Add `xyxy2xywhn()` (#3765)

* Edit Comments for numpy2torch tensor process

Edit Comments for numpy2torch tensor process

* add xyxy2xywhn

add xyxy2xywhn

* add xyxy2xywhn

* formatting

* pass arguments

pass arguments

* edit comment for xyxy2xywhn()

edit comment for xyxy2xywhn()

* cleanup datasets.py

Co-authored-by: Glenn Jocher <[email protected]>

utils/datasets.py

@@ -23,8 +23,8 @@ from PIL import Image, ExifTags
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from utils.general import check_requirements, check_file, check_dataset, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, \
-    segment2box, segments2boxes, resample_segments, clean_str
+from utils.general import check_requirements, check_file, check_dataset, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, \
+    xyn2xy, segment2box, segments2boxes, resample_segments, clean_str
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
@@ -192,7 +192,7 @@ class LoadImages:  # for inference
         img = letterbox(img0, self.img_size, stride=self.stride)[0]
 
         # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB and HWC to CHW
         img = np.ascontiguousarray(img)
 
         return path, img, img0, self.cap
@@ -255,7 +255,7 @@ class LoadWebcam:  # for inference
         img = letterbox(img0, self.img_size, stride=self.stride)[0]
 
         # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB and HWC to CHW
         img = np.ascontiguousarray(img)
 
         return img_path, img, img0, None
@@ -336,7 +336,7 @@ class LoadStreams:  # multiple IP or RTSP cameras
         img = np.stack(img, 0)
 
         # Convert
-        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
+        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB and BHWC to BCHW
         img = np.ascontiguousarray(img)
 
         return self.sources, img, img0, None
@@ -552,9 +552,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
         nL = len(labels)  # number of labels
         if nL:
-            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
-            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
-            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1
+            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0])  # xyxy to xywh normalized
 
         if self.augment:
             # flip up-down

utils/general.py

@@ -393,6 +393,16 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
     return y
 
 
+def xyxy2xywhn(x, w=640, h=640):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
+    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
+    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
+    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
+    return y
+
+
 def xyn2xy(x, w=640, h=640, padw=0, padh=0):
     # Convert normalized segments into pixel segments, shape (n,2)
     y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)