guide for pose estimation - flipping (#884)

docs/modules/prepro.rst

@@ -1,4 +1,4 @@
-API - Data Pre-processing
+API - Data Pre-Processing
 =========================
 
 .. automodule:: tensorlayer.prepro
@@ -221,7 +221,7 @@ The sequential transformation can be represented as ``y = Tk (... T1(x))``,
 and the time complexity is ``O(k N)`` where ``N`` is the cost of applying one transformation to image ``x``.
 ``N`` is linear to the size of ``x``.
 For the combined transformation ``y = (Tk ... T1) (x)``
-the time complexity is ``O(27(k - 1) + N) = max{O(27k), O(N)} = O(N)`` (assuming 27k << N) where 27 = 3^3 is the cost for combine two transformations.
+the time complexity is ``O(27(k - 1) + N) = max{O(27k), O(N)} = O(N)`` (assuming 27k << N) where 27 = 3^3 is the cost for combining two transformations.
 
 
 Get rotation matrix

tensorlayer/prepro.py

@@ -581,6 +581,9 @@ def affine_transform_keypoints(coords_list, transform_matrix):
     """Transform keypoint coordinates according to a given affine transform matrix.
     OpenCV format, x is width.
 
+    Note that, for pose estimation task, flipping requires maintaining the left and right body information.
+    We should not flip the left and right body, so please use ``tl.prepro.keypoint_random_flip``.
+
     Parameters
     -----------
     coords_list : list of list of tuple/list
@@ -3995,7 +3998,10 @@ def keypoint_random_flip(
     prob : float, 0 to 1
         The probability to flip the image, if 1, always flip the image.
     flip_list : tuple of int
-        Denotes how the keypoints number be changed after flipping. Default COCO format.
+        Denotes how the keypoints number be changed after flipping which is required for pose estimation task.
+        The left and right body should be maintained rather than switch.
+        (Default COCO format).
+        Set to an empty tuple if you don't need to maintain left and right information.
 
     Returns
     ----------