Add a preprocessing method to adjust gamma.

PiperOrigin-RevId: 342373365

research/object_detection/builders/preprocessor_builder.py

@@ -109,6 +109,8 @@ PREPROCESSING_FUNCTION_MAP = {
         preprocessor.subtract_channel_mean,
     'convert_class_logits_to_softmax':
         preprocessor.convert_class_logits_to_softmax,
+    'adjust_gamma':
+        preprocessor.adjust_gamma,
 }
 
 

research/object_detection/builders/preprocessor_builder_test.py

@@ -753,6 +753,19 @@ class PreprocessorBuilderTest(tf.test.TestCase):
         'max_border': 128
     })
 
+  def test_adjust_gamma(self):
+    preprocessor_text_proto = """
+    adjust_gamma {
+      gamma: 2.2
+      gain: 2.0
+    }
+    """
+    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
+    text_format.Parse(preprocessor_text_proto, preprocessor_proto)
+    function, args = preprocessor_builder.build(preprocessor_proto)
+    self.assertEqual(function, preprocessor.adjust_gamma)
+    self.assert_dictionary_close(args, {'gamma': 2.2, 'gain': 2.0})
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/core/preprocessor.py

@@ -1049,6 +1049,22 @@ def random_rgb_to_gray(image,
   return image
 
 
+def adjust_gamma(image, gamma=1.0, gain=1.0):
+  """Adjusts the gamma.
+
+  Args:
+    image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
+           with pixel values varying between [0, 255].
+    gamma: the gamma value. Must be a non-negative real number.
+    gain: a constant multiplier.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.name_scope('AdjustGamma', values=[image]):
+    return tf.image.adjust_gamma(image, gamma, gain)
+
+
 def random_adjust_brightness(image,
                              max_delta=0.2,
                              seed=None,
@@ -1069,7 +1085,6 @@ def random_adjust_brightness(image,
 
   Returns:
     image: image which is the same shape as input image.
-    boxes: boxes which is the same shape as input boxes.
   """
   with tf.name_scope('RandomAdjustBrightness', values=[image]):
     generator_func = functools.partial(tf.random_uniform, [],
@@ -4504,6 +4519,7 @@ def get_default_func_arg_map(include_label_weights=True,
            fields.InputDataFields.groundtruth_classes,
            groundtruth_label_weights, groundtruth_instance_masks,
            groundtruth_keypoints, groundtruth_label_confidences),
+      adjust_gamma: (fields.InputDataFields.image,),
   }
 
   return prep_func_arg_map

research/object_detection/core/preprocessor_test.py

@@ -3957,6 +3957,28 @@ class PreprocessorTest(test_case.TestCase, parameterized.TestCase):
     self.assertLen(boxes, 2)
     self.assertAllEqual(confidences, [-1.0, 1.0])
 
+  def testAdjustGamma(self):
+
+    def graph_fn():
+      preprocessing_options = []
+      preprocessing_options.append((preprocessor.normalize_image, {
+          'original_minval': 0,
+          'original_maxval': 255,
+          'target_minval': 0,
+          'target_maxval': 1
+      }))
+      preprocessing_options.append((preprocessor.adjust_gamma, {}))
+      images_original = self.createTestImages()
+      tensor_dict = {fields.InputDataFields.image: images_original}
+      tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
+      images_gamma = tensor_dict[fields.InputDataFields.image]
+      image_original_shape = tf.shape(images_original)
+      image_gamma_shape = tf.shape(images_gamma)
+      return [image_original_shape, image_gamma_shape]
+
+    (image_original_shape_, image_gamma_shape_) = self.execute_cpu(graph_fn, [])
+    self.assertAllEqual(image_original_shape_, image_gamma_shape_)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/protos/preprocessor.proto

@@ -4,7 +4,7 @@ package object_detection.protos;
 
 // Message for defining a preprocessing operation on input data.
 // See: //third_party/tensorflow_models/object_detection/core/preprocessor.py
-// Next ID: 39
+// Next ID: 40
 message PreprocessingStep {
   oneof preprocessing_step {
     NormalizeImage normalize_image = 1;
@@ -45,6 +45,7 @@ message PreprocessingStep {
     RandomPatchGaussian random_patch_gaussian = 36;
     RandomSquareCropByScale random_square_crop_by_scale = 37;
     RandomScaleCropAndPadToSquare random_scale_crop_and_pad_to_square = 38;
+    AdjustGamma adjust_gamma = 39;
   }
 }
 
@@ -590,3 +591,9 @@ message RandomScaleCropAndPadToSquare {
   optional float scale_min = 2 [default=0.1];
   optional float scale_max = 3 [default=2.0];
 }
+
+// Adjusts the gamma of the image using the specified gamma and gain values.
+message AdjustGamma {
+  optional float gamma = 1 [default=1.0];
+  optional float gain = 2 [default=1.0];
+}