ultralytics 8.0.197 save P, R, F1 curves to metrics (#5354)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: erminkev1 <83356055+erminkev1@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>

ultralytics/data/augment.py

@@ -491,7 +491,7 @@ class RandomPerspective:
         border = labels.pop('mosaic_border', self.border)
         self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2  # w, h
         # M is affine matrix
-        # scale for func:`box_candidates`
+        # Scale for func:`box_candidates`
         img, M, scale = self.affine_transform(img, border)
 
         bboxes = self.apply_bboxes(instances.bboxes, M)
@@ -894,7 +894,7 @@ class Format:
         return labels
 
     def _format_img(self, img):
-        """Format the image for YOLOv5 from Numpy array to PyTorch tensor."""
+        """Format the image for YOLO from Numpy array to PyTorch tensor."""
         if len(img.shape) < 3:
             img = np.expand_dims(img, -1)
         img = np.ascontiguousarray(img.transpose(2, 0, 1)[::-1])

ultralytics/data/converter.py

@@ -1,14 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import json
-import shutil
 from collections import defaultdict
 from pathlib import Path
 
 import cv2
 import numpy as np
 
-from ultralytics.utils import TQDM
+from ultralytics.utils import LOGGER, TQDM
+from ultralytics.utils.files import increment_path
 
 
 def coco91_to_coco80_class():
@@ -48,12 +48,12 @@ def coco80_to_coco91_class():  #
 
 
 def convert_coco(labels_dir='../coco/annotations/',
-                 save_dir='.',
+                 save_dir='coco_converted/',
                  use_segments=False,
                  use_keypoints=False,
                  cls91to80=True):
     """
-    Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
+    Converts COCO dataset annotations to a YOLO annotation format  suitable for training YOLO models.
 
     Args:
         labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
@@ -74,9 +74,7 @@ def convert_coco(labels_dir='../coco/annotations/',
     """
 
     # Create dataset directory
-    save_dir = Path(save_dir)
-    if save_dir.exists():
-        shutil.rmtree(save_dir)  # delete dir
+    save_dir = increment_path(save_dir)  # increment if save directory already exists
     for p in save_dir / 'labels', save_dir / 'images':
         p.mkdir(parents=True, exist_ok=True)  # make dir
 
@@ -147,6 +145,8 @@ def convert_coco(labels_dir='../coco/annotations/',
                                  if use_segments and len(segments[i]) > 0 else bboxes[i]),  # cls, box or segments
                     file.write(('%g ' * len(line)).rstrip() % line + '\n')
 
+    LOGGER.info(f'COCO data converted successfully.\nResults saved to {save_dir.resolve()}')
+
 
 def convert_dota_to_yolo_obb(dota_root_path: str):
     """
@@ -271,26 +271,25 @@ def merge_multi_segment(segments):
     segments = [np.array(i).reshape(-1, 2) for i in segments]
     idx_list = [[] for _ in range(len(segments))]
 
-    # record the indexes with min distance between each segment
+    # Record the indexes with min distance between each segment
     for i in range(1, len(segments)):
         idx1, idx2 = min_index(segments[i - 1], segments[i])
         idx_list[i - 1].append(idx1)
         idx_list[i].append(idx2)
 
-    # use two round to connect all the segments
+    # Use two round to connect all the segments
     for k in range(2):
-        # forward connection
+        # Forward connection
         if k == 0:
             for i, idx in enumerate(idx_list):
-                # middle segments have two indexes
-                # reverse the index of middle segments
+                # Middle segments have two indexes, reverse the index of middle segments
                 if len(idx) == 2 and idx[0] > idx[1]:
                     idx = idx[::-1]
                     segments[i] = segments[i][::-1, :]
 
                 segments[i] = np.roll(segments[i], -idx[0], axis=0)
                 segments[i] = np.concatenate([segments[i], segments[i][:1]])
-                # deal with the first segment and the last one
+                # Deal with the first segment and the last one
                 if i in [0, len(idx_list) - 1]:
                     s.append(segments[i])
                 else:

ultralytics/data/dataset.py

@@ -162,7 +162,7 @@ class YOLODataset(BaseDataset):
     def update_labels_info(self, label):
         """Custom your label format here."""
         # NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
-        # we can make it also support classification and semantic segmentation by add or remove some dict keys there.
+        # We can make it also support classification and semantic segmentation by add or remove some dict keys there.
         bboxes = label.pop('bboxes')
         segments = label.pop('segments')
         keypoints = label.pop('keypoints', None)