修改\ultralytics-main\ultralytics\engine\predictor.py，即可
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ yolo mode=predict model=yolov8n.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=yolov8n.pt # PyTorch
yolov8n.torchscript # TorchScript
yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlpackage # 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
yolov8n_ncnn_model # NCNN
"""
import platform
import re
import threading
from pathlib import Path
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, classify_transforms
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 select_device, smart_inference_mode
STREAM_WARNING = """
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:
"""
BasePredictor.
A base class for creating predictors.
Attributes:
args (SimpleNamespace): Configuration for the predictor.
save_dir (Path): Directory to save results.
done_warmup (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_writer (dict): Dictionary of {save_path: video_writer, ...} writer for saving video output.
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""
Initializes the BasePredictor class.
Args:
cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
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):
"""
Prepares input image before inference.
Args:
im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
"""
not_tensor = not isinstance(im, torch.Tensor)
if not_tensor:
im = np.stack(self.pre_transform(im))
im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, 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, *args, **kwargs):
"""Runs 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):
"""
Pre-transform input image before inference.
Args:
im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
Returns:
(list): A list of transformed images.
"""
same_shapes = len({x.shape for x in im}) == 1
letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride)
return [letterbox(image=x) for x in im]
def postprocess(self, preds, img, orig_imgs):
"""Post-processes predictions for an image and returns them."""
return preds
def __call__(self, source=None, model=None, stream=False, *args, **kwargs):
"""Performs inference on an image or stream."""
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 CLI prediction.
It uses always generator as outputs as not required by CLI mode.
"""
gen = self.stream_inference(source, model)
for _ in gen: # noqa, running CLI inference without accumulating any outputs (do not modify)
pass
def setup_source(self, source):
"""Sets up source and inference mode."""
self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
self.transforms = (
getattr(
self.model.model,
"transforms",
classify_transforms(self.imgsz[0], crop_fraction=self.args.crop_fraction),
)
if self.args.task == "classify"
else None
)
self.dataset = load_inference_source(
source=source,
batch=self.args.batch,
vid_stride=self.args.vid_stride,
buffer=self.args.stream_buffer,
)
self.source_type = self.dataset.source_type
if not getattr(self, "stream", True) and (
self.source_type.stream
or self.source_type.screenshot
or len(self.dataset) > 1000 # many images
or any(getattr(self.dataset, "video_flag", [False]))
): # videos
LOGGER.warning(STREAM_WARNING)
self.vid_writer = {}
@smart_inference_mode()
def stream_inference(self, source=None, model=None, *args, **kwargs):
"""Streams real-time inference on camera feed and saves results to file."""
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, 3, *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)
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)
# 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()
# 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), 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=True):
"""Initialize YOLO model with given parameters and set it to evaluation mode."""
self.model = AutoBackend(
weights=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,
batch=self.args.batch,
fuse=True,
verbose=verbose,
)
self.device = self.model.device # update device
self.args.half = self.model.fp16 # update half
self.model.eval()
def write_results(self, i, p, im, s):
"""Write inference results to a file or directory."""
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.group(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 += "%gx%g " % im.shape[2:]
result = self.results[i]
result.save_dir = self.save_dir.__str__() # used in other locations
string += result.verbose() + f"{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(str(self.save_dir / p.name), frame)
return string
def save_predicted_images(self, save_path="", frame=0):
"""Save video predictions as mp4 at specified path."""
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 = f'{save_path.split(".", 1)[0]}_frames/'
if save_path not in self.vid_writer: # new video
if self.args.save_frames:
Path(frames_path).mkdir(parents=True, exist_ok=True)
# Always save as MP4 regardless of OS
suffix, fourcc = (".mp4", "avc1")
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}{frame}.jpg", im)
# Save images
else:
cv2.imwrite(save_path, im)
def show(self, p=""):
"""Display an image in a window using OpenCV imshow()."""
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)
cv2.waitKey(300 if self.dataset.mode == "image" else 1) # 1 millisecond
def run_callbacks(self, event: str):
"""Runs all registered callbacks for a specific event."""
for callback in self.callbacks.get(event, []):
callback(self)
def add_callback(self, event: str, func):
"""Add callback."""
self.callbacks[event].append(func)