Tring to make better predictions.

Samo Penic

2018-11-28 7621b38ff23a963724adcafe8946acce48e48abe

 aoiOcr.py

@@ -1,19 +1,20 @@
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)

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}
settings = {"sid_mask": "11x0xxxx", "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)
# 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",
@@ -24,15 +25,16 @@
    "processed_scans/20141028095553745_0018.tif",
    "processed_scans/20151013180545275_0011.tif",
    "processed_scans/20160408140801098_0004.tif",
    "processed_scans/20160510075445995_0026.tif"
    "processed_scans/20160510075445995_0026.tif",
]
p=Paper(filename=pa[9], sid_classifier=classifier, settings=settings)
# p=Paper(filename=pa[9], sid_classifier=classifier, settings=settings)
p = Paper(filename="sizif-test.tif", sid_classifier=classifier, settings=settings)

# print(p.QRData)
# print(p.errors)

# print(p.getSkewAngle())
# print(p.locateUpMarkers())
# print(p.locateUpMarkers())%%
# print(p.locateRightMarkers())
# print(p.answerMatrix)
# p.get_enhanced_sid()
@@ -40,19 +42,21 @@

print(p.get_paper_ocr_data())


exit(0)
filelist = glob("processed_scans/*.tif")
wrong_sid=0;
total=0
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))
    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))
print("Total:{}, wrong SID: {}".format(total, wrong_sid))