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0.2: variable filename
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‎LICENSE

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Public Domain Mark 1.0
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No Copyright
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This work has been identified as being free of known restrictions
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under copyright law, including all related and neighboring rights.
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You can copy, modify, distribute and perform the work, even for
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commercial purposes, all without asking permission. See Other
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Information below.
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Other Information
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The work may not be free of known copyright restrictions in all
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jurisdictions.
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Persons may have other rights in or related to the work, such as
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patent or trademark rights, and others may have rights in how the
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work is used, such as publicity or privacy rights.
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In some jurisdictions moral rights of the author may persist beyond
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the term of copyright. These rights may include the right to be
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identified as the author and the right to object to derogatory
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treatments.
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Unless expressly stated otherwise, the person who identified the work
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makes no warranties about the work, and disclaims liability for all
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uses of the work, to the fullest extent permitted by applicable law.
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When using or citing the work, you should not imply endorsement by
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the author or the person who identified the work.

‎img2txt.py

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import cv2
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import numpy as np
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#import pytesseract
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#pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract'
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#from PIL import Image
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#import image
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image = cv2.imread('test.jpg')
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#cv2.imshow('orig',image)
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#cv2.waitKey(0)
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#Initial Processing of the image starts...!!!!!!!!!!!
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#grayscale
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gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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cv2.imshow('gray',gray)
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cv2.waitKey(0)
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#binary
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ret,thresh = cv2.threshold(gray,160,255,cv2.THRESH_BINARY_INV)
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cv2.imshow('Binarized',thresh)
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cv2.waitKey(0)
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#detecting edges>>>>
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edges=cv2.Canny(thresh,140,200)
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#cv2.imshow("edges",edges)
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#dilation
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kernel = np.ones((5,5), np.uint8)
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img_dilation = cv2.dilate(edges, kernel, iterations=1)
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cv2.imshow('dilated',img_dilation)
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cv2.waitKey(0)
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#Localization Horizontal projection
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(x,y)=img_dilation.shape
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z=[sum(y) for y in img_dilation]
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Tx=(((sum(z))/(len(z))/20))
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#print Tx
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#Vertical Projection
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ndilation=zip(*img_dilation)
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X=[sum(row) for row in ndilation]
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mean=(sum(X))/(len(X))
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maximofX=(max(X)/10)
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Ty=mean+maximofX
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#print Ty
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#Adaptive threashold for horizontal projection
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th1 = cv2.adaptiveThreshold(img_dilation,Tx,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2) #block size and constant.
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#Adaptive threashold for vertical projection
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th2 = cv2.adaptiveThreshold(img_dilation,Ty,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
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fthreashold=cv2.add(th1,th2)
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cv2.imshow("threashold1",fthreashold)
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#Initial Processing of the image finishes....!!!!!
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#find contours
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im2,ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
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#sort contours
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sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
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for i, ctr in enumerate(sorted_ctrs):
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# Get bounding box
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#(x,y) be the top-left coordinate of the rectangle and (w,h) be its width and height
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x, y, w, h = cv2.boundingRect(ctr)
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#Removing the false area that are not textes.
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if w<35 and h<35:
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continue
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# Getting ROI
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roi = image[y:y+h, x:x+w]
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#result = pytesseract.image_to_string(Image.open(roi))
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# show ROI
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#cv2.imshow(roi)
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cv2.imshow('segment no:'+str(i),roi)
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cv2.waitKey(0)
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cv2.rectangle(image,(x,y),( x + w, y + h ),(90,0,255),1)
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#print result
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'''cv2.imshow('marked areas',image)
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cv2.waitKey(0)
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cv2.imwrite('final.jpg', image)
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result = pytesseract.image_to_string(Image.open('final.jpg'))
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with open('fiel12.txt',mode='w') as file:
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file.write(result)
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print("Done")
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'''
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#!/usr/bin/env python
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import sys
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import cv2
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import numpy as np
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#import pytesseract
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#pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract'
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#from PIL import Image
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#import image
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def imagetoregion(imagename, imagedest, segmentview):
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image = cv2.imread(imagename)
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cv2.imshow('orig',image)
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cv2.waitKey(0)
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#Initial Processing of the image starts...!!!!!!!!!!!
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#grayscale
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gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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cv2.imshow('gray',gray)
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cv2.waitKey(0)
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#binary
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ret,thresh = cv2.threshold(gray,160,255,cv2.THRESH_BINARY_INV)
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cv2.imshow('Binarized',thresh)
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cv2.waitKey(0)
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#detecting edges>>>>
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edges=cv2.Canny(thresh,140,200)
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cv2.imshow("edges",edges)
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cv2.waitKey(0)
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#dilation
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kernel = np.ones((15,15), np.uint8)
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img_dilation = cv2.dilate(edges, kernel, iterations=1)
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cv2.imshow('dilated',img_dilation)
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cv2.waitKey(0)
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#Localization Horizontal projection
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(x,y)=img_dilation.shape
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z=[sum(y) for y in img_dilation]
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Tx=(((sum(z))/(len(z))/20))
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#print Tx
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#Vertical Projection
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ndilation=zip(*img_dilation)
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X=[sum(row) for row in ndilation]
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mean=(sum(X))/(len(X))
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maximofX=(max(X)/10)
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Ty=mean+maximofX
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#print Ty
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#Adaptive threashold for horizontal projection
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th1 = cv2.adaptiveThreshold(img_dilation,Tx,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2) #block size and constant.
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#Adaptive threashold for vertical projection
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th2 = cv2.adaptiveThreshold(img_dilation,Ty,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
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fthreashold=cv2.add(th1,th2)
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cv2.imshow("threashold1",fthreashold)
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cv2.waitKey(0)
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#Initial Processing of the image finishes....!!!!!
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#find contours
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im2,ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
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#sort contours
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sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
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for i, ctr in enumerate(sorted_ctrs):
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# Get bounding box
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#(x,y) be the top-left coordinate of the rectangle and (w,h) be its width and height
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x, y, w, h = cv2.boundingRect(ctr)
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#Removing the false area that are not textes.
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if w<35 and h<35:
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continue
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if segmentview:
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# Getting ROI
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roi = image[y:y+h, x:x+w]
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#result = pytesseract.image_to_string(Image.open(roi))
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# show ROI
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#cv2.imshow(roi)
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cv2.imshow('segment no:'+str(i),roi)
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cv2.waitKey(0)
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cv2.rectangle(image,(x,y),( x + w, y + h ),(90,0,255),1)
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#print result
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cv2.namedWindow('marked areas', cv2.WINDOW_NORMAL)
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cv2.resizeWindow('marked areas', 800, 600)
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cv2.imshow('marked areas',image)
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cv2.waitKey(0)
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if not(segmentview):
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cv2.imwrite(imagedest, image)
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'''
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result = pytesseract.image_to_string(Image.open('final.jpg'))
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with open('fiel12.txt',mode='w') as file:
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file.write(result)
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print("Done")
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'''
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if __name__ == '__main__':
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segmentview = True
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filename = 'test.jpg'
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if len(sys.argv) > 1:
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filename = sys.argv[1]
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filedest = 'final.jpg'
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if len(sys.argv) > 2:
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filedest = sys.argv[2]
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segmentview = False
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imagetoregion(filename, filedest, segmentview)

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