autoclose files opened in load_img

keras_preprocessing/image/utils.py

@@ -4,6 +4,7 @@
 from __future__ import division
 from __future__ import print_function
 
+import io
 import os
 import warnings
 
@@ -109,32 +110,33 @@ 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.')
-    img = pil_image.open(path)
-    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
+    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
 
 
 def list_pictures(directory, ext=('jpg', 'jpeg', 'bmp', 'png', 'ppm', 'tif',

keras_preprocessing/text.py

@@ -43,8 +43,10 @@ def text_to_word_sequence(text,
         text = text.lower()
 
     if sys.version_info < (3,):
-        if isinstance(text, unicode):
-            translate_map = dict((ord(c), unicode(split)) for c in filters)
+        if isinstance(text, unicode):  # noqa: F821
+            translate_map = dict(
+                (ord(c), unicode(split)) for c in filters  # noqa: F821
+            )
             text = text.translate(translate_map)
         elif len(split) == 1:
             translate_map = maketrans(filters, split * len(filters))

tests/image/utils_test.py

@@ -1,5 +1,7 @@
 import numpy as np
 import pytest
+import resource
+import PIL
 
 from keras_preprocessing.image import utils
 
@@ -326,5 +328,25 @@ def test_array_to_img_and_img_to_array():
         img = utils.img_to_array(x, data_format='channels_last')
 
 
+def write_sample_image(tmpdir):
+    im = utils.array_to_img(np.random.rand(1, 1, 3))
+    path = str(tmpdir / 'sample_image.png')
+    utils.save_img(path, im)
+    return path
+
+
+def test_image_file_handlers_close(tmpdir):
+    path = write_sample_image(tmpdir)
+    max_open_files, _ = resource.getrlimit(resource.RLIMIT_NOFILE)
+    for i in range(max_open_files+1):
+        utils.load_img(path)
+
+
+def test_load_img_returns_image(tmpdir):
+    path = write_sample_image(tmpdir)
+    im = utils.load_img(path)
+    assert isinstance(im, PIL.Image.Image)
+
+
 if __name__ == '__main__':
     pytest.main([__file__])