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GitOrigin-RevId: ff83882955f1a1e2a043ff4e71278be9d7217bbe

docs/solutions/holistic.md

@@ -135,12 +135,11 @@ another detection until it loses track, on reducing computation and latency. If
 set to `true`, person detection runs every input image, ideal for processing a
 batch of static, possibly unrelated, images. Default to `false`.
 
-#### upper_body_only
+#### model_complexity
 
-If set to `true`, the solution outputs only the 25 upper-body pose landmarks
-(535 in total) instead of the full set of 33 pose landmarks (543 in total). Note
-that upper-body-only prediction may be more accurate for use cases where the
-lower-body parts are mostly out of view. Default to `false`.
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
+well as inference latency generally go up with the model complexity. Default to
+`1`.
 
 #### smooth_landmarks
 
@@ -207,7 +206,7 @@ install MediaPipe Python package, then learn more in the companion
 Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
-*   [upper_body_only](#upper_body_only)
+*   [model_complexity](#model_complexity)
 *   [smooth_landmarks](#smooth_landmarks)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
@@ -219,7 +218,9 @@ mp_drawing = mp.solutions.drawing_utils
 mp_holistic = mp.solutions.holistic
 
 # For static images:
-with mp_holistic.Holistic(static_image_mode=True) as holistic:
+with mp_holistic.Holistic(
+    static_image_mode=True,
+    model_complexity=2) as holistic:
   for idx, file in enumerate(file_list):
     image = cv2.imread(file)
     image_height, image_width, _ = image.shape
@@ -240,8 +241,6 @@ with mp_holistic.Holistic(static_image_mode=True) as holistic:
         annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
     mp_drawing.draw_landmarks(
         annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    # Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when
-    # upper_body_only is set to True.
     mp_drawing.draw_landmarks(
         annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
     cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
@@ -291,7 +290,7 @@ and the following usage example.
 
 Supported configuration options:
 
-*   [upperBodyOnly](#upper_body_only)
+*   [modelComplexity](#model_complexity)
 *   [smoothLandmarks](#smooth_landmarks)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
@@ -348,7 +347,7 @@ const holistic = new Holistic({locateFile: (file) => {
   return `https://cdn.jsdelivr.net/npm/@mediapipe/holistic/${file}`;
 }});
 holistic.setOptions({
-  upperBodyOnly: false,
+  modelComplexity: 1,
   smoothLandmarks: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5