Revised Interactive Segmenter API and added more tests

mediapipe/tasks/python/test/vision/BUILD

@@ -102,9 +102,11 @@ py_test(
     deps = [
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/tasks/python/components/containers:keypoint",
+        "//mediapipe/tasks/python/components/containers:rect",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/vision:interactive_segmenter",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )

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

@@ -24,20 +24,24 @@ import numpy as np
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.python._framework_bindings import image_frame
 from mediapipe.tasks.python.components.containers import keypoint as keypoint_module
+from mediapipe.tasks.python.components.containers import rect
 from mediapipe.tasks.python.core import base_options as base_options_module
 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
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
 _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
 _Format = interactive_segmenter.RegionOfInterest.Format
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
 
 _MODEL_FILE = 'ptm_512_hdt_ptm_woid.tflite'
@@ -50,11 +54,11 @@ _TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
 
 
 def _calculate_soft_iou(m1, m2):
-  intersection = np.sum(m1 * m2)
-  union = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection
+  intersection_sum = np.sum(m1 * m2)
+  union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
 
-  if union > 0:
-    return intersection / union
+  if union_sum > 0:
+    return intersection_sum / union_sum
   else:
     return 0
 
@@ -189,9 +193,9 @@ class InteractiveSegmenterTest(parameterized.TestCase):
 
   @parameterized.parameters(
       (_RegionOfInterest.Format.KEYPOINT, _NormalizedKeypoint(0.44, 0.7),
-       _CATS_AND_DOGS_MASK_DOG_1, 0.58),
+       _CATS_AND_DOGS_MASK_DOG_1, 0.84),
       (_RegionOfInterest.Format.KEYPOINT, _NormalizedKeypoint(0.66, 0.66),
-       _CATS_AND_DOGS_MASK_DOG_2, 0.60)
+       _CATS_AND_DOGS_MASK_DOG_2, _MASK_SIMILARITY_THRESHOLD)
   )
   def test_segment_succeeds_with_confidence_mask(
       self, format, keypoint, output_mask, similarity_threshold):
@@ -203,26 +207,66 @@ class InteractiveSegmenterTest(parameterized.TestCase):
     options = _InteractiveSegmenterOptions(
         base_options=base_options,
         output_type=_OutputType.CONFIDENCE_MASK)
-    segmenter = _InteractiveSegmenter.create_from_options(options)
 
-    # Perform segmentation
-    confidence_masks = segmenter.segment(self.test_image, roi)
+    with _InteractiveSegmenter.create_from_options(options) as segmenter:
+      # Perform segmentation
+      confidence_masks = segmenter.segment(self.test_image, roi)
 
-    # Check if confidence mask shape is correct.
-    self.assertLen(
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks, 2,
+          'Number of confidence masks must match with number of categories.')
+
+      # Loads ground truth segmentation file.
+      expected_mask = self._load_segmentation_mask(output_mask)
+
+      self.assertTrue(
+          _similar_to_float_mask(confidence_masks[1], expected_mask,
+                                 similarity_threshold))
+
+  def test_segment_succeeds_with_rotation(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    roi = _RegionOfInterest(
+        format=_RegionOfInterest.Format.KEYPOINT,
+        keypoint=_NormalizedKeypoint(0.66, 0.66)
+    )
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _InteractiveSegmenterOptions(
+      base_options=base_options,
+      output_type=_OutputType.CONFIDENCE_MASK)
+
+    with _InteractiveSegmenter.create_from_options(options) as segmenter:
+      # Perform segmentation
+      image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
+      confidence_masks = segmenter.segment(self.test_image, roi)
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
         confidence_masks, 2,
         'Number of confidence masks must match with number of categories.')
 
-    # Loads ground truth segmentation file.
-    expected_mask = self._load_segmentation_mask(output_mask)
+  def test_segment_fails_with_roi_in_image_processing_options(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    roi = _RegionOfInterest(
+      format=_RegionOfInterest.Format.KEYPOINT,
+      keypoint=_NormalizedKeypoint(0.66, 0.66)
+    )
 
-    self.assertTrue(
-        _similar_to_float_mask(confidence_masks[1], expected_mask,
-                               similarity_threshold))
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _InteractiveSegmenterOptions(
+      base_options=base_options,
+      output_type=_OutputType.CONFIDENCE_MASK)
 
-    # Closes the segmenter explicitly when the segmenter is not used in
-    # a context.
-    segmenter.close()
+    with self.assertRaisesRegex(
+        ValueError, "This task doesn't support region-of-interest."):
+      with _InteractiveSegmenter.create_from_options(options) as segmenter:
+        # Perform segmentation
+        image_processing_options = _ImageProcessingOptions(
+            _Rect(left=0.1, top=0, right=0.9, bottom=1))
+        segmenter.segment(self.test_image, roi, image_processing_options)
 
 
 if __name__ == '__main__':

mediapipe/tasks/python/vision/interactive_segmenter.py

@@ -254,7 +254,8 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If image segmentation failed to run.
     """
-    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options,
+                                                      roi_allowed=False)
     render_data_proto = _convert_roi_to_render_data(roi)
     output_packets = self._process_image_data(
         {