title: Error on loading OpenCV EAST text detector in Python
body: <p>I'm trying to use EAST text detector to detect areas of text in images, but am having trouble on loading the pre-trained EAST text detector.</p>
  
  <p>The following is my text_detection.py file</p>
  
  <pre><code>from imutils.object_detection import non_max_suppression
  import numpy as np
  import argparse
  import time
  import cv2
  import requests
  import urllib
  
  # construct the argument parser and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", type=str,help="path to input image")
  ap.add_argument("-east", "--east", type=str,help="path to input EAST text detector")
  ap.add_argument("-c", "--min-confidence", type=float, default=0.5,help="minimum probability required to inspect a region")
  ap.add_argument("-w", "--width", type=int, default=320,help="resized image width (should be multiple of 32)")
  ap.add_argument("-e", "--height", type=int, default=320,help="resized image height (should be multiple of 32)")
  args = vars(ap.parse_args())
  
  # load the input image and grab the image dimensions
  req = urllib.request.urlopen('https://hips.hearstapps.com/ghk.h-cdn.co/assets/18/02/mandy-hale-inspirational-quote.jpg')
  arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
  image = cv2.imdecode(arr, -1)
  orig = image.copy()
  
  (H, W) = image.shape[:2]
  
  # set the new width and height and then determine the ratio in change
  # for both the width and height
  (newW, newH) = (args["width"], args["height"])
  rW = W / float(newW)
  rH = H / float(newH)
  
  # resize the image and grab the new image dimensions
  image = cv2.resize(image, (newW, newH))
  (H, W) = image.shape[:2]
  
  # define the two output layer names for the EAST detector model that
  # we are interested -- the first is the output probabilities and the
  # second can be used to derive the bounding box coordinates of text
  layerNames = [
      "feature_fusion/Conv_7/Sigmoid",
      "feature_fusion/concat_3"]
  
  # load the pre-trained EAST text detector
  print("[INFO] loading EAST text detector...")
  net = cv2.dnn.readNet(args["east"])
  
  # construct a blob from the image and then perform a forward pass of
  # the model to obtain the two output layer sets
  blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
                               (123.68, 116.78, 103.94), swapRB=True, crop=False)
  start = time.time()
  net.setInput(blob)
  (scores, geometry) = net.forward(layerNames)
  end = time.time()
  
  # show timing information on text prediction
  print("[INFO] text detection took {:.6f} seconds".format(end - start))
  
  # grab the number of rows and columns from the scores volume, then
  # initialize our set of bounding box rectangles and corresponding
  # confidence scores
  (numRows, numCols) = scores.shape[2:4]
  rects = []
  confidences = []
  
  # loop over the number of rows
  for y in range(0, numRows):
      # extract the scores (probabilities), followed by the geometrical
      # data used to derive potential bounding box coordinates that
      # surround text
      scoresData = scores[0, 0, y]
      xData0 = geometry[0, 0, y]
      xData1 = geometry[0, 1, y]
      xData2 = geometry[0, 2, y]
      xData3 = geometry[0, 3, y]
      anglesData = geometry[0, 4, y]
  
  # loop over the number of columns
  for x in range(0, numCols):
      # if our score does not have sufficient probability, ignore it
      if scoresData[x] &lt; args["min_confidence"]:
          continue
  
      # compute the offset factor as our resulting feature maps will
      # be 4x smaller than the input image
      (offsetX, offsetY) = (x * 4.0, y * 4.0)
  
      # extract the rotation angle for the prediction and then
      # compute the sin and cosine
      angle = anglesData[x]
      cos = np.cos(angle)
      sin = np.sin(angle)
  
      # use the geometry volume to derive the width and height of
      # the bounding box
      h = xData0[x] + xData2[x]
      w = xData1[x] + xData3[x]
  
      # compute both the starting and ending (x, y)-coordinates for
      # the text prediction bounding box
      endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
      endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
      startX = int(endX - w)
      startY = int(endY - h)
  
      # add the bounding box coordinates and probability score to
      # our respective lists
      rects.append((startX, startY, endX, endY))
      confidences.append(scoresData[x])
  
  # apply non-maxima suppression to suppress weak, overlapping bounding
  # boxes
  boxes = non_max_suppression(np.array(rects), probs=confidences)
  
  # loop over the bounding boxes
  for (startX, startY, endX, endY) in boxes:
      # scale the bounding box coordinates based on the respective
      # ratios
      startX = int(startX * rW)
      startY = int(startY * rH)
      endX = int(endX * rW)
      endY = int(endY * rH)
  
      # draw the bounding box on the image
      cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
  
  # show the output image
  cv2.imshow("Text Detection", orig)
  cv2.waitKey(0)
  
  # apply non-maxima suppression to suppress weak, overlapping bounding
  # boxes
  boxes = non_max_suppression(np.array(rects), probs=confidences)
  
  # loop over the bounding boxes
  for (startX, startY, endX, endY) in boxes:
      # scale the bounding box coordinates based on the respective
      # ratios
      startX = int(startX * rW)
      startY = int(startY * rH)
      endX = int(endX * rW)
      endY = int(endY * rH)
  
      # draw the bounding box on the image
      cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
  
  # show the output image
  cv2.imshow("Text Detection", orig)
  cv2.waitKey(0)
  </code></pre>
  
  <p>An error</p>
  
  <p>net = cv2.dnn.readNet(args["east"])
  cv2.error: OpenCV(3.4.3) C:\projects\opencv-python\opencv\modules\dnn\src\dnn.cpp:3443: error: (-2:Unspecified error) Cannot determine an origin framework of files:  in function 'cv::dnn::experimental_dnn_34_v7::readNet'</p>
  
  <p>is shown on loading the EAST text detector</p>
  
  <p>I am using opencv-python 3.4.3.18. What is the cause for this error? Does it have anything to do with the Python version?</p>