Fix documentation of crop functionals

torchvision/transforms/functional.py

@@ -349,54 +349,61 @@ def pad(img, padding, fill=0, padding_mode='constant'):
         return Image.fromarray(img)
 
 
-def crop(img, i, j, h, w):
+def crop(img, top, left, height, width):
     """Crop the given PIL Image.
-
     Args:
-        img (PIL Image): Image to be cropped.
-        i (int): i in (i,j) i.e coordinates of the upper left corner.
-        j (int): j in (i,j) i.e coordinates of the upper left corner.
-        h (int): Height of the cropped image.
-        w (int): Width of the cropped image.
-
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
+        top (int): Vertical component of the top left corner of the crop box.
+        left (int): Horizontal component of the top left corner of the crop box.
+        height (int): Height of the crop box.
+        width (int): Width of the crop box.
     Returns:
         PIL Image: Cropped image.
     """
     if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
-    return img.crop((j, i, j + w, i + h))
+    return img.crop((left, top, left + width, top + height))
 
 
 def center_crop(img, output_size):
+    """Crop the given PIL Image and resize it to desired size.
+
+        Args:
+            img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
+            output_size (sequence or int): (height, width) of the crop box. If int,
+                it is used for both directions
+        Returns:
+            PIL Image: Cropped image.
+        """
     if isinstance(output_size, numbers.Number):
         output_size = (int(output_size), int(output_size))
-    w, h = img.size
-    th, tw = output_size
-    i = int(round((h - th) / 2.))
-    j = int(round((w - tw) / 2.))
-    return crop(img, i, j, th, tw)
+    image_width, image_height = img.size
+    crop_height, crop_width = output_size
+    crop_top = int(round((image_height - crop_height) / 2.))
+    crop_left = int(round((image_width - crop_width) / 2.))
+    return crop(img, crop_top, crop_left, crop_height, crop_width)
 
 
-def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
+def resized_crop(img, top, left, height, width, size, interpolation=Image.BILINEAR):
     """Crop the given PIL Image and resize it to desired size.
 
     Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
 
     Args:
-        img (PIL Image): Image to be cropped.
-        i (int): i in (i,j) i.e coordinates of the upper left corner
-        j (int): j in (i,j) i.e coordinates of the upper left corner
-        h (int): Height of the cropped image.
-        w (int): Width of the cropped image.
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
+        top (int): Vertical component of the top left corner of the crop box.
+        left (int): Horizontal component of the top left corner of the crop box.
+        height (int): Height of the crop box.
+        width (int): Width of the crop box.
         size (sequence or int): Desired output size. Same semantics as ``resize``.
         interpolation (int, optional): Desired interpolation. Default is
             ``PIL.Image.BILINEAR``.
     Returns:
         PIL Image: Cropped image.
     """
     assert _is_pil_image(img), 'img should be PIL Image'
-    img = crop(img, i, j, h, w)
+    img = crop(img, top, left, height, width)
     img = resize(img, size, interpolation)
     return img
 
@@ -495,16 +502,18 @@ def five_crop(img, size):
     else:
         assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
 
-    w, h = img.size
-    crop_h, crop_w = size
-    if crop_w > w or crop_h > h:
-        raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
-                                                                                      (h, w)))
-    tl = img.crop((0, 0, crop_w, crop_h))
-    tr = img.crop((w - crop_w, 0, w, crop_h))
-    bl = img.crop((0, h - crop_h, crop_w, h))
-    br = img.crop((w - crop_w, h - crop_h, w, h))
-    center = center_crop(img, (crop_h, crop_w))
+    image_width, image_height = img.size
+    crop_height, crop_width = size
+    if crop_width > image_width or crop_height > image_height:
+        msg = "Requested crop size {} is bigger than input size {}"
+        raise ValueError(msg.format(size, (image_height, image_width)))
+
+    tl = img.crop((0, 0, crop_width, crop_height))
+    tr = img.crop((image_width - crop_width, 0, image_width, crop_height))
+    bl = img.crop((0, image_height - crop_height, crop_width, image_height))
+    br = img.crop((image_width - crop_width, image_height - crop_height,
+                   image_width, image_height))
+    center = center_crop(img, (crop_height, crop_width))
     return (tl, tr, bl, br, center)