Fix rotation of rectangular images

utils.py

@@ -49,26 +49,57 @@ def binarize_images(x):
 
 def rotate(image, angle):
     """
-    Rotate an OpenCV 2 / NumPy image around it's centre by the given angle
-    (in degrees). The returned image will have the same size as the new image.
+    Rotates an OpenCV 2 / NumPy image about it's centre by the given angle
+    (in degrees). The returned image will be large enough to hold the entire
+    new image, with a black background
 
-    Adapted from: http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
+    Source: http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
     """
-
     # Get the image size
     # No that's not an error - NumPy stores image matricies backwards
     image_size = (image.shape[1], image.shape[0])
-    image_center = tuple(np.array(image_size) / 2 - 0.5)
+    image_center = tuple(np.array(image_size) / 2)
 
     # Convert the OpenCV 3x2 rotation matrix to 3x3
     rot_mat = np.vstack(
         [cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]
     )
 
+    rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2])
+
+    # Shorthand for below calcs
+    image_w2 = image_size[0] * 0.5
+    image_h2 = image_size[1] * 0.5
+
+    # Obtain the rotated coordinates of the image corners
+    rotated_coords = [
+        (np.array([-image_w2,  image_h2]) * rot_mat_notranslate).A[0],
+        (np.array([ image_w2,  image_h2]) * rot_mat_notranslate).A[0],
+        (np.array([-image_w2, -image_h2]) * rot_mat_notranslate).A[0],
+        (np.array([ image_w2, -image_h2]) * rot_mat_notranslate).A[0]
+    ]
+
+    # Find the size of the new image
+    x_coords = [pt[0] for pt in rotated_coords]
+    x_pos = [x for x in x_coords if x > 0]
+    x_neg = [x for x in x_coords if x < 0]
+
+    y_coords = [pt[1] for pt in rotated_coords]
+    y_pos = [y for y in y_coords if y > 0]
+    y_neg = [y for y in y_coords if y < 0]
+
+    right_bound = max(x_pos)
+    left_bound = min(x_neg)
+    top_bound = max(y_pos)
+    bot_bound = min(y_neg)
+
+    new_w = int(abs(right_bound - left_bound))
+    new_h = int(abs(top_bound - bot_bound))
+
     # We require a translation matrix to keep the image centred
     trans_mat = np.matrix([
-        [1, 0, int(image_size[0] * 0.5 - image_center[1])],
-        [0, 1, int(image_size[0] * 0.5 - image_center[1])],
+        [1, 0, int(new_w * 0.5 - image_w2)],
+        [0, 1, int(new_h * 0.5 - image_h2)],
         [0, 0, 1]
     ])
 
@@ -79,7 +110,7 @@ def rotate(image, angle):
     result = cv2.warpAffine(
         image,
         affine_mat,
-        image_size,
+        (new_w, new_h),
         flags=cv2.INTER_LINEAR
     )
 
@@ -179,7 +210,6 @@ def generate_rotated_image(image, angle, size=None, crop_center=False,
             height = width
         else:
             width = height
-        image = crop_around_center(image, height, width)
 
     image = rotate(image, angle)