Move DELF resizing code into utils.py, which is more appropriate than extractor.py. (#9108)

* Merged commit includes the following changes:
326723075  by Andre Araujo:

    Move image resize utility into utils.py.

--

PiperOrigin-RevId: 326723075

* Adding back matched_images_demo.png

research/delf/delf/python/examples/extractor.py

@@ -19,81 +19,18 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-from PIL import Image
 import tensorflow as tf
 
 from delf import datum_io
 from delf import feature_extractor
+from delf import utils
 
-# Minimum dimensions below which DELF features are not extracted (empty
+# Minimum dimensions below which features are not extracted (empty
 # features are returned). This applies after any resizing is performed.
 _MIN_HEIGHT = 10
 _MIN_WIDTH = 10
 
 
-def ResizeImage(image, config, resize_factor=1.0):
-  """Resizes image according to config.
-
-  Args:
-    image: Uint8 array with shape (height, width, 3).
-    config: DelfConfig proto containing the model configuration.
-    resize_factor: Optional float resize factor for the input image. If given,
-      the maximum and minimum allowed image sizes in `config` are scaled by this
-      factor. Must be non-negative.
-
-  Returns:
-    resized_image: Uint8 array with resized image.
-    scale_factors: 2D float array, with factors used for resizing along height
-      and width (If upscaling, larger than 1; if downscaling, smaller than 1).
-
-  Raises:
-    ValueError: If `image` has incorrect number of dimensions/channels.
-  """
-  if resize_factor < 0.0:
-    raise ValueError('negative resize_factor is not allowed: %f' %
-                     resize_factor)
-  if image.ndim != 3:
-    raise ValueError('image has incorrect number of dimensions: %d' %
-                     image.ndims)
-  height, width, channels = image.shape
-
-  # Take into account resize factor.
-  max_image_size = resize_factor * config.max_image_size
-  min_image_size = resize_factor * config.min_image_size
-
-  if channels != 3:
-    raise ValueError('image has incorrect number of channels: %d' % channels)
-
-  largest_side = max(width, height)
-
-  if max_image_size >= 0 and largest_side > max_image_size:
-    scale_factor = max_image_size / largest_side
-  elif min_image_size >= 0 and largest_side < min_image_size:
-    scale_factor = min_image_size / largest_side
-  elif config.use_square_images and (height != width):
-    scale_factor = 1.0
-  else:
-    # No resizing needed, early return.
-    return image, np.ones(2, dtype=float)
-
-  # Note that new_shape is in (width, height) format (PIL convention), while
-  # scale_factors are in (height, width) convention (NumPy convention).
-  if config.use_square_images:
-    new_shape = (int(round(largest_side * scale_factor)),
-                 int(round(largest_side * scale_factor)))
-  else:
-    new_shape = (int(round(width * scale_factor)),
-                 int(round(height * scale_factor)))
-
-  scale_factors = np.array([new_shape[1] / height, new_shape[0] / width],
-                           dtype=float)
-
-  pil_image = Image.fromarray(image)
-  resized_image = np.array(pil_image.resize(new_shape, resample=Image.BILINEAR))
-
-  return resized_image, scale_factors
-
-
 def MakeExtractor(config):
   """Creates a function to extract global and/or local features from an image.
 
@@ -206,7 +143,7 @@ def MakeExtractor(config):
         features (key 'local_features' mapping to a dict with keys 'locations',
         'descriptors', 'scales', 'attention').
     """
-    resized_image, scale_factors = ResizeImage(
+    resized_image, scale_factors = utils.ResizeImage(
         image, config, resize_factor=resize_factor)
 
     # If the image is too small, returns empty features.

research/delf/delf/python/utils.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 from PIL import Image
 from PIL import ImageFile
 import tensorflow as tf
@@ -39,3 +40,65 @@ def RgbLoader(path):
     img = Image.open(f)
     return img.convert('RGB')
 
+
+def ResizeImage(image, config, resize_factor=1.0):
+  """Resizes image according to config.
+
+  Args:
+    image: Uint8 array with shape (height, width, 3).
+    config: DelfConfig proto containing the model configuration.
+    resize_factor: Optional float resize factor for the input image. If given,
+      the maximum and minimum allowed image sizes in `config` are scaled by this
+      factor. Must be non-negative.
+
+  Returns:
+    resized_image: Uint8 array with resized image.
+    scale_factors: 2D float array, with factors used for resizing along height
+      and width (If upscaling, larger than 1; if downscaling, smaller than 1).
+
+  Raises:
+    ValueError: If `image` has incorrect number of dimensions/channels.
+  """
+  if resize_factor < 0.0:
+    raise ValueError('negative resize_factor is not allowed: %f' %
+                     resize_factor)
+  if image.ndim != 3:
+    raise ValueError('image has incorrect number of dimensions: %d' %
+                     image.ndims)
+  height, width, channels = image.shape
+
+  # Take into account resize factor.
+  max_image_size = resize_factor * config.max_image_size
+  min_image_size = resize_factor * config.min_image_size
+
+  if channels != 3:
+    raise ValueError('image has incorrect number of channels: %d' % channels)
+
+  largest_side = max(width, height)
+
+  if max_image_size >= 0 and largest_side > max_image_size:
+    scale_factor = max_image_size / largest_side
+  elif min_image_size >= 0 and largest_side < min_image_size:
+    scale_factor = min_image_size / largest_side
+  elif config.use_square_images and (height != width):
+    scale_factor = 1.0
+  else:
+    # No resizing needed, early return.
+    return image, np.ones(2, dtype=float)
+
+  # Note that new_shape is in (width, height) format (PIL convention), while
+  # scale_factors are in (height, width) convention (NumPy convention).
+  if config.use_square_images:
+    new_shape = (int(round(largest_side * scale_factor)),
+                 int(round(largest_side * scale_factor)))
+  else:
+    new_shape = (int(round(width * scale_factor)),
+                 int(round(height * scale_factor)))
+
+  scale_factors = np.array([new_shape[1] / height, new_shape[0] / width],
+                           dtype=float)
+
+  pil_image = Image.fromarray(image)
+  resized_image = np.array(pil_image.resize(new_shape, resample=Image.BILINEAR))
+
+  return resized_image, scale_factors

research/delf/delf/python/examples/extractor_test.py → research/delf/delf/python/utils_test.py

-# Copyright 2019 The TensorFlow Authors All Rights Reserved.
+# Copyright 2020 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for DELF feature extractor."""
+"""Tests for helper utilities."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,10 +23,10 @@ import numpy as np
 import tensorflow as tf
 
 from delf import delf_config_pb2
-from delf import extractor
+from delf import utils
 
 
-class ExtractorTest(tf.test.TestCase, parameterized.TestCase):
+class UtilsTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.named_parameters(
       ('Max-1Min-1', -1, -1, 1.0, False, [4, 2, 3], [1.0, 1.0]),
@@ -70,8 +70,8 @@ class ExtractorTest(tf.test.TestCase, parameterized.TestCase):
         min_image_size=min_image_size,
         use_square_images=square_output)
 
-    resized_image, scale_factors = extractor.ResizeImage(
-        image, config, resize_factor)
+    resized_image, scale_factors = utils.ResizeImage(image, config,
+                                                     resize_factor)
     self.assertAllEqual(resized_image.shape, expected_shape)
     self.assertAllClose(scale_factors, expected_scale_factors)
 
@@ -93,8 +93,8 @@ class ExtractorTest(tf.test.TestCase, parameterized.TestCase):
         min_image_size=min_image_size,
         use_square_images=square_output)
 
-    resized_image, scale_factors = extractor.ResizeImage(
-        image, config, resize_factor)
+    resized_image, scale_factors = utils.ResizeImage(image, config,
+                                                     resize_factor)
     self.assertAllEqual(resized_image.shape, expected_shape)
     self.assertAllClose(scale_factors, expected_scale_factors)