          print("Preparing to group...")
          if group_method == 'group_blur':
              temp_list = [[x, self.estimate_blur(cv2.imread(x))] for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
          elif group_method == 'group_face':
              temp_list = [[x, face_recognition.face_encodings(cv2.imread(x))] for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
          elif group_method == 'group_face_cnn':
              import_FaceLandmarksExtractor()
              temp_list = []
              for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout):
                  d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
 
</s> remove             temp_list = [[x, self.estimate_blur(cv2.imread(x))] for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
 </s> add             temp_list = [[x, self.estimate_blur(cv2.imread(x))]
                         for x in
                         tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)] </s> remove         input_dir = self.arguments.input_dir
 </s> add         input_dir = self.args.input_dir </s> remove                 d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
 </s> add                 d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True,
                                                   input_is_predetected_face=True) </s> remove             temp_list = [[x, cv2.calcHist([cv2.imread(x)], [0], None, [256], [0, 256])] for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
 </s> add             temp_list = [
                [x, cv2.calcHist([cv2.imread(x)], [0], None, [256], [0, 256])]
                for x in
                tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)
            ] </s> remove         for x in tqdm( self.find_images(input_dir), desc="Loading", file=sys.stdout):
 </s> add         for x in tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout):
            temp_list = [[x, face_recognition.face_encodings(cv2.imread(x))]
                         for x in
                         tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
          print("Preparing to group...")
          if group_method == 'group_blur':
              temp_list = [[x, self.estimate_blur(cv2.imread(x))] for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
          elif group_method == 'group_face':
            temp_list = [[x, face_recognition.face_encodings(cv2.imread(x))]
                         for x in
                         tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
          elif group_method == 'group_face_cnn':
              import_FaceLandmarksExtractor()
              temp_list = []
              for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout):
                  d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
keep keep keep keep replace keep keep keep keep keep
 GUI v0.2.1b (#352)

* Code refactor

* Console Added

* Add graphing with matplotlib

* Add GUI preview support

* Improve import handling.

* Add GUI integration for tools.py (Launch tools.py with the gui command)

* Reformat to conform to PEP8.

* Add Tooltips, 

* Make panels user adjustable

* Fix convert.py Sharpen default value 
tools/sort.py