Support random_crop_resize function in preprocess_ops_3d.

PiperOrigin-RevId: 348500556

Author: tensorflower-gardener

official/vision/beta/ops/preprocess_ops_3d.py

@@ -15,7 +15,7 @@
 # ==============================================================================
 """Utils for processing video dataset features."""
 
-from typing import Optional
+from typing import Optional, Tuple
 import tensorflow as tf
 
 
@@ -217,6 +217,55 @@ def resize_fn():
   return frames
 
 
+def random_crop_resize(frames: tf.Tensor,
+                       output_h: int,
+                       output_w: int,
+                       num_frames: int,
+                       num_channels: int,
+                       aspect_ratio: Tuple[float, float],
+                       area_range: Tuple[float, float]) -> tf.Tensor:
+  """First crops clip with jittering and then resizes to (output_h, output_w).
+
+  Args:
+    frames: A Tensor of dimension [timesteps, input_h, input_w, channels].
+    output_h: Resized image height.
+    output_w: Resized image width.
+    num_frames: Number of input frames per clip.
+    num_channels: Number of channels of the clip.
+    aspect_ratio: Float tuple with the aspect range for cropping.
+    area_range: Float tuple with the area range for cropping.
+  Returns:
+    A Tensor of shape [timesteps, output_h, output_w, channels] of type
+      frames.dtype.
+  """
+  shape = tf.shape(frames)
+  seq_len, _, _, channels = shape[0], shape[1], shape[2], shape[3]
+  bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
+  factor = output_w / output_h
+  aspect_ratio = (aspect_ratio[0] * factor, aspect_ratio[1] * factor)
+  sample_distorted_bbox = tf.image.sample_distorted_bounding_box(
+      shape[1:],
+      bounding_boxes=bbox,
+      min_object_covered=0.1,
+      aspect_ratio_range=aspect_ratio,
+      area_range=area_range,
+      max_attempts=100,
+      use_image_if_no_bounding_boxes=True)
+  bbox_begin, bbox_size, _ = sample_distorted_bbox
+  offset_y, offset_x, _ = tf.unstack(bbox_begin)
+  target_height, target_width, _ = tf.unstack(bbox_size)
+  size = tf.convert_to_tensor((
+      seq_len, target_height, target_width, channels))
+  offset = tf.convert_to_tensor((
+      0, offset_y, offset_x, 0))
+  frames = tf.slice(frames, offset, size)
+  frames = tf.cast(
+      tf.image.resize(frames, (output_h, output_w)),
+      frames.dtype)
+  frames.set_shape((num_frames, output_h, output_w, num_channels))
+  return frames
+
+
 def random_flip_left_right(
     frames: tf.Tensor,
     seed: Optional[int] = None) -> tf.Tensor:

official/vision/beta/ops/preprocess_ops_3d_test.py

@@ -119,6 +119,20 @@ def test_resize_smallest(self):
     self.assertAllEqual(resized_frames_3.shape, (6, 90, 120, 3))
     self.assertAllEqual(resized_frames_4.shape, (6, 60, 45, 3))
 
+  def test_random_crop_resize(self):
+    resized_frames_1 = preprocess_ops_3d.random_crop_resize(
+        self._frames, 256, 256, 6, 3, (0.5, 2), (0.3, 1))
+    resized_frames_2 = preprocess_ops_3d.random_crop_resize(
+        self._frames, 224, 224, 6, 3, (0.5, 2), (0.3, 1))
+    resized_frames_3 = preprocess_ops_3d.random_crop_resize(
+        self._frames, 256, 256, 6, 3, (0.8, 1.2), (0.3, 1))
+    resized_frames_4 = preprocess_ops_3d.random_crop_resize(
+        self._frames, 256, 256, 6, 3, (0.5, 2), (0.1, 1))
+    self.assertAllEqual(resized_frames_1.shape, (6, 256, 256, 3))
+    self.assertAllEqual(resized_frames_2.shape, (6, 224, 224, 3))
+    self.assertAllEqual(resized_frames_3.shape, (6, 256, 256, 3))
+    self.assertAllEqual(resized_frames_4.shape, (6, 256, 256, 3))
+
   def test_random_flip_left_right(self):
     flipped_frames = preprocess_ops_3d.random_flip_left_right(self._frames)