Added vertical flip augmentation, along with its random version.

data_aug/data_aug.py

@@ -36,18 +36,19 @@ def __init__(self, p=0.5):
         self.p = p
 
     def __call__(self, img, bboxes):
-            img_center = np.array(img.shape[:2])[::-1]/2
-            img_center = np.hstack((img_center, img_center))
-            if random.random() < self.p:
-                img = img[:, ::-1, :]
-                bboxes[:, [0, 2]] += 2*(img_center[[0, 2]] - bboxes[:, [0, 2]])
+        img_center = np.array(img.shape[:2])[::-1]/2
+        img_center = np.hstack((img_center, img_center))
 
-                box_w = abs(bboxes[:, 0] - bboxes[:, 2])
+        if random.random() < self.p:
+            img = img[:, ::-1, :]
+            bboxes[:, [0, 2]] += 2*(img_center[[0, 2]] - bboxes[:, [0, 2]])
 
-                bboxes[:, 0] -= box_w
-                bboxes[:, 2] += box_w
+            box_w = abs(bboxes[:, 0] - bboxes[:, 2])
 
-            return img, bboxes
+            bboxes[:, 0] -= box_w
+            bboxes[:, 2] += box_w
+
+        return img, bboxes
 
 
 class HorizontalFlip(object):
@@ -90,6 +91,86 @@ def __call__(self, img, bboxes):
         return img, bboxes
 
 
+class RandomVerticalFlip(object):
+
+    """Randomly vertically flips the Image with the probability *p*
+
+    Parameters
+    ----------
+    p: float
+        The probability with which the image is flipped
+
+
+    Returns
+    -------
+
+    numpy.ndaaray
+        Flipped image in the numpy format of shape `HxWxC`
+
+    numpy.ndarray
+        Tranformed bounding box co-ordinates of the format `n x 4` where n is
+        number of bounding boxes and 4 represents `x1,y1,x2,y2` of the box
+
+    """
+
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, img, bboxes):
+        img_center = np.array(img.shape[:2])[::-1]/2
+        img_center = np.hstack((img_center, img_center))
+
+        if random.random() < self.p:
+            img = img[::-1]
+            bboxes[:, [1, 3]] += 2*(img_center[[1, 3]] - bboxes[:, [1, 3]])
+
+            box_h = abs(bboxes[:, 1] - bboxes[:, 3])
+
+            bboxes[:, 1] -= box_h
+            bboxes[:, 3] += box_h
+
+        return img, bboxes
+
+
+class VerticalFlip(object):
+
+    """Vertically flips the Image
+
+    Parameters
+    ----------
+    None
+
+
+    Returns
+    -------
+
+    numpy.ndaaray
+        Flipped image in the numpy format of shape `HxWxC`
+
+    numpy.ndarray
+        Tranformed bounding box co-ordinates of the format `n x 4` where n is
+        number of bounding boxes and 4 represents `x1,y1,x2,y2` of the box
+
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, img, bboxes):
+        img_center = np.array(img.shape[:2])[::-1]/2
+        img_center = np.hstack((img_center, img_center))    
+
+        img = img[::-1]
+        bboxes[:, [1, 3]] += 2*(img_center[[1, 3]] - bboxes[:, [1, 3]])
+
+        box_h = abs(bboxes[:, 1] - bboxes[:, 3])
+
+        bboxes[:, 1] -= box_h
+        bboxes[:, 3] += box_h
+
+        return img, bboxes
+
+
 class RandomScale(object):
     """Randomly scales an image    
 

readme.md

@@ -12,6 +12,9 @@ We support a variety of data augmentations, like.
 ### Horizontal Flipping
 ![Horizontal Flip](Images/hflip.png)
 
+### Vertical Flipping
+![Vertical Flip](Images/vflip.png)
+
 ### Scaling
 ![Scaling](Images/scale_aug.png)