          count = sum(1 for val in self.alignments.alignments.values() if val)
          landmarks_all = np.zeros((68, 2, int(count)))
  
          end = 0
          for key in tqdm(sorted(self.alignments.alignments.keys()),
                          desc="Compiling"):
              val = self.alignments.alignments[key]
              if not val:
                  continue
              # We should only be normalizing a single face, so just take
 
</s> remove             val = self.alignments.alignments[key]
 </s> add             val = self.alignments.data[key] </s> remove         count = sum(1 for val in self.alignments.alignments.values() if val)
 </s> add         count = sum(1 for val in self.alignments.data.values() if val) </s> remove         for alignment in tqdm(self.alignments.get_alignments_one_image(),
 </s> add         for alignment in tqdm(self.alignments.yield_faces(), </s> remove                for val in self.alignments.alignments.values()
 </s> add                for val in self.alignments.data.values() </s> remove                               total=self.alignments.count):
 </s> add                               total=self.alignments.frames_count):
        for key in tqdm(sorted(self.alignments.data.keys()),
          count = sum(1 for val in self.alignments.alignments.values() if val)
          landmarks_all = np.zeros((68, 2, int(count)))
  
          end = 0
        for key in tqdm(sorted(self.alignments.data.keys()),
                          desc="Compiling"):
              val = self.alignments.alignments[key]
              if not val:
                  continue
              # We should only be normalizing a single face, so just take
keep keep keep keep replace keep keep keep keep keep
 Fix convert-adjust (#531)

* Add dimensions to alignments + refactor

* Add frame_dims + funcs to DetectFaces. Add alignments lib

* Convert Adjust working

* Refactor and tidy up 
tools/lib_alignments/jobs.py