Add adjustment operations for RGB Tensor Images. (#1525)

* Add adjustment operations for RGB Tensor Images.

Right now, we have operations on PIL images, but we want to have a version of the opeartions that act directly on Tensor images.

Here, we add such operations for adjust_brightness, adjust_contrast and adjust_saturation.

In PIL, those functions are implemented by generating an degenerate image from the first, and then interpolating them together.
- https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageEnhance.py
- https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Blend.c

A few caveats:
* Since PIL operates on uint8, and the tensor operations might be on float, we can get slightly different values because of int truncation.
* We assume here the images are RGB; in particular, to handle an alpha channel, we need to check whether it is present, in which case we copy it to the final image.

* Keep dtype and use broadcast in adjust operations

- We make our operations have input.dtype == output.dtype, at the cost of
adding a few type checks and branches.

- By using Tensor broadcast, we can simplify the calls to _blend.

* Use is_floating_point to check dtype.

* Remove unpacking in tuple

It seems Python 2 does not support this type of unpacking, so it broke
Python 2 builds. This should fix it.

* Add from __future__ import division for Python 2

Author: pedrofreire

test/test_functional_tensor.py

@@ -1,3 +1,4 @@
+from __future__ import division
 import torch
 import torchvision.transforms as transforms
 import torchvision.transforms.functional_tensor as F_t
@@ -36,6 +37,37 @@ def test_crop(self):
         self.assertTrue(torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
                         "functional_tensor crop not working")
 
+    def test_adjustments(self):
+        fns = ((F.adjust_brightness, F_t.adjust_brightness),
+               (F.adjust_contrast, F_t.adjust_contrast),
+               (F.adjust_saturation, F_t.adjust_saturation))
+
+        for _ in range(20):
+            channels = 3
+            dims = torch.randint(1, 50, (2,))
+            shape = (channels, dims[0], dims[1])
+
+            if torch.randint(0, 2, (1,)) == 0:
+                img = torch.rand(*shape, dtype=torch.float)
+            else:
+                img = torch.randint(0, 256, shape, dtype=torch.uint8)
+
+            factor = 3 * torch.rand(1)
+            for f, ft in fns:
+
+                ft_img = ft(img, factor)
+                if not img.dtype.is_floating_point:
+                    ft_img = ft_img.to(torch.float) / 255
+
+                img_pil = transforms.ToPILImage()(img)
+                f_img_pil = f(img_pil, factor)
+                f_img = transforms.ToTensor()(f_img_pil)
+
+                # F uses uint8 and F_t uses float, so there is a small
+                # difference in values caused by (at most 5) truncations.
+                max_diff = (ft_img - f_img).abs().max()
+                self.assertLess(max_diff, 5 / 255 + 1e-5)
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional_tensor.py

@@ -48,3 +48,69 @@ def crop(img, top, left, height, width):
         raise TypeError('tensor is not a torch image.')
 
     return img[..., top:top + height, left:left + width]
+
+
+def adjust_brightness(img, brightness_factor):
+    """Adjust brightness of an RGB image.
+
+    Args:
+        img (Tensor): Image to be adjusted.
+        brightness_factor (float):  How much to adjust the brightness. Can be
+            any non negative number. 0 gives a black image, 1 gives the
+            original image while 2 increases the brightness by a factor of 2.
+
+    Returns:
+        Tensor: Brightness adjusted image.
+    """
+    if not F._is_tensor_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    return _blend(img, 0, brightness_factor)
+
+
+def adjust_contrast(img, contrast_factor):
+    """Adjust contrast of an RGB image.
+
+    Args:
+        img (Tensor): Image to be adjusted.
+        contrast_factor (float): How much to adjust the contrast. Can be any
+            non negative number. 0 gives a solid gray image, 1 gives the
+            original image while 2 increases the contrast by a factor of 2.
+
+    Returns:
+        Tensor: Contrast adjusted image.
+    """
+    if not F._is_tensor_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    mean = torch.mean(_rgb_to_grayscale(img).to(torch.float))
+
+    return _blend(img, mean, contrast_factor)
+
+
+def adjust_saturation(img, saturation_factor):
+    """Adjust color saturation of an RGB image.
+
+    Args:
+        img (Tensor): Image to be adjusted.
+        saturation_factor (float):  How much to adjust the saturation. 0 will
+            give a black and white image, 1 will give the original image while
+            2 will enhance the saturation by a factor of 2.
+
+    Returns:
+        Tensor: Saturation adjusted image.
+    """
+    if not F._is_tensor_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    return _blend(img, _rgb_to_grayscale(img), saturation_factor)
+
+
+def _blend(img1, img2, ratio):
+    bound = 1 if img1.dtype.is_floating_point else 255
+    return (ratio * img1 + (1 - ratio) * img2).clamp(0, bound).to(img1.dtype)
+
+
+def _rgb_to_grayscale(img):
+    # ITU-R 601-2 luma transform, as used in PIL.
+    return (0.2989 * img[0] + 0.5870 * img[1] + 0.1140 * img[2]).to(img.dtype)