Amazing-Python-Scripts

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'''
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output : word detection on document (Simple OCR type of application)
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'''
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import cv2
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import numpy as np
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import imutils
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# frame read
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frame = cv2.imread('test.jpeg')
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# resize
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frame = cv2.resize(frame, (600, 600))
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# grayscale
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# remove noise
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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# otsu thresh (bimodel thresold)
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thresh = cv2.threshold(blur, 0, 255,
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                       cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
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# get structuring element
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horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 1))
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vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 25))
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print('horizontal kernel : {}'.format(horizontal_kernel))
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print('vertical kernel : {}'.format(vertical_kernel))
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# opening (erosion followed by dilation)
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horizontal_lines = cv2.morphologyEx(thresh,
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                                    cv2.MORPH_OPEN,
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                                    horizontal_kernel,
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                                    iterations=2)
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vertical_lines = cv2.morphologyEx(thresh,
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                                  cv2.MORPH_OPEN,
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                                  vertical_kernel,
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                                  iterations=2)
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# contours apply on detected lines
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# First one is source image, second is contour retrieval mode, third is contour approximation method
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cnts = cv2.findContours(horizontal_lines, cv2.RETR_EXTERNAL,
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                        cv2.CHAIN_APPROX_SIMPLE)
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cntsv = cv2.findContours(vertical_lines, cv2.RETR_EXTERNAL,
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                         cv2.CHAIN_APPROX_SIMPLE)
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# find contours
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cnts = cnts[0] if len(cnts) == 2 else cnts[1]
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cntsv = cntsv[0] if len(cntsv) == 2 else cntsv[1]
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for c in cnts:
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    cv2.drawContours(frame, [c], -1, (255, 255, 255), 2)
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for c in cntsv:
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    cv2.drawContours(frame, [c], -1, (255, 255, 255), 2)
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# imshow
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cv2.imshow('thresh', thresh)
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cv2.imshow('horizontal_lines', horizontal_lines)
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cv2.imshow('vertical_lines', vertical_lines)
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cv2.imshow('frame', frame)
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# grayscale
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gray1 = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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thresh1 = cv2.adaptiveThreshold(gray1, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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                                cv2.THRESH_BINARY, 23, 30)
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canny = imutils.auto_canny(thresh1)
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output = cv2.bitwise_not(canny)
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kernel = np.ones((5, 5), np.uint8)
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opening = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernel)
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dilation = cv2.dilate(canny, kernel, iterations=1)
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contour, hierachy = cv2.findContours(dilation, cv2.RETR_TREE,
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                                     cv2.CHAIN_APPROX_SIMPLE)
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for i in contour:
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    area = cv2.contourArea(i)
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    if area > 20:
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        x, y, w, h = cv2.boundingRect(i)
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        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 120, 255), 2)
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cv2.imshow('output', output)
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cv2.imshow('dilate', dilation)
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cv2.imshow('opening', opening)
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cv2.imshow('original_frame', frame)
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cv2.imshow('canny', canny)
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cv2.imshow('thresh1', thresh1)
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# Saving output image
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cv2.imwrite('output.jpg', frame)
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# destroy all window
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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