              for j in range(i+1,len(img_list)):
              
                  f1encs = img_list[i][1]
                  f2encs = img_list[j][1]
                  if f1encs is not None and f2encs is not None and len(f1encs) > 0 and len(f2encs) > 0:
                      score = face_recognition.face_distance(f1encs[0], f2encs)[0]
                  else: 
                      score = float("inf")
                  
                  if score < min_score:
 
</s> remove             j_min_score = i+1
            for j in range(i+1,len(img_list)):
            
 </s> add             j_min_score = i + 1
            for j in range(i + 1, len(img_list)): </s> remove                 score = np.sum ( np.absolute ( (fl2 - fl1).flatten() ) )
 </s> add                 score = np.sum(np.absolute((fl2 - fl1).flatten())) </s> remove             j_min_score = i+1
            for j in range(i+1,len(img_list)):

 </s> add             j_min_score = i + 1
            for j in range(i + 1, len(img_list)): </s> remove             j_min_score = i+1
            for j in range(i+1,len(img_list)):
                score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA)
 </s> add             j_min_score = i + 1
            for j in range(i + 1, len(img_list)):
                score = cv2.compareHist(img_list[i][1],
                                        img_list[j][1],
                                        cv2.HISTCMP_BHATTACHARYYA) </s> remove         return sum(scores)/len(scores)
 </s> add         return sum(scores) / len(scores)
                if f1encs is not None and f2encs is not None and len(
                        f1encs) > 0 and len(f2encs) > 0:
              for j in range(i+1,len(img_list)):
              
                  f1encs = img_list[i][1]
                  f2encs = img_list[j][1]
                if f1encs is not None and f2encs is not None and len(
                        f1encs) > 0 and len(f2encs) > 0:
                      score = face_recognition.face_distance(f1encs[0], f2encs)[0]
                  else: 
                      score = float("inf")
                  
                  if score < min_score:
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