New LetterBox(size) CenterCrop(size), ToTensor() transforms (#9213)

* New LetterBox transform

YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([T.ToTensor(), LetterBox(size)])

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update augmentations.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update augmentations.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update augmentations.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

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

utils/dataloaders.py

@@ -251,7 +251,7 @@ class LoadImages:
             s = f'image {self.count}/{self.nf} {path}: '
 
         if self.transforms:
-            im = self.transforms(cv2.cvtColor(im0, cv2.COLOR_BGR2RGB))  # transforms
+            im = self.transforms(im0)  # transforms
         else:
             im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
             im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
@@ -386,7 +386,7 @@ class LoadStreams:
 
         im0 = self.imgs.copy()
         if self.transforms:
-            im = np.stack([self.transforms(cv2.cvtColor(x, cv2.COLOR_BGR2RGB)) for x in im0])  # transforms
+            im = np.stack([self.transforms(x) for x in im0])  # transforms
         else:
             im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0])  # resize
             im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
@@ -1113,18 +1113,18 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
 
     def __getitem__(self, i):
         f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
+        if self.cache_ram and im is None:
+            im = self.samples[i][3] = cv2.imread(f)
+        elif self.cache_disk:
+            if not fn.exists():  # load npy
+                np.save(fn.as_posix(), cv2.imread(f))
+            im = np.load(fn)
+        else:  # read image
+            im = cv2.imread(f)  # BGR
         if self.album_transforms:
-            if self.cache_ram and im is None:
-                im = self.samples[i][3] = cv2.imread(f)
-            elif self.cache_disk:
-                if not fn.exists():  # load npy
-                    np.save(fn.as_posix(), cv2.imread(f))
-                im = np.load(fn)
-            else:  # read image
-                im = cv2.imread(f)  # BGR
             sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
         else:
-            sample = self.torch_transforms(self.loader(f))
+            sample = self.torch_transforms(im)
         return sample, j