Revised API implementation and added more tests for segment_for_video and segment_async

mediapipe/tasks/python/test/vision/BUILD

@@ -47,7 +47,7 @@ py_test(
     deps = [
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/tasks/python/core:base_options",
-        "//mediapipe/tasks/python/test:test_util",
+        "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/components/proto:segmenter_options",
         "//mediapipe/tasks/python/vision:image_segmenter",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",

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

@@ -16,6 +16,8 @@
 import enum
 import numpy as np
 import cv2
+from typing import List
+from unittest import mock
 
 from absl.testing import absltest
 from absl.testing import parameterized
@@ -24,7 +26,7 @@ from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.python._framework_bindings import image_frame as image_frame_module
 from mediapipe.tasks.python.components.proto import segmenter_options
 from mediapipe.tasks.python.core import base_options as base_options_module
-from mediapipe.tasks.python.test import test_util
+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 as running_mode_module
 
@@ -42,7 +44,22 @@ _MODEL_FILE = 'deeplabv3.tflite'
 _IMAGE_FILE = 'segmentation_input_rotation0.jpg'
 _SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
 _MASK_MAGNIFICATION_FACTOR = 10
-_MATCH_PIXELS_THRESHOLD = 0.01
+_MASK_SIMILARITY_THRESHOLD = 0.98
+
+
+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()
+
+  consistent_pixels = 0
+  num_pixels = len(expected_mask_pixels)
+
+  for index in range(num_pixels):
+    consistent_pixels += (
+        actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR ==
+        expected_mask_pixels[index])
+
+  return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
 
 
 class ModelFileType(enum.Enum):
@@ -54,10 +71,14 @@ class ImageSegmenterTest(parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
-    self.test_image = test_util.read_test_image(
+    # Load the test input image.
-        test_util.get_test_data_path(_IMAGE_FILE))
+    self.test_image = _Image.create_from_file(
-    self.test_seg_path = test_util.get_test_data_path(_SEGMENTATION_FILE)
+        test_utils.get_test_data_path(_IMAGE_FILE))
-    self.model_path = test_util.get_test_data_path(_MODEL_FILE)
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+      test_utils.get_test_data_path(_SEGMENTATION_FILE), cv2.IMREAD_GRAYSCALE)
+    self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
+    self.model_path = test_utils.get_test_data_path(_MODEL_FILE)
 
   def test_create_from_file_succeeds_with_valid_model_path(self):
     # Creates with default option and valid model file successfully.
@@ -76,7 +97,7 @@ class ImageSegmenterTest(parameterized.TestCase):
     with self.assertRaisesRegex(
         ValueError,
         r"ExternalFile must specify at least one of 'file_content', "
-        r"'file_name' or 'file_descriptor_meta'."):
+        r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
       base_options = _BaseOptions(model_asset_path='')
       options = _ImageSegmenterOptions(base_options=base_options)
       _ImageSegmenter.create_from_options(options)
@@ -112,34 +133,16 @@ class ImageSegmenterTest(parameterized.TestCase):
     # Performs image segmentation on the input.
     category_masks = segmenter.segment(self.test_image)
     self.assertEqual(len(category_masks), 1)
-    result_pixels = category_masks[0].numpy_view().flatten()
+    category_mask = category_masks[0]
+    result_pixels = category_mask.numpy_view().flatten()
 
-    # Check if data type of `category_masks` is correct.
+    # Check if data type of `category_mask` is correct.
     self.assertEqual(result_pixels.dtype, np.uint8)
 
-    # Loads ground truth segmentation file.
+    self.assertTrue(
-    image_data = cv2.imread(self.test_seg_path, cv2.IMREAD_GRAYSCALE)
+      _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
-    gt_segmentation = _Image(_ImageFormat.GRAY8, image_data)
-    gt_segmentation_array = gt_segmentation.numpy_view()
-    gt_segmentation_shape = gt_segmentation_array.shape
-    num_pixels = gt_segmentation_shape[0] * gt_segmentation_shape[1]
-    ground_truth_pixels = gt_segmentation_array.flatten()
-
-    self.assertEqual(
-      len(result_pixels), len(ground_truth_pixels),
-      'Segmentation mask size does not match the ground truth mask size.')
-
-    inconsistent_pixels = 0
-
-    for index in range(num_pixels):
-      inconsistent_pixels += (
-          result_pixels[index] * _MASK_MAGNIFICATION_FACTOR !=
-          ground_truth_pixels[index])
-
-    self.assertLessEqual(
-      inconsistent_pixels / num_pixels, _MATCH_PIXELS_THRESHOLD,
       f'Number of pixels in the candidate mask differing from that of the '
-      f'ground truth mask exceeds {_MATCH_PIXELS_THRESHOLD}.')
+      f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
 
     # Closes the segmenter explicitly when the segmenter is not used in
     # a context.
@@ -188,6 +191,174 @@ class ImageSegmenterTest(parameterized.TestCase):
     # 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.')
+
+    segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
+    options = _ImageSegmenterOptions(base_options=base_options,
+                                     segmenter_options=segmenter_options)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      # Performs image segmentation on the input.
+      category_masks = segmenter.segment(self.test_image)
+      self.assertEqual(len(category_masks), 1)
+
+      self.assertTrue(
+        _similar_to_uint8_mask(category_masks[0], 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}.')
+
+  def test_missing_result_callback(self):
+    options = _ImageSegmenterOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM)
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback must be provided'):
+      with _ImageSegmenter.create_from_options(options) as unused_segmenter:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _ImageSegmenterOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=running_mode,
+      result_callback=mock.MagicMock())
+    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)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(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)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(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)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(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)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_detect_for_video_with_out_of_order_timestamp(self):
+    options = _ImageSegmenterOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      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'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_segment_for_video(self):
+    segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
+    options = _ImageSegmenterOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      segmenter_options=segmenter_options,
+      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)
+        self.assertEqual(len(category_masks), 1)
+        self.assertTrue(
+          _similar_to_uint8_mask(category_masks[0], 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}.')
+
+  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())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(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())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(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())
+    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'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_segment_async_calls(self):
+    observed_timestamp_ms = -1
+
+    def check_result(result: List[image_module.Image],
+                     output_image: _Image,
+                     timestamp_ms: int):
+      # Get the output category mask.
+      category_mask = result[0]
+      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}.')
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
+    options = _ImageSegmenterOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      segmenter_options=segmenter_options,
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      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)
+
 
 if __name__ == '__main__':
   absltest.main()

mediapipe/tasks/python/vision/image_segmenter.py

@@ -42,6 +42,7 @@ _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
 _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
 
 
 @dataclasses.dataclass
@@ -52,9 +53,9 @@ class ImageSegmenterOptions:
     base_options: Base options for the image segmenter task.
     running_mode: The running mode of the task. Default to the image mode.
       Image segmenter task has three running modes:
-      1) The image mode for detecting objects on single image inputs.
+      1) The image mode for segmenting objects on single image inputs.
-      2) The video mode for detecting objects on the decoded frames of a video.
+      2) The video mode for segmenting objects on the decoded frames of a video.
-      3) The live stream mode for detecting objects on a live stream of input
+      3) The live stream mode for segmenting objects on a live stream of input
          data, such as from camera.
     segmenter_options: Options for the image segmenter task.
     result_callback: The user-defined result callback for processing live stream
@@ -86,7 +87,8 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
 
   @classmethod
   def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
-    """Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
+    """Creates an `ImageSegmenter` object from a TensorFlow Lite model and the
+    default `ImageSegmenterOptions`.
 
     Note that the created `ImageSegmenter` instance is in image mode, for
     performing image segmentation on single image inputs.
@@ -131,8 +133,9 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
       segmentation_result = packet_getter.get_image_list(
           output_packets[_SEGMENTATION_OUT_STREAM_NAME])
       image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
-      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
-      options.result_callback(segmentation_result, image, timestamp)
+      options.result_callback(segmentation_result, image,
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
 
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,
@@ -148,7 +151,6 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
             _RunningMode.LIVE_STREAM), options.running_mode,
         packets_callback if options.result_callback else None)
 
-  # TODO: Create an Image class for MediaPipe Tasks.
   def segment(self,
               image: image_module.Image) -> List[image_module.Image]:
     """Performs the actual segmentation task on the provided MediaPipe Image.
@@ -162,10 +164,74 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
 
     Raises:
       ValueError: If any of the input arguments is invalid.
-      RuntimeError: If object detection failed to run.
+      RuntimeError: If image segmentation failed to run.
     """
     output_packets = self._process_image_data(
         {_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
     segmentation_result = packet_getter.get_image_list(
         output_packets[_SEGMENTATION_OUT_STREAM_NAME])
     return segmentation_result
+
+  def segment_for_video(self, image: image_module.Image,
+                        timestamp_ms: int) -> List[image_module.Image]:
+    """Performs segmentation on the provided video frames.
+
+    Only use this method when the ImageSegmenter is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+
+    Returns:
+      A segmentation result object that contains a list of segmentation masks
+      as images.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image segmentation failed to run.
+    """
+    output_packets = self._process_video_data({
+      _IMAGE_IN_STREAM_NAME:
+        packet_creator.create_image(image).at(
+          timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+    segmentation_result = packet_getter.get_image_list(
+      output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+    return segmentation_result
+
+  def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
+    """Sends live image data (an Image with a unique timestamp) to perform
+    image segmentation.
+
+    Only use this method when the ImageSegmenter is created with the live stream
+    running mode. The input timestamps should be monotonically increasing for
+    adjacent calls of this method. This method will return immediately after the
+    input image is accepted. The results will be available via the
+    `result_callback` provided in the `ImageSegmenterOptions`. The
+    `segment_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, image segmenter may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` prvoides:
+      - A segmentation result object that contains a list of segmentation masks
+        as images.
+      - The input image that the image segmenter runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the image
+        segmenter has already processed.
+    """
+    self._send_live_stream_data({
+      _IMAGE_IN_STREAM_NAME:
+        packet_creator.create_image(image).at(
+          timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })