from aoi_ocr.Ocr import Paper from sklearn.externals import joblib import pkg_resources path = '/filename.joblib' # always use slash filepath = pkg_resources.resource_filename('aoi_ocr', path) from glob import glob settings = {"sid_mask": "64xx0xxx", "answer_threshold": 0.25} classifier = joblib.load(filepath) #p = Paper(filename="testpage300dpi_scan1.png") #p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings={"sid_mask": "11xx0xxx", "answer_threshold": 0.25}) #p=Paper(filename='processed_scans/20141016095134535_0006.tif', sid_classifier=classifier, settings=settings) #p = Paper(filename="processed_scans/20151111080408825_0001.tif",sid_classifier=classifier,settings=settings,) #p=Paper(filename='processed_scans/20151028145444607_0028.tif', sid_classifier=classifier, settings=settings) pa = [ "processed_scans/20141016095134535_0006.tif", "processed_scans/20141016095134535_0028.tif", "processed_scans/20141016095134535_0028.tif", "processed_scans/20141016095134535_0037.tif", "processed_scans/20141021095744144_0005.tif", "processed_scans/20141021095744144_0009.tif", "processed_scans/20141028095553745_0018.tif", "processed_scans/20151013180545275_0011.tif", "processed_scans/20160408140801098_0004.tif", "processed_scans/20160510075445995_0026.tif" ] p=Paper(filename=pa[9], sid_classifier=classifier, settings=settings) # print(p.QRData) # print(p.errors) # print(p.getSkewAngle()) # print(p.locateUpMarkers()) # print(p.locateRightMarkers()) # print(p.answerMatrix) # p.get_enhanced_sid() print(p.get_paper_ocr_data()) filelist = glob("processed_scans/*.tif") wrong_sid=0; total=0 for f in sorted(filelist): print("processing: {}".format(f)) p=Paper(filename=f, sid_classifier=classifier, settings=settings).get_paper_ocr_data() print(f,p) if(p['page_no']==0): total+=1 if(len(p['errors'])!=0): wrong_sid+=1 if total%10 == 0: print("Total:{}, wrong SID: {}".format(total,wrong_sid)) print("Total:{}, wrong SID: {}".format(total,wrong_sid))