added changes to use grayscale images during training

ultralytics/cfg/models/v8/yolov8.yaml

@@ -3,6 +3,7 @@
 
 # Parameters
 nc: 80  # number of classes
+ch: 1  # number of channels
 scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
   # [depth, width, max_channels]
   n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs

ultralytics/data/augment.py

@@ -741,9 +741,15 @@ class Format:
 
     def _format_img(self, img):
         """Format the image for YOLOv5 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 = torch.from_numpy(img)
+        # return img
+
         if len(img.shape) < 3:
-            img = np.expand_dims(img, -1)
-        img = np.ascontiguousarray(img.transpose(2, 0, 1)[::-1])
+            img = img.reshape([1, *img.shape])
+        img = np.ascontiguousarray(img)
         img = torch.from_numpy(img)
         return img
 

ultralytics/data/base.py

@@ -148,7 +148,7 @@ class BaseDataset(Dataset):
             if fn.exists():  # load npy
                 im = np.load(fn)
             else:  # read image
-                im = cv2.imread(f)  # BGR
+                im = cv2.imread(f, cv2.IMREAD_GRAYSCALE)  # BGR
                 if im is None:
                     raise FileNotFoundError(f'Image Not Found {f}')
             h0, w0 = im.shape[:2]  # orig hw