Add Perspective fill option (#1973)

* Add fill option to RandomPerspective #1972

* Minor fix to docstring syntax

* Add _parse_fill() to get fillcolor (#1972)

* Minor refactoring as per comments.

* Added test for RandomPerspective with fillcolor.

* Force perspective transform in test.

Author: WillieMaddox

test/test_transforms.py

@@ -177,6 +177,41 @@ def test_randomperspective(self):
             self.assertGreater(torch.nn.functional.mse_loss(tr_img, F.to_tensor(img)) + 0.3,
                                torch.nn.functional.mse_loss(tr_img2, F.to_tensor(img)))
 
+    def test_randomperspective_fill(self):
+        height = 100
+        width = 100
+        img = torch.ones(3, height, width)
+        to_pil_image = transforms.ToPILImage()
+        img = to_pil_image(img)
+
+        modes = ("L", "RGB", "F")
+        nums_bands = [len(mode) for mode in modes]
+        fill = 127
+
+        for mode, num_bands in zip(modes, nums_bands):
+            img_conv = img.convert(mode)
+            perspective = transforms.RandomPerspective(p=1, fill=fill)
+            tr_img = perspective(img_conv)
+            pixel = tr_img.getpixel((0, 0))
+
+            if not isinstance(pixel, tuple):
+                pixel = (pixel,)
+            self.assertTupleEqual(pixel, tuple([fill] * num_bands))
+
+        for mode, num_bands in zip(modes, nums_bands):
+            img_conv = img.convert(mode)
+            startpoints, endpoints = transforms.RandomPerspective.get_params(width, height, 0.5)
+            tr_img = F.perspective(img_conv, startpoints, endpoints, fill=fill)
+            pixel = tr_img.getpixel((0, 0))
+        
+            if not isinstance(pixel, tuple):
+                pixel = (pixel,)
+            self.assertTupleEqual(pixel, tuple([fill] * num_bands))
+
+            for wrong_num_bands in set(nums_bands) - {num_bands}:
+                with self.assertRaises(ValueError):
+                    F.perspective(img_conv, startpoints, endpoints, fill=tuple([fill] * wrong_num_bands))
+
     def test_resize(self):
         height = random.randint(24, 32) * 2
         width = random.randint(24, 32) * 2

torchvision/transforms/functional.py

@@ -425,6 +425,41 @@ def hflip(img):
     return img.transpose(Image.FLIP_LEFT_RIGHT)
 
 
+def _parse_fill(fill, img, min_pil_version):
+    """Helper function to get the fill color for rotate and perspective transforms.
+
+    Args:
+        fill (n-tuple or int or float): Pixel fill value for area outside the transformed
+            image. If int or float, the value is used for all bands respectively.
+            Defaults to 0 for all bands.
+        img (PIL Image): Image to be filled.
+        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option 
+            was first introduced in the calling function. (e.g. rotate->5.2.0, perspective->5.0.0)
+
+    Returns:
+        dict: kwarg for ``fillcolor``
+    """
+    if PILLOW_VERSION < min_pil_version:
+        if fill is None:
+            return {}
+        else:
+            msg = ("The option to fill background area of the transformed image, "
+                   "requires pillow>={}")
+            raise RuntimeError(msg.format(min_pil_version))
+
+    num_bands = len(img.getbands())
+    if fill is None:
+        fill = 0
+    if isinstance(fill, (int, float)) and num_bands > 1:
+        fill = tuple([fill] * num_bands)
+    if not isinstance(fill, (int, float)) and len(fill) != num_bands:
+        msg = ("The number of elements in 'fill' does not match the number of "
+               "bands of the image ({} != {})")
+        raise ValueError(msg.format(len(fill), num_bands))
+
+    return {"fillcolor": fill}
+
+
 def _get_perspective_coeffs(startpoints, endpoints):
     """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
 
@@ -450,22 +485,29 @@ def _get_perspective_coeffs(startpoints, endpoints):
     return res.squeeze_(1).tolist()
 
 
-def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):
+def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC, fill=None):
     """Perform perspective transform of the given PIL Image.
 
     Args:
         img (PIL Image): Image to be transformed.
         startpoints: List containing [top-left, top-right, bottom-right, bottom-left] of the orignal image
         endpoints: List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image
         interpolation: Default- Image.BICUBIC
+        fill (n-tuple or int or float): Pixel fill value for area outside the rotated
+            image. If int or float, the value is used for all bands respectively.
+            This option is only available for ``pillow>=5.0.0``.
+
     Returns:
         PIL Image:  Perspectively transformed Image.
     """
+
     if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
+    opts = _parse_fill(fill, img, '5.0.0')
+
     coeffs = _get_perspective_coeffs(startpoints, endpoints)
-    return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation)
+    return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, **opts)
 
 
 def vflip(img):
@@ -721,30 +763,10 @@ def rotate(img, angle, resample=False, expand=False, center=None, fill=None):
     .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
 
     """
-    def parse_fill(fill, num_bands):
-        if PILLOW_VERSION < "5.2.0":
-            if fill is None:
-                return {}
-            else:
-                msg = ("The option to fill background area of the rotated image, "
-                       "requires pillow>=5.2.0")
-                raise RuntimeError(msg)
-
-        if fill is None:
-            fill = 0
-        if isinstance(fill, (int, float)) and num_bands > 1:
-            fill = tuple([fill] * num_bands)
-        if not isinstance(fill, (int, float)) and len(fill) != num_bands:
-            msg = ("The number of elements in 'fill' does not match the number of "
-                   "bands of the image ({} != {})")
-            raise ValueError(msg.format(len(fill), num_bands))
-
-        return {"fillcolor": fill}
-
     if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
-    opts = parse_fill(fill, len(img.getbands()))
+    opts = _parse_fill(fill, img, '5.2.0')
 
     return img.rotate(angle, resample, expand, center, **opts)
 

torchvision/transforms/transforms.py

@@ -550,12 +550,15 @@ class RandomPerspective(object):
 
         distortion_scale(float): it controls the degree of distortion and ranges from 0 to 1. Default value is 0.5.
 
+        fill (3-tuple or int): RGB pixel fill value for area outside the rotated image.
+            If int, it is used for all channels respectively. Default value is 0.
     """
 
-    def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BICUBIC):
+    def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BICUBIC, fill=0):
         self.p = p
         self.interpolation = interpolation
         self.distortion_scale = distortion_scale
+        self.fill = fill
 
     def __call__(self, img):
         """
@@ -571,7 +574,7 @@ def __call__(self, img):
         if random.random() < self.p:
             width, height = img.size
             startpoints, endpoints = self.get_params(width, height, self.distortion_scale)
-            return F.perspective(img, startpoints, endpoints, self.interpolation)
+            return F.perspective(img, startpoints, endpoints, self.interpolation, self.fill)
         return img
 
     @staticmethod