59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
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#!/usr/bin/python
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import sys
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import os
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import cv2 as cv
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import numpy as np
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print('\ntextdetection.py')
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print(' A demo script of the Extremal Region Filter algorithm described in:')
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print(' Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012\n')
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if (len(sys.argv) < 2):
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print(' (ERROR) You must call this script with an argument (path_to_image_to_be_processed)\n')
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quit()
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pathname = os.path.dirname(sys.argv[0])
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img = cv.imread(str(sys.argv[1]))
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# for visualization
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vis = img.copy()
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# Extract channels to be processed individually
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channels = cv.text.computeNMChannels(img)
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# Append negative channels to detect ER- (bright regions over dark background)
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cn = len(channels)-1
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for c in range(0,cn):
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channels.append((255-channels[c]))
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# Apply the default cascade classifier to each independent channel (could be done in parallel)
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print("Extracting Class Specific Extremal Regions from "+str(len(channels))+" channels ...")
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print(" (...) this may take a while (...)")
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for channel in channels:
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erc1 = cv.text.loadClassifierNM1(pathname+'/trained_classifierNM1.xml')
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er1 = cv.text.createERFilterNM1(erc1,16,0.00015,0.13,0.2,True,0.1)
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erc2 = cv.text.loadClassifierNM2(pathname+'/trained_classifierNM2.xml')
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er2 = cv.text.createERFilterNM2(erc2,0.5)
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regions = cv.text.detectRegions(channel,er1,er2)
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rects = cv.text.erGrouping(img,channel,[r.tolist() for r in regions])
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#rects = cv.text.erGrouping(img,channel,[x.tolist() for x in regions], cv.text.ERGROUPING_ORIENTATION_ANY,'../../GSoC2014/opencv_contrib/modules/text/samples/trained_classifier_erGrouping.xml',0.5)
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#Visualization
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for r in range(0,np.shape(rects)[0]):
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rect = rects[r]
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cv.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (0, 0, 0), 2)
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cv.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (255, 255, 255), 1)
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#Visualization
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cv.imshow("Text detection result", vis)
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cv.waitKey(0)
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