Merge pull request #4280 from kinaryml:image-segmenter-python-api-updates

PiperOrigin-RevId: 525204893

mediapipe/tasks/python/test/vision/image_segmenter_test.py

@@ -15,7 +15,6 @@
 
 import enum
 import os
-from typing import List
 from unittest import mock
 
 from absl.testing import absltest
@@ -30,11 +29,10 @@ from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import image_segmenter
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
+ImageSegmenterResult = image_segmenter.ImageSegmenterResult
 _BaseOptions = base_options_module.BaseOptions
 _Image = image_module.Image
 _ImageFormat = image_frame.ImageFormat
-_OutputType = image_segmenter.ImageSegmenterOptions.OutputType
-_Activation = image_segmenter.ImageSegmenterOptions.Activation
 _ImageSegmenter = image_segmenter.ImageSegmenter
 _ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
 _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
@@ -42,11 +40,33 @@ _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
 _MODEL_FILE = 'deeplabv3.tflite'
 _IMAGE_FILE = 'segmentation_input_rotation0.jpg'
 _SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
+_CAT_IMAGE = 'cat.jpg'
+_CAT_MASK = 'cat_mask.jpg'
 _MASK_MAGNIFICATION_FACTOR = 10
 _MASK_SIMILARITY_THRESHOLD = 0.98
 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
 
 
+def _calculate_soft_iou(m1, m2):
+  intersection_sum = np.sum(m1 * m2)
+  union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
+
+  if union_sum > 0:
+    return intersection_sum / union_sum
+  else:
+    return 0
+
+
+def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
+  actual_mask = actual_mask.numpy_view()
+  expected_mask = expected_mask.numpy_view() / 255.0
+
+  return (
+      actual_mask.shape == expected_mask.shape
+      and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
+  )
+
+
 def _similar_to_uint8_mask(actual_mask, expected_mask):
   actual_mask_pixels = actual_mask.numpy_view().flatten()
   expected_mask_pixels = expected_mask.numpy_view().flatten()
@@ -56,8 +76,9 @@ def _similar_to_uint8_mask(actual_mask, expected_mask):
 
   for index in range(num_pixels):
     consistent_pixels += (
-        actual_mask_pixels[index] *
+        actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR
-        _MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
+        == expected_mask_pixels[index]
+    )
 
   return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
 
@@ -73,16 +94,27 @@ class ImageSegmenterTest(parameterized.TestCase):
     super().setUp()
     # Load the test input image.
     self.test_image = _Image.create_from_file(
-        test_utils.get_test_data_path(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
-            os.path.join(_TEST_DATA_DIR, _IMAGE_FILE)))
+    )
     # Loads ground truth segmentation file.
     gt_segmentation_data = cv2.imread(
         test_utils.get_test_data_path(
-            os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)),
+            os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)
-        cv2.IMREAD_GRAYSCALE)
+        ),
+        cv2.IMREAD_GRAYSCALE,
+    )
     self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
     self.model_path = test_utils.get_test_data_path(
-        os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
+        os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
+    )
+
+  def _load_segmentation_mask(self, file_path: str):
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
+        cv2.IMREAD_GRAYSCALE,
+    )
+    return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
 
   def test_create_from_file_succeeds_with_valid_model_path(self):
     # Creates with default option and valid model file successfully.
@@ -98,9 +130,11 @@ class ImageSegmenterTest(parameterized.TestCase):
 
   def test_create_from_options_fails_with_invalid_model_path(self):
     with self.assertRaisesRegex(
-        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
+    ):
       base_options = _BaseOptions(
-          model_asset_path='/path/to/invalid/model.tflite')
+          model_asset_path='/path/to/invalid/model.tflite'
+      )
       options = _ImageSegmenterOptions(base_options=base_options)
       _ImageSegmenter.create_from_options(options)
 
@@ -112,8 +146,9 @@ class ImageSegmenterTest(parameterized.TestCase):
       segmenter = _ImageSegmenter.create_from_options(options)
       self.assertIsInstance(segmenter, _ImageSegmenter)
 
-  @parameterized.parameters((ModelFileType.FILE_NAME,),
+  @parameterized.parameters(
-                            (ModelFileType.FILE_CONTENT,))
+      (ModelFileType.FILE_NAME,), (ModelFileType.FILE_CONTENT,)
+  )
   def test_segment_succeeds_with_category_mask(self, model_file_type):
     # Creates segmenter.
     if model_file_type is ModelFileType.FILE_NAME:
@@ -127,22 +162,27 @@ class ImageSegmenterTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _ImageSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+        base_options=base_options,
+        output_category_mask=True,
+        output_confidence_masks=False,
+    )
     segmenter = _ImageSegmenter.create_from_options(options)
 
     # Performs image segmentation on the input.
-    category_masks = segmenter.segment(self.test_image)
+    segmentation_result = segmenter.segment(self.test_image)
-    self.assertLen(category_masks, 1)
+    category_mask = segmentation_result.category_mask
-    category_mask = category_masks[0]
     result_pixels = category_mask.numpy_view().flatten()
 
     # Check if data type of `category_mask` is correct.
     self.assertEqual(result_pixels.dtype, np.uint8)
 
     self.assertTrue(
-        _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+        _similar_to_uint8_mask(category_mask, self.test_seg_image),
-        f'Number of pixels in the candidate mask differing from that of the '
+        (
-        f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+            'Number of pixels in the candidate mask differing from that of the'
+            f' ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+        ),
+    )
 
     # Closes the segmenter explicitly when the segmenter is not used in
     # a context.
@@ -152,74 +192,46 @@ class ImageSegmenterTest(parameterized.TestCase):
     # Creates segmenter.
     base_options = _BaseOptions(model_asset_path=self.model_path)
 
-    # Run segmentation on the model in CATEGORY_MASK mode.
+    # Load the cat image.
-    options = _ImageSegmenterOptions(
+    test_image = _Image.create_from_file(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
-    segmenter = _ImageSegmenter.create_from_options(options)
+    )
-    category_masks = segmenter.segment(self.test_image)
-    category_mask = category_masks[0].numpy_view()
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _ImageSegmenterOptions(
         base_options=base_options,
-        output_type=_OutputType.CONFIDENCE_MASK,
+        output_category_mask=False,
-        activation=_Activation.SOFTMAX)
+        output_confidence_masks=True,
-    segmenter = _ImageSegmenter.create_from_options(options)
+    )
-    confidence_masks = segmenter.segment(self.test_image)
+
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      segmentation_result = segmenter.segment(test_image)
+      confidence_masks = segmentation_result.confidence_masks
 
       # Check if confidence mask shape is correct.
       self.assertLen(
-        confidence_masks, 21,
+          confidence_masks,
-        'Number of confidence masks must match with number of categories.')
+          21,
+          'Number of confidence masks must match with number of categories.',
+      )
 
-    # Gather the confidence masks in a single array `confidence_mask_array`.
+      # Loads ground truth segmentation file.
-    confidence_mask_array = np.array(
+      expected_mask = self._load_segmentation_mask(_CAT_MASK)
-        [confidence_mask.numpy_view() for confidence_mask in confidence_masks])
-
-    # Check if data type of `confidence_masks` are correct.
-    self.assertEqual(confidence_mask_array.dtype, np.float32)
-
-    # Compute the category mask from the created confidence mask.
-    calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
-    self.assertListEqual(
-        calculated_category_mask.tolist(), category_mask.tolist(),
-        'Confidence mask does not match with the category mask.')
-
-    # Closes the segmenter explicitly when the segmenter is not used in
-    # a context.
-    segmenter.close()
-
-  @parameterized.parameters((ModelFileType.FILE_NAME),
-                            (ModelFileType.FILE_CONTENT))
-  def test_segment_in_context(self, model_file_type):
-    if model_file_type is ModelFileType.FILE_NAME:
-      base_options = _BaseOptions(model_asset_path=self.model_path)
-    elif model_file_type is ModelFileType.FILE_CONTENT:
-      with open(self.model_path, 'rb') as f:
-        model_contents = f.read()
-      base_options = _BaseOptions(model_asset_buffer=model_contents)
-    else:
-      # Should never happen
-      raise ValueError('model_file_type is invalid.')
-
-    options = _ImageSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
-    with _ImageSegmenter.create_from_options(options) as segmenter:
-      # Performs image segmentation on the input.
-      category_masks = segmenter.segment(self.test_image)
-      self.assertLen(category_masks, 1)
 
       self.assertTrue(
-          _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+          _similar_to_float_mask(
-          f'Number of pixels in the candidate mask differing from that of the '
+              confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
-          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+          )
+      )
 
   def test_missing_result_callback(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.LIVE_STREAM)
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
-    with self.assertRaisesRegex(ValueError,
+    )
-                                r'result callback must be provided'):
+    with self.assertRaisesRegex(
+        ValueError, r'result callback must be provided'
+    ):
       with _ImageSegmenter.create_from_options(options) as unused_segmenter:
         pass
 
@@ -228,130 +240,236 @@ class ImageSegmenterTest(parameterized.TestCase):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=running_mode,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
-    with self.assertRaisesRegex(ValueError,
+    )
-                                r'result callback should not be provided'):
+    with self.assertRaisesRegex(
+        ValueError, r'result callback should not be provided'
+    ):
       with _ImageSegmenter.create_from_options(options) as unused_segmenter:
         pass
 
   def test_calling_segment_for_video_in_image_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.IMAGE)
+        running_mode=_RUNNING_MODE.IMAGE,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the video mode'):
+          ValueError, r'not initialized with the video mode'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
   def test_calling_segment_async_in_image_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.IMAGE)
+        running_mode=_RUNNING_MODE.IMAGE,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the live stream mode'):
+          ValueError, r'not initialized with the live stream mode'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
   def test_calling_segment_in_video_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the image mode'):
+          ValueError, r'not initialized with the image mode'
+      ):
         segmenter.segment(self.test_image)
 
   def test_calling_segment_async_in_video_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the live stream mode'):
+          ValueError, r'not initialized with the live stream mode'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
   def test_segment_for_video_with_out_of_order_timestamp(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        running_mode=_RUNNING_MODE.VIDEO)
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       unused_result = segmenter.segment_for_video(self.test_image, 1)
       with self.assertRaisesRegex(
-          ValueError, r'Input timestamp must be monotonically increasing'):
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
-  def test_segment_for_video(self):
+  def test_segment_for_video_in_category_mask_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        output_type=_OutputType.CATEGORY_MASK,
+        output_category_mask=True,
-        running_mode=_RUNNING_MODE.VIDEO)
+        output_confidence_masks=False,
+        running_mode=_RUNNING_MODE.VIDEO,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       for timestamp in range(0, 300, 30):
-        category_masks = segmenter.segment_for_video(self.test_image, timestamp)
+        segmentation_result = segmenter.segment_for_video(
-        self.assertLen(category_masks, 1)
+            self.test_image, timestamp
+        )
+        category_mask = segmentation_result.category_mask
         self.assertTrue(
-            _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+            _similar_to_uint8_mask(category_mask, self.test_seg_image),
-            f'Number of pixels in the candidate mask differing from that of the '
+            (
-            f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+                'Number of pixels in the candidate mask differing from that of'
+                f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+            ),
+        )
+
+  def test_segment_for_video_in_confidence_mask_mode(self):
+    # Load the cat image.
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
+    )
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO,
+        output_category_mask=False,
+        output_confidence_masks=True,
+    )
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmentation_result = segmenter.segment_for_video(test_image, timestamp)
+        confidence_masks = segmentation_result.confidence_masks
+
+        # Check if confidence mask shape is correct.
+        self.assertLen(
+            confidence_masks,
+            21,
+            'Number of confidence masks must match with number of categories.',
+        )
+
+        # Loads ground truth segmentation file.
+        expected_mask = self._load_segmentation_mask(_CAT_MASK)
+        self.assertTrue(
+            _similar_to_float_mask(
+                confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
+            )
+        )
 
   def test_calling_segment_in_live_stream_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the image mode'):
+          ValueError, r'not initialized with the image mode'
+      ):
         segmenter.segment(self.test_image)
 
   def test_calling_segment_for_video_in_live_stream_mode(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
-      with self.assertRaisesRegex(ValueError,
+      with self.assertRaisesRegex(
-                                  r'not initialized with the video mode'):
+          ValueError, r'not initialized with the video mode'
+      ):
         segmenter.segment_for_video(self.test_image, 0)
 
   def test_segment_async_calls_with_illegal_timestamp(self):
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=mock.MagicMock())
+        result_callback=mock.MagicMock(),
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       segmenter.segment_async(self.test_image, 100)
       with self.assertRaisesRegex(
-          ValueError, r'Input timestamp must be monotonically increasing'):
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
         segmenter.segment_async(self.test_image, 0)
 
-  def test_segment_async_calls(self):
+  def test_segment_async_calls_in_category_mask_mode(self):
     observed_timestamp_ms = -1
 
-    def check_result(result: List[image_module.Image], output_image: _Image,
+    def check_result(
-                     timestamp_ms: int):
+        result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
+    ):
       # Get the output category mask.
-      category_mask = result[0]
+      category_mask = result.category_mask
       self.assertEqual(output_image.width, self.test_image.width)
       self.assertEqual(output_image.height, self.test_image.height)
       self.assertEqual(output_image.width, self.test_seg_image.width)
       self.assertEqual(output_image.height, self.test_seg_image.height)
       self.assertTrue(
           _similar_to_uint8_mask(category_mask, self.test_seg_image),
-          f'Number of pixels in the candidate mask differing from that of the '
+          (
-          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+              'Number of pixels in the candidate mask differing from that of'
+              f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
+          ),
+      )
       self.assertLess(observed_timestamp_ms, timestamp_ms)
       self.observed_timestamp_ms = timestamp_ms
 
     options = _ImageSegmenterOptions(
         base_options=_BaseOptions(model_asset_path=self.model_path),
-        output_type=_OutputType.CATEGORY_MASK,
+        output_category_mask=True,
+        output_confidence_masks=False,
         running_mode=_RUNNING_MODE.LIVE_STREAM,
-        result_callback=check_result)
+        result_callback=check_result,
+    )
     with _ImageSegmenter.create_from_options(options) as segmenter:
       for timestamp in range(0, 300, 30):
         segmenter.segment_async(self.test_image, timestamp)
 
+  def test_segment_async_calls_in_confidence_mask_mode(self):
+    # Load the cat image.
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
+    )
+
+    # Loads ground truth segmentation file.
+    expected_mask = self._load_segmentation_mask(_CAT_MASK)
+    observed_timestamp_ms = -1
+
+    def check_result(
+        result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
+    ):
+      # Get the output category mask.
+      confidence_masks = result.confidence_masks
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks,
+          21,
+          'Number of confidence masks must match with number of categories.',
+      )
+      self.assertEqual(output_image.width, test_image.width)
+      self.assertEqual(output_image.height, test_image.height)
+      self.assertTrue(
+          _similar_to_float_mask(
+              confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
+          )
+      )
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        output_category_mask=False,
+        output_confidence_masks=True,
+        result_callback=check_result,
+    )
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmenter.segment_async(test_image, timestamp)
+
 
 if __name__ == '__main__':
   absltest.main()

mediapipe/tasks/python/test/vision/interactive_segmenter_test.py

@@ -30,12 +30,12 @@ from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import interactive_segmenter
 from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 
+InteractiveSegmenterResult = interactive_segmenter.InteractiveSegmenterResult
 _BaseOptions = base_options_module.BaseOptions
 _Image = image_module.Image
 _ImageFormat = image_frame.ImageFormat
 _NormalizedKeypoint = keypoint_module.NormalizedKeypoint
 _Rect = rect.Rect
-_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
 _InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
 _InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
 _RegionOfInterest = interactive_segmenter.RegionOfInterest
@@ -200,15 +200,16 @@ class InteractiveSegmenterTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CATEGORY_MASK
+        base_options=base_options,
+        output_category_mask=True,
+        output_confidence_masks=False,
     )
     segmenter = _InteractiveSegmenter.create_from_options(options)
 
     # Performs image segmentation on the input.
     roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
-    category_masks = segmenter.segment(self.test_image, roi)
+    segmentation_result = segmenter.segment(self.test_image, roi)
-    self.assertLen(category_masks, 1)
+    category_mask = segmentation_result.category_mask
-    category_mask = category_masks[0]
     result_pixels = category_mask.numpy_view().flatten()
 
     # Check if data type of `category_mask` is correct.
@@ -219,7 +220,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     self.assertTrue(
         _similar_to_uint8_mask(
-            category_masks[0], test_seg_image, similarity_threshold
+            category_mask, test_seg_image, similarity_threshold
         ),
         (
             'Number of pixels in the candidate mask differing from that of the'
@@ -254,12 +255,15 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with _InteractiveSegmenter.create_from_options(options) as segmenter:
       # Perform segmentation
-      confidence_masks = segmenter.segment(self.test_image, roi)
+      segmentation_result = segmenter.segment(self.test_image, roi)
+      confidence_masks = segmentation_result.confidence_masks
 
       # Check if confidence mask shape is correct.
       self.assertLen(
@@ -287,15 +291,18 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with _InteractiveSegmenter.create_from_options(options) as segmenter:
       # Perform segmentation
       image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
-      confidence_masks = segmenter.segment(
+      segmentation_result = segmenter.segment(
           self.test_image, roi, image_processing_options
       )
+      confidence_masks = segmentation_result.confidence_masks
 
       # Check if confidence mask shape is correct.
       self.assertLen(
@@ -314,7 +321,9 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
     # Run segmentation on the model in CONFIDENCE_MASK mode.
     options = _InteractiveSegmenterOptions(
-        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+        base_options=base_options,
+        output_category_mask=False,
+        output_confidence_masks=True,
     )
 
     with self.assertRaisesRegex(

mediapipe/tasks/python/vision/image_segmenter.py

@@ -14,7 +14,6 @@
 """MediaPipe image segmenter task."""
 
 import dataclasses
-import enum
 from typing import Callable, List, Mapping, Optional
 
 from mediapipe.python import packet_creator
@@ -31,7 +30,6 @@ from mediapipe.tasks.python.vision.core import base_vision_task_api
 from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
-ImageSegmenterResult = List[image_module.Image]
 _NormalizedRect = rect.NormalizedRect
 _BaseOptions = base_options_module.BaseOptions
 _SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
@@ -42,8 +40,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
-_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
-_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
+_CATEGORY_MASK_STREAM_NAME = 'category_mask'
+_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
@@ -53,6 +53,21 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
 _MICRO_SECONDS_PER_MILLISECOND = 1000
 
 
+@dataclasses.dataclass
+class ImageSegmenterResult:
+  """Output result of ImageSegmenter.
+
+  confidence_masks: multiple masks of float image where, for each mask, each
+  pixel represents the prediction confidence, usually in the [0, 1] range.
+
+  category_mask: a category mask of uint8 image where each pixel represents the
+  class which the pixel in the original image was predicted to belong to.
+  """
+
+  confidence_masks: Optional[List[image_module.Image]] = None
+  category_mask: Optional[image_module.Image] = None
+
+
 @dataclasses.dataclass
 class ImageSegmenterOptions:
   """Options for the image segmenter task.
@@ -64,28 +79,17 @@ class ImageSegmenterOptions:
       objects on single image inputs. 2) The video mode for segmenting objects
       on the decoded frames of a video. 3) The live stream mode for segmenting
       objects on a live stream of input data, such as from camera.
-    output_type: The output mask type allows specifying the type of
+    output_confidence_masks: Whether to output confidence masks.
-      post-processing to perform on the raw model results.
+    output_category_mask: Whether to output category mask.
-    activation: Activation function to apply to input tensor.
     result_callback: The user-defined result callback for processing live stream
       data. The result callback should only be specified when the running mode
       is set to the live stream mode.
   """
 
-  class OutputType(enum.Enum):
-    UNSPECIFIED = 0
-    CATEGORY_MASK = 1
-    CONFIDENCE_MASK = 2
-
-  class Activation(enum.Enum):
-    NONE = 0
-    SIGMOID = 1
-    SOFTMAX = 2
-
   base_options: _BaseOptions
   running_mode: _RunningMode = _RunningMode.IMAGE
-  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+  output_confidence_masks: bool = True
-  activation: Optional[Activation] = Activation.NONE
+  output_category_mask: bool = False
   result_callback: Optional[
       Callable[[ImageSegmenterResult, image_module.Image, int], None]
   ] = None
@@ -97,9 +101,7 @@ class ImageSegmenterOptions:
     base_options_proto.use_stream_mode = (
         False if self.running_mode == _RunningMode.IMAGE else True
     )
-    segmenter_options_proto = _SegmenterOptionsProto(
+    segmenter_options_proto = _SegmenterOptionsProto()
-        output_type=self.output_type.value, activation=self.activation.value
-    )
     return _ImageSegmenterGraphOptionsProto(
         base_options=base_options_proto,
         segmenter_options=segmenter_options_proto,
@@ -177,27 +179,48 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
     def packets_callback(output_packets: Mapping[str, packet.Packet]):
       if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
         return
-      segmentation_result = packet_getter.get_image_list(
+
-          output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+      segmentation_result = ImageSegmenterResult()
+
+      if options.output_confidence_masks:
+        segmentation_result.confidence_masks = packet_getter.get_image_list(
+            output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
         )
+
+      if options.output_category_mask:
+        segmentation_result.category_mask = packet_getter.get_image(
+            output_packets[_CATEGORY_MASK_STREAM_NAME]
+        )
+
       image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
-      timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
       options.result_callback(
           segmentation_result,
           image,
           timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
       )
 
+    output_streams = [
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_confidence_masks:
+      output_streams.append(
+          ':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
+      )
+
+    if options.output_category_mask:
+      output_streams.append(
+          ':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
+      )
+
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
             ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
             ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
-        output_streams=[
+        output_streams=output_streams,
-            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
-            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
-        ],
         task_options=options,
     )
     return cls(
@@ -240,9 +263,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = ImageSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
       )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result
 
   def segment_for_video(
@@ -285,9 +317,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = ImageSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
       )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result
 
   def segment_async(

mediapipe/tasks/python/vision/interactive_segmenter.py

@@ -41,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
-_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
-_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
+_CATEGORY_MASK_STREAM_NAME = 'category_mask'
+_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _ROI_STREAM_NAME = 'roi_in'
@@ -55,32 +57,41 @@ _TASK_GRAPH_NAME = (
 )
 
 
+@dataclasses.dataclass
+class InteractiveSegmenterResult:
+  """Output result of InteractiveSegmenter.
+
+  confidence_masks: multiple masks of float image where, for each mask, each
+  pixel represents the prediction confidence, usually in the [0, 1] range.
+
+  category_mask: a category mask of uint8 image where each pixel represents the
+  class which the pixel in the original image was predicted to belong to.
+  """
+
+  confidence_masks: Optional[List[image_module.Image]] = None
+  category_mask: Optional[image_module.Image] = None
+
+
 @dataclasses.dataclass
 class InteractiveSegmenterOptions:
   """Options for the interactive segmenter task.
 
   Attributes:
     base_options: Base options for the interactive segmenter task.
-    output_type: The output mask type allows specifying the type of
+    output_confidence_masks: Whether to output confidence masks.
-      post-processing to perform on the raw model results.
+    output_category_mask: Whether to output category mask.
   """
 
-  class OutputType(enum.Enum):
-    UNSPECIFIED = 0
-    CATEGORY_MASK = 1
-    CONFIDENCE_MASK = 2
-
   base_options: _BaseOptions
-  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+  output_confidence_masks: bool = True
+  output_category_mask: bool = False
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
     """Generates an InteractiveSegmenterOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
     base_options_proto.use_stream_mode = False
-    segmenter_options_proto = _SegmenterOptionsProto(
+    segmenter_options_proto = _SegmenterOptionsProto()
-        output_type=self.output_type.value
-    )
     return _ImageSegmenterGraphOptionsProto(
         base_options=base_options_proto,
         segmenter_options=segmenter_options_proto,
@@ -192,6 +203,20 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
       RuntimeError: If other types of error occurred.
     """
 
+    output_streams = [
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_confidence_masks:
+      output_streams.append(
+          ':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
+      )
+
+    if options.output_category_mask:
+      output_streams.append(
+          ':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
+      )
+
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
@@ -199,10 +224,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
             ':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
             ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
-        output_streams=[
+        output_streams=output_streams,
-            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
-            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
-        ],
         task_options=options,
     )
     return cls(
@@ -216,7 +238,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
       image: image_module.Image,
       roi: RegionOfInterest,
       image_processing_options: Optional[_ImageProcessingOptions] = None,
-  ) -> List[image_module.Image]:
+  ) -> InteractiveSegmenterResult:
     """Performs the actual segmentation task on the provided MediaPipe Image.
 
     The image can be of any size with format RGB.
@@ -248,7 +270,16 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
             normalized_rect.to_pb2()
         ),
     })
-    segmentation_result = packet_getter.get_image_list(
+    segmentation_result = InteractiveSegmenterResult()
-        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+
+    if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
+      segmentation_result.confidence_masks = packet_getter.get_image_list(
+          output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
       )
+
+    if _CATEGORY_MASK_STREAM_NAME in output_packets:
+      segmentation_result.category_mask = packet_getter.get_image(
+          output_packets[_CATEGORY_MASK_STREAM_NAME]
+      )
+
     return segmentation_result