Added Face Stylizer unit test files

mediapipe/tasks/python/test/vision/BUILD

@@ -162,3 +162,21 @@ py_test(
         "@com_google_protobuf//:protobuf_python",
     ],
 )
+
+py_test(
+    name = "face_stylizer_test",
+    srcs = ["face_stylizer_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:face_stylizer",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

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

@@ -0,0 +1,343 @@
+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for face stylizer."""
+
+import enum
+import os
+from unittest import mock
+
+import numpy as np
+from absl.testing import absltest
+from absl.testing import parameterized
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.components.containers import rect
+from mediapipe.tasks.python.test import test_utils
+from mediapipe.tasks.python.vision import face_stylizer
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+
+
+_BaseOptions = base_options_module.BaseOptions
+_Rect = rect.Rect
+_Image = image_module.Image
+_FaceStylizer = face_stylizer.FaceStylizer
+_FaceStylizerOptions = face_stylizer.FaceStylizerOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_MODEL = 'face_stylizer.task'
+_LARGE_FACE_IMAGE = "portrait.jpg"
+_MODEL_IMAGE_SIZE = 256
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class FaceStylizerTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+            os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)))
+    self.model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _MODEL))
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _FaceStylizer.create_from_model_path(self.model_path) as stylizer:
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceStylizerOptions(base_options=base_options)
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  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'):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite')
+      options = _FaceStylizerOptions(base_options=base_options)
+      _FaceStylizer.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _FaceStylizerOptions(base_options=base_options)
+      stylizer = _FaceStylizer.create_from_options(options)
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _LARGE_FACE_IMAGE),
+      (ModelFileType.FILE_CONTENT, _LARGE_FACE_IMAGE)
+  )
+  def test_stylize(self, model_file_type, image_file_name):
+    # Load the test image.
+    self.test_image = _Image.create_from_file(
+      test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, image_file_name)))
+    # Creates stylizer.
+    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_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _FaceStylizerOptions(base_options=base_options)
+    stylizer = _FaceStylizer.create_from_options(options)
+
+    # Performs face stylization on the input.
+    stylized_image = stylizer.stylize(self.test_image)
+    self.assertIsInstance(stylized_image, _Image)
+    # Closes the stylizer explicitly when the stylizer is not used in
+    # a context.
+    stylizer.close()
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _LARGE_FACE_IMAGE),
+      (ModelFileType.FILE_CONTENT, _LARGE_FACE_IMAGE)
+  )
+  def test_stylize_in_context(self, model_file_type, image_file_name):
+    # Load the test image.
+    self.test_image = _Image.create_from_file(
+      test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, image_file_name)))
+    # Creates stylizer.
+    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_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _FaceStylizerOptions(base_options=base_options)
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      # Performs face stylization on the input.
+      stylized_image = stylizer.stylize(self.test_image)
+      self.assertIsInstance(stylized_image, _Image)
+      self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
+      self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
+
+  def test_stylize_succeeds_with_region_of_interest(self):
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceStylizerOptions(base_options=base_options)
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      # Load the test image.
+      test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+          os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)
+        )
+      )
+      # Region-of-interest around the face.
+      roi = _Rect(left=0.32, top=0.02, right=0.67, bottom=0.32)
+      image_processing_options = _ImageProcessingOptions(roi)
+      # Performs face stylization on the input.
+      stylized_image = stylizer.stylize(test_image, image_processing_options)
+      self.assertIsInstance(stylized_image, _Image)
+      self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
+      self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
+
+  def test_stylize_succeeds_with_no_face_detected(self):
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceStylizerOptions(base_options=base_options)
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      # Load the test image.
+      test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+          os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)
+        )
+      )
+      # Region-of-interest that doesn't contain a human face.
+      roi = _Rect(left=0.1, top=0.1, right=0.2, bottom=0.2)
+      image_processing_options = _ImageProcessingOptions(roi)
+      # Performs face stylization on the input.
+      stylized_image = stylizer.stylize(test_image, image_processing_options)
+      self.assertIsNone(stylized_image)
+
+  def test_missing_result_callback(self):
+    options = _FaceStylizerOptions(
+      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 _FaceStylizer.create_from_options(options) as unused_stylizer:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _FaceStylizerOptions(
+      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 _FaceStylizer.create_from_options(options) as unused_stylizer:
+        pass
+
+  def test_calling_stylize_for_video_in_image_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the video mode'
+      ):
+        stylizer.stylize_for_video(self.test_image, 0)
+
+  def test_calling_stylize_async_in_image_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the live stream mode'
+      ):
+        stylizer.stylize_async(self.test_image, 0)
+
+  def test_calling_classify_in_video_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the image mode'
+      ):
+        stylizer.stylize(self.test_image)
+
+  def test_calling_classify_async_in_video_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the live stream mode'
+      ):
+        stylizer.stylize_async(self.test_image, 0)
+
+  def test_classify_for_video_with_out_of_order_timestamp(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      unused_result = stylizer.stylize_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
+        stylizer.stylize_for_video(self.test_image, 0)
+
+  def test_stylize_for_video(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      for timestamp in range(0, 300, 30):
+        stylized_image = stylizer.stylize_for_video(
+          self.test_image, timestamp
+        )
+        self.assertIsInstance(stylized_image, _Image)
+        self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
+        self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
+
+  def test_calling_stylize_in_live_stream_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock(),
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the image mode'
+      ):
+        stylizer.stylize(self.test_image)
+
+  def test_calling_stylize_for_video_in_live_stream_mode(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock(),
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      with self.assertRaisesRegex(
+          ValueError, r'not initialized with the video mode'
+      ):
+        stylizer.stylize_for_video(self.test_image, 0)
+
+  def test_stylize_async_calls_with_illegal_timestamp(self):
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock(),
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      stylizer.stylize_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'
+      ):
+        stylizer.stylize_async(self.test_image, 0)
+
+  def test_stylize_async_calls(self):
+    observed_timestamp_ms = -1
+
+    def check_result(
+        stylized_image: _Image, output_image: _Image, timestamp_ms: int
+    ):
+      self.assertIsInstance(stylized_image, _Image)
+      self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
+      self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
+      self.assertTrue(
+        np.array_equal(
+          output_image.numpy_view(), self.test_image.numpy_view()
+        )
+      )
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _FaceStylizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=check_result,
+    )
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      for timestamp in range(0, 300, 30):
+        stylizer.stylize_async(self.test_image, timestamp)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/testdata/vision/BUILD

@@ -47,6 +47,7 @@ mediapipe_files(srcs = [
     "face_landmark.tflite",
     "face_landmarker.task",
     "face_landmarker_v2.task",
+    "face_stylizer_color_ink.task",
     "fist.jpg",
     "fist.png",
     "hair_segmentation.tflite",
@@ -172,6 +173,7 @@ filegroup(
         "face_detection_short_range.tflite",
         "face_landmarker.task",
         "face_landmarker_v2.task",
+        "face_stylizer_color_ink.task",
         "hair_segmentation.tflite",
         "hand_landmark_full.tflite",
         "hand_landmark_lite.tflite",