Merged dd1a04a into tools folder

Merged #278 sortool into tools/sort.py

sorttool.py → tools/sort.py

@@ -5,6 +5,8 @@
 import numpy as np
 import cv2
 from tqdm import tqdm
+import face_recognition
+
 if sys.version_info[0] < 3:
     raise Exception("This program requires at least python3.2")
 if sys.version_info[0] == 3 and sys.version_info[1] < 2:
@@ -28,16 +30,18 @@ def init_parser_arguments(self, parser):
 
         parser.add_argument('-by', '--by',
                             type=str,
-                            choices=("blur", "similarity"), # case sensitive because this is used to load a plugin.
+                            choices=("blur", "hist", "face"),
                             dest='method',
-                            default="similarity",
+                            default="hist",
                             help="Sort by method.")
 
     def process(self):        
         if self.arguments.method.lower() == 'blur':
             self.process_blur()
-        elif self.arguments.method.lower() == 'similarity':
-            self.process_similarity()
+        elif self.arguments.method.lower() == 'hist':
+            self.process_hist()
+        elif self.arguments.method.lower() == 'face':
+            self.process_face()
 
     def process_blur(self):
         input_dir = self.arguments.input_dir
@@ -49,10 +53,10 @@ def process_blur(self):
         self.process_final_rename(input_dir, img_list)        
         print ("Done.")
 
-    def process_similarity(self):
+    def process_hist(self):
         input_dir = self.arguments.input_dir
 
-        print ("Sorting by similarity...")
+        print ("Sorting by histogram similarity...")
 
         img_list = [ [x, cv2.calcHist([cv2.imread(x)], [0], None, [256], [0, 256]) ] for x in tqdm( self.find_images(input_dir), desc="Loading") ]
 
@@ -71,6 +75,35 @@ def process_similarity(self):
 
         print ("Done.")
 
+    def process_face(self):
+        input_dir = self.arguments.input_dir
+
+        print ("Sorting by face similarity...")
+
+        img_list = [ [x, face_recognition.face_encodings(cv2.imread(x)) ] for x in tqdm( self.find_images(input_dir), desc="Loading") ]
+
+        img_list_len = len(img_list)
+        for i in tqdm ( range(0, img_list_len-1), desc="Sorting"):
+            min_score = 9999.9
+            j_min_score = i+1
+            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 = 9999.9
+
+                if score < min_score:
+                    min_score = score
+                    j_min_score = j            
+            img_list[i+1], img_list[j_min_score] = img_list[j_min_score], img_list[i+1]
+
+        self.process_final_rename (input_dir, img_list)
+
+        print ("Done.")
+
     def process_final_rename(self, input_dir, img_list):
         for i in tqdm( range(0,len(img_list)), desc="Renaming" , leave=False):
             src = img_list[i][0]