1 files changed, 39 insertions, 1 deletions
diff --git a/megapixels/app/models/bbox.py b/megapixels/app/models/bbox.py
index 40874691..608aaaf8 100644
--- a/megapixels/app/models/bbox.py
+++ b/megapixels/app/models/bbox.py
@@ -1,4 +1,5 @@
 import math
+import random
 
 from dlib import rectangle as dlib_rectangle
 import numpy as np
@@ -127,9 +128,39 @@ class BBox:
     d = int(math.sqrt(math.pow(dcx, 2) + math.pow(dcy, 2)))
     return d
 
+
   # -----------------------------------------------------------------
   # Modify
 
+  def jitter(self, amt):
+    '''Jitters BBox in x,y,w,h values. Used for face feature extraction
+    :param amt: (float) percentage of BBox for maximum translation
+    :returns (BBox)
+    '''
+    w = self._width + (self._width * random.uniform(-amt, amt))
+    h = self._height + (self._height * random.uniform(-amt, amt))
+    cx = self._cx + (self._cx * random.uniform(-amt, amt))
+    cy = self._cy + (self._cy * random.uniform(-amt, amt))
+    x1, y1 = np.clip((cx - w/2, cy - h/2), 0.0, 1.0)
+    x2, y2 = np.clip((cx + w/2, cy + h/2), 0.0, 1.0)
+    return BBox(x1, y1, x2, y2)
+
+  def expand(self, per):
+    """Expands BBox by percentage
+    :param per: (float) percentage to expand 0.0 - 1.0
+    :param dim: (int, int) image width, height
+    :returns (BBox) expanded
+    """
+    # expand
+    dw, dh = [(self._width * per), (self._height * per)]
+    r = list(np.array(self._rect) + np.array([-dw, -dh, dw, dh]))
+    # threshold expanded rectangle
+    r[0] = max(r[0], 0.0)
+    r[1] = max(r[1], 0.0)
+    r[2] = min(r[2], 1.0)
+    r[3] = min(r[3], 1.0)
+    return BBox(*r)
+
   def expand_dim(self, amt, bounds):
     """Expands BBox within dim
     :param box: (tuple) left, top, right, bottom
@@ -170,7 +201,7 @@ class BBox:
       # print(adj)
       r = np.add(np.array(r), adj)
 
-    return BBox(*r)    
+    return BBox(*r)  # updats all BBox values
 
 
   # -----------------------------------------------------------------
@@ -221,6 +252,13 @@ class BBox:
   # Create from
 
   @classmethod
+  def from_xywh_norm(cls, x, y, w, h):
+    """Converts w, y, w, h to normalized BBox
+    :returns BBox
+    """
+    return cls(x, y, x + w, y + h)
+
+  @classmethod
   def from_xyxy_dim(cls, x1, y1, x2, y2, dim):
     """Converts x1, y1, w, h to BBox and normalizes
     :returns BBox