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

Co-authored-by: Kengo Miyakawa <s_flashback@yahoo.co.jp>

docs/guides/hyperparameter-tuning.md

@@ -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).

ultralytics/__init__.py

@@ -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

ultralytics/engine/results.py

@@ -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
 
 
@@ -153,21 +153,20 @@ class Results(SimpleClass):
         return Results(orig_img=self.orig_img, path=self.path, names=self.names)
 
     def plot(
-            self,
-            conf=True,
-            line_width=None,
-            font_size=None,
-            font='Arial.ttf',
-            pil=False,
-            img=None,
-            im_gpu=None,
-            kpt_radius=5,
-            kpt_line=True,
-            labels=True,
-            boxes=True,
-            masks=True,
-            probs=True,
-            **kwargs  # deprecated args TODO: remove support in 8.2
+        self,
+        conf=True,
+        line_width=None,
+        font_size=None,
+        font='Arial.ttf',
+        pil=False,
+        img=None,
+        im_gpu=None,
+        kpt_radius=5,
+        kpt_line=True,
+        labels=True,
+        boxes=True,
+        masks=True,
+        probs=True,
     ):
         """
         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

ultralytics/utils/plotting.py

@@ -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."""