diff --git a/docs/reference/hub/__init__.md b/docs/reference/hub/__init__.md
index 86ca1e5698..4019369135 100644
--- a/docs/reference/hub/__init__.md
+++ b/docs/reference/hub/__init__.md
@@ -17,10 +17,6 @@ keywords: Ultralytics, hub functions, model export, dataset check, reset model,
## ::: ultralytics.hub.logout
----
-## ::: ultralytics.hub.start
-
-
---
## ::: ultralytics.hub.reset_model
diff --git a/docs/reference/utils/metrics.md b/docs/reference/utils/metrics.md
index f208df0d12..9b762404c1 100644
--- a/docs/reference/utils/metrics.md
+++ b/docs/reference/utils/metrics.md
@@ -33,10 +33,6 @@ keywords: Ultralytics, YOLO, YOLOv3, YOLOv4, metrics, confusion matrix, detectio
## ::: ultralytics.utils.metrics.ClassifyMetrics
----
-## ::: ultralytics.utils.metrics.box_area
-
-
---
## ::: ultralytics.utils.metrics.bbox_ioa
diff --git a/docs/reference/utils/ops.md b/docs/reference/utils/ops.md
index 86bb53daba..9595f5ee07 100644
--- a/docs/reference/utils/ops.md
+++ b/docs/reference/utils/ops.md
@@ -57,10 +57,6 @@ keywords: Ultralytics YOLO, Utility Operations, segment2box, make_divisible, cli
## ::: ultralytics.utils.ops.xyxy2xywhn
----
-## ::: ultralytics.utils.ops.xyn2xy
-
-
---
## ::: ultralytics.utils.ops.xywh2ltwh
diff --git a/tests/conftest.py b/tests/conftest.py
index b418863bce..235e2b774d 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,6 +6,7 @@ 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
@@ -32,6 +33,7 @@ def pytest_sessionstart(session):
"""
Called after the 'Session' object has been created and before performing test collection.
"""
+ 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
diff --git a/tests/test_python.py b/tests/test_python.py
index fa81295fb5..0c4cc67607 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -128,7 +128,7 @@ def test_track_stream():
def test_val():
model = YOLO(MODEL)
- model.val(data='coco8.yaml', imgsz=32)
+ model.val(data='coco8.yaml', imgsz=32, save_hybrid=True)
def test_train_scratch():
@@ -348,9 +348,20 @@ def test_utils_downloads():
def test_utils_ops():
- from ultralytics.utils.ops import make_divisible
+ from ultralytics.utils.ops import (ltwh2xywh, ltwh2xyxy, make_divisible, xywh2ltwh, xywh2xyxy, xywhn2xyxy,
+ xywhr2xyxyxyxy, xyxy2ltwh, xyxy2xywh, xyxy2xywhn, xyxyxyxy2xywhr)
- make_divisible(17, 8)
+ make_divisible(17, torch.tensor([8]))
+
+ boxes = torch.rand(10, 4) # xywh
+ torch.allclose(boxes, xyxy2xywh(xywh2xyxy(boxes)))
+ torch.allclose(boxes, xyxy2xywhn(xywhn2xyxy(boxes)))
+ torch.allclose(boxes, ltwh2xywh(xywh2ltwh(boxes)))
+ torch.allclose(boxes, xyxy2ltwh(ltwh2xyxy(boxes)))
+
+ boxes = torch.rand(10, 5) # xywhr for OBB
+ boxes[:, 4] = torch.randn(10) * 30
+ torch.allclose(boxes, xyxyxyxy2xywhr(xywhr2xyxyxyxy(boxes)), rtol=1e-3)
def test_utils_files():
@@ -364,3 +375,42 @@ def test_utils_files():
path.mkdir(parents=True, exist_ok=True)
with spaces_in_path(path) as new_path:
print(new_path)
+
+
+def test_nn_modules_conv():
+ from ultralytics.nn.modules.conv import CBAM, Conv2, ConvTranspose, DWConvTranspose2d, Focus
+
+ c1, c2 = 8, 16 # input and output channels
+ x = torch.zeros(4, c1, 10, 10) # BCHW
+
+ # Run all modules not otherwise covered in tests
+ DWConvTranspose2d(c1, c2)(x)
+ ConvTranspose(c1, c2)(x)
+ Focus(c1, c2)(x)
+ CBAM(c1)(x)
+
+ # Fuse ops
+ m = Conv2(c1, c2)
+ m.fuse_convs()
+ m(x)
+
+
+def test_nn_modules_block():
+ from ultralytics.nn.modules.block import C1, C3TR, BottleneckCSP, C3Ghost, C3x
+
+ c1, c2 = 8, 16 # input and output channels
+ x = torch.zeros(4, c1, 10, 10) # BCHW
+
+ # Run all modules not otherwise covered in tests
+ C1(c1, c2)(x)
+ C3x(c1, c2)(x)
+ C3TR(c1, c2)(x)
+ C3Ghost(c1, c2)(x)
+ BottleneckCSP(c1, c2)(x)
+
+
+def test_hub():
+ from ultralytics.hub import export_fmts_hub, logout
+
+ export_fmts_hub()
+ logout()
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 87f2b67570..6ecd8a281b 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -2,7 +2,6 @@
__version__ = '8.0.159'
-from ultralytics.hub import start
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM
from ultralytics.models.nas import NAS
@@ -10,4 +9,4 @@ from ultralytics.utils import SETTINGS as settings
from ultralytics.utils.checks import check_yolo as checks
from ultralytics.utils.downloads import download
-__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'start', 'settings' # allow simpler import
+__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings' # allow simpler import
diff --git a/ultralytics/hub/__init__.py b/ultralytics/hub/__init__.py
index 2ffd21aeac..daed439c22 100644
--- a/ultralytics/hub/__init__.py
+++ b/ultralytics/hub/__init__.py
@@ -5,7 +5,7 @@ import requests
from ultralytics.data.utils import HUBDatasetStats
from ultralytics.hub.auth import Auth
from ultralytics.hub.utils import HUB_API_ROOT, HUB_WEB_ROOT, PREFIX
-from ultralytics.utils import LOGGER, SETTINGS, USER_CONFIG_DIR, yaml_save
+from ultralytics.utils import LOGGER, SETTINGS
def login(api_key=''):
@@ -37,29 +37,10 @@ def logout():
```
"""
SETTINGS['api_key'] = ''
- yaml_save(USER_CONFIG_DIR / 'settings.yaml', SETTINGS)
+ SETTINGS.save()
LOGGER.info(f"{PREFIX}logged out ✅. To log in again, use 'yolo hub login'.")
-def start(key=''):
- """
- Start training models with Ultralytics HUB (DEPRECATED).
-
- Args:
- key (str, optional): A string containing either the API key and model ID combination (apikey_modelid),
- or the full model URL (https://hub.ultralytics.com/models/apikey_modelid).
- """
- api_key, model_id = key.split('_')
- LOGGER.warning(f"""
-WARNING ⚠️ ultralytics.start() is deprecated after 8.0.60. Updated usage to train Ultralytics HUB models is:
-
-from ultralytics import YOLO, hub
-
-hub.login('{api_key}')
-model = YOLO('{HUB_WEB_ROOT}/models/{model_id}')
-model.train()""")
-
-
def reset_model(model_id=''):
"""Reset a trained model to an untrained state."""
r = requests.post(f'{HUB_API_ROOT}/model-reset', json={'apiKey': Auth().api_key, 'modelId': model_id})
@@ -117,7 +98,3 @@ def check_dataset(path='', task='detect'):
"""
HUBDatasetStats(path=path, task=task).get_json()
LOGGER.info(f'Checks completed correctly ✅. Upload this dataset to {HUB_WEB_ROOT}/datasets/.')
-
-
-if __name__ == '__main__':
- start()
diff --git a/ultralytics/hub/auth.py b/ultralytics/hub/auth.py
index 833a9671fb..9963d79c09 100644
--- a/ultralytics/hub/auth.py
+++ b/ultralytics/hub/auth.py
@@ -73,8 +73,7 @@ class Auth:
bool: True if authentication is successful, False otherwise.
"""
try:
- header = self.get_auth_header()
- if header:
+ if header := self.get_auth_header():
r = requests.post(f'{HUB_API_ROOT}/v1/auth', headers=header)
if not r.json().get('success', False):
raise ConnectionError('Unable to authenticate.')
@@ -117,23 +116,4 @@ class Auth:
return {'authorization': f'Bearer {self.id_token}'}
elif self.api_key:
return {'x-api-key': self.api_key}
- else:
- return None
-
- def get_state(self) -> bool:
- """
- Get the authentication state.
-
- Returns:
- bool: True if either id_token or API key is set, False otherwise.
- """
- return self.id_token or self.api_key
-
- def set_api_key(self, key: str):
- """
- Set the API key for authentication.
-
- Args:
- key (str): The API key string.
- """
- self.api_key = key
+ # else returns None
diff --git a/ultralytics/models/sam/modules/sam.py b/ultralytics/models/sam/modules/sam.py
index a9dac842e5..6fb71b33c4 100644
--- a/ultralytics/models/sam/modules/sam.py
+++ b/ultralytics/models/sam/modules/sam.py
@@ -30,11 +30,10 @@ class Sam(nn.Module):
SAM predicts object masks from an image and input prompts.
Args:
- image_encoder (ImageEncoderViT): The backbone used to encode the
- image into image embeddings that allow for efficient mask prediction.
+ image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings that allow for
+ efficient mask prediction.
prompt_encoder (PromptEncoder): Encodes various types of input prompts.
- mask_decoder (MaskDecoder): Predicts masks from the image embeddings
- and encoded prompts.
+ mask_decoder (MaskDecoder): Predicts masks from the image embeddings and encoded prompts.
pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
pixel_std (list(float)): Std values for normalizing pixels in the input image.
"""
@@ -65,34 +64,25 @@ class Sam(nn.Module):
Args:
batched_input (list(dict)): A list over input images, each a dictionary with the following keys. A prompt
- key can be excluded if it is not present.
- 'image': The image as a torch tensor in 3xHxW format,
- already transformed for input to the model.
- 'original_size': (tuple(int, int)) The original size of
- the image before transformation, as (H, W).
- 'point_coords': (torch.Tensor) Batched point prompts for
- this image, with shape BxNx2. Already transformed to the
- input frame of the model.
- 'point_labels': (torch.Tensor) Batched labels for point prompts,
- with shape BxN.
- 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
- Already transformed to the input frame of the model.
- 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
- in the form Bx1xHxW.
+ key can be excluded if it is not present.
+ 'image': The image as a torch tensor in 3xHxW format, already transformed for input to the model.
+ 'original_size': (tuple(int, int)) The original size of the image before transformation, as (H, W).
+ 'point_coords': (torch.Tensor) Batched point prompts for this image, with shape BxNx2. Already
+ transformed to the input frame of the model.
+ 'point_labels': (torch.Tensor) Batched labels for point prompts, with shape BxN.
+ 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. Already transformed to the input frame of
+ the model.
+ 'mask_inputs': (torch.Tensor) Batched mask inputs to the model, in the form Bx1xHxW.
multimask_output (bool): Whether the model should predict multiple disambiguating masks, or return a single
mask.
Returns:
(list(dict)): A list over input images, where each element is as dictionary with the following keys.
- 'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input prompts,
- C is determined by multimask_output, and (H, W) is the
- original size of the image.
- 'iou_predictions': (torch.Tensor) The model's predictions
- of mask quality, in shape BxC.
- 'low_res_logits': (torch.Tensor) Low resolution logits with
- shape BxCxHxW, where H=W=256. Can be passed as mask input
- to subsequent iterations of prediction.
+ 'masks': (torch.Tensor) Batched binary mask predictions, with shape BxCxHxW, where B is the number of
+ input prompts, C is determined by multimask_output, and (H, W) is the original size of the image.
+ 'iou_predictions': (torch.Tensor) The model's predictions of mask quality, in shape BxC.
+ 'low_res_logits': (torch.Tensor) Low resolution logits with shape BxCxHxW, where H=W=256. Can be passed
+ as mask input to subsequent iterations of prediction.
"""
input_images = torch.stack([self.preprocess(x['image']) for x in batched_input], dim=0)
image_embeddings = self.image_encoder(input_images)
@@ -137,16 +127,12 @@ class Sam(nn.Module):
Remove padding and upscale masks to the original image size.
Args:
- masks (torch.Tensor): Batched masks from the mask_decoder,
- in BxCxHxW format.
- input_size (tuple(int, int)): The size of the image input to the
- model, in (H, W) format. Used to remove padding.
- original_size (tuple(int, int)): The original size of the image
- before resizing for input to the model, in (H, W) format.
+ masks (torch.Tensor): Batched masks from the mask_decoder, in BxCxHxW format.
+ input_size (tuple(int, int)): The size of the model input image, in (H, W) format. Used to remove padding.
+ original_size (tuple(int, int)): The original image size before resizing for input to the model, in (H, W).
Returns:
- (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
- is given by original_size.
+ (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) is given by original_size.
"""
masks = F.interpolate(
masks,
diff --git a/ultralytics/nn/modules/conv.py b/ultralytics/nn/modules/conv.py
index 9577ba0329..77e99c009e 100644
--- a/ultralytics/nn/modules/conv.py
+++ b/ultralytics/nn/modules/conv.py
@@ -9,7 +9,7 @@ import numpy as np
import torch
import torch.nn as nn
-__all__ = ('Conv', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
+__all__ = ('Conv', 'Conv2', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'RepConv')
@@ -54,6 +54,10 @@ class Conv2(Conv):
"""Apply convolution, batch normalization and activation to input tensor."""
return self.act(self.bn(self.conv(x) + self.cv2(x)))
+ def forward_fuse(self, x):
+ """Apply fused convolution, batch normalization and activation to input tensor."""
+ return self.act(self.bn(self.conv(x)))
+
def fuse_convs(self):
"""Fuse parallel convolutions."""
w = torch.zeros_like(self.conv.weight.data)
@@ -61,6 +65,7 @@ class Conv2(Conv):
w[:, :, i[0]:i[0] + 1, i[1]:i[1] + 1] = self.cv2.weight.data.clone()
self.conv.weight.data += w
self.__delattr__('cv2')
+ self.forward = self.forward_fuse
class LightConv(nn.Module):
diff --git a/ultralytics/trackers/utils/kalman_filter.py b/ultralytics/trackers/utils/kalman_filter.py
index 7bccecbbac..28ebc89e96 100644
--- a/ultralytics/trackers/utils/kalman_filter.py
+++ b/ultralytics/trackers/utils/kalman_filter.py
@@ -6,20 +6,13 @@ import scipy.linalg
class KalmanFilterXYAH:
"""
- For bytetrack
- A simple Kalman filter for tracking bounding boxes in image space.
+ For bytetrack. A simple Kalman filter for tracking bounding boxes in image space.
- The 8-dimensional state space
-
- x, y, a, h, vx, vy, va, vh
-
- contains the bounding box center position (x, y), aspect ratio a, height h,
- and their respective velocities.
-
- Object motion follows a constant velocity model. The bounding box location
- (x, y, a, h) is taken as direct observation of the state space (linear
- observation model).
+ The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y),
+ aspect ratio a, height h, and their respective velocities.
+ Object motion follows a constant velocity model. The bounding box location (x, y, a, h) is taken as direct
+ observation of the state space (linear observation model).
"""
def __init__(self):
@@ -32,14 +25,14 @@ class KalmanFilterXYAH:
self._motion_mat[i, ndim + i] = dt
self._update_mat = np.eye(ndim, 2 * ndim)
- # Motion and observation uncertainty are chosen relative to the current
- # state estimate. These weights control the amount of uncertainty in
- # the model. This is a bit hacky.
+ # Motion and observation uncertainty are chosen relative to the current state estimate. These weights control
+ # the amount of uncertainty in the model. This is a bit hacky.
self._std_weight_position = 1. / 20
self._std_weight_velocity = 1. / 160
def initiate(self, measurement):
- """Create track from unassociated measurement.
+ """
+ Create track from unassociated measurement.
Parameters
----------
@@ -53,7 +46,6 @@ class KalmanFilterXYAH:
Returns the mean vector (8 dimensional) and covariance matrix (8x8
dimensional) of the new track. Unobserved velocities are initialized
to 0 mean.
-
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
@@ -67,23 +59,21 @@ class KalmanFilterXYAH:
return mean, covariance
def predict(self, mean, covariance):
- """Run Kalman filter prediction step.
+ """
+ Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
- The 8 dimensional mean vector of the object state at the previous
- time step.
+ The 8 dimensional mean vector of the object state at the previous time step.
covariance : ndarray
- The 8x8 dimensional covariance matrix of the object state at the
- previous time step.
+ The 8x8 dimensional covariance matrix of the object state at the previous time step.
Returns
-------
(ndarray, ndarray)
- Returns the mean vector and covariance matrix of the predicted
- state. Unobserved velocities are initialized to 0 mean.
-
+ Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
+ initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-2,
@@ -100,7 +90,8 @@ class KalmanFilterXYAH:
return mean, covariance
def project(self, mean, covariance):
- """Project state distribution to measurement space.
+ """
+ Project state distribution to measurement space.
Parameters
----------
@@ -112,9 +103,7 @@ class KalmanFilterXYAH:
Returns
-------
(ndarray, ndarray)
- Returns the projected mean and covariance matrix of the given state
- estimate.
-
+ Returns the projected mean and covariance matrix of the given state estimate.
"""
std = [
self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-1,
@@ -126,20 +115,21 @@ class KalmanFilterXYAH:
return mean, covariance + innovation_cov
def multi_predict(self, mean, covariance):
- """Run Kalman filter prediction step (Vectorized version).
+ """
+ Run Kalman filter prediction step (Vectorized version).
+
Parameters
----------
mean : ndarray
- The Nx8 dimensional mean matrix of the object states at the previous
- time step.
+ The Nx8 dimensional mean matrix of the object states at the previous time step.
covariance : ndarray
- The Nx8x8 dimensional covariance matrix of the object states at the
- previous time step.
+ The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
+
Returns
-------
(ndarray, ndarray)
- Returns the mean vector and covariance matrix of the predicted
- state. Unobserved velocities are initialized to 0 mean.
+ Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
+ initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[:, 3], self._std_weight_position * mean[:, 3],
@@ -159,7 +149,8 @@ class KalmanFilterXYAH:
return mean, covariance
def update(self, mean, covariance, measurement):
- """Run Kalman filter correction step.
+ """
+ Run Kalman filter correction step.
Parameters
----------
@@ -168,14 +159,13 @@ class KalmanFilterXYAH:
covariance : ndarray
The state's covariance matrix (8x8 dimensional).
measurement : ndarray
- The 4 dimensional measurement vector (x, y, a, h), where (x, y)
- is the center position, a is the aspect ratio, and h is the height of the bounding box.
+ The 4 dimensional measurement vector (x, y, a, h), where (x, y) is the center position, a the aspect
+ ratio, and h the height of the bounding box.
Returns
-------
(ndarray, ndarray)
Returns the measurement-corrected state distribution.
-
"""
projected_mean, projected_cov = self.project(mean, covariance)
@@ -195,10 +185,11 @@ class KalmanFilterXYAH:
return new_mean, new_covariance
def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
- """Compute gating distance between state distribution and measurements.
- A suitable distance threshold can be obtained from `chi2inv95`. If
- `only_position` is False, the chi-square distribution has 4 degrees of
+ """
+ Compute gating distance between state distribution and measurements. A suitable distance threshold can be
+ obtained from `chi2inv95`. If `only_position` is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
+
Parameters
----------
mean : ndarray
@@ -206,18 +197,16 @@ class KalmanFilterXYAH:
covariance : ndarray
Covariance of the state distribution (8x8 dimensional).
measurements : ndarray
- An Nx4 dimensional matrix of N measurements, each in
- format (x, y, a, h) where (x, y) is the bounding box center
- position, a the aspect ratio, and h the height.
+ An Nx4 dimensional matrix of N measurements, each in format (x, y, a, h) where (x, y) is the bounding box
+ center position, a the aspect ratio, and h the height.
only_position : Optional[bool]
- If True, distance computation is done with respect to the bounding
- box center position only.
+ If True, distance computation is done with respect to the bounding box center position only.
+
Returns
-------
ndarray
- Returns an array of length N, where the i-th element contains the
- squared Mahalanobis distance between (mean, covariance) and
- `measurements[i]`.
+ Returns an array of length N, where the i-th element contains the squared Mahalanobis distance between
+ (mean, covariance) and `measurements[i]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
@@ -237,38 +226,29 @@ class KalmanFilterXYAH:
class KalmanFilterXYWH(KalmanFilterXYAH):
"""
- For BoT-SORT
- A simple Kalman filter for tracking bounding boxes in image space.
+ For BoT-SORT. A simple Kalman filter for tracking bounding boxes in image space.
- The 8-dimensional state space
-
- x, y, w, h, vx, vy, vw, vh
-
- contains the bounding box center position (x, y), width w, height h,
- and their respective velocities.
-
- Object motion follows a constant velocity model. The bounding box location
- (x, y, w, h) is taken as direct observation of the state space (linear
- observation model).
+ The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y),
+ width w, height h, and their respective velocities.
+ Object motion follows a constant velocity model. The bounding box location (x, y, w, h) is taken as direct
+ observation of the state space (linear observation model).
"""
def initiate(self, measurement):
- """Create track from unassociated measurement.
+ """
+ Create track from unassociated measurement.
Parameters
----------
measurement : ndarray
- Bounding box coordinates (x, y, w, h) with center position (x, y),
- width w, and height h.
+ Bounding box coordinates (x, y, w, h) with center position (x, y), width w, and height h.
Returns
-------
(ndarray, ndarray)
- Returns the mean vector (8 dimensional) and covariance matrix (8x8
- dimensional) of the new track. Unobserved velocities are initialized
- to 0 mean.
-
+ Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track.
+ Unobserved velocities are initialized to 0 mean.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
@@ -283,23 +263,21 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def predict(self, mean, covariance):
- """Run Kalman filter prediction step.
+ """
+ Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
- The 8 dimensional mean vector of the object state at the previous
- time step.
+ The 8 dimensional mean vector of the object state at the previous time step.
covariance : ndarray
- The 8x8 dimensional covariance matrix of the object state at the
- previous time step.
+ The 8x8 dimensional covariance matrix of the object state at the previous time step.
Returns
-------
(ndarray, ndarray)
- Returns the mean vector and covariance matrix of the predicted
- state. Unobserved velocities are initialized to 0 mean.
-
+ Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
+ initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
@@ -315,7 +293,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def project(self, mean, covariance):
- """Project state distribution to measurement space.
+ """
+ Project state distribution to measurement space.
Parameters
----------
@@ -327,9 +306,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
Returns
-------
(ndarray, ndarray)
- Returns the projected mean and covariance matrix of the given state
- estimate.
-
+ Returns the projected mean and covariance matrix of the given state estimate.
"""
std = [
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
@@ -341,20 +318,21 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance + innovation_cov
def multi_predict(self, mean, covariance):
- """Run Kalman filter prediction step (Vectorized version).
+ """
+ Run Kalman filter prediction step (Vectorized version).
+
Parameters
----------
mean : ndarray
- The Nx8 dimensional mean matrix of the object states at the previous
- time step.
+ The Nx8 dimensional mean matrix of the object states at the previous time step.
covariance : ndarray
- The Nx8x8 dimensional covariance matrix of the object states at the
- previous time step.
+ The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
+
Returns
-------
(ndarray, ndarray)
- Returns the mean vector and covariance matrix of the predicted
- state. Unobserved velocities are initialized to 0 mean.
+ Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
+ initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[:, 2], self._std_weight_position * mean[:, 3],
@@ -374,7 +352,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def update(self, mean, covariance, measurement):
- """Run Kalman filter correction step.
+ """
+ Run Kalman filter correction step.
Parameters
----------
@@ -383,13 +362,12 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
covariance : ndarray
The state's covariance matrix (8x8 dimensional).
measurement : ndarray
- The 4 dimensional measurement vector (x, y, w, h), where (x, y) is the center position, w is the width, and
- h is the height of the bounding box.
+ The 4 dimensional measurement vector (x, y, w, h), where (x, y) is the center position, w the width,
+ and h the height of the bounding box.
Returns
-------
(ndarray, ndarray)
Returns the measurement-corrected state distribution.
-
"""
return super().update(mean, covariance, measurement)
diff --git a/ultralytics/utils/downloads.py b/ultralytics/utils/downloads.py
index dd197c4464..3171c038a3 100644
--- a/ultralytics/utils/downloads.py
+++ b/ultralytics/utils/downloads.py
@@ -212,21 +212,18 @@ def get_google_drive_file_info(link):
"""
file_id = link.split('/d/')[1].split('/view')[0]
drive_url = f'https://drive.google.com/uc?export=download&id={file_id}'
+ filename = None
# Start session
- filename = None
with requests.Session() as session:
response = session.get(drive_url, stream=True)
if 'quota exceeded' in str(response.content.lower()):
raise ConnectionError(
emojis(f'❌ Google Drive file download quota exceeded. '
f'Please try again later or download this file manually at {link}.'))
- token = None
- for key, value in response.cookies.items():
- if key.startswith('download_warning'):
- token = value
- if token:
- drive_url = f'https://drive.google.com/uc?export=download&confirm={token}&id={file_id}'
+ for k, v in response.cookies.items():
+ if k.startswith('download_warning'):
+ drive_url += f'&confirm={v}' # v is token
cd = response.headers.get('content-disposition')
if cd:
filename = re.findall('filename="(.+)"', cd)[0]
diff --git a/ultralytics/utils/metrics.py b/ultralytics/utils/metrics.py
index a0463fff15..a1dddf6f33 100644
--- a/ultralytics/utils/metrics.py
+++ b/ultralytics/utils/metrics.py
@@ -15,12 +15,6 @@ from ultralytics.utils import LOGGER, SimpleClass, TryExcept, plt_settings
OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
-# Boxes
-def box_area(box):
- """Return box area, where box shape is xyxy(4,n)."""
- return (box[2] - box[0]) * (box[3] - box[1])
-
-
def bbox_ioa(box1, box2, iou=False, eps=1e-7):
"""
Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.
@@ -869,11 +863,6 @@ class PoseMetrics(SegmentMetrics):
self.pose = Metric()
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
- def __getattr__(self, attr):
- """Raises an AttributeError if an invalid attribute is accessed."""
- name = self.__class__.__name__
- raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
-
def process(self, tp_b, tp_p, conf, pred_cls, target_cls):
"""
Processes the detection and pose metrics over the given set of predictions.
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index 08cb47f02b..ef741ad53b 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -13,8 +13,6 @@ import torchvision
from ultralytics.utils import LOGGER
-from .metrics import box_iou
-
class Profile(contextlib.ContextDecorator):
"""
@@ -32,23 +30,17 @@ class Profile(contextlib.ContextDecorator):
self.cuda = torch.cuda.is_available()
def __enter__(self):
- """
- Start timing.
- """
+ """Start timing."""
self.start = self.time()
return self
def __exit__(self, type, value, traceback): # noqa
- """
- Stop timing.
- """
+ """Stop timing."""
self.dt = self.time() - self.start # delta-time
self.t += self.dt # accumulate dt
def time(self):
- """
- Get current time.
- """
+ """Get current time."""
if self.cuda:
torch.cuda.synchronize()
return time.time()
@@ -56,15 +48,15 @@ class Profile(contextlib.ContextDecorator):
def segment2box(segment, width=640, height=640):
"""
- Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
+ Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy).
Args:
- segment (torch.Tensor): the segment label
- width (int): the width of the image. Defaults to 640
- height (int): The height of the image. Defaults to 640
+ segment (torch.Tensor): the segment label
+ width (int): the width of the image. Defaults to 640
+ height (int): The height of the image. Defaults to 640
Returns:
- (np.ndarray): the minimum and maximum x and y values of the segment.
+ (np.ndarray): the minimum and maximum x and y values of the segment.
"""
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
x, y = segment.T # segment xy
@@ -80,16 +72,16 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None, padding=True):
(img1_shape) to the shape of a different image (img0_shape).
Args:
- img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width).
- boxes (torch.Tensor): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2)
- img0_shape (tuple): the shape of the target image, in the format of (height, width).
- ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be
- calculated based on the size difference between the two images.
- padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
- rescaling.
+ img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width).
+ boxes (torch.Tensor): the bounding boxes of the objects in the image, in the format of (x1, y1, x2, y2)
+ img0_shape (tuple): the shape of the target image, in the format of (height, width).
+ ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be
+ calculated based on the size difference between the two images.
+ padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
+ rescaling.
Returns:
- boxes (torch.Tensor): The scaled bounding boxes, in the format of (x1, y1, x2, y2)
+ boxes (torch.Tensor): The scaled bounding boxes, in the format of (x1, y1, x2, y2)
"""
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
@@ -186,9 +178,7 @@ def non_max_suppression(
# Settings
# min_wh = 2 # (pixels) minimum box width and height
time_limit = 0.5 + max_time_img * bs # seconds to quit after
- redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
- merge = False # use merge-NMS
prediction = prediction.transpose(-1, -2) # shape(1,84,6300) to shape(1,6300,84)
prediction[..., :4] = xywh2xyxy(prediction[..., :4]) # xywh to xyxy
@@ -226,10 +216,6 @@ def non_max_suppression(
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
- # Apply finite constraint
- # if not torch.isfinite(x).all():
- # x = x[torch.isfinite(x).all(1)]
-
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
@@ -242,13 +228,18 @@ def non_max_suppression(
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
i = i[:max_det] # limit detections
- if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
- # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
- iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
- weights = iou * scores[None] # box weights
- x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
- if redundant:
- i = i[iou.sum(1) > 1] # require redundancy
+
+ # # Experimental
+ # merge = False # use merge-NMS
+ # if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
+ # # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ # from .metrics import box_iou
+ # iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ # weights = iou * scores[None] # box weights
+ # x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ # redundant = True # require redundant detections
+ # if redundant:
+ # i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if mps:
@@ -262,8 +253,7 @@ def non_max_suppression(
def clip_boxes(boxes, shape):
"""
- It takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the
- shape
+ Takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the shape.
Args:
boxes (torch.Tensor): the bounding boxes to clip
@@ -303,12 +293,12 @@ def scale_image(masks, im0_shape, ratio_pad=None):
Takes a mask, and resizes it to the original image size
Args:
- masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
- im0_shape (tuple): the original image shape
- ratio_pad (tuple): the ratio of the padding to the original image.
+ masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
+ im0_shape (tuple): the original image shape
+ ratio_pad (tuple): the ratio of the padding to the original image.
Returns:
- masks (torch.Tensor): The masks that are being returned.
+ masks (torch.Tensor): The masks that are being returned.
"""
# Rescale coordinates (xyxy) from im1_shape to im0_shape
im1_shape = masks.shape
@@ -340,6 +330,7 @@ def xyxy2xywh(x):
Args:
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
+
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
"""
@@ -359,6 +350,7 @@ def xywh2xyxy(x):
Args:
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.
+
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
"""
@@ -407,6 +399,7 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
h (int): The height of the image. Defaults to 640
clip (bool): If True, the boxes will be clipped to the image boundaries. Defaults to False
eps (float): The minimum value of the box's width and height. Defaults to 0.0
+
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height, normalized) format
"""
@@ -421,31 +414,13 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
return y
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
- """
- Convert normalized coordinates to pixel coordinates of shape (n,2)
-
- Args:
- x (np.ndarray | torch.Tensor): The input tensor of normalized bounding box coordinates
- w (int): The width of the image. Defaults to 640
- h (int): The height of the image. Defaults to 640
- padw (int): The width of the padding. Defaults to 0
- padh (int): The height of the padding. Defaults to 0
- Returns:
- y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box
- """
- y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
- y[..., 0] = w * x[..., 0] + padw # top left x
- y[..., 1] = h * x[..., 1] + padh # top left y
- return y
-
-
def xywh2ltwh(x):
"""
Convert the bounding box format from [x, y, w, h] to [x1, y1, w, h], where x1, y1 are the top-left coordinates.
Args:
x (np.ndarray | torch.Tensor): The input tensor with the bounding box coordinates in the xywh format
+
Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format
"""
@@ -460,9 +435,10 @@ def xyxy2ltwh(x):
Convert nx4 bounding boxes from [x1, y1, x2, y2] to [x1, y1, w, h], where xy1=top-left, xy2=bottom-right
Args:
- x (np.ndarray | torch.Tensor): The input tensor with the bounding boxes coordinates in the xyxy format
+ x (np.ndarray | torch.Tensor): The input tensor with the bounding boxes coordinates in the xyxy format
+
Returns:
- y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
+ y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 2] = x[..., 2] - x[..., 0] # width
@@ -475,7 +451,10 @@ def ltwh2xywh(x):
Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
Args:
- x (torch.Tensor): the input tensor
+ x (torch.Tensor): the input tensor
+
+ Returns:
+ y (np.ndarray | torch.Tensor): The bounding box coordinates in the xywh format.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] + x[..., 2] / 2 # center x
@@ -493,14 +472,8 @@ def xyxyxyxy2xywhr(corners):
Returns:
(numpy.ndarray | torch.Tensor): Converted data in [cx, cy, w, h, rotation] format of shape (n, 5).
"""
- if isinstance(corners, torch.Tensor):
- is_numpy = False
- atan2 = torch.atan2
- sqrt = torch.sqrt
- else:
- is_numpy = True
- atan2 = np.arctan2
- sqrt = np.sqrt
+ is_numpy = isinstance(corners, np.ndarray)
+ atan2, sqrt = (np.arctan2, np.sqrt) if is_numpy else (torch.atan2, torch.sqrt)
x1, y1, x2, y2, x3, y3, x4, y4 = corners.T
cx = (x1 + x3) / 2
@@ -527,14 +500,8 @@ def xywhr2xyxyxyxy(center):
Returns:
(numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 8).
"""
- if isinstance(center, torch.Tensor):
- is_numpy = False
- cos = torch.cos
- sin = torch.sin
- else:
- is_numpy = True
- cos = np.cos
- sin = np.sin
+ is_numpy = isinstance(center, np.ndarray)
+ cos, sin = (np.cos, np.sin) if is_numpy else (torch.cos, torch.sin)
cx, cy, w, h, rotation = center.T
rotation *= math.pi / 180.0 # degrees to radians
@@ -567,10 +534,10 @@ def ltwh2xyxy(x):
It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
Args:
- x (np.ndarray | torch.Tensor): the input image
+ x (np.ndarray | torch.Tensor): the input image
Returns:
- y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
+ y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
"""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 2] = x[..., 2] + x[..., 0] # width
@@ -583,10 +550,10 @@ def segments2boxes(segments):
It converts segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
Args:
- segments (list): list of segments, each segment is a list of points, each point is a list of x, y coordinates
+ segments (list): list of segments, each segment is a list of points, each point is a list of x, y coordinates
Returns:
- (np.ndarray): the xywh coordinates of the bounding boxes.
+ (np.ndarray): the xywh coordinates of the bounding boxes.
"""
boxes = []
for s in segments:
@@ -600,11 +567,11 @@ def resample_segments(segments, n=1000):
Inputs a list of segments (n,2) and returns a list of segments (n,2) up-sampled to n points each.
Args:
- segments (list): a list of (n,2) arrays, where n is the number of points in the segment.
- n (int): number of points to resample the segment to. Defaults to 1000
+ segments (list): a list of (n,2) arrays, where n is the number of points in the segment.
+ n (int): number of points to resample the segment to. Defaults to 1000
Returns:
- segments (list): the resampled segments.
+ segments (list): the resampled segments.
"""
for i, s in enumerate(segments):
s = np.concatenate((s, s[0:1, :]), axis=0)
@@ -617,14 +584,14 @@ def resample_segments(segments, n=1000):
def crop_mask(masks, boxes):
"""
- It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box
+ It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box.
Args:
- masks (torch.Tensor): [n, h, w] tensor of masks
- boxes (torch.Tensor): [n, 4] tensor of bbox coordinates in relative point form
+ masks (torch.Tensor): [n, h, w] tensor of masks
+ boxes (torch.Tensor): [n, 4] tensor of bbox coordinates in relative point form
Returns:
- (torch.Tensor): The masks are being cropped to the bounding box.
+ (torch.Tensor): The masks are being cropped to the bounding box.
"""
n, h, w = masks.shape
x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(n,1,1)
@@ -636,17 +603,17 @@ def crop_mask(masks, boxes):
def process_mask_upsample(protos, masks_in, bboxes, shape):
"""
- It takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
+ Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
quality but is slower.
Args:
- protos (torch.Tensor): [mask_dim, mask_h, mask_w]
- masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
- bboxes (torch.Tensor): [n, 4], n is number of masks after nms
- shape (tuple): the size of the input image (h,w)
+ protos (torch.Tensor): [mask_dim, mask_h, mask_w]
+ masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
+ bboxes (torch.Tensor): [n, 4], n is number of masks after nms
+ shape (tuple): the size of the input image (h,w)
Returns:
- (torch.Tensor): The upsampled masks.
+ (torch.Tensor): The upsampled masks.
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
@@ -692,13 +659,13 @@ def process_mask_native(protos, masks_in, bboxes, shape):
It takes the output of the mask head, and crops it after upsampling to the bounding boxes.
Args:
- protos (torch.Tensor): [mask_dim, mask_h, mask_w]
- masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
- bboxes (torch.Tensor): [n, 4], n is number of masks after nms
- shape (tuple): the size of the input image (h,w)
+ protos (torch.Tensor): [mask_dim, mask_h, mask_w]
+ masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
+ bboxes (torch.Tensor): [n, 4], n is number of masks after nms
+ shape (tuple): the size of the input image (h,w)
Returns:
- masks (torch.Tensor): The returned masks with dimensions [h, w, n]
+ masks (torch.Tensor): The returned masks with dimensions [h, w, n]
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
@@ -733,19 +700,19 @@ def scale_masks(masks, shape, padding=True):
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False, padding=True):
"""
- Rescale segment coordinates (xyxy) from img1_shape to img0_shape
+ Rescale segment coordinates (xy) from img1_shape to img0_shape
Args:
- img1_shape (tuple): The shape of the image that the coords are from.
- coords (torch.Tensor): the coords to be scaled
- img0_shape (tuple): the shape of the image that the segmentation is being applied to
- ratio_pad (tuple): the ratio of the image size to the padded image size.
- normalize (bool): If True, the coordinates will be normalized to the range [0, 1]. Defaults to False
- padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
- rescaling.
+ img1_shape (tuple): The shape of the image that the coords are from.
+ coords (torch.Tensor): the coords to be scaled of shape n,2.
+ img0_shape (tuple): the shape of the image that the segmentation is being applied to.
+ ratio_pad (tuple): the ratio of the image size to the padded image size.
+ normalize (bool): If True, the coordinates will be normalized to the range [0, 1]. Defaults to False.
+ padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular
+ rescaling.
Returns:
- coords (torch.Tensor): the segmented image.
+ coords (torch.Tensor): The scaled coordinates.
"""
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
@@ -771,11 +738,11 @@ def masks2segments(masks, strategy='largest'):
It takes a list of masks(n,h,w) and returns a list of segments(n,xy)
Args:
- masks (torch.Tensor): the output of the model, which is a tensor of shape (batch_size, 160, 160)
- strategy (str): 'concat' or 'largest'. Defaults to largest
+ masks (torch.Tensor): the output of the model, which is a tensor of shape (batch_size, 160, 160)
+ strategy (str): 'concat' or 'largest'. Defaults to largest
Returns:
- segments (List): list of segment masks
+ segments (List): list of segment masks
"""
segments = []
for x in masks.int().cpu().numpy().astype('uint8'):
@@ -796,9 +763,9 @@ def clean_str(s):
Cleans a string by replacing special characters with underscore _
Args:
- s (str): a string needing special characters replaced
+ s (str): a string needing special characters replaced
Returns:
- (str): a string with special characters replaced by an underscore _
+ (str): a string with special characters replaced by an underscore _
"""
return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s)