Add bgr hyperparameter (#9139)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/cfg/__init__.py

@@ -112,6 +112,7 @@ CFG_FRACTION_KEYS = {
     "perspective",
     "flipud",
     "fliplr",
+    "bgr",
     "mosaic",
     "mixup",
     "copy_paste",

ultralytics/cfg/default.yaml

@@ -111,6 +111,7 @@ shear: 0.0 # (float) image shear (+/- deg)
 perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
 flipud: 0.0 # (float) image flip up-down (probability)
 fliplr: 0.5 # (float) image flip left-right (probability)
+bgr: 0.0 # (float) image channel BGR (probability)
 mosaic: 1.0 # (float) image mosaic (probability)
 mixup: 0.0 # (float) image mixup (probability)
 copy_paste: 0.0 # (float) segment copy-paste (probability)

ultralytics/data/augment.py

@@ -886,6 +886,7 @@ class Format:
         mask_ratio (int): Downsample ratio for masks. Default is 4.
         mask_overlap (bool): Whether to overlap masks. Default is True.
         batch_idx (bool): Keep batch indexes. Default is True.
+        bgr (float): The probability to return BGR images. Default is 0.0.
     """
 
     def __init__(
@@ -898,6 +899,7 @@ class Format:
         mask_ratio=4,
         mask_overlap=True,
         batch_idx=True,
+        bgr=0.0,
     ):
         """Initializes the Format class with given parameters."""
         self.bbox_format = bbox_format
@@ -908,6 +910,7 @@ class Format:
         self.mask_ratio = mask_ratio
         self.mask_overlap = mask_overlap
         self.batch_idx = batch_idx  # keep the batch indexes
+        self.bgr = bgr
 
     def __call__(self, labels):
         """Return formatted image, classes, bounding boxes & keypoints to be used by 'collate_fn'."""
@@ -948,7 +951,8 @@ class Format:
         """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])
+        img = img.transpose(2, 0, 1)
+        img = np.ascontiguousarray(img[::-1] if random.uniform(0, 1) > self.bgr else img)
         img = torch.from_numpy(img)
         return img
 

ultralytics/data/dataset.py

@@ -167,6 +167,7 @@ class YOLODataset(BaseDataset):
                 batch_idx=True,
                 mask_ratio=hyp.mask_ratio,
                 mask_overlap=hyp.overlap_mask,
+                bgr=hyp.bgr if self.augment else 0.0,  # only affect training.
             )
         )
         return transforms

ultralytics/engine/tuner.py

@@ -95,6 +95,7 @@ class Tuner:
             "perspective": (0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
             "flipud": (0.0, 1.0),  # image flip up-down (probability)
             "fliplr": (0.0, 1.0),  # image flip left-right (probability)
+            "bgr": (0.0, 1.0),  # image channel bgr (probability)
             "mosaic": (0.0, 1.0),  # image mixup (probability)
             "mixup": (0.0, 1.0),  # image mixup (probability)
             "copy_paste": (0.0, 1.0),  # segment copy-paste (probability)

ultralytics/utils/tuner.py

@@ -78,6 +78,7 @@ def run_ray_tune(
         "perspective": tune.uniform(0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
         "flipud": tune.uniform(0.0, 1.0),  # image flip up-down (probability)
         "fliplr": tune.uniform(0.0, 1.0),  # image flip left-right (probability)
+        "bgr": tune.uniform(0.0, 1.0),  # image channel BGR (probability)
         "mosaic": tune.uniform(0.0, 1.0),  # image mixup (probability)
         "mixup": tune.uniform(0.0, 1.0),  # image mixup (probability)
         "copy_paste": tune.uniform(0.0, 1.0),  # segment copy-paste (probability)