Fix FPS truncated to integer in multiple locations

src/python/pose_format/numpy/pose_body.py

@@ -298,13 +298,13 @@ def bbox(self, header: PoseHeader):
 
         return NumPyPoseBody(self.fps, new_data, confidence)
 
-    def interpolate(self, new_fps: int = None, kind='cubic'):
+    def interpolate(self, new_fps: float = None, kind='cubic'):
         """
         Interpolates the pose data to match a new frame rate.
 
         Parameters
         ----------
-        new_fps : int, optional
+        new_fps : float, optional
             The desired frame rate for interpolation.
         kind : str, optional
             The type of interpolation. Options include: "linear", "quadratic", and "cubic".

src/python/pose_format/tensorflow/pose_body.py

@@ -7,7 +7,7 @@
 from .masked.tensor import MaskedTensor
 
 TF_POSE_RECORD_DESCRIPTION = {
-    'fps': tf.io.FixedLenFeature([], tf.int64, default_value=0),
+    'fps': tf.io.FixedLenFeature([], tf.float32, default_value=0.0),
     'pose_data': tf.io.FixedLenFeature([], tf.string),
     'pose_confidence': tf.io.FixedLenFeature([], tf.string),
 }
@@ -218,7 +218,7 @@ def as_tfrecord(self):
         confidence = tf.io.serialize_tensor(self.confidence).numpy()
 
         return {
-            'fps': tf.train.Feature(int64_list=tf.train.Int64List(value=[self.fps])),
+            'fps': tf.train.Feature(float_list=tf.train.FloatList(value=[self.fps])),
             'pose_data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[data])),
             'pose_confidence': tf.train.Feature(bytes_list=tf.train.BytesList(value=[confidence]))
         }
@@ -238,7 +238,7 @@ def from_tfrecord(cls, tfrecord_dict: dict):
         TensorflowPoseBody
             An instance constructed from given TensorFlow record data
         """
-        fps = tf.cast(tfrecord_dict['fps'], dtype=tf.float32)
+        fps = tfrecord_dict['fps']
         data = tf.io.parse_tensor(tfrecord_dict['pose_data'], out_type=tf.float32)
         confidence = tf.io.parse_tensor(tfrecord_dict['pose_confidence'], out_type=tf.float32)
         return cls(fps=fps, data=data, confidence=confidence)

src/python/pose_format/utils/generic.py

@@ -213,7 +213,7 @@ def get_standard_components_for_known_format(known_pose_format: KnownPoseFormat)
     raise NotImplementedError(f"Unsupported pose header schema {known_pose_format}")
 
 
-def fake_pose(num_frames: int, fps: int=25, components: Union[List[PoseHeaderComponent],None]=None)->Pose:
+def fake_pose(num_frames: int, fps: float=25.0, components: Union[List[PoseHeaderComponent],None]=None)->Pose:
     if components is None:
         components = copy.deepcopy(OpenPose_Components) # fixes W0102, dangerous default value
 
@@ -230,7 +230,7 @@ def fake_pose(num_frames: int, fps: int=25, components: Union[List[PoseHeaderCom
     confidence = np.random.randn(num_frames, 1, total_points)
     masked_data = ma.masked_array(data)
 
-    body = NumPyPoseBody(fps=int(fps), data=masked_data, confidence=confidence)
+    body = NumPyPoseBody(fps=fps, data=masked_data, confidence=confidence)
 
     return Pose(header, body)
 

src/python/pose_format/utils/holistic.py

@@ -367,7 +367,7 @@ def formatted_holistic_pose(width: int, height: int, additional_face_points: int
                                {"FACE_LANDMARKS": FACEMESH_CONTOURS_POINTS})
 
 
-def load_mediapipe_directory(directory: str, fps: int, width: int, height: int, num_face_points: int = 128) -> Pose:
+def load_mediapipe_directory(directory: str, fps: float, width: int, height: int, num_face_points: int = 128) -> Pose:
     """
     Load pose data from a directory of MediaPipe
 

src/python/pose_format/utils/openpose.py

@@ -279,7 +279,7 @@ def load_openpose(frames: OpenPoseFrames,
     stacked_confidence = np.stack([mask, mask], axis=3)
     masked_data = ma.masked_array(data, mask=stacked_confidence)
 
-    body = NumPyPoseBody(fps=int(fps), data=masked_data, confidence=confidence)
+    body = NumPyPoseBody(fps=fps, data=masked_data, confidence=confidence)
 
     return Pose(header, body)