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56 lines
1.9 KiB
56 lines
1.9 KiB
#!/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 = list(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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erc1 = cv.text.loadClassifierNM1('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('trained_classifierNM2.xml') |
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er2 = cv.text.createERFilterNM2(erc2,0.5) |
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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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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 rect in rects: |
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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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