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59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import cv2
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import numpy as np
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import imutils
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import pytesseract
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# read image from disk
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image = cv2.imread('test13.jpg')
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# make it gray
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img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# blur it to remove noise
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img = cv2.GaussianBlur(img, (7,7), 0)
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# perform edge detection, then perform a dilation + erosion to
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# close gaps in between object edges
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edged = cv2.Canny(img, 40, 90)
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dilate = cv2.dilate(edged, None, iterations=2)
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# perform erosion if necessay, it completely depends on the image
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# erode = cv2.erode(dilate, None, iterations=1)
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# create an empty masks
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mask = np.ones(img.shape[:2], dtype="uint8") * 255
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# find contours
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cnts = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
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cnts = cnts[0] if imutils.is_cv2() else cnts[1]
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orig = img.copy()
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for c in cnts:
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# if the contour is not sufficiently large, ignore it
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print('contouring...')
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if cv2.contourArea(c) < 300:
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cv2.drawContours(mask, [c], -1, 0, -1)
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x,y,w,h = cv2.boundingRect(c)
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# filter more contours if nessesary
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if(w>h):
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cv2.drawContours(mask, [c], -1, 0, -1)
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print('contourint done');
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newimage = cv2.bitwise_and(dilate.copy(), dilate.copy(), mask=mask)
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img2 = cv2.dilate(newimage, None, iterations=3)
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ret2,th1 = cv2.threshold(img2 ,0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
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# Tesseract OCR on the image
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temp = pytesseract.image_to_string(th1)
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print(temp)
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# Write results on the image
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cv2.putText(image, temp, (100,100), cv2.FONT_HERSHEY_SIMPLEX, 1.8, (0,255,255), 3)
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# show the outputs
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cv2.imshow('Original image', cv2.resize(image,(640,480)))
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cv2.imshow('Dilated', cv2.resize(dilate,(640,480)))
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cv2.imshow('New Image', cv2.resize(newimage,(640,480)))
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cv2.imshow('Inverted Threshold', cv2.resize(th1,(640,480)))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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