensor): Preprocessed image tensor of shape (N, 3, H, W).
"""
not_tensor = not isinstance(im, torch.Tensor)
if not_tensor:
im = np.stack(self.pre_transform(im))
if im.shape[-1] == 3:
im = im[..., ::-1] # BGR to RGB
im = im.transpose((0, 3, 1, 2)) # BHWC to BCHW, (n, 3, h, w)
im = np.ascontiguousarray(im) # contiguous
im = torch.from_numpy(im)
im = im.to(self.device)
im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32
if not_tensor:
im /= 255 # 0 - 255 to 0.0 - 1.0
return im
def inference(self, im: torch.Tensor, *args, **kwargs):
"""Run inference on a given image using the specified model and arguments."""
visualize = (
increment_path(self.save_dir / Path(self.batch[0][0]).stem, mkdir=True)
if self.args.visualize and (not self.source_type.tensor)
else False
)
return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs)
def pre_transform(self, im: list[np.ndarray]) -> list[np.ndarray]:
"""
Pre-transform input image before inference.
Args:
im (list[np.ndarray]): List of images with shape [(H, W, 3) x N].
Returns:
(list[np.ndarray]): List of transformed images.
"""
same_shapes = len({x.shape for x in im}) == 1
letterbox = LetterBox(
self.imgsz,
auto=same_shapes
and self.args.rect
and (self.model.pt or (getattr(self.model, "dynamic", False) and not self.model.imx)),
stride=self.model.stride,
)
return [letterbox(image=x) for x in im]
def postprocess(self, preds, img, orig_imgs):
"""Post-process predictions for an image and return them."""
return preds
def __call__(self, source=None, model=None, stream: bool = False, *args, **kwargs):
"""
Perform inference on an image or stream.
Args:
source (str | Path | list[str] | list[Path] | list[np.ndarray] | np.ndarray | torch.Tensor, optional):
Source for inference.
model (str | Path | torch.nn.Module, optional): Model for inference.
stream (bool): Whether to stream the inference results. If True, returns a generator.
*args (Any): Additional arguments for the inference method.
**kwargs (Any): Additional keyword arguments for the inference method.
Returns:
(list[ultralytics.engine.results.Results] | generator): Results objects or generator of Results objects.
"""
self.stream = stream
if stream:
return self.stream_inference(source, model, *args, **kwargs)
else:
return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one
def predict_cli(self, source=None, model=None):
"""
Method used for Command Line Interface (CLI) prediction.
This function is designed to run predictions using the CLI. It sets up the source and model, then processes
the inputs in a streaming manner. This method ensures that no outputs accumulate in memory by consuming the
generator without storing results.
Args:
source (str | Path | list[str] | list[Path] | list[np.ndarray] | np.ndarray | torch.Tensor, optional):
Source for inference.
model (str | Path | torch.nn.Module, optional): Model for inference.
Note:
Do not modify this function or remove the generator. The generator ensures that no outputs are
accumulated in memory, which is critical for preventing memory issues during long-running predictions.
"""
gen = self.stream_inference(source, model)
for _ in gen: # sourcery skip: remove-empty-nested-block, noqa
pass
def setup_source(self, source):
"""
Set up source and inference mode.
Args:
source (str | Path | list[str] | list[Path] | list[np.ndarray] | np.ndarray | torch.Tensor):
Source for inference.
"""
self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
self.dataset = load_inference_source(
source=source,
batch=self.args.batch,
vid_stride=self.args.vid_stride,
buffer=self.args.stream_buffer,
channels=getattr(self.model, "ch", 3),
)
self.source_type = self.dataset.source_type
long_sequence = (
self.source_type.stream
or self.source_type.screenshot
or len(self.dataset) > 1000 # many images
or any(getattr(self.dataset, "video_flag", [False]))
)
if long_sequence:
import torchvision # noqa (import here triggers torchvision NMS use in nms.py)
if not getattr(self, "stream", True): # videos
LOGGER.warning(STREAM_WARNING)
self.vid_writer = {}
@smart_inference_mode()
def stream_inference(self, source=None, model=None, *args, **kwargs):
"""
Stream real-time inference on camera feed and save results to file.
Args:
source (str | Path | list[str] | list[Path] | list[np.ndarray] | np.ndarray | torch.Tensor, optional):
Source for inference.
model (str | Path | torch.nn.Module, optional): Model for inference.
*args (Any): Additional arguments for the inference method.
**kwargs (Any): Additional keyword arguments for the inference method.
Yields:
(ultralytics.engine.results.Results): Results objects.
"""
if self.args.verbose:
LOGGER.info("")
# Setup model
if not self.model:
self.setup_model(model)
with self._lock: # for thread-safe inference
# Setup source every time predict is called
self.setup_source(source if source is not None else self.args.source)
# Check if save_dir/ label file exists
if self.args.save or self.args.save_txt:
(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.dataset.bs, self.model.ch, *self.imgsz)
)
self.done_warmup = True
self.seen, self.windows, self.batch = 0, [], None
profilers = (
ops.Profile(device=self.device),
ops.Profile(device=self.device),
ops.Profile(device=self.device),
)
self.run_callbacks("on_predict_start")
for self.batch in self.dataset:
self.run_callbacks("on_predict_batch_start")
paths, im0s, s = self.batch
# Preprocess
with profilers[0]:
im = self.preprocess(im0s)
# Inference
with profilers[1]:
preds = self.inference(im, *args, **kwargs)
if self.args.embed:
yield from [preds] if isinstance(preds, torch.Tensor) else preds # yield embedding tensors
continue
# Postprocess
with profilers[2]:
self.results = self.postprocess(preds, im, im0s)
self.run_callbacks("on_predict_postprocess_end")
# Visualize, save, write results
n = len(im0s)
try:
for i in range(n):
self.seen += 1
self.results[i].speed = {
"preprocess": profilers[0].dt * 1e3 / n,
"inference": profilers[1].dt * 1e3 / n,
"postprocess": profilers[2].dt * 1e3 / n,
}
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
s[i] += self.write_results(i, Path(paths[i]), im, s)
except StopIteration:
break
# Print batch results
if self.args.verbose:
LOGGER.info("\n".join(s))
self.run_callbacks("on_predict_batch_end")
yield from self.results
# Release assets
for v in self.vid_writer.values():
if isinstance(v, cv2.VideoWriter):
v.release()
if self.args.show:
cv2.destroyAllWindows() # close any open windows
# Print final results
if self.args.verbose and self.seen:
t = tuple(x.t / self.seen * 1e3 for x in profilers) # speeds per image
LOGGER.info(
f"Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape "
f"{(min(self.args.batch, self.seen), getattr(self.model, 'ch', 3), *im.shape[2:])}" % t
)
if self.args.save or self.args.save_txt or self.args.save_crop:
nl = len(list(self.save_dir.glob("labels/*.txt"))) # number of labels
s = f"\n{nl} label{'s' * (nl > 1)} 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, verbose: bool = True):
"""
Initialize YOLO model with given parameters and set it to evaluation mode.
Args:
model (str | Path | torch.nn.Module, optional): Model to load or use.
verbose (bool): Whether to print verbose output.
"""
self.model = AutoBackend(
model=model or self.args.model,
device=select_device(self.args.device, verbose=verbose),
dnn=self.args.dnn,
data=self.args.data,
fp16=self.args.half,
fuse=True,
verbose=verbose,
)
self.device = self.model.device # update device
self.args.half = self.model.fp16 # update half
if hasattr(self.model, "imgsz") and not getattr(self.model, "dynamic", False):
self.args.imgsz = self.model.imgsz # reuse imgsz from export metadata
self.model.eval()
self.model = attempt_compile(self.model, device=self.device, mode=self.args.compile)
def write_results(self, i: int, p: Path, im: torch.Tensor, s: list[str]) -> str:
"""
Write inference results to a file or directory.
Args:
i (int): Index of the current image in the batch.
p (Path): Path to the current image.
im (torch.Tensor): Preprocessed image tensor.
s (list[str]): List of result strings.
Returns:
(str): String with result information.
"""
string = "" # print string
if len(im.shape) == 3:
im = im[None] # expand for batch dim
if self.source_type.stream or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1
string += f"{i}: "
frame = self.dataset.count
else:
match = re.search(r"frame (\d+)/", s[i])
frame = int(match[1]) if match else None # 0 if frame undetermined
self.txt_path = self.save_dir / "labels" / (p.stem + ("" if self.dataset.mode == "image" else f"_{frame}"))
string += "{:g}x{:g} ".format(*im.shape[2:])
result = self.results[i]
result.save_dir = self.save_dir.__str__() # used in other locations
string += f"{result.verbose()}{result.speed['inference']:.1f}ms"
# Add predictions to image
if self.args.save or self.args.show:
self.plotted_img = result.plot(
line_width=self.args.line_width,
boxes=self.args.show_boxes,
conf=self.args.show_conf,
labels=self.args.show_labels,
im_gpu=None if self.args.retina_masks else im[i],
)
# Save results
if self.args.save_txt:
result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf)
if self.args.save_crop:
result.save_crop(save_dir=self.save_dir / "crops", file_name=self.txt_path.stem)
if self.args.show:
self.show(str(p))
if self.args.save:
self.save_predicted_images(self.save_dir / p.name, frame)
return string
def save_predicted_images(self, save_path: Path, frame: int = 0):
"""
Save video predictions as mp4 or images as jpg at specified path.
Args:
save_path (Path): Path to save the results.
frame (int): Frame number for video mode.
"""
im = self.plotted_img
# Save videos and streams
if self.dataset.mode in {"stream", "video"}:
fps = self.dataset.fps if self.dataset.mode == "video" else 30
frames_path = self.save_dir / f"{save_path.stem}_frames" # save frames to a separate directory
if save_path not in self.vid_writer: # new video
if self.args.save_frames:
Path(frames_path).mkdir(parents=True, exist_ok=True)
suffix, fourcc = (".mp4", "avc1") if MACOS else (".avi", "WMV2") if WINDOWS else (".avi", "MJPG")
self.vid_writer[save_path] = cv2.VideoWriter(
filename=str(Path(save_path).with_suffix(suffix)),
fourcc=cv2.VideoWriter_fourcc(*fourcc),
fps=fps, # integer required, floats produce error in MP4 codec
frameSize=(im.shape[1], im.shape[0]), # (width, height)
)
# Save video
self.vid_writer[save_path].write(im)
if self.args.save_frames:
cv2.imwrite(f"{frames_path}/{save_path.stem}_{frame}.jpg", im)
# Save images
else:
cv2.imwrite(str(save_path.with_suffix(".jpg")), im) # save to JPG for best support
def show(self, p: str = ""):
"""Display an image in a window."""
im = self.plotted_img
if platform.system() == "Linux" and p not in self.windows:
self.windows.append(p)
cv2.namedWindow(p, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(p, im.shape[1], im.shape[0]) # (width, height)
cv2.imshow(p, im)
if cv2.waitKey(300 if self.dataset.mode == "image" else 1) & 0xFF == ord("q"): # 300ms if image; else 1ms
raise StopIteration
def run_callbacks(self, event: str):
"""Run all registered callbacks for a specific event."""
for callback in self.callbacks.get(event, []):
callback(self)
def add_callback(self, event: str, func: callable):
"""Add a callback function for a specific event."""
self.callbacks[event].append(func)