def generatePairedTableOnlyOcr(imageTable):
foundOneMatch = False
singular = []
# used imagehashing to figure out pairs
for elemOneKey, elemOneValue in imageTable.items():
one = elemOneValue["ocr_text"]
for elemTwoKey, elemTwoValue in imageTable2.items():
two = elemTwoValue["ocr_text"]
if elemOneKey != elemTwoKey:
ocrDiff = commonMethods.percentageEditDistance(one, two)
if ocrDiff <= 0.3:
imageTable[elemOneKey]["strictMatch"].add(elemTwoKey)
foundOneMatch = True
elif ocrDiff < 0.26:
imageTable[elemOneKey]["matchNamesRemove"].add(elemTwoKey)
foundOneMatch = True
if (not foundOneMatch):
singular.append(elemOneKey)
foundOneMatch = False # reset value for next element
#end of outer for loop
# the singulars are taken care of
imageTable = commonMethods.addSingulars(imageTable, singular)
return imageTable
def findImagePairs(imageTable):
addList = [] # ( name, ocr_difference, hash_difference )
removeList = []
for key, value in imageTable.iteritems():
# HERE
# print("KEY :"),
# print(key)
for matchKey in value["matchNamesRemove"]:
diffOCRdistance = commonMethods.percentageEditDistance( imageTable[key]["ocr_text"],\
imageTable[matchKey]["ocr_text"] )
diffHashDistance = commonMethods.percentHashDifference( imageTable[key]["hash_value"],\
imageTable[matchKey]["hash_value"] )
# print(matchKey, " " , diffOCRdistance, diffHashDistance)
# a picture is a strict match if the ocr is within 3 percent and
# image structure is within a 5 percent difference
if diffHashDistance <= THRESH_HASH_DISTANCE_STRICT and diffOCRdistance <= THRESH_OCR_DISTANCE_STRICT:
imageTable[key]['strictMatch'].add(matchKey)
# if image is imageDominant
elif imageTable[key]["imageDominant"] or imageTable[matchKey][
"imageDominant"]:
if diffHashDistance < THRESH_HASH_DISTANCE_STRICT_IMAGE_DOMINANT:
imageTable[key]['strictMatch'].add(matchKey)
elif diffHashDistance > THRESH_HASH_DISTANCE_REMOVE_IMAGE_DOMINANT or diffOCRdistance > THRESH_OCR_DISTANCE_REMOVE_IMAGE_DOMINANT:
removeList.append(matchKey)
# if the picture is both not similar in image structure and writing
elif diffHashDistance > THRESH_HASH_DISTANCE_REMOVE and diffOCRdistance > THRESH_OCR_DISTANCE_REMOVE:
removeList.append(matchKey)
# if not a strict match or needing to be removed checked it against
# other elements that also do not need to be removed
# CAN BE DONE WITH removed elements also but for the time being
# have not checked the feasibility with all elements
else:
addList.append((matchKey, diffOCRdistance, diffHashDistance))
# populate the matchNamesAdd
experimentGraph(imageTable, key, addList)
# prune the matchNamesRemove
commonMethods.removeItemsFromSet(imageTable, key, removeList)
removeList = []
addList = []
return imageTable
def generatePairedTableOnlyOcr2(imageTable, imageTable2):
# What format do I want results?
# Image1 Image2 confidence (0/1/2)
# UIC.CS108F17/L1709171030_Q5.jpg UIC.CS108F18/L1709101035_Q4.jpg
result = ''
# used imagehashing to figure out pairs
for elemOneKey, elemOneValue in imageTable.items():
one = elemOneValue["ocr_text"]
for elemTwoKey, elemTwoValue in imageTable2.items():
two = elemTwoValue["ocr_text"]
if elemOneKey != elemTwoKey:
ocrDiff = commonMethods.percentageEditDistance(one, two)
if ocrDiff <= 0.3:
# 1 means probable match
result += "{}\t{}\t1\n".format(elemOneKey, elemTwoKey)
elif ocrDiff < 0.26:
# 0 means (almost) positive match
result += "{}\t{}\t0\n".format(elemOneKey, elemTwoKey)
# the singulars are taken care of
return result
def find_image_matches(class1, table1, class2, table2, htmlout=False):
'''Match the images in class1 against the images from class2
Returns either an HTML file for checking visually,
or a
For output, the matches show:
course1/image1 course2/image2 match_score
possible match_score values are:
0 for high confidence match (both phash and text-ocr are below threshold)
1 medium-high confidence (text match, but no phash match)
2 medium confidence match (phash and text at threshold 2, or text at threshold 1)
anything else we leave it out.
'''
print("comparing {} with {}".format(len(table1), len(table2)))
done = set()
result = ''
html = ''
both = 0
neither = 0
phash = 0
text = 0
for filename1, image1 in table1.items():
for filename2, image2 in table2.items():
key = "{}-{}".format(filename1, filename2)
if key in done:
continue
done.add(key)
# also add the opposite of the key, so f1-f2 and f2-f1
done.add("{}-{}".format(filename2, filename1))
diff_text = percentageEditDistance(image1.text, image2.text)
diff_phash = percentHashDifference(image1.image_hash,
image2.image_hash)
msg = '{:.2} {:.2} for {} and {}'.format(float(diff_text),
float(diff_phash),
filename1, filename2)
if diff_text < THRESHOLD_TEXT and diff_phash < THRESHOLD_PHASH:
#print("both: {}".format(msg))
# BEST: both phash and ocr text indicate a match
both += 1
html += tohtml('both!', diff_text, diff_phash, class1,
filename1, class2, filename2)
result += "{}\t{}\t0\n".format(filename1, filename2)
elif diff_text < THRESHOLD_TEXT:
#print("text: {}".format(msg))
# second best: text matches
text += 1
html += tohtml('text only', diff_text, diff_phash, class1,
filename1, class2, filename2)
result += "{}\t{}\t1\n".format(filename1, filename2)
elif diff_phash < THRESHOLD_PHASH and diff_text < THRESHOLD_TEXT_WITH_PHASH:
#print("phash: {}".format(msg))
# third best: phash matches, text is below a less strict threshold
phash += 1
html += tohtml('phash only', diff_text, diff_phash, class1,
filename1, class2, filename2)
result += "{}\t{}\t2\n".format(filename1, filename2)
else:
# print("neither: {}".format(msg))
neither += 1
# result += "{}\t{}\t{:.2}\t{:.2}\n".format(filename1, filename2, float(diff_text), float(diff_phash))
print("both: {} phash: {} text: {} neither: {}".format(
both, phash, text, neither))
if htmlout:
return '''
<html>
<head><title></title></head>
<body>
<table>
<tr>
<th>message</th>
<th>diff text</th>
<th>diff phash</th>
<th>file1</th>
<th>file2</th>
</tr>
{}
</table>
</body>
</html>
'''.format(html)
else:
return result