bugfix (#43)

* bugfix

* np.random => random

albumentations/augmentations/functional.py

@@ -5,6 +5,7 @@
 
 import cv2
 import numpy as np
+import random
 from scipy.ndimage.filters import gaussian_filter
 
 MAX_VALUES_BY_DTYPE = {
@@ -50,8 +51,8 @@ def cutout(img, num_holes, max_h_size, max_w_size):
     height, width = img.shape[:2]
 
     for n in range(num_holes):
-        y = np.random.randint(height)
-        x = np.random.randint(width)
+        y = random.randint(0, height)
+        x = random.randint(0, width)
 
         y1 = np.clip(y - max_h_size // 2, 0, height)
         y2 = np.clip(y + max_h_size // 2, 0, height)
@@ -71,7 +72,7 @@ def rotate(img, angle, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_RE
 
 def scale(img, scale, interpolation=cv2.INTER_LINEAR):
     height, width = img.shape[:2]
-    new_height, new_width = int(height * scale), int(height * scale)
+    new_height, new_width = int(height * scale), int(width * scale)
     img = cv2.resize(img, (new_width, new_height), interpolation=interpolation)
     return img
 
@@ -250,8 +251,8 @@ def median_blur(img, ksize):
 
 def motion_blur(img, ksize):
     kernel = np.zeros((ksize, ksize))
-    xs, ys = np.random.randint(0, ksize), np.random.randint(0, ksize)
-    xe, ye = np.random.randint(0, ksize), np.random.randint(0, ksize)
+    xs, ys = random.randint(0, ksize), random.randint(0, ksize)
+    xe, ye = random.randint(0, ksize), random.randint(0, ksize)
     cv2.line(kernel, (xs, ys), (xe, ye), 1, thickness=1)
     return cv2.filter2D(img, -1, kernel / np.sum(kernel))
 
@@ -395,7 +396,7 @@ def invert(img):
 
 def channel_shuffle(img):
     ch_arr = [0, 1, 2]
-    np.random.shuffle(ch_arr)
+    random.shuffle(ch_arr)
     img = img[..., ch_arr]
     return img
 

albumentations/augmentations/transforms.py

@@ -3,6 +3,7 @@
 import cv2
 
 import numpy as np
+import random
 
 from ..core.transforms_interface import to_tuple, DualTransform, ImageOnlyTransform
 from . import functional as F
@@ -239,7 +240,7 @@ def apply(self, img, scale=0, interpolation=cv2.INTER_LINEAR, **params):
         return F.scale(img, scale, interpolation)
 
     def get_params(self):
-        return {'scale': np.random.uniform(1 + self.scale_limit[0], 1 + self.scale_limit[1])}
+        return {'scale': random.uniform(self.scale_limit[0], self.scale_limit[1])}
 
 
 class ShiftScaleRotate(DualTransform):

tests/test_functional.py

@@ -597,21 +597,23 @@ def test_from_float_with_max_value_specified(max_value):
 
 
 @pytest.mark.parametrize('target', ['image', 'mask'])
-def test_scale_rotate(target):
+def test_scale(target):
     img = np.array([
-        [1, 2, 3, 4],
-        [5, 6, 7, 8],
-        [9, 10, 11, 12],
-        [13, 14, 15, 16]], dtype=np.uint8)
-    expected = np.array([[1, 1, 2, 3, 3, 4],
-                         [3, 4, 4, 5, 6, 6],
-                         [5, 6, 7, 7, 8, 8],
-                         [8, 9, 9, 10, 11, 11],
-                         [11, 12, 12, 13, 14, 14],
-                         [13, 13, 14, 15, 15, 16]], dtype=np.uint8)
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+        [10, 11, 12]], dtype=np.uint8)
+    expected = np.array([[1, 1, 2, 2, 3, 3],
+                         [2, 2, 2, 3, 3, 4],
+                         [3, 3, 4, 4, 5, 5],
+                         [5, 5, 5, 6, 6, 7],
+                         [6, 6, 7, 7, 8, 8],
+                         [8, 8, 8, 9, 9, 10],
+                         [9, 9, 10, 10, 11, 11],
+                         [10, 10, 11, 11, 12, 12]], dtype=np.uint8)
 
     img, expected = convert_2d_to_target_format([img, expected], target=target)
-    scaled = F.scale(img, scale=1.5, interpolation=cv2.INTER_LINEAR)
+    scaled = F.scale(img, scale=2, interpolation=cv2.INTER_LINEAR)
     assert np.array_equal(scaled, expected)