Fixed the software code.

Facial_Recognition_Inference.py

@@ -17,9 +17,7 @@ def inference():
     FACE_RECOGNITION_MODEL_PATH = pwd + '/Facial_models/dlib_face_recognition_resnet_model_v1.dat'
 
     SKIP_FRAMES = 1
-    MAXIMUM_TIME = 10
     THRESHOLD = 0.4
-    DEMO_SHOW = False
 
     faceDetector = dlib.get_frontal_face_detector()
     shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
@@ -28,7 +26,7 @@ def inference():
     index = np.load(pwd + '/Facial_models/index.pkl', allow_pickle=True)
     faceDescriptorsEnrolled = np.load(pwd + '/Facial_models/descriptors.npy')
 
-    cam = cv2.VideoCapture(1)
+    cam = cv2.VideoCapture(0)
     count = 0
 
     x1 = x2 = y1 = y2 = 0
@@ -38,8 +36,6 @@ def inference():
     label = 'unknown'
     start = time.time()
 
-    label_counter = {}
-
     while DBHelper.get_power() == "on":
         success, im = cam.read()
 
@@ -78,60 +74,38 @@ def inference():
 
                 if minDistance <= THRESHOLD:
                     label = DBHelper.get_firstname(index[argmin]) + "_" + DBHelper.get_lastname(index[argmin])
-                    #cond = True
+                    cond = True
                 else:
                     label = 'unknown'
-                    #cond = False
+                    cond = False
-
-                if label in label_counter.keys():
-                    label_counter[label] += 1
-                else:
-                    label_counter[label] = 1
 
                 # print("time taken = {:.3f} seconds".format(time.time() - t))
-                if DEMO_SHOW:
-                    cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                    font_face = cv2.FONT_HERSHEY_SIMPLEX
-                    font_scale = 0.8
-                    text_color = (0, 255, 0)
-                    printLabel = '{} {:0.4f}'.format(label, minDistance)
-                    cv2.putText(im, printLabel, (int(x1), int(y1)), font_face, font_scale, text_color, thickness=2)
 
-            if DEMO_SHOW:
+        cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                cv2.imshow('img', im)
+        font_face = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 0.8
+        text_color = (0, 255, 0)
+        printLabel = '{} {:0.4f}'.format(label, minDistance)
+        cv2.putText(im, printLabel, (int(x1), int(y1)), font_face, font_scale, text_color, thickness=2)
+
+        cv2.imshow('img', im)
 
         k = cv2.waitKey(1) & 0xff
         if k == 27:
             break
 
         count += 1
-
+        if cond:
-        if(count == MAXIMUM_TIME):
+            DBHelper.set_motor("on")
-            break
+            DBHelper.set_alarm("off")
-
+            start = time.time()
-    print("The statics of 10 frames: ", label_counter)
+        elif not cond:
-
+            DBHelper.set_motor("off")
-    output_label =sorted(label_counter,key=lambda x:label_counter[x])[-1]
+            DBHelper.set_alarm("on")
-
+            end = time.time()
-    print("Final results: ", output_label)
+            if end - start >= 30:
-
+                thief = True
-    if output_label == 'unknown':
+                DBHelper.set_power("off")
-        cond = False
-    else:
-        cond = True
-
-    if cond:
-        DBHelper.set_motor("on")
-        DBHelper.set_alarm("off")
-        start = time.time()
-    elif not cond:
-        DBHelper.set_motor("off")
-        DBHelper.set_alarm("on")
-        end = time.time()
-        if end - start >= 30:
-            thief = True
-            DBHelper.set_power("off")
-
 
     DBHelper.set_alarm("off")
     DBHelper.set_motor("off")
@@ -141,4 +115,4 @@ def inference():
 
 
 if __name__ == "__main__":
-    inference()
+    inference()