Nsryjdtyk

Hello I'm trying to recognize text from Image using Tesseract but unable to get result.
I'm using EAST technique to detect text. I've one more question how can I extend padding of the box. cv2.putText does not work in this case.
original code for text detection: https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp

import cv2

import numpy as np

import argparse

import time

import math

import matplotlib.pyplot as plt

import skimage.io as io

import os

from imutils.object_detection import non_max_suppression

import pytesseract

print(np.__version__)



def decode_predictions(scores, geometry):

    **# 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]

    boxes = 

    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 the rotated rect for

            # the text prediction bounding box**

            offset = (offsetX + (cos * xData1[x]) + (sin * xData2[x]), offsetY - (sin * xData1[x]) + (cos * xData2[x]))

            p1 = (-sin * h + offset[0], -cos * h + offset[1])

            p3 = (-cos * w + offset[0],  sin * w + offset[1])

            center = (0.5*(p1[0]+p3[0]), 0.5*(p1[1]+p3[1]))



            **# add the bounding box coordinates and probability score to

            # our respective lists**

            boxes.append((center, (w,h), -angle * 180.0 / math.pi))

            confidences.append(float(scoresData[x]))

    return (boxes, confidences)

args = {

    "image":"C:\Users\ckunwar\Test_Images\licence_plate1\52.jpg",

    "east": "frozen_east_text_detection.pb",

    "min_confidence":0.25,

    "nms_thresh": 0.24,

    "width":480,

    "height":320,

    "padding":0.0

}



    **# load the input image and grab the image dimensions**

image = cv2.imread(args["image"])

orig = image.copy()

(H, W) = image.shape[:2]

#print(H,W)

**# 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))



(boxes, confidences) = decode_predictions(scores, geometry)



**# apply non-maxima suppression to suppress weak, overlapping bounding boxes**

indices = cv2.dnn.NMSBoxesRotated(boxes, confidences, args["min_confidence"], args["nms_thresh"])



results = 





**# loop over the bounding boxes**

for i in indices:

  **# get 4 corners of the rotated rect**

  vertices = cv2.boxPoints(boxes[i[0]])



  **# scale the bounding box coordinates based on the respective ratios**

  for j in [0,1,2,3]:

    vertices[j][0] *= rW

    vertices[j][1] *= rH



  **# draw the bounding box on the image**

  for j in [0,1,2,3]:

    p1 = (vertices[j][0], vertices[j][1])

    p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])





    config = ("-l eng --oem 3 --psm 11")

    text = pytesseract.image_to_string(orig,config=config)



    results.append(((p1,p2), text))



results = sorted(results, key=lambda r:r[0][1])



output = orig.copy()



for ((p1,p2), text) in results:

    print("OCR TEXT")

    print("========")

    print("{}n".format(text))



    text = "".join([c if ord(c) < 128 else "" for c in text]).strip()

    cv2.line(output, p1, p2, (0, 255, 0), 2)

    #cv2.rectangle(output, p1, p2,(0, 255, 0), 2)

    cv2.putText(output, text,cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 255), 2)







**# show the output image**

#orig = cv2.cvtColor(orig, cv2.COLOR_BGR2RGB)



cv2.imshow("Text Detection", output)

cv2.waitKey(0)