Revised gesture recognizer implementation

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

@@ -47,22 +47,26 @@ _GestureRecognitionResult = gesture_recognizer.GestureRecognitionResult
 _RUNNING_MODE = running_mode_module.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
-_GESTURE_RECOGNIZER_MODEL_FILE = 'gesture_recognizer.task'
+_GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE = 'gesture_recognizer.task'
+_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE = 'gesture_recognizer_with_custom_classifier.task'
 _NO_HANDS_IMAGE = 'cats_and_dogs.jpg'
 _TWO_HANDS_IMAGE = 'right_hands.jpg'
+_FIST_IMAGE = 'fist.jpg'
+_FIST_LANDMARKS = 'fist_landmarks.pbtxt'
+_FIST_LABEL = 'Closed_Fist'
 _THUMB_UP_IMAGE = 'thumb_up.jpg'
 _THUMB_UP_LANDMARKS = 'thumb_up_landmarks.pbtxt'
 _THUMB_UP_LABEL = 'Thumb_Up'
-_THUMB_UP_INDEX = 5
 _POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg'
 _POINTING_UP_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt'
 _POINTING_UP_LABEL = 'Pointing_Up'
-_POINTING_UP_INDEX = 3
+_ROCK_LABEL = "Rock"
 _LANDMARKS_ERROR_TOLERANCE = 0.03
+_GESTURE_EXPECTED_INDEX = -1
 
 
 def _get_expected_gesture_recognition_result(
-    file_path: str, gesture_label: str, gesture_index: int
+    file_path: str, gesture_label: str
 ) -> _GestureRecognitionResult:
   landmarks_detection_result_file_path = test_utils.get_test_data_path(
     file_path)
@@ -73,7 +77,8 @@ def _get_expected_gesture_recognition_result(
     text_format.Parse(f.read(), landmarks_detection_result_proto)
     landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2(
         landmarks_detection_result_proto)
-  gesture = _Category(category_name=gesture_label, index=gesture_index,
+  gesture = _Category(category_name=gesture_label,
+                      index=_GESTURE_EXPECTED_INDEX,
                       display_name='')
   return _GestureRecognitionResult(
       gestures=[[gesture]],
@@ -94,7 +99,7 @@ class GestureRecognizerTest(parameterized.TestCase):
     self.test_image = _Image.create_from_file(
         test_utils.get_test_data_path(_THUMB_UP_IMAGE))
     self.model_path = test_utils.get_test_data_path(
-        _GESTURE_RECOGNIZER_MODEL_FILE)
+        _GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE)
 
   def _assert_actual_result_approximately_matches_expected_result(
       self,
@@ -127,7 +132,7 @@ class GestureRecognizerTest(parameterized.TestCase):
     # Actual gesture with top score matches expected gesture.
     actual_top_gesture = actual_result.gestures[0][0]
     expected_top_gesture = expected_result.gestures[0][0]
-    self.assertEqual(actual_top_gesture.index, expected_top_gesture.index)
+    self.assertEqual(actual_top_gesture.index, _GESTURE_EXPECTED_INDEX)
     self.assertEqual(actual_top_gesture.category_name,
                      expected_top_gesture.category_name)
 
@@ -163,10 +168,10 @@ class GestureRecognizerTest(parameterized.TestCase):
 
   @parameterized.parameters(
       (ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL
       )),
       (ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL
       )))
   def test_recognize(self, model_file_type, expected_recognition_result):
     # Creates gesture recognizer.
@@ -194,10 +199,10 @@ class GestureRecognizerTest(parameterized.TestCase):
 
   @parameterized.parameters(
       (ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL
       )),
       (ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL
       )))
   def test_recognize_in_context(self, model_file_type,
                                 expected_recognition_result):
@@ -224,12 +229,12 @@ class GestureRecognizerTest(parameterized.TestCase):
     # Creates gesture recognizer.
     base_options = _BaseOptions(model_asset_path=self.model_path)
     options = _GestureRecognizerOptions(base_options=base_options,
-                                        min_gesture_confidence=2)
+                                        min_gesture_confidence=0.5)
     with _GestureRecognizer.create_from_options(options) as recognizer:
       # Performs hand gesture recognition on the input.
       recognition_result = recognizer.recognize(self.test_image)
       expected_result = _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)
       # Only contains one top scoring gesture.
       self.assertLen(recognition_result.gestures[0], 1)
       # Actual gesture with top score matches expected gesture.
@@ -266,7 +271,25 @@ class GestureRecognizerTest(parameterized.TestCase):
       recognition_result = recognizer.recognize(test_image,
                                                 image_processing_options)
       expected_recognition_result = _get_expected_gesture_recognition_result(
-          _POINTING_UP_LANDMARKS, _POINTING_UP_LABEL, _POINTING_UP_INDEX)
+          _POINTING_UP_LANDMARKS, _POINTING_UP_LABEL)
+      # Comparing results.
+      self._assert_actual_result_approximately_matches_expected_result(
+          recognition_result, expected_recognition_result)
+
+  def test_recognize_succeeds_with_custom_gesture_fist(self):
+    # Creates gesture recognizer.
+    model_path = test_utils.get_test_data_path(
+        _GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      # Load the fist image.
+      test_image = _Image.create_from_file(
+          test_utils.get_test_data_path(_FIST_IMAGE))
+      # Performs hand gesture recognition on the input.
+      recognition_result = recognizer.recognize(test_image)
+      expected_recognition_result = _get_expected_gesture_recognition_result(
+          _FIST_LANDMARKS, _ROCK_LABEL)
       # Comparing results.
       self._assert_actual_result_approximately_matches_expected_result(
         recognition_result, expected_recognition_result)
@@ -373,7 +396,7 @@ class GestureRecognizerTest(parameterized.TestCase):
         recognition_result = recognizer.recognize_for_video(self.test_image,
                                                             timestamp)
         expected_recognition_result = _get_expected_gesture_recognition_result(
-           _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)
+           _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)
         self._assert_actual_result_approximately_matches_expected_result(
             recognition_result, expected_recognition_result)
 
@@ -410,7 +433,7 @@ class GestureRecognizerTest(parameterized.TestCase):
 
   @parameterized.parameters(
       (_THUMB_UP_IMAGE, _get_expected_gesture_recognition_result(
-          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)),
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)),
       (_NO_HANDS_IMAGE, _GestureRecognitionResult([], [], [], [])))
   def test_recognize_async_calls(self, image_path, expected_result):
     test_image = _Image.create_from_file(

mediapipe/tasks/python/vision/BUILD

@@ -87,12 +87,9 @@ py_library(
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
-        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_classifier_graph_options_py_pb2",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_py_pb2",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:hand_gesture_recognizer_graph_options_py_pb2",
-        "//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_py_pb2",
         "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_py_pb2",
-        "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:landmark",
         "//mediapipe/tasks/python/components/processors:classifier_options",

mediapipe/tasks/python/vision/gesture_recognizer.py

@@ -20,13 +20,9 @@ from mediapipe.python import packet_creator
 from mediapipe.python import packet_getter
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.python._framework_bindings import packet as packet_module
-from mediapipe.python._framework_bindings import task_runner as task_runner_module
-from mediapipe.tasks.cc.vision.gesture_recognizer.proto import gesture_classifier_graph_options_pb2
 from mediapipe.tasks.cc.vision.gesture_recognizer.proto import gesture_recognizer_graph_options_pb2
 from mediapipe.tasks.cc.vision.gesture_recognizer.proto import hand_gesture_recognizer_graph_options_pb2
-from mediapipe.tasks.cc.vision.hand_detector.proto import hand_detector_graph_options_pb2
 from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarker_graph_options_pb2
-from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarks_detector_graph_options_pb2
 from mediapipe.tasks.python.components.containers import category as category_module
 from mediapipe.tasks.python.components.containers import landmark as landmark_module
 from mediapipe.tasks.python.components.processors import classifier_options
@@ -38,12 +34,9 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
 from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 
 _BaseOptions = base_options_module.BaseOptions
-_GestureClassifierGraphOptionsProto = gesture_classifier_graph_options_pb2.GestureClassifierGraphOptions
 _GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
 _HandGestureRecognizerGraphOptionsProto = hand_gesture_recognizer_graph_options_pb2.HandGestureRecognizerGraphOptions
-_HandDetectorGraphOptionsProto = hand_detector_graph_options_pb2.HandDetectorGraphOptions
 _HandLandmarkerGraphOptionsProto = hand_landmarker_graph_options_pb2.HandLandmarkerGraphOptions
-_HandLandmarksDetectorGraphOptionsProto = hand_landmarks_detector_graph_options_pb2.HandLandmarksDetectorGraphOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _RunningMode = running_mode_module.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
@@ -64,6 +57,7 @@ _HAND_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
 _HAND_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
 _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph'
 _MICRO_SECONDS_PER_MILLISECOND = 1000
+_GESTURE_DEFAULT_INDEX = -1
 
 
 @dataclasses.dataclass
@@ -72,8 +66,9 @@ class GestureRecognitionResult:
   element represents a single hand detected in the image.
 
   Attributes:
-    gestures:  Recognized hand gestures with sorted order such that the
+    gestures: Recognized hand gestures of detected hands. Note that the index
-      winning label is the first item in the list.
+      of the gesture is always 0, because the raw indices from multiple gesture
+      classifiers cannot consolidate to a meaningful index.
     handedness: Classification of handedness.
     hand_landmarks: Detected hand landmarks in normalized image coordinates.
     hand_world_landmarks: Detected hand landmarks in world coordinates.
@@ -101,16 +96,16 @@ def _build_recognition_result(
     [
       [
         category_module.Category(
-          index=gesture.index, score=gesture.score,
+          index=_GESTURE_DEFAULT_INDEX, score=gesture.score,
           display_name=gesture.display_name, category_name=gesture.label)
         for gesture in gesture_classifications.classification]
       for gesture_classifications in gestures_proto_list
     ], [
       [
         category_module.Category(
-          index=gesture.index, score=gesture.score,
+          index=handedness.index, score=handedness.score,
-          display_name=gesture.display_name, category_name=gesture.label)
+          display_name=handedness.display_name, category_name=handedness.label)
-        for gesture in handedness_classifications.classification]
+        for handedness in handedness_classifications.classification]
       for handedness_classifications in handedness_proto_list
     ], [
       [landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
@@ -170,26 +165,17 @@ class GestureRecognizerOptions:
     base_options_proto = self.base_options.to_pb2()
     base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
 
-    # Configure hand detector options.
+    # Configure hand detector and hand landmarker options.
-    hand_detector_options_proto = _HandDetectorGraphOptionsProto(
+    hand_landmarker_options_proto = _HandLandmarkerGraphOptionsProto()
-        num_hands=self.num_hands,
+    hand_landmarker_options_proto.min_tracking_confidence = self.min_tracking_confidence
-        min_detection_confidence=self.min_hand_detection_confidence)
+    hand_landmarker_options_proto.hand_detector_graph_options.num_hands = self.num_hands
-
+    hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = self.min_hand_detection_confidence
-    # Configure hand landmarker options.
+    hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = self.min_hand_presence_confidence
-    hand_landmarks_detector_options_proto = _HandLandmarksDetectorGraphOptionsProto(
-        min_detection_confidence=self.min_hand_presence_confidence)
-    hand_landmarker_options_proto = _HandLandmarkerGraphOptionsProto(
-        hand_detector_graph_options=hand_detector_options_proto,
-        hand_landmarks_detector_graph_options=hand_landmarks_detector_options_proto,
-        min_tracking_confidence=self.min_tracking_confidence)
 
     # Configure hand gesture recognizer options.
-    classifier_options = _ClassifierOptions(
+    hand_gesture_recognizer_options_proto = _HandGestureRecognizerGraphOptionsProto()
-        score_threshold=self.min_gesture_confidence)
+    hand_gesture_recognizer_options_proto.canned_gesture_classifier_graph_options.classifier_options.score_threshold = self.min_gesture_confidence
-    gesture_classifier_options = _GestureClassifierGraphOptionsProto(
+    hand_gesture_recognizer_options_proto.custom_gesture_classifier_graph_options.classifier_options.score_threshold = self.min_gesture_confidence
-        classifier_options=classifier_options.to_pb2())
-    hand_gesture_recognizer_options_proto = _HandGestureRecognizerGraphOptionsProto(
-        canned_gesture_classifier_graph_options=gesture_classifier_options)
 
     return _GestureRecognizerGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/testdata/vision/BUILD

@@ -130,6 +130,7 @@ filegroup(
         "hand_landmark_lite.tflite",
         "hand_landmarker.task",
         "gesture_recognizer.task",
+        "gesture_recognizer_with_custom_classifier.task",
         "mobilenet_v1_0.25_192_quantized_1_default_1.tflite",
         "mobilenet_v1_0.25_224_1_default_1.tflite",
         "mobilenet_v1_0.25_224_1_metadata_1.tflite",