xclele/yolov10
1
0
Fork 0
mirror of https://github.com/THU-MIG/yolov10.git synced 2025-05-23 05:24:22 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

ultralytics 8.0.191 fix yolo checks for missing packages (#5179)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Muhammad Rizwan Munawar <62513924+RizwanMunawar@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-10-02 22:45:30 +02:00 committed by GitHub
parent 9aaa5d5ed0
commit 525c8b0294
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
17 changed files with 110 additions and 74 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
docs/help/contributing.md
View File

@ -52,22 +52,23 @@ When adding new functions or classes, please include a [Google-style docstring](
Example Google-style docstring:
```python
def example_function(arg1: int, arg2: str) -> bool:
"""Example function that demonstrates Google-style docstrings.
def example_function(arg1: int, arg2: int) -> bool:
"""
Example function that demonstrates Google-style docstrings.
Args:
arg1 (int): The first argument.
arg2 (str): The second argument.
arg2 (int): The second argument.
Returns:
bool: True if successful, False otherwise.
(bool): True if successful, False otherwise.
Raises:
ValueError: If `arg1` is negative or `arg2` is empty.
Examples:
>>> result = example_function(1, 2) # returns False
"""
if arg1 < 0 or not arg2:
raise ValueError("Invalid input values")
return True
if arg1 == arg2:
return True
return False
```
### GitHub Actions CI Tests

73
docs/modes/track.md
View File

@ -32,10 +32,10 @@ The output from Ultralytics trackers is consistent with standard object detectio
## Real-world Applications
| Transportation | Retail | Aquaculture |
|:-----------------------------------:|:-----------------------:|:-----------:|
| Transportation | Retail | Aquaculture |
|:----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| ![Vehicle Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab) | ![People Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527) | ![Fish Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142) |
| Vehicle Tracking | People Tracking | Fish Tracking |
| Vehicle Tracking | People Tracking | Fish Tracking |
## Features at a Glance
@ -264,7 +264,7 @@ Multithreaded tracking provides the capability to run object tracking on multipl
In the provided Python script, we make use of Python's `threading` module to run multiple instances of the tracker concurrently. Each thread is responsible for running the tracker on one video file, and all the threads run simultaneously in the background.
To ensure that each thread receives the correct parameters (the video file and the model to use), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
To ensure that each thread receives the correct parameters (the video file, the model to use and the file index), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
Two different models are used in this example: `yolov8n.pt` and `yolov8n-seg.pt`, each tracking objects in a different video file. The video files are specified in `video_file1` and `video_file2`.
@ -276,44 +276,67 @@ Finally, after all threads have completed their task, the windows displaying the
```python
import threading
import cv2
from ultralytics import YOLO
def run_tracker_in_thread(filename, model, file_index):
"""
Runs a video file or webcam stream concurrently with the YOLOv8 model using threading.
This function captures video frames from a given file or camera source and utilizes the YOLOv8 model for object
tracking. The function runs in its own thread for concurrent processing.
def run_tracker_in_thread(filename, model):
video = cv2.VideoCapture(filename)
frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
for _ in range(frames):
ret, frame = video.read()
if ret:
results = model.track(source=frame, persist=True)
res_plotted = results[0].plot()
cv2.imshow('p', res_plotted)
if cv2.waitKey(1) == ord('q'):
break
Args:
filename (str): The path to the video file or the identifier for the webcam/external camera source.
model (obj): The YOLOv8 model object.
file_index (int): An index to uniquely identify the file being processed, used for display purposes.
Note:
Press 'q' to quit the video display window.
"""
video = cv2.VideoCapture(filename) # Read the video file
while True:
ret, frame = video.read() # Read the video frames
# Exit the loop if no more frames in either video
if not ret:
break
# Track objects in frames if available
results = model.track(frame, persist=True)
res_plotted = results[0].plot()
cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)
key = cv2.waitKey(1)
if key == ord('q'):
break
# Release video sources
video.release()
# Load the models
model1 = YOLO('yolov8n.pt')
model2 = YOLO('yolov8n-seg.pt')
# Define the video files for the trackers
video_file1 = 'path/to/video1.mp4'
video_file2 = 'path/to/video2.mp4'
video_file1 = "path/to/video1.mp4" # Path to video file, 0 for webcam
video_file2 = 0 # Path to video file, 0 for webcam, 1 for external camera
# Create the tracker threads
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2), daemon=True)
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1, 1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2, 2), daemon=True)
# Start the tracker threads
tracker_thread1.start()
tracker_thread2.start()
# Wait for the tracker threads to finish
tracker_thread1.join()
tracker_thread2.join()
# Clean up and close windows
cv2.destroyAllWindows()
```

2
docs/tasks/detect.md
View File

@ -81,7 +81,7 @@ Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a ful
### Dataset format
YOLO detection dataset format can be found in detail in the [Dataset Guide](../datasets/detect/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
YOLO detection dataset format can be found in detail in the [Dataset Guide](../datasets/detect/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics.
## Val

2
docs/tasks/pose.md
View File

@ -84,7 +84,7 @@ Train a YOLOv8-pose model on the COCO128-pose dataset.
### Dataset format
YOLO pose dataset format can be found in detail in the [Dataset Guide](../datasets/pose/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
YOLO pose dataset format can be found in detail in the [Dataset Guide](../datasets/pose/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics.
## Val

2
docs/tasks/segment.md
View File

@ -81,7 +81,7 @@ Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. F
### Dataset format
YOLO segmentation dataset format can be found in detail in the [Dataset Guide](../datasets/segment/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
YOLO segmentation dataset format can be found in detail in the [Dataset Guide](../datasets/segment/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics.
## Val

15
tests/conftest.py
View File

@ -5,10 +5,7 @@ from pathlib import Path
import pytest
from ultralytics.utils import ROOT
from ultralytics.utils.torch_utils import init_seeds
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
TMP = Path(__file__).resolve().parent / 'tmp' # temp directory for test files
def pytest_addoption(parser):
@ -62,6 +59,8 @@ def pytest_sessionstart(session):
Args:
session (pytest.Session): The pytest session object.
"""
from ultralytics.utils.torch_utils import init_seeds
init_seeds()
shutil.rmtree(TMP, ignore_errors=True) # delete any existing tests/tmp directory
TMP.mkdir(parents=True, exist_ok=True) # create a new empty directory
@ -79,10 +78,14 @@ def pytest_terminal_summary(terminalreporter, exitstatus, config):
exitstatus (int): The exit status of the test run.
config (pytest.config.Config): The pytest config object.
"""
from ultralytics.utils import WEIGHTS_DIR
# Remove files
for file in ['bus.jpg', 'decelera_landscape_min.mov']:
models = [path for x in ['*.onnx', '*.torchscript'] for path in WEIGHTS_DIR.rglob(x)]
for file in ['bus.jpg', 'yolov8n.onnx', 'yolov8n.torchscript'] + models:
Path(file).unlink(missing_ok=True)
# Remove directories
for directory in [ROOT / '../.pytest_cache', TMP]:
models = [path for x in ['*.mlpackage', '*_openvino_model'] for path in WEIGHTS_DIR.rglob(x)]
for directory in [TMP.parents[1] / '.pytest_cache', TMP] + models:
shutil.rmtree(directory, ignore_errors=True)

4
tests/test_cli.py
View File

@ -1,16 +1,14 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import subprocess
from pathlib import Path
import pytest
from ultralytics.utils import ASSETS, SETTINGS
from ultralytics.utils import ASSETS, WEIGHTS_DIR
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
CUDA_IS_AVAILABLE = cuda_is_available()
CUDA_DEVICE_COUNT = cuda_device_count()
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
TASK_ARGS = [
('detect', 'yolov8n', 'coco8.yaml'),
('segment', 'yolov8n-seg', 'coco8-seg.yaml'),

7
tests/test_cuda.py
View File

@ -1,19 +1,16 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
from pathlib import Path
import pytest
import torch
from ultralytics import YOLO, download
from ultralytics.utils import ASSETS, SETTINGS
from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
CUDA_IS_AVAILABLE = cuda_is_available()
CUDA_DEVICE_COUNT = cuda_device_count()
DATASETS_DIR = Path(SETTINGS['datasets_dir'])
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
DATA = 'coco8.yaml'
BUS = ASSETS / 'bus.jpg'
@ -91,7 +88,7 @@ def test_predict_sam():
model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
# Create SAMPredictor
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model='mobile_sam.pt')
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model=WEIGHTS_DIR / 'mobile_sam.pt')
predictor = SAMPredictor(overrides=overrides)
# Set image

6
tests/test_engine.py
View File

@ -1,18 +1,16 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
from ultralytics import YOLO
from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import ASSETS, DEFAULT_CFG, SETTINGS
from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
CFG_DET = 'yolov8n.yaml'
CFG_SEG = 'yolov8n-seg.yaml'
CFG_CLS = 'yolov8n-cls.yaml' # or 'squeezenet1_0'
CFG = get_cfg(DEFAULT_CFG)
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
MODEL = WEIGHTS_DIR / 'yolov8n'
def test_func(*args): # noqa

18
tests/test_python.py
View File

@ -14,11 +14,11 @@ from torchvision.transforms import ToTensor
from ultralytics import RTDETR, YOLO
from ultralytics.cfg import TASK2DATA
from ultralytics.data.build import load_inference_source
from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS, is_dir_writeable
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_PATH, LINUX, MACOS, ONLINE, ROOT, WEIGHTS_DIR, WINDOWS,
is_dir_writeable)
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
CFG = 'yolov8n.yaml'
SOURCE = ASSETS / 'bus.jpg'
@ -260,12 +260,12 @@ def test_predict_callback_and_setup():
def test_results():
for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
results = YOLO(m)([SOURCE, SOURCE], imgsz=160)
results = YOLO(WEIGHTS_DIR / m)([SOURCE, SOURCE], imgsz=160)
for r in results:
r = r.cpu().numpy()
r = r.to(device='cpu', dtype=torch.float32)
r.save_txt(txt_file='runs/tests/label.txt', save_conf=True)
r.save_crop(save_dir='runs/tests/crops/')
r.save_txt(txt_file=TMP / 'runs/tests/label.txt', save_conf=True)
r.save_crop(save_dir=TMP / 'runs/tests/crops/')
r.tojson(normalize=True)
r.plot(pil=True)
r.plot(conf=True, boxes=True)
@ -299,14 +299,17 @@ def test_data_converter():
file = 'instances_val2017.json'
download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}', dir=TMP)
convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
convert_coco(labels_dir=TMP, save_dir=TMP / 'yolo_labels', use_segments=True, use_keypoints=False, cls91to80=True)
coco80_to_coco91_class()
def test_data_annotator():
from ultralytics.data.annotator import auto_annotate
auto_annotate(ASSETS, det_model='yolov8n.pt', sam_model='mobile_sam.pt', output_dir=TMP / 'auto_annotate_labels')
auto_annotate(ASSETS,
det_model=WEIGHTS_DIR / 'yolov8n.pt',
sam_model=WEIGHTS_DIR / 'mobile_sam.pt',
output_dir=TMP / 'auto_annotate_labels')
def test_events():
@ -326,6 +329,7 @@ def test_cfg_init():
with contextlib.suppress(SyntaxError):
check_dict_alignment({'a': 1}, {'b': 2})
copy_default_cfg()
(Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')).unlink(missing_ok=False)
[smart_value(x) for x in ['none', 'true', 'false']]

2
ultralytics/__init__.py
View File

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.190'
__version__ = '8.0.191'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

9
ultralytics/cfg/__init__.py
View File

@ -7,9 +7,9 @@ from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Union
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS,
SETTINGS_YAML, IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn,
yaml_load, yaml_print)
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, RANK, ROOT, SETTINGS,
SETTINGS_YAML, TESTS_RUNNING, IterableSimpleNamespace, __version__, checks, colorstr,
deprecation_warn, yaml_load, yaml_print)
# Define valid tasks and modes
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
@ -153,7 +153,8 @@ def get_save_dir(args, name=None):
else:
from ultralytics.utils.files import increment_path
project = args.project or Path(SETTINGS['runs_dir']) / args.task
project = args.project or (ROOT /
'../tests/tmp/runs' if TESTS_RUNNING else Path(SETTINGS['runs_dir'])) / args.task
name = name or args.name or f'{args.mode}'
save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in (-1, 0) else True)

9
ultralytics/data/converter.py
View File

@ -46,11 +46,16 @@ def coco80_to_coco91_class(): #
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keypoints=False, cls91to80=True):
def convert_coco(labels_dir='../coco/annotations/',
save_dir='.',
use_segments=False,
use_keypoints=False,
cls91to80=True):
"""Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
Args:
labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
save_dir (str, optional): Path to directory to save results to.
use_segments (bool, optional): Whether to include segmentation masks in the output.
use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
@ -67,7 +72,7 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
"""
# Create dataset directory
save_dir = Path('yolo_labels')
save_dir = Path(save_dir)
if save_dir.exists():
shutil.rmtree(save_dir) # delete dir
for p in save_dir / 'labels', save_dir / 'images':

3
ultralytics/models/sam/build.py
View File

@ -148,11 +148,12 @@ sam_model_map = {
def build_sam(ckpt='sam_b.pt'):
"""Build a SAM model specified by ckpt."""
model_builder = None
ckpt = str(ckpt) # to allow Path ckpt types
for k in sam_model_map.keys():
if ckpt.endswith(k):
model_builder = sam_model_map.get(k)
if not model_builder:
raise FileNotFoundError(f'{ckpt} is not a supported sam model. Available models are: \n {sam_model_map.keys()}')
raise FileNotFoundError(f'{ckpt} is not a supported SAM model. Available models are: \n {sam_model_map.keys()}')
return model_builder(ckpt)

1
ultralytics/utils/__init__.py
View File

@ -917,6 +917,7 @@ def url2file(url):
PREFIX = colorstr('Ultralytics: ')
SETTINGS = SettingsManager() # initialize settings
DATASETS_DIR = Path(SETTINGS['datasets_dir']) # global datasets directory
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
ENVIRONMENT = 'Colab' if is_colab() else 'Kaggle' if is_kaggle() else 'Jupyter' if is_jupyter() else \
'Docker' if is_docker() else platform.system()
TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()

4
ultralytics/utils/benchmarks.py
View File

@ -36,13 +36,13 @@ import torch.cuda
from ultralytics import YOLO
from ultralytics.cfg import TASK2DATA, TASK2METRIC
from ultralytics.engine.exporter import export_formats
from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, SETTINGS, TQDM
from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
from ultralytics.utils.checks import check_requirements, check_yolo
from ultralytics.utils.files import file_size
from ultralytics.utils.torch_utils import select_device
def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
def benchmark(model=WEIGHTS_DIR / 'yolov8n.pt',
data=None,
imgsz=160,
half=False,

8
ultralytics/utils/checks.py
View File

@ -516,8 +516,12 @@ def collect_system_info():
f"{'CUDA':<20}{torch.version.cuda if torch and torch.cuda.is_available() else None}\n")
for r in parse_requirements(package='ultralytics'):
current = metadata.version(r.name)
is_met = '✅ ' if check_version(current, str(r.specifier)) else '❌ '
try:
current = metadata.version(r.name)
is_met = '✅ ' if check_version(current, str(r.specifier), hard=True) else '❌ '
except metadata.PackageNotFoundError:
current = '(not installed)'
is_met = '❌ '
LOGGER.info(f'{r.name:<20}{is_met}{current}{r.specifier}')