Feature/optional pose norm info (#141)

* CDL: minor doc typo fix

* Undoing some changes that got mixed in

* automatically set pose normalization info if not provided, add some readme notes. Fixes #140

README.md

@@ -97,7 +97,7 @@ Maintaining data consistency is very important and data normalization is one met
 
 For instance, you can set the shoulder width to a consistent measurement across all data points. This is useful for comparing poses across different individuals. 
 
-* See this example for using a standard body feature, such as the shoulder width, for normalization:
+* See this example for manually specifying a standard body feature, such as the shoulder width, for normalization:
 
 ```python
 pose.normalize(p.header.normalization_info(
@@ -106,6 +106,12 @@ pose.normalize(p.header.normalization_info(
 ))
 ```
 
+* If normalization info is not specified, normalize() will automatically base normalization on shoulder joints.
+
+```python
+pose.normalize() # same result as above, but attempts to automatically select shoulder points based on format
+```
+
 * Keypoint values can be standardized to have a mean of zero and unit variance:
 
 ```python
@@ -116,7 +122,6 @@ pose.normalize_distribution()
 
 The usual way to do this is to compute a separate mean and standard deviation for each keypoint and each dimension (usually x and y). This can be achieved with the `axis` argument of `normalize_distribution`. 
 
-
 ```python
 
 # Normalize each keypoint separately:

src/python/pose_format/pose.py

@@ -10,6 +10,7 @@
                                      PoseNormalizationInfo)
 from pose_format.utils.fast_math import distance_batch
 from pose_format.utils.reader import BufferReader
+from pose_format.utils.generic import pose_normalization_info
 
 
 class Pose:
@@ -87,7 +88,7 @@ def focus(self):
         dimensions = (maxs - mins).tolist()
         self.header.dimensions = PoseHeaderDimensions(*dimensions)
 
-    def normalize(self, info: PoseNormalizationInfo, scale_factor: float = 1) -> "Pose":
+    def normalize(self, info: PoseNormalizationInfo|None=None, scale_factor: float = 1) -> "Pose":
         """
         Normalize the points to a fixed distance between two particular points.
 
@@ -103,6 +104,9 @@ def normalize(self, info: PoseNormalizationInfo, scale_factor: float = 1) -> "Po
         Pose
             The normalized Pose object.
         """
+        if info is None:
+            info = pose_normalization_info(self)
+
         transposed = self.body.points_perspective()
 
         p1s = transposed[info.p1]