Error on loading OpenCV EAST text detector in Python












1















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?










share|improve this question

























  • Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

    – Dmitry Kurtaev
    Nov 23 '18 at 10:11











  • It returns None. Any idea why it is so?

    – Dilinieee
    Nov 23 '18 at 10:40
















1















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?










share|improve this question

























  • Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

    – Dmitry Kurtaev
    Nov 23 '18 at 10:11











  • It returns None. Any idea why it is so?

    – Dilinieee
    Nov 23 '18 at 10:40














1












1








1








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?










share|improve this question
















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?







python opencv pycharm ocr






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 23 '18 at 7:49







Dilinieee

















asked Nov 23 '18 at 7:39









DilinieeeDilinieee

267




267













  • Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

    – Dmitry Kurtaev
    Nov 23 '18 at 10:11











  • It returns None. Any idea why it is so?

    – Dilinieee
    Nov 23 '18 at 10:40



















  • Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

    – Dmitry Kurtaev
    Nov 23 '18 at 10:11











  • It returns None. Any idea why it is so?

    – Dilinieee
    Nov 23 '18 at 10:40

















Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

– Dmitry Kurtaev
Nov 23 '18 at 10:11





Please print args["east"] before net = cv2.dnn.readNet(args["east"]) .

– Dmitry Kurtaev
Nov 23 '18 at 10:11













It returns None. Any idea why it is so?

– Dilinieee
Nov 23 '18 at 10:40





It returns None. Any idea why it is so?

– Dilinieee
Nov 23 '18 at 10:40












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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






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    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






    share|improve this answer




























      0














      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






      share|improve this answer


























        0












        0








        0







        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






        share|improve this answer













        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







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 28 '18 at 6:27









        DilinieeeDilinieee

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