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neurons.py

@@ -11,13 +11,13 @@ class Neurons:
         output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
         return output_layers
     def whatIsIt(self, path):
-        """image = cv2.imread(path)
+        image = cv2.imread(path)
         scale = 0.00392
         classes = None
         with open("yolov3.txt", 'r') as f:
             classes = [line.strip() for line in f.readlines()]
         net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
-        blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)
+        blob = cv2.dnn.blobFromImage(image, scale, (608, 608), (0, 0, 0), True, crop=False)
         net.setInput(blob)
         outs = net.forward(self.get_output_layers(net))
         class_ids = []
@@ -26,19 +26,30 @@ class Neurons:
                 scores = detection[5:]
                 class_id = np.argmax(scores)
                 confidence = scores[class_id]
-                if confidence > 0.5:
+                if confidence > 0.2:
                     class_ids.append(class_id)
-        print([classes[ids] for ids in  class_ids])"""
-        x = [1, 0, 0, 0, 0]
-        numpy.random.shuffle(x)
+        y = [classes[ids] for ids in  class_ids]
+        print(y)
+        x = [0, 0, 0, 0, 0]
+        if "kruche" in y:
+            x[1]=1
+        elif "niebezpieczne" in y:
+            x[4]=1
+        elif "biohazard" in y:
+            x[4]=1
+        elif "radioaktywne" in y:
+            x[3]=1
+        elif "latwopalne" in y:
+            x[2]=1
+        print(x)
         if x[0]==1:
             print("Zwykła")
-        elif x[1]==1:
+        if x[1]==1:
             print("Kruchy")
-        elif x[2]==1:
+        if x[2]==1:
             print("Łatwopalny")
-        elif x[3]==1:
+        if x[3]==1:
             print("Radioaktywny")
-        elif x[4]==1:
+        if x[4]==1:
             print("Niebezpieczny")
         return [list(x)]