Fix lint and allow iobytes to load image

keras_preprocessing/image/utils.py

@@ -81,7 +81,7 @@ def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
     """Loads an image into PIL format.
 
     # Arguments
-        path: Path to image file.
+        path: Path or io.BytesIO stream to image file.
         grayscale: DEPRECATED use `color_mode="grayscale"`.
         color_mode: The desired image format. One of "grayscale", "rgb", "rgba".
             "grayscale" supports 8-bit images and 32-bit signed integer images.
@@ -110,33 +110,38 @@ def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
     if pil_image is None:
         raise ImportError('Could not import PIL.Image. '
                           'The use of `load_img` requires PIL.')
-    with open(path, 'rb') as f:
-        img = pil_image.open(io.BytesIO(f.read()))
-        if color_mode == 'grayscale':
-            # if image is not already an 8-bit, 16-bit or 32-bit grayscale image
-            # convert it to an 8-bit grayscale image.
-            if img.mode not in ('L', 'I;16', 'I'):
-                img = img.convert('L')
-        elif color_mode == 'rgba':
-            if img.mode != 'RGBA':
-                img = img.convert('RGBA')
-        elif color_mode == 'rgb':
-            if img.mode != 'RGB':
-                img = img.convert('RGB')
-        else:
-            raise ValueError('color_mode must be "grayscale", "rgb", or "rgba"')
-        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
+    if isinstance(path, io.BytesIO):
+        buf = path
+    else:
+        with open(path, 'rb') as f:
+            buf = io.BytesIO(f.read())
+
+    img = pil_image.open(buf)
+    if color_mode == 'grayscale':
+        # if image is not already an 8-bit, 16-bit or 32-bit grayscale image
+        # convert it to an 8-bit grayscale image.
+        if img.mode not in ('L', 'I;16', 'I'):
+            img = img.convert('L')
+    elif color_mode == 'rgba':
+        if img.mode != 'RGBA':
+            img = img.convert('RGBA')
+    elif color_mode == 'rgb':
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+    else:
+        raise ValueError('color_mode must be "grayscale", "rgb", or "rgba"')
+    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
 
 
 def list_pictures(directory, ext=('jpg', 'jpeg', 'bmp', 'png', 'ppm', 'tif',

tests/image/dataframe_iterator_test.py

@@ -256,7 +256,7 @@ def test_dataframe_iterator_class_mode_categorical_multi_label(all_test_images,
     assert isinstance(batch_y, np.ndarray)
     assert batch_y.shape == (len(batch_x), 2)
     for labels in batch_y:
-        assert all(l in {0, 1} for l in labels)
+        assert all(lbl in {0, 1} for lbl in labels)
 
     # on first 3 batches
     df = pd.DataFrame({
@@ -272,7 +272,7 @@ def test_dataframe_iterator_class_mode_categorical_multi_label(all_test_images,
     assert isinstance(batch_y, np.ndarray)
     assert batch_y.shape == (len(batch_x), 3)
     for labels in batch_y:
-        assert all(l in {0, 1} for l in labels)
+        assert all(lbl in {0, 1} for lbl in labels)
     assert (batch_y[0] == np.array([1, 1, 0])).all()
     assert (batch_y[1] == np.array([0, 1, 0])).all()
     assert (batch_y[2] == np.array([0, 0, 1])).all()

tests/image/utils_test.py

@@ -1,3 +1,5 @@
+import io
+
 import numpy as np
 import pytest
 import resource
@@ -192,6 +194,14 @@ def test_load_img(tmpdir):
     loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
     assert loaded_im_array.shape == (25, 25, 1)
 
+    with open(filename_grayscale_32bit, 'rb') as f:
+        buf = io.BytesIO(f.read())
+    loaded_im = utils.load_img(buf,
+                               color_mode='grayscale',
+                               target_size=(25, 25), interpolation="nearest")
+    loaded_im_array = utils.img_to_array(loaded_im, dtype='int32')
+    assert loaded_im_array.shape == (25, 25, 1)
+
     # Check that exception is raised if interpolation not supported.
 
     loaded_im = utils.load_img(filename_rgb, interpolation="unsupported")

tests/sequence_test.py

@@ -101,8 +101,8 @@ def test_skipgrams():
                                          categorical=True)
     for couple in couples:
         assert couple[0] - couple[1] <= 3
-    for l in labels:
-        assert len(l) == 2
+    for lbl in labels:
+        assert len(lbl) == 2
 
 
 def test_remove_long_seq():