lint(): yapf, isort, fix pydoc

Author: AmitMY

Makefile

@@ -0,0 +1,7 @@
+lint:
+	cd src/python && yapf -i -r .
+	cd src/python && isort .
+	cd src/python && pydoclint .
+
+test:
+	cd src/python && pytest .

src/python/pose_format/numpy/pose_body.py

@@ -3,16 +3,17 @@
 import numpy as np
 import numpy.ma as ma
 
-from ..pose_body import PoseBody, POINTS_DIMS
+from ..pose_body import POINTS_DIMS, PoseBody
 from ..pose_header import PoseHeader
 from ..utils.reader import BufferReader, ConstStructs
 
-
 # import numpy as np
 # np.seterr(all='raise')
 
+
 class NumPyPoseBody(PoseBody):
-    """Represents pose information leveraging NumPy operations and structures.
+    """
+    Represents pose information leveraging NumPy operations and structures.
 
      * Inherits from:  `PoseBody`
      * Implements pose info using NumPy operations and structures.
@@ -34,16 +35,14 @@ class NumPyPoseBody(PoseBody):
         Pose data either as a masked array or a regular numpy array.
     confidence : np.ndarray
         confidence array of the pose keypoints.
-    
     """
 
-    tensor_reader = 'unpack_numpy' 
-    """Specifies the method name for unpacking a numpy array (Value: 'unpack_numpy')."""
+    tensor_reader = 'unpack_numpy' """Specifies the method name for unpacking a numpy array (Value: 'unpack_numpy')."""
 
     def __init__(self, fps: float, data: Union[ma.MaskedArray, np.ndarray], confidence: np.ndarray):
         """
         Initializes the NumPyPoseBody instance
-        """   
+        """
         if isinstance(data, np.ndarray):  # If array is not masked
             mask = confidence == 0  # 0 means no-mask, 1 means with-mask
             stacked_mask = np.stack([mask] * data.shape[-1], axis=3)
@@ -131,7 +130,8 @@ def mask(self):
         return self.data.mask
 
     def torch(self):
-        """converts current instance into a TorchPoseBody instance.
+        """
+        converts current instance into a TorchPoseBody instance.
 
         Returns
         -------
@@ -141,11 +141,10 @@ def torch(self):
         try:
             import torch
         except ImportError:
-            raise ImportError(
-                "Please install torch. https://pytorch.org/"
-            )
+            raise ImportError("Please install torch. https://pytorch.org/")
 
         import torch
+
         from ..torch.pose_body import TorchPoseBody
 
         torch_confidence = torch.from_numpy(self.confidence)
@@ -162,6 +161,7 @@ def tensorflow(self):
             pose body data represented in TensorFlow tensors
         """
         import tensorflow
+
         from ..tensorflow.pose_body import TensorflowPoseBody
 
         tf_confidence = tensorflow.constant(self.confidence)
@@ -361,11 +361,12 @@ def interpolate(self, new_fps: int = None, kind='cubic'):
                             new_frames.append(np.zeros((len(new_steps), frames.shape[1])))
                         else:
                             frame_data = f(new_steps[first_step_index:last_step_index])
-                            new_frames.append(np.concatenate([
-                                np.zeros((first_step_index, frames.shape[1])),
-                                np.array(frame_data),
-                                np.zeros((len(new_steps) - last_step_index, frames.shape[1]))
-                            ]))
+                            new_frames.append(
+                                np.concatenate([
+                                    np.zeros((first_step_index, frames.shape[1])),
+                                    np.array(frame_data),
+                                    np.zeros((len(new_steps) - last_step_index, frames.shape[1]))
+                                ]))
             new_people.append(np.stack(new_frames, axis=0))
 
         new_data = np.stack(new_people, axis=0).transpose([2, 1, 0, 3])
@@ -375,13 +376,14 @@ def interpolate(self, new_fps: int = None, kind='cubic'):
         return NumPyPoseBody(fps=new_fps, data=dimensions, confidence=confidence)
 
     def flatten(self):
-        """Flattens data and confidence arrays.
+        """
+        Flattens data and confidence arrays.
 
         method reshapes data and confidence arrays to a two-dimensional array.
         The flattened array is filtered to remove rows where confidence is zero.
 
         Returns
-        --------
+        -------
         numpy.ndarray
             flattened and filtered version of the data array.
 

src/python/pose_format/numpy/representation/distance.py

@@ -2,8 +2,10 @@
 
 
 class DistanceRepresentation:
-    """A class to compute the Euclidean distance between two sets of points.
     """
+    A class to compute the Euclidean distance between two sets of points.
+    """
+
     def distance(self, p1s: ma.MaskedArray, p2s: ma.MaskedArray) -> ma.MaskedArray:
         """
         Compute the Euclidean distance between two sets of points.
@@ -21,7 +23,7 @@ def distance(self, p1s: ma.MaskedArray, p2s: ma.MaskedArray) -> ma.MaskedArray:
             Euclidean distances between the two sets of points. The returned array has one fewer dimension than the input arrays, as the distance calculation collapses the last dimension.
 
         Note
-        -----
+        ----
         this method assumes that input arrays `p1s` and `p2s` have same shape.
         """
         diff = p1s - p2s

src/python/pose_format/numpy/representation/distance_test.py

@@ -1,8 +1,8 @@
+import math
 from unittest import TestCase
 
 import numpy as np
 import numpy.ma as ma
-import math
 
 from pose_format.numpy.representation.distance import DistanceRepresentation
 
@@ -13,17 +13,18 @@ class TestDistanceRepresentation(TestCase):
     """
     Unit tests for DistanceRepresentation class to computes Euclidean distance between sets of 3D points.
     """
+
     def test_call_value_should_be_distance(self):
         """
         Test if computed distance between two points is correct.
 
         Note
-        -----
+        ----
         This test initializes two sets of 3D points:
         p1 = (1, 2, 3) and p2 = (4, 5, 6). It then checks if the 
         computed Euclidean distance between p1 and p2 is sqrt(27).
         """
-        
+
         p1s = ma.array([[[[1, 2, 3]]]], dtype=np.float32)
         p2s = ma.array([[[[4, 5, 6]]]], dtype=np.float32)
         distances = representation(p1s, p2s)
@@ -34,11 +35,11 @@ def test_call_masked_value_should_be_zero(self):
         Test if the distance for masked values is zero.
 
         Note
-        -----
+        ----
         This test checks scenario where one set of points 
         is masked.The computed distance in such in such a case be 0.
         """
-        
+
         mask = [[[[True, True, True]]]]
         p1s = ma.array([[[[1, 2, 3]]]], mask=mask, dtype=np.float32)
         p2s = ma.array([[[[4, 5, 6]]]], dtype=np.float32)

src/python/pose_format/pose.py

@@ -1,18 +1,21 @@
 from itertools import chain
-from typing import List, BinaryIO, Dict, Type, Tuple
+from typing import BinaryIO, Dict, List, Tuple, Type
 
 import numpy as np
 import numpy.ma as ma
 
 from pose_format.numpy import NumPyPoseBody
 from pose_format.pose_body import PoseBody
-from pose_format.pose_header import PoseHeader, PoseHeaderDimensions, PoseNormalizationInfo, PoseHeaderComponent
+from pose_format.pose_header import (PoseHeader, PoseHeaderComponent,
+                                     PoseHeaderDimensions,
+                                     PoseNormalizationInfo)
 from pose_format.utils.fast_math import distance_batch
 from pose_format.utils.reader import BufferReader
 
 
 class Pose:
-    """File IO for '.pose' file format, including the header and body.
+    """
+    File IO for '.pose' file format, including the header and body.
     
     Parameters
     ----------
@@ -28,7 +31,8 @@ def __init__(self, header: PoseHeader, body: PoseBody):
 
     @staticmethod
     def read(buffer: bytes, pose_body: Type[PoseBody] = NumPyPoseBody, **kwargs):
-        """Read Pose object from buffer.
+        """
+        Read Pose object from buffer.
 
         Parameters
         ----------
@@ -49,7 +53,8 @@ def read(buffer: bytes, pose_body: Type[PoseBody] = NumPyPoseBody, **kwargs):
         return Pose(header, body)
 
     def write(self, buffer: BinaryIO):
-        """Write Pose object to buffer.
+        """
+        Write Pose object to buffer.
 
         Parameters
         ----------
@@ -60,7 +65,8 @@ def write(self, buffer: BinaryIO):
         self.body.write(self.header.version, buffer)
 
     def focus(self):
-        """Gets the pose to start at (0,0) and have dimensions as big as needed
+        """
+        Gets the pose to start at (0,0) and have dimensions as big as needed
         """
         mins = ma.min(self.body.data, axis=(0, 1, 2))
         maxs = ma.max(self.body.data, axis=(0, 1, 2))
@@ -145,10 +151,7 @@ def unnormalize_distribution(self, mu, std):
         """
         self.body.data = (self.body.data * std) + mu
 
-    def frame_dropout_uniform(self,
-                              dropout_min: float = 0.2,
-                              dropout_max: float = 1.0) -> Tuple["Pose", List[int]]:
-        
+    def frame_dropout_uniform(self, dropout_min: float = 0.2, dropout_max: float = 1.0) -> Tuple["Pose", List[int]]:
         """
         Perform uniform frame dropout on Pose
 
@@ -167,9 +170,7 @@ def frame_dropout_uniform(self,
         body, selected_indexes = self.body.frame_dropout_uniform(dropout_min=dropout_min, dropout_max=dropout_max)
         return Pose(header=self.header, body=body), selected_indexes
 
-    def frame_dropout_normal(self,
-                             dropout_mean: float = 0.5,
-                             dropout_std: float = 0.1) -> Tuple["Pose", List[int]]:
+    def frame_dropout_normal(self, dropout_mean: float = 0.5, dropout_std: float = 0.1) -> Tuple["Pose", List[int]]:
         """
         Normal frame dropout on Pose.
 
@@ -189,7 +190,8 @@ def frame_dropout_normal(self,
         return Pose(header=self.header, body=body), selected_indexes
 
     def get_components(self, components: List[str], points: Dict[str, List[str]] = None):
-        """get pose components based on criteria.
+        """
+        get pose components based on criteria.
 
         Parameters
         ----------
@@ -209,13 +211,17 @@ def get_components(self, components: List[str], points: Dict[str, List[str]] = N
         idx = 0
         for component in self.header.components:
             if component.name in components:
-                new_component = PoseHeaderComponent(component.name, component.points,
-                                                    component.limbs, component.colors, component.format)
+                new_component = PoseHeaderComponent(component.name, component.points, component.limbs, component.colors,
+                                                    component.format)
                 if points is not None and component.name in points:  # copy and permute points
-                    new_component.points = points[component.name] 
-                    point_index_mapping = {component.points.index(point): i for i, point in enumerate(new_component.points)}
+                    new_component.points = points[component.name]
+                    point_index_mapping = {
+                        component.points.index(point): i for i, point in enumerate(new_component.points)
+                    }
                     old_indexes_set = set(point_index_mapping.keys())
-                    new_component.limbs = [(point_index_mapping[l1], point_index_mapping[l2]) for l1, l2 in component.limbs if l1 in old_indexes_set and l2 in old_indexes_set]
+                    new_component.limbs = [(point_index_mapping[l1], point_index_mapping[l2])
+                                           for l1, l2 in component.limbs
+                                           if l1 in old_indexes_set and l2 in old_indexes_set]
 
                     indexes[component.name] = [idx + component.points.index(p) for p in new_component.points]
                 else:  # Copy component as is
@@ -235,7 +241,8 @@ def get_components(self, components: List[str], points: Dict[str, List[str]] = N
         return Pose(header=new_header, body=new_body)
 
     def bbox(self):
-        """Calculates bounding box for Pose.
+        """
+        Calculates bounding box for Pose.
 
         Returns
         -------
@@ -254,7 +261,8 @@ def bbox(self):
         "tensorflow",  # Convert body to tensorflow
         "slice_step",  # Step through the data
     }
-    """A set of method names which define actions that can be applied to the pose data.
+    """
+A set of method names which define actions that can be applied to the pose data.
 
     Parameters
     ----------
@@ -271,8 +279,6 @@ def bbox(self):
     slice_step : str
         Represents a method to step through the data.
     """
-    
-    
 
     def __getattr__(self, attr):
         """