1. Add image_ops.is_jpeg Op to decide if a input string is a jpeg string or not.

2. Change tfexample_decoder in slim/objection_detection to accept different JPEG decompression method.
Defaults to ""/None which maps to a system-specific default. Currently valid values are ["INTEGER_FAST", "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg library changes to a version that does not have that specific option.)

PiperOrigin-RevId: 185486653

1 files changed, 27 insertions, 4 deletions

diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
index 0544404e9e..b3b61e1dfe 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
@@ -349,7 +349,8 @@ class Image(ItemHandler):
                shape=None,
                channels=3,
                dtype=dtypes.uint8,
-               repeated=False):
+               repeated=False,
+               dct_method=''):
     """Initializes the image.
 
     Args:
@@ -368,6 +369,11 @@ class Image(ItemHandler):
               tf.decode_raw,
       repeated: if False, decodes a single image. If True, decodes a
         variable number of image strings from a 1D tensor of strings.
+      dct_method: An optional string. Defaults to empty string. It only takes
+        effect when image format is jpeg, used to specify a hint about the
+        algorithm used for jpeg decompression. Currently valid values
+        are ['INTEGER_FAST', 'INTEGER_ACCURATE']. The hint may be ignored, for
+        example, the jpeg library does not have that specific option.
     """
     if not image_key:
       image_key = 'image/encoded'
@@ -381,6 +387,7 @@ class Image(ItemHandler):
     self._channels = channels
     self._dtype = dtype
     self._repeated = repeated
+    self._dct_method = dct_method
 
   def tensors_to_item(self, keys_to_tensors):
     """See base class."""
@@ -406,9 +413,25 @@ class Image(ItemHandler):
       A tensor that represents decoded image of self._shape, or
       (?, ?, self._channels) if self._shape is not specified.
     """
+
     def decode_image():
-      """Decodes a png or jpg based on the headers."""
-      return image_ops.decode_image(image_buffer, self._channels)
+      """Decodes a image based on the headers."""
+      return image_ops.decode_image(image_buffer, channels=self._channels)
+
+    def decode_jpeg():
+      """Decodes a jpeg image with specified '_dct_method'."""
+      return image_ops.decode_jpeg(
+          image_buffer, channels=self._channels, dct_method=self._dct_method)
+
+    def check_jpeg():
+      """Checks if an image is jpeg."""
+      # For jpeg, we directly use image_ops.decode_jpeg rather than decode_image
+      # in order to feed the jpeg specify parameter 'dct_method'.
+      return control_flow_ops.cond(
+          image_ops.is_jpeg(image_buffer),
+          decode_jpeg,
+          decode_image,
+          name='cond_jpeg')
 
     def decode_raw():
       """Decodes a raw image."""
@@ -420,7 +443,7 @@ class Image(ItemHandler):
             math_ops.equal(image_format, 'RAW')): decode_raw,
     }
     image = control_flow_ops.case(
-        pred_fn_pairs, default=decode_image, exclusive=True)
+        pred_fn_pairs, default=check_jpeg, exclusive=True)
 
     image.set_shape([None, None, self._channels])
     if self._shape is not None: