ignoring the giraffe

Application/Classifiers/Classifier.py

@@ -7,6 +7,7 @@ import tensorflow as tf
 import cv2
 import os
 import json
+import imutils
 from Application.Classifiers.ClassifierInterface import ClassifierInterface
 
 
@@ -32,20 +33,36 @@ class Classifier(ClassifierInterface):
                     #print(self.classes[classes[i]])
                     return self.classes[classes[i]]
 
-
     def tagLayer(self, data):
         res = []
         for cnts in data:
             for cnt in cnts:
                 if cnt.any():
+                    cnt= imutils.resize(cnt, width=320)
                     x = self.detect(cnt)
-                    if x not in res:
+
-                        res.append(x)
+                    res.append(x)
-                    if x is not None:
+
-                        print(x)
+        di = dict()
-                        cv2.imshow("changes x", cnt)
+        for re in res:
-                        cv2.waitKey(10) & 0XFF
+            if re not in di:
-        return res
+                di[re] = 0
+            di[re]+=1
+
+        # remove all tags that occour infrequently
+        # if a giraff is only seen in 2 out of 100 frames, there probably wasn't a giraff in the layer
+        # 
+        di.pop(None, None)
+        total = 0
+        for value in di.values():
+            total += value
+
+        result = []
+        for key, value in di.items():
+            if value > len(data) / len(di) / 2:
+                result.append(key)
+
+        return result
 
     # Detector API can be changed out given the I/O remains the same
     # this way you can use a different N-Net if you like to

README.md

@@ -6,3 +6,5 @@ Time consumed reading video:  240.s     3.06GB 26min 1080p downscaled 500p 30fps
 contour extraction: 10.5 Sec. when only 2 Threads
                     8 secs when also mapping getContours()
                     
+
+install tensorflow==1.15.0 and tensorflow-gpu==1.15.0, cuda 10.2 and 10.0, copy missing files from 10.0 to 10.2, restart computer, set maximum vram