title: How can I use a custom tensorflow model with the CV2 DNN module?
body: <p>I retrained an object detection model based on Google's Tensorflow object detection API. I exported it as a frozen inference graph. I would like to use it with CV2's DNN module: </p>
  
  <pre><code>cap = cv2.VideoCapture(URL)
  
  cvNet = cv2.dnn.readNetFromTensorflow('graph.pb', 'graph.pbtxt')
  
  while True:
      ret, img = cap.read()
      rows = img.shape[0]
      cols = img.shape[1]
      cvNet.setInput(cv2.dnn.blobFromImage(img, 1.0/127.5, (300, 300), (127.5, 127.5, 127.5), swapRB=True, crop=False))
      cvOut = cv2Net.forward()
  
      for detection in cv2Out[0,0,:,:]:
          score = float(detection[2])
          if score &gt; 0.3:
              left = detection[3] * cols
              top = detection[4] * rows
              right = detection[5] * cols
              bottom = detection[6] * rows
              cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)
      cv2.imshow('img', img)
      if cv2.waitKey(1) ==27:
          exit(0)
  </code></pre>
  
  <p>I get this error: <code>Const input blob for weights not found in function getConstBlob</code></p>
  
  <p>From my research, I believe I have to optimize the inference graph. I can't find any documentation as how to do this.</p>
  
  <p>If anyone could point me in the right direction, it would be very much appreciated.</p>