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ultralytics 8.0.184 https://yolovision.ultralytics.com #YV23 (#5008)

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Co-authored-by: Kengo Miyakawa <s_flashback@yahoo.co.jp>
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Glenn Jocher 2023-09-21 17:37:09 +02:00 committed by GitHub
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4
docs/guides/hyperparameter-tuning.md
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@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
<img width="640" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
</p>
For a full list of augmentation hyperparameters used in YOLOv8 please refer to [https://docs.ultralytics.com/usage/cfg/#augmentation)(https://docs.ultralytics.com/usage/cfg/#augmentation).
For a full list of augmentation hyperparameters used in YOLOv8 please refer to [https://docs.ultralytics.com/usage/cfg/#augmentation](https://docs.ultralytics.com/usage/cfg/#augmentation).
### Genetic Evolution and Mutation
@ -203,4 +203,4 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
2. [YOLOv5 Hyperparameter Evolution Guide](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](https://docs.ultralytics.com/integrations/ray-tune/)
For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord)
For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord).

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.183'
__version__ = '8.0.184'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

14
ultralytics/engine/results.py
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@ -13,7 +13,7 @@ import numpy as np
import torch
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER, SimpleClass, deprecation_warn, ops
from ultralytics.utils import LOGGER, SimpleClass, ops
from ultralytics.utils.plotting import Annotator, colors, save_one_box
@ -167,7 +167,6 @@ class Results(SimpleClass):
boxes=True,
masks=True,
probs=True,
**kwargs # deprecated args TODO: remove support in 8.2
):
"""
Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.
@ -207,17 +206,6 @@ class Results(SimpleClass):
if img is None and isinstance(self.orig_img, torch.Tensor):
img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).cpu().numpy()
# Deprecation warn TODO: remove in 8.2
if 'show_conf' in kwargs:
deprecation_warn('show_conf', 'conf')
conf = kwargs['show_conf']
assert isinstance(conf, bool), '`show_conf` should be of boolean type, i.e, show_conf=True/False'
if 'line_thickness' in kwargs:
deprecation_warn('line_thickness', 'line_width')
line_width = kwargs['line_thickness']
assert isinstance(line_width, int), '`line_width` should be of int type, i.e, line_width=3'
names = self.names
pred_boxes, show_boxes = self.boxes, boxes
pred_masks, show_masks = self.masks, masks

17
ultralytics/utils/plotting.py
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@ -76,6 +76,7 @@ class Annotator:
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
self.pil = pil or non_ascii
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
if self.pil: # use PIL
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
@ -90,7 +91,8 @@ class Annotator:
self.font.getsize = lambda x: self.font.getbbox(x)[2:4] # text width, height
else: # use cv2
self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
self.tf = max(self.lw - 1, 1) # font thickness
self.sf = self.lw / 3 # font scale
# Pose
self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
@ -118,17 +120,16 @@ class Annotator:
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
if label:
tf = max(self.lw - 1, 1) # font thickness
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0] # text width, height
outside = p1[1] - h >= 3
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
cv2.putText(self.im,
label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
0,
self.lw / 3,
self.sf,
txt_color,
thickness=tf,
thickness=self.tf,
lineType=cv2.LINE_AA)
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
@ -240,15 +241,13 @@ class Annotator:
self.draw.text(xy, text, fill=txt_color, font=self.font)
else:
if box_style:
tf = max(self.lw - 1, 1) # font thickness
w, h = cv2.getTextSize(text, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
w, h = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0] # text width, height
outside = xy[1] - h >= 3
p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA) # filled
# Using `txt_color` for background and draw fg with white color
txt_color = (255, 255, 255)
tf = max(self.lw - 1, 1) # font thickness
cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
cv2.putText(self.im, text, xy, 0, self.sf, txt_color, thickness=self.tf, lineType=cv2.LINE_AA)
def fromarray(self, im):
"""Update self.im from a numpy array."""