Add option to specify resampling method in load_img

keras/preprocessing/image.py

@@ -25,6 +25,22 @@
     pil_image = None
 
 
+if pil_image is not None:
+    _PIL_INTERPOLATION_METHODS = {
+        'nearest': pil_image.NEAREST,
+        'bilinear': pil_image.BILINEAR,
+        'bicubic': pil_image.BICUBIC,
+    }
+    # These methods were only introduced in version 3.4.0 (2016).
+    if hasattr(pil_image, 'HAMMING'):
+        _PIL_INTERPOLATION_METHODS['hamming'] = pil_image.HAMMING
+    if hasattr(pil_image, 'BOX'):
+        _PIL_INTERPOLATION_METHODS['box'] = pil_image.BOX
+    # This method is new in version 1.1.3 (2013).
+    if hasattr(pil_image, 'LANCZOS'):
+        _PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS
+
+
 def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,
                     fill_mode='nearest', cval=0.):
     """Performs a random rotation of a Numpy image tensor.
@@ -302,20 +318,28 @@ def img_to_array(img, data_format=None):
     return x
 
 
-def load_img(path, grayscale=False, target_size=None):
+def load_img(path, grayscale=False, target_size=None,
+             interpolation='bilinear'):
     """Loads an image into PIL format.
 
     # Arguments
         path: Path to image file
         grayscale: Boolean, whether to load the image as grayscale.
         target_size: Either `None` (default to original size)
             or tuple of ints `(img_height, img_width)`.
+        interpolation: Interpolation method used to resample the image if the
+            target size is different from that of the loaded image.
+            Supported methods are "nearest", "bilinear", and "bicubic".
+            If PIL version 1.1.3 or newer is installed, "lanczos" is also
+            supported. If PIL version 3.4.0 or newer is installed, "box" and
+            "hamming" are also supported. By default, "bilinear" is used.
 
     # Returns
         A PIL Image instance.
 
     # Raises
         ImportError: if PIL is not available.
+        ValueError: if interpolation method is not supported.
     """
     if pil_image is None:
         raise ImportError('Could not import PIL.Image. '
@@ -327,10 +351,17 @@ def load_img(path, grayscale=False, target_size=None):
     else:
         if img.mode != 'RGB':
             img = img.convert('RGB')
-    if target_size:
-        hw_tuple = (target_size[1], target_size[0])
-        if img.size != hw_tuple:
-            img = img.resize(hw_tuple)
+    if target_size is not None:
+        width_height_tuple = (target_size[1], target_size[0])
+        if img.size != width_height_tuple:
+            if interpolation not in _PIL_INTERPOLATION_METHODS:
+                raise ValueError(
+                    'Invalid interpolation method {} specified. Supported '
+                    'methods are {}'.format(
+                        interpolation,
+                        ", ".join(_PIL_INTERPOLATION_METHODS.keys())))
+            resample = _PIL_INTERPOLATION_METHODS[interpolation]
+            img = img.resize(width_height_tuple, resample)
     return img
 
 

tests/keras/preprocessing/image_test.py

@@ -307,6 +307,65 @@ def test_batch_standardize(self):
                 transformed[i] = generator.random_transform(im)
             transformed = generator.standardize(transformed)
 
+    def test_load_img(self, tmpdir):
+        filename = str(tmpdir / 'image.png')
+
+        original_im_array = np.array(255 * np.random.rand(100, 100, 3),
+                                     dtype=np.uint8)
+        original_im = image.array_to_img(original_im_array, scale=False)
+        original_im.save(filename)
+
+        # Test that loaded image is exactly equal to original.
+
+        loaded_im = image.load_img(filename)
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == original_im_array.shape
+        assert np.all(loaded_im_array == original_im_array)
+
+        loaded_im = image.load_img(filename, grayscale=True)
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == (original_im_array.shape[0],
+                                         original_im_array.shape[1], 1)
+
+        # Test that nothing is changed when target size is equal to original.
+
+        loaded_im = image.load_img(filename, target_size=(100, 100))
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == original_im_array.shape
+        assert np.all(loaded_im_array == original_im_array)
+
+        loaded_im = image.load_img(filename, grayscale=True,
+                                   target_size=(100, 100))
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == (original_im_array.shape[0],
+                                         original_im_array.shape[1], 1)
+
+        # Test down-sampling with bilinear interpolation.
+
+        loaded_im = image.load_img(filename, target_size=(25, 25))
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == (25, 25, 3)
+
+        loaded_im = image.load_img(filename, grayscale=True,
+                                   target_size=(25, 25))
+        loaded_im_array = image.img_to_array(loaded_im)
+        assert loaded_im_array.shape == (25, 25, 1)
+
+        # Test down-sampling with nearest neighbor interpolation.
+
+        loaded_im_nearest = image.load_img(filename, target_size=(25, 25),
+                                           interpolation="nearest")
+        loaded_im_array_nearest = image.img_to_array(loaded_im_nearest)
+        assert loaded_im_array_nearest.shape == (25, 25, 3)
+        assert np.any(loaded_im_array_nearest != loaded_im_array)
+
+        # Check that exception is raised if interpolation not supported.
+
+        loaded_im = image.load_img(filename, interpolation="unsupported")
+        with pytest.raises(ValueError):
+            loaded_im = image.load_img(filename, target_size=(25, 25),
+                                       interpolation="unsupported")
+
 
 if __name__ == '__main__':
     pytest.main([__file__])