increased speed of sort by hist

mainscripts/Sorter.py

@@ -238,9 +238,96 @@ def sort_by_face_yaw(input_path):
     img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
     
     return img_list
+
+class HistSimSubprocessor(SubprocessorBase):
+    #override
+    def __init__(self, img_list): 
+        self.img_list = img_list
+        self.img_list_range = [i for i in range(0, len(img_list) )]       
+        self.result = [0]*len(img_list)
+        super().__init__('HistSim', 60)           
+
+    #override
+    def onHostClientsInitialized(self):
+        pass
+
+    #override
+    def process_info_generator(self):    
+        for i in range(0, multiprocessing.cpu_count()):
+            yield 'CPU%d' % (i), {}, {'device_idx': i,
+                                      'device_name': 'CPU%d' % (i), 
+                                      'img_list' : self.img_list
+                                      }
+
+    #override
+    def get_no_process_started_message(self):
+        print ( 'Unable to start CPU processes.')
+        
+    #override
+    def onHostGetProgressBarDesc(self):
+        return "Computing"
+        
+    #override
+    def onHostGetProgressBarLen(self):
+        return len (self.img_list)
+        
+    #override
+    def onHostGetData(self):
+        if len (self.img_list_range) > 0:        
+            return [self.img_list_range.pop(0)]
+        
+        return None
+    
+    #override
+    def onHostDataReturn (self, data):
+        self.img_list_range.insert(0, data[0])   
+        
+    #override
+    def onClientInitialize(self, client_dict):
+        self.img_list = client_dict['img_list']
+        self.img_list_len = len(self.img_list)
+        
+        self.safe_print ('Running on %s.' % (client_dict['device_name']) )
+        return None
+
+    #override
+    def onClientFinalize(self):
+        pass
+        
+    #override
+    def onClientProcessData(self, data):  
+        i = data[0]
+
+        result = [0]*self.img_list_len
+        for j in range( 0, self.img_list_len):
+            if i == j:
+                continue
+                
+            result[j] = cv2.compareHist(self.img_list[i][1], self.img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
+                        cv2.compareHist(self.img_list[i][2], self.img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
+                        cv2.compareHist(self.img_list[i][3], self.img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
+
+        return result
+
+    #override
+    def onClientGetDataName (self, data):
+        #return string identificator of your data
+        return self.img_list[data[0]][0]
+        
+    #override
+    def onHostResult (self, data, result):
+        self.result[data[0]] = result
+        return 1
+    
+    #override    
+    def onHostProcessEnd(self):
+        pass
+             
+    #override
+    def get_start_return(self):
+        return self.result
     
 def sort_by_hist(input_path):
-
     print ("Sorting by histogram similarity...")
 
     img_list = []
@@ -250,21 +337,29 @@ def sort_by_hist(input_path):
                              cv2.calcHist([img], [1], None, [256], [0, 256]),
                              cv2.calcHist([img], [2], None, [256], [0, 256])
                          ])
-
+     
-    img_list_len = len(img_list)
+    img_list_2D = HistSimSubprocessor(img_list).process() 
-    for i in tqdm( range(0, img_list_len-1), desc="Sorting"):
+     
-        min_score = float("inf")
+    rem_list = [i for i in range(len(img_list))]
-        j_min_score = i+1
+    
-        for j in range(i+1,len(img_list)):
+    s_list = []
-            score = cv2.compareHist(img_list[i][1], img_list[j][1], cv2.HISTCMP_BHATTACHARYYA) + \
+    s_list.append ( rem_list.pop(0) )
-                    cv2.compareHist(img_list[i][2], img_list[j][2], cv2.HISTCMP_BHATTACHARYYA) + \
+    
-                    cv2.compareHist(img_list[i][3], img_list[j][3], cv2.HISTCMP_BHATTACHARYYA)
+    for x in tqdm( range(len(rem_list)), desc="Sorting"):
+        i = s_list[ len(s_list) -1 ]
+        
+        min_score_id = rem_list[0]        
+        min_score = img_list_2D[i][min_score_id]
+        
+        for score_id in rem_list:
+            score = img_list_2D[i][score_id]
             if score < min_score:
                 min_score = score
-                j_min_score = j
+                min_score_id = score_id
-        img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
+                
+        s_list.append ( rem_list.pop(rem_list.index(min_score_id)) )
 
-    return img_list
+    return [ (img_list[i][0],) for i in s_list ]
 
 class HistDissimSubprocessor(SubprocessorBase):
     #override
@@ -337,7 +432,7 @@ class HistDissimSubprocessor(SubprocessorBase):
     #override
     def onClientGetDataName (self, data):
         #return string identificator of your data
-        return data[1]
+        return self.img_list[data[0]][0]
         
     #override
     def onHostResult (self, data, result):