Merge remote-tracking branch 'origin/docstrings' into docstrings

utils/plots.py

@@ -31,7 +31,11 @@
 class Colors:
     # Ultralytics color palette https://ultralytics.com/
     def __init__(self):
-        """Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB. Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`."""
+        """
+        Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.
+
+        Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`.
+        """
         hexs = (
             "FF3838",
             "FF9D97",
@@ -101,7 +105,11 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detec
 
 
 def hist2d(x, y, n=100):
-    """Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions. Used in used in labels.png and evolve.png."""
+    """
+    Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions.
+
+    Used in used in labels.png and evolve.png.
+    """
     xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
     hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
     xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
@@ -124,7 +132,9 @@ def butter_lowpass(cutoff, fs, order):
 
 
 def output_to_target(output, max_det=300):
-    """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections to `max_det`."""
+    """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections
+    to `max_det`.
+    """
     targets = []
     for i, o in enumerate(output):
         box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
@@ -399,7 +409,11 @@ def plot_evolve(evolve_csv="path/to/evolve.csv"):  # from utils.plots import *;
 
 
 def plot_results(file="path/to/results.csv", dir=""):
-    """Plots training results from a 'results.csv' file; accepts file path and directory as arguments. Example: from utils.plots import *; plot_results('path/to/results.csv')"""
+    """
+    Plots training results from a 'results.csv' file; accepts file path and directory as arguments.
+
+    Example: from utils.plots import *; plot_results('path/to/results.csv')
+    """
     save_dir = Path(file).parent if file else Path(dir)
     fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
     ax = ax.ravel()
@@ -426,7 +440,11 @@ def plot_results(file="path/to/results.csv", dir=""):
 
 
 def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
-    """Plots per-image iDetection logs, comparing metrics like storage and performance over time. Example: from utils.plots import *; profile_idetection()"""
+    """
+    Plots per-image iDetection logs, comparing metrics like storage and performance over time.
+
+    Example: from utils.plots import *; profile_idetection()
+    """
     ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
     s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"]
     files = list(Path(save_dir).glob("frames*.txt"))
@@ -457,7 +475,9 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
 
 
 def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
-    """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts for BGR."""
+    """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts
+    for BGR.
+    """
     xyxy = torch.tensor(xyxy).view(-1, 4)
     b = xyxy2xywh(xyxy)  # boxes
     if square: