changes

scripts/welspun_classifier.py

@@ -56,7 +56,6 @@ class Welspun_Classifier(ModelWrapper):
         self.defect_type = ""
         self.frame_skip_count = 25
         self.counter = 0
-        self.k = 0
 
     def _pre_process(self, x):
         """
@@ -189,8 +188,6 @@ class Welspun_Classifier(ModelWrapper):
         return exp_vec / np.sum(exp_vec)
 
     def process_frame(self, frame):
-        if self.k % 10 == 0:
-            self.k = self.k + 1
         starttime = time.time()
         vino_frame = frame.copy()
         # vino_frame = vino_frame[20:600,150:650]
@@ -212,6 +209,7 @@ class Welspun_Classifier(ModelWrapper):
         # log.info("Batch size is {}".format(n))
         #
         # log.info("Starting inference in synchronous mode")
+        if self.counter % 10 == 0:
             start = time.time()
             res = self.exec_net.infer(inputs={self.input_blob: images})
             print(f"Inference time: {time.time() - start}")
@@ -293,8 +291,7 @@ class Welspun_Classifier(ModelWrapper):
                                 color=(0, 0, 255),
                                 thickness=2,
                                 fontScale=1, fontFace=cv2.LINE_AA)
-                    self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020",
-                                      "#ed2020",
+                    self.send_payload("Split Defect Detected", resized_frame, "Split " + str(prob[3]), "#472020", "#ed2020",
                                       "sound_1")
                     logger.info(f"Probability: {prob}")
                 # if self.counter % 25 == 0:
@@ -354,5 +351,6 @@ class Welspun_Classifier(ModelWrapper):
             print("total time taken to process-------------> ", str(time.time() - starttime))
             # logger.info(f"total time taken to process----------------- {time.time()-starttime}")
             # cv2.imshow('res', frame)
-
+        else:
+            pass
         return frame