New scale_segments() function (#9570)

* Rename scale_coords to scale_boxes

* add scale_segments

segment/predict.py

@@ -42,7 +42,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
+                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
 from utils.segment.general import process_mask
 from utils.torch_utils import select_device, smart_inference_mode
@@ -152,7 +152,7 @@ def run(
                 masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
 
                 # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
 
                 # Print results
                 for c in det[:, 5].unique():

segment/val.py

@@ -44,7 +44,7 @@ from models.yolo import SegmentationModel
 from utils.callbacks import Callbacks
 from utils.general import (LOGGER, NUM_THREADS, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
                            coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
-                           scale_coords, xywh2xyxy, xyxy2xywh)
+                           scale_boxes, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, box_iou
 from utils.plots import output_to_target, plot_val_study
 from utils.segment.dataloaders import create_dataloader
@@ -298,12 +298,12 @@ def run(
             if single_cls:
                 pred[:, 5] = 0
             predn = pred.clone()
-            scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
+            scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
 
             # Evaluate
             if nl:
                 tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
-                scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
+                scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
                 labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
                 correct_bboxes = process_batch(predn, labelsn, iouv)
                 correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)