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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.

The following is my text_detection.py file

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] < 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)

An error

net = cv2.dnn.readNet(args["east"])
cv2.error: OpenCV(3.4.3) C:projectsopencv-pythonopencvmodulesdnnsrcdnn.cpp:3443: error: (-2:Unspecified error) Cannot determine an origin framework of files: in function 'cv::dnn::experimental_dnn_34_v7::readNet'

is shown on loading the EAST text detector

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?

The issue was that I hadn't passed the arguments.
To pass the arguments using PyCharm, on the 'run'menu select "edit configurations" and pass the arguments
--image : The path to the input image.
--east : The EAST scene text detector model file path.
--min-confidence : Probability threshold to determine text.
--width : Resized image width
--height : Resized image height