Replace inline comments with docstrings (#12764)

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* Update plots.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

utils/augmentations.py

@@ -310,8 +310,13 @@ def mixup(im, labels, im2, labels2):
     return im, labels
 
 
-def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
-    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
+def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
+    """
+    Filters bounding box candidates by minimum width-height threshold `wh_thr` (pixels), aspect ratio threshold
+    `ar_thr`, and area ratio threshold `area_thr`.
+
+    box1(4,n) is before augmentation, box2(4,n) is after augmentation.
+    """
     w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
     w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
     ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
@@ -380,7 +385,12 @@ class LetterBox:
         self.auto = auto  # pass max size integer, automatically solve for short side using stride
         self.stride = stride  # used with auto
 
-    def __call__(self, im):  # im = np.array HWC
+    def __call__(self, im):
+        """
+        Resizes and pads input image `im` (HWC format) to specified dimensions, maintaining aspect ratio.
+
+        im = np.array HWC
+        """
         imh, imw = im.shape[:2]
         r = min(self.h / imh, self.w / imw)  # ratio of new/old
         h, w = round(imh * r), round(imw * r)  # resized image
@@ -398,7 +408,12 @@ class CenterCrop:
         super().__init__()
         self.h, self.w = (size, size) if isinstance(size, int) else size
 
-    def __call__(self, im):  # im = np.array HWC
+    def __call__(self, im):
+        """
+        Applies center crop to the input image and resizes it to a specified size, maintaining aspect ratio.
+
+        im = np.array HWC
+        """
         imh, imw = im.shape[:2]
         m = min(imh, imw)  # min dimension
         top, left = (imh - m) // 2, (imw - m) // 2
@@ -412,7 +427,13 @@ class ToTensor:
         super().__init__()
         self.half = half
 
-    def __call__(self, im):  # im = np.array HWC in BGR order
+    def __call__(self, im):
+        """
+        Converts BGR np.array image from HWC to RGB CHW format, and normalizes to [0, 1], with support for FP16 if
+        `half=True`.
+
+        im = np.array HWC in BGR order
+        """
         im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1])  # HWC to CHW -> BGR to RGB -> contiguous
         im = torch.from_numpy(im)  # to torch
         im = im.half() if self.half else im.float()  # uint8 to fp16/32