|
|
# 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 |
|
|
""" |
|
|
import platform |
|
|
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_path (str): Path to video file. |
|
|
vid_writer (cv2.VideoWriter): Video writer for saving video output. |
|
|
data_path (str): Path to data. |
|
|
""" |
|
|
|
|
|
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_path, self.vid_writer, self.vid_frame = None, None, None |
|
|
self.plotted_img = None |
|
|
self.data_path = None |
|
|
self.source_type = None |
|
|
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 = all(x.shape == im[0].shape for x in im) |
|
|
letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride) |
|
|
return [letterbox(image=x) for x in im] |
|
|
|
|
|
def write_results(self, idx, results, batch): |
|
|
"""Write inference results to a file or directory.""" |
|
|
p, im, _ = batch |
|
|
log_string = '' |
|
|
if len(im.shape) == 3: |
|
|
im = im[None] # expand for batch dim |
|
|
if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1 |
|
|
log_string += f'{idx}: ' |
|
|
frame = self.dataset.count |
|
|
else: |
|
|
frame = getattr(self.dataset, 'frame', 0) |
|
|
self.data_path = p |
|
|
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') |
|
|
log_string += '%gx%g ' % im.shape[2:] # print string |
|
|
result = results[idx] |
|
|
log_string += result.verbose() |
|
|
|
|
|
if self.args.save or self.args.show: # Add bbox to image |
|
|
plot_args = { |
|
|
'line_width': self.args.line_width, |
|
|
'boxes': self.args.show_boxes, |
|
|
'conf': self.args.show_conf, |
|
|
'labels': self.args.show_labels} |
|
|
if not self.args.retina_masks: |
|
|
plot_args['im_gpu'] = im[idx] |
|
|
self.plotted_img = result.plot(**plot_args) |
|
|
# Write |
|
|
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.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}')) |
|
|
|
|
|
return log_string |
|
|
|
|
|
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: # 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, |
|
|
imgsz=self.imgsz, |
|
|
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.dataset.mode == 'stream' or # streams |
|
|
len(self.dataset) > 1000 or # images |
|
|
any(getattr(self.dataset, 'video_flag', [False]))): # videos |
|
|
LOGGER.warning(STREAM_WARNING) |
|
|
self.vid_path = [None] * self.dataset.bs |
|
|
self.vid_writer = [None] * self.dataset.bs |
|
|
self.vid_frame = [None] * self.dataset.bs |
|
|
|
|
|
@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, profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile()) |
|
|
self.run_callbacks('on_predict_start') |
|
|
|
|
|
for batch in self.dataset: |
|
|
self.run_callbacks('on_predict_batch_start') |
|
|
self.batch = batch |
|
|
path, im0s, vid_cap, s = 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} |
|
|
p, im0 = path[i], None if self.source_type.tensor else im0s[i].copy() |
|
|
p = Path(p) |
|
|
|
|
|
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show: |
|
|
s += self.write_results(i, self.results, (p, im, im0)) |
|
|
if self.args.save or self.args.save_txt: |
|
|
self.results[i].save_dir = self.save_dir.__str__() |
|
|
if self.args.show and self.plotted_img is not None: |
|
|
self.show(p) |
|
|
if self.args.save and self.plotted_img is not None: |
|
|
self.save_preds(vid_cap, i, str(self.save_dir / p.name)) |
|
|
|
|
|
self.run_callbacks('on_predict_batch_end') |
|
|
yield from self.results |
|
|
|
|
|
# Print time (inference-only) |
|
|
if self.args.verbose: |
|
|
LOGGER.info(f'{s}{profilers[1].dt * 1E3:.1f}ms') |
|
|
|
|
|
# Release assets |
|
|
if isinstance(self.vid_writer[-1], cv2.VideoWriter): |
|
|
self.vid_writer[-1].release() # release final video writer |
|
|
|
|
|
# Print 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'{(1, 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(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 |
|
|
self.model.eval() |
|
|
|
|
|
def show(self, p): |
|
|
"""Display an image in a window using OpenCV imshow().""" |
|
|
im0 = self.plotted_img |
|
|
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(500 if self.batch[3].startswith('image') else 1) # 1 millisecond |
|
|
|
|
|
def save_preds(self, vid_cap, idx, save_path): |
|
|
"""Save video predictions as mp4 at specified path.""" |
|
|
im0 = self.plotted_img |
|
|
# Save imgs |
|
|
if self.dataset.mode == 'image': |
|
|
cv2.imwrite(save_path, im0) |
|
|
else: # 'video' or 'stream' |
|
|
frames_path = f'{save_path.split(".", 1)[0]}_frames/' |
|
|
if self.vid_path[idx] != save_path: # new video |
|
|
self.vid_path[idx] = save_path |
|
|
if self.args.save_frames: |
|
|
Path(frames_path).mkdir(parents=True, exist_ok=True) |
|
|
self.vid_frame[idx] = 0 |
|
|
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] |
|
|
suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG') |
|
|
self.vid_writer[idx] = cv2.VideoWriter(str(Path(save_path).with_suffix(suffix)), |
|
|
cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) |
|
|
# Write video |
|
|
self.vid_writer[idx].write(im0) |
|
|
|
|
|
# Write frame |
|
|
if self.args.save_frames: |
|
|
cv2.imwrite(f'{frames_path}{self.vid_frame[idx]}.jpg', im0) |
|
|
self.vid_frame[idx] += 1 |
|
|
|
|
|
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)
|
|
|
|
