Update Python models to allow extracting any set of graph outputs

mediapipe/python/solutions/face_mesh.py

@@ -15,7 +15,7 @@
 # Lint as: python3
 """MediaPipe FaceMesh."""
 
-from typing import NamedTuple
+from typing import NamedTuple, Optional, Tuple
 
 import numpy as np
 
@@ -249,7 +249,8 @@ class FaceMesh(SolutionBase):
                static_image_mode=False,
                max_num_faces=2,
                min_detection_confidence=0.5,
-               min_tracking_confidence=0.5):
+               min_tracking_confidence=0.5,
+               outputs: Optional[Tuple[str]] = ('multi_face_landmarks',)):
     """Initializes a MediaPipe FaceMesh object.
 
     Args:
@@ -274,6 +275,9 @@ class FaceMesh(SolutionBase):
         robustness of the solution, at the expense of a higher latency. Ignored
         if "static_image_mode" is True, where face detection simply runs on
         every image. Default to 0.5.
+      outputs: A list of the graph output stream names to observe. If the list
+        is empty, all the output streams listed in the graph config will be
+        automatically observed by default.
     """
     super().__init__(
         binary_graph_path=BINARYPB_FILE_PATH,
@@ -287,7 +291,7 @@ class FaceMesh(SolutionBase):
             'facelandmarkcpu__ThresholdingCalculator.threshold':
                 min_tracking_confidence,
         },
-        outputs=['multi_face_landmarks'])
+        outputs=list(outputs) if outputs else [])
 
   def process(self, image: np.ndarray) -> NamedTuple:
     """Processes an RGB image and returns the face landmarks on each detected face.
@@ -300,8 +304,12 @@ class FaceMesh(SolutionBase):
       ValueError: If the input image is not three channel RGB.
 
     Returns:
-      A NamedTuple object with a "multi_face_landmarks" field that contains the
+      A NamedTuple object with fields corresponding to the set of outputs passed to the
-      face landmarks on each detected face.
+      constructor. Fields may include:
+        "multi_hand_landmarks" The face landmarks on each detected face
+        "face_detections" The detected faces
+        "face_rects_from_landmarks" Regions of interest calculated based on landmarks
+        "face_rects_from_detections" Regions of interest calculated based on face detections
     """
 
     return super().process(input_data={'image': image})

mediapipe/python/solutions/hands.py

@@ -16,7 +16,7 @@
 """MediaPipe Hands."""
 
 import enum
-from typing import NamedTuple
+from typing import NamedTuple, Optional, Tuple
 
 import numpy as np
 
@@ -168,7 +168,8 @@ class Hands(SolutionBase):
                static_image_mode=False,
                max_num_hands=2,
                min_detection_confidence=0.7,
-               min_tracking_confidence=0.5):
+               min_tracking_confidence=0.5,
+               outputs: Optional[Tuple[str]] = ('multi_hand_landmarks', 'multi_handedness')):
     """Initializes a MediaPipe Hand object.
 
     Args:
@@ -193,6 +194,9 @@ class Hands(SolutionBase):
         robustness of the solution, at the expense of a higher latency. Ignored
         if "static_image_mode" is True, where hand detection simply runs on
         every image. Default to 0.5.
+      outputs: A tuple of the graph output stream names to observe. If the tuple
+        is empty, all the output streams listed in the graph config will be
+        automatically observed by default.
     """
     super().__init__(
         binary_graph_path=BINARYPB_FILE_PATH,
@@ -206,7 +210,7 @@ class Hands(SolutionBase):
             'handlandmarkcpu__ThresholdingCalculator.threshold':
                 min_tracking_confidence,
         },
-        outputs=['multi_hand_landmarks', 'multi_handedness'])
+        outputs=list(outputs) if outputs else [])
 
   def process(self, image: np.ndarray) -> NamedTuple:
     """Processes an RGB image and returns the hand landmarks and handedness of each detected hand.
@@ -219,10 +223,13 @@ class Hands(SolutionBase):
       ValueError: If the input image is not three channel RGB.
 
     Returns:
-      A NamedTuple object with two fields: a "multi_hand_landmarks" field that
+      A NamedTuple object with fields corresponding to the set of outputs passed to the
-      contains the hand landmarks on each detected hand and a "multi_handedness"
+      constructor. Fields may include:
-      field that contains the handedness (left v.s. right hand) of the detected
+        "multi_hand_landmarks" The hand landmarks on each detected hand
-      hand.
+        "multi_handedness" The handedness (left v.s. right hand) of the detected hand
+        "palm_detections" The detected palms
+        "hand_rects" Regions of interest calculated based on landmarks
+        "hand_rects_from_palm_detections" Regions of interest calculated based on palm detections
     """
 
     return super().process(input_data={'image': image})

mediapipe/python/solutions/pose.py

@@ -16,7 +16,7 @@
 """MediaPipe Pose."""
 
 import enum
-from typing import NamedTuple
+from typing import NamedTuple, Optional, Tuple
 
 import numpy as np
 
@@ -159,7 +159,8 @@ class Pose(SolutionBase):
   def __init__(self,
                static_image_mode=False,
                min_detection_confidence=0.5,
-               min_tracking_confidence=0.5):
+               min_tracking_confidence=0.5,
+               outputs: Optional[Tuple[str]] = ('pose_landmarks',)):
     """Initializes a MediaPipe Pose object.
 
     Args:
@@ -181,6 +182,9 @@ class Pose(SolutionBase):
         increase robustness of the solution, at the expense of a higher latency.
         Ignored if "static_image_mode" is True, where person detection simply
         runs on every image. Default to 0.5.
+      outputs: A list of the graph output stream names to observe. If the list
+        is empty, all the output streams listed in the graph config will be
+        automatically observed by default.
     """
     super().__init__(
         binary_graph_path=BINARYPB_FILE_PATH,
@@ -193,7 +197,7 @@ class Pose(SolutionBase):
             'poselandmarkupperbodycpu__poselandmarkupperbodybyroicpu__ThresholdingCalculator.threshold':
                 min_tracking_confidence,
         },
-        outputs=['pose_landmarks'])
+        outputs=list(outputs) if outputs else [])
 
   def process(self, image: np.ndarray) -> NamedTuple:
     """Processes an RGB image and returns the pose landmarks on the most prominent person detected.
@@ -206,8 +210,12 @@ class Pose(SolutionBase):
       ValueError: If the input image is not three channel RGB.
 
     Returns:
-      A NamedTuple object with a "pose_landmarks" field that contains the pose
+      A NamedTuple object with fields corresponding to the set of outputs passed to the
-      landmarks on the most prominent person detected.
+      constructor. Fields may include:
+        "pose_landmarks" The pose landmarks on the most prominent person detected
+        "pose_detection" The detected pose
+        "pose_rect_from_landmarks" Region of interest calculated based on landmarks
+        "pose_rect_from_detection" Region of interest calculated based on pose detection
     """
 
     return super().process(input_data={'image': image})