ultralytics 8.0.80 single-line docstring fixes (#2060)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/utils/plotting.py

@@ -21,7 +21,7 @@ from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
 class Colors:
     # Ultralytics color palette https://ultralytics.com/
     def __init__(self):
-        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
         hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
                 '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
         self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
@@ -63,7 +63,7 @@ class Annotator:
         else:  # use cv2
             self.im = im
         self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-        # pose
+        # 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]]
 
@@ -115,7 +115,7 @@ class Annotator:
             alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
         """
         if self.pil:
-            # convert to numpy first
+            # Convert to numpy first
             self.im = np.asarray(self.im).copy()
         if len(masks) == 0:
             self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
@@ -136,7 +136,7 @@ class Annotator:
         im_mask_np = im_mask.byte().cpu().numpy()
         self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
         if self.pil:
-            # convert im back to PIL and update draw
+            # Convert im back to PIL and update draw
             self.fromarray(self.im)
 
     def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
@@ -152,7 +152,7 @@ class Annotator:
         Note: `kpt_line=True` currently only supports human pose plotting.
         """
         if self.pil:
-            # convert to numpy first
+            # Convert to numpy first
             self.im = np.asarray(self.im).copy()
         nkpt, ndim = kpts.shape
         is_pose = nkpt == 17 and ndim == 3
@@ -183,11 +183,11 @@ class Annotator:
                     continue
                 cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
         if self.pil:
-            # convert im back to PIL and update draw
+            # Convert im back to PIL and update draw
             self.fromarray(self.im)
 
     def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
+        """Add rectangle to image (PIL-only)."""
         self.draw.rectangle(xy, fill, outline, width)
 
     def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
@@ -202,12 +202,12 @@ class Annotator:
             cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
 
     def fromarray(self, im):
-        # Update self.im from a numpy array
+        """Update self.im from a numpy array."""
         self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
         self.draw = ImageDraw.Draw(self.im)
 
     def result(self):
-        # Return annotated image as array
+        """Return annotated image as array."""
         return np.asarray(self.im)
 
 
@@ -217,18 +217,18 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     import pandas as pd
     import seaborn as sn
 
-    # plot dataset labels
+    # Plot dataset labels
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
     b = boxes.transpose()  # classes, boxes
     nc = int(cls.max() + 1)  # number of classes
     x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
 
-    # seaborn correlogram
+    # Seaborn correlogram
     sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
     plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
     plt.close()
 
-    # matplotlib labels
+    # Matplotlib labels
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
     with contextlib.suppress(Exception):  # color histogram bars by class
@@ -242,7 +242,7 @@ def plot_labels(boxes, cls, names=(), save_dir=Path('')):
     sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
     sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
 
-    # rectangles
+    # Rectangles
     boxes[:, 0:2] = 0.5  # center
     boxes = xywh2xyxy(boxes) * 1000
     img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
@@ -401,7 +401,7 @@ def plot_images(images,
 
 @plt_settings()
 def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
-    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+    """Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')."""
     import pandas as pd
     save_dir = Path(file).parent if file else Path(dir)
     if segment:
@@ -436,7 +436,7 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
 
 
 def output_to_target(output, max_det=300):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
+    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
     targets = []
     for i, o in enumerate(output):
         box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)