def get_context_data(self, **kwargs):
self.create_context(**kwargs)
matches = []
for h in MTGHash.objects.all():
s = pHash.hamming_distance(long(self.context['hash']), long(h.hash))
if s <= 15:
matches.append((h.card, s))
self.context['matches'] = matches
return self.context
#!/usr/bin/env python
# coding: utf-8
import sys
import pHash
if __name__ == "__main__":
if len(sys.argv) != 3:
print "usage: ${prog} src dst"
sys.exit(0)
d1 = pHash.image_digest(sys.argv[1], 1.0, 1.0, 180)
d2 = pHash.image_digest(sys.argv[2], 1.0, 1.0, 180)
print 'digest', pHash.crosscorr(d1, d2)[1]
h1 = pHash.imagehash(sys.argv[1])
h2 = pHash.imagehash(sys.argv[2])
print 'hash', pHash.hamming_distance(h1, h2)
# logo_hash = pHash.imagehash('video_wp.png')
# logo1_hash = pHash.imagehash('logo1.jpg')
# logo2_hash = pHash.imagehash('logo2.jpg')
# logo3_hash = pHash.imagehash('logo3.jpg')
# logo4_hash = pHash.imagehash('logo4.jpg')
#
# print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(logo_hash, logo1_hash), logo_hash, logo1_hash)
# print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(logo_hash, logo2_hash), logo_hash, logo2_hash)
# print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(logo_hash, logo3_hash), logo_hash, logo3_hash)
# print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(logo_hash, logo4_hash), logo_hash, logo4_hash)
#
#
# print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(logo1_hash, logo3_hash), logo1_hash, logo3_hash)
#
o4n_hash = pHash.imagehash('frame_o4n_03.jpg')
wp_hash = pHash.imagehash('frame_wp_003.jpg')
wpc_hash = pHash.imagehash('frame_wpc_0003.jpg')
wpstar_hash = pHash.imagehash('frame_wpstar_000056.jpg')
print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(o4n_hash, wp_hash), o4n_hash, wp_hash)
print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(o4n_hash, wpc_hash), o4n_hash, wpc_hash)
print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(wp_hash, wpc_hash), wp_hash, wpc_hash)
print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(wpstar_hash, wpc_hash), wpstar_hash, wpc_hash)
hash1 = pHash.imagehash(temp.name)
command = 'blur-detection ' + temp.name
output = commands.getoutput(command)
p = re.compile('.*density: (\d+\.\d+)')
image_quality = float(p.match(output).group(1))
images = conn.get_images(user_id=user['_id'], is_duplicate=False)
is_duplicate = False
group = 1
is_unique = True
for image in images:
if pHash.hamming_distance(hash1, long(image['hash'])) < 15:
is_unique = False
if image_quality < image['quality']:
group = image['group']
is_duplicate = True
break
elif not image['is_duplicate']:
group = image['group']
conn.mark_image_duplicate(image["_id"])
break
if is_unique:
highest_group += 1
group = highest_group
print "adding image for user " + str(user) + " -- " + data[
"path"] + " quality " + str(image_quality)
conn.insert_image(user["_id"],
class CompressionSimilarityError(Exception):
pass
def _images_are_similar(filename1,filename2):
try:
import pHash
except FileNotFoundError, fe:
logging.info("Could not determine similarity - missing pHash module")
return True
hash1 = pHash.imagehash(filename1)
hash2 = pHash.imagehash(filename2)
hd = pHash.hamming_distance(hash1,hash2)
logging.info('Hamming distance: %d (%08x / %08x)' % ( hd, hash1, hash2 ))
if hd <= SIMILARITY_THRESHOLD:
return True
return False
"""
digest1 = pHash.image_digest(filename1, 1.0, 1.0, 180 )
digest2 = pHash.image_digest(filename2, 1.0, 1.0, 180 )
print 'Cross-correelation: %d' % ( pHash.crosscorr( digest1, digest2 ) )
"""
def _images_are_equal(filename1, filename2):
urllib.urlretrieve(albumPhoto["images"][-1]["source"], photoName);
compare_photos.append(photoName)
compare_hashes.append(pHash.imagehash(photoName))
print "Saved %s" % (photoName)
response = graph.request("search",{"q":profile["name"], "fields":"id,name,picture", "type":"user"})
next = response["paging"]["next"].replace("https://graph.facebook.com/v1.0/", "")
print "Hunt commencing!"
while next:
for user in response["data"]:
urllib.urlretrieve(user["picture"]["data"]["url"], "compared.jpg");
compared_hash = pHash.imagehash("compared.jpg")
compare_count += 1
for compare_hash in compare_hashes:
hamming_distance = pHash.hamming_distance( compare_hash, compared_hash )
if hamming_distance < 8:
print 'Potential scammer: http://graph.facebook.com/%s Hamming distance: %d (%08x / %08x)' % (user["id"], hamming_distance, compare_hash, compared_hash)
elif DEBUG:
print 'http://graph.facebook.com/%s Hamming distance: %d' % (user["id"], hamming_distance)
response = graph.request(next)
if ("next" in response["paging"].keys()):
next = response["paging"]["next"].replace("https://graph.facebook.com/v1.0/", "")
else:
next = None
print "Compared %d profiles!" % (compare_count)
def get_phash_similarity(self, other):
return pHash.hamming_distance(self.phash, other.phash)
hash1 = pHash.imagehash(temp.name)
command = 'blur-detection ' + temp.name
output = commands.getoutput(command)
p=re.compile('.*density: (\d+\.\d+)')
image_quality = float(p.match(output).group(1))
images = conn.get_images(user_id=user['_id'], is_duplicate=False)
is_duplicate = False
group = 1
is_unique = True
for image in images:
if pHash.hamming_distance(hash1, long(image['hash'])) < 15:
is_unique = False
if image_quality < image['quality']:
group = image['group']
is_duplicate = True
break
elif not image['is_duplicate']:
group = image['group']
conn.mark_image_duplicate(image["_id"])
break
if is_unique:
highest_group += 1
group = highest_group
print "adding image for user " + str(user) + " -- " + data["path"] + " quality " + str(image_quality)
conn.insert_image(user["_id"],data["path"],[latitude, longitude],"xx",hash1,timestamp,group,image_quality,is_duplicate=is_duplicate)
os.unlink(temp.name)
def ia896_capstone_calculate_dist(img1, img2):
hash1 = long(img1['phash'])
hash2 = long(img2['phash'])
if not hash1 or not hash2:
return 9999
return pHash.hamming_distance(hash1, hash2)
import pHash
import sys
hash1 = pHash.imagehash(sys.argv[1])
hash2 = pHash.imagehash(sys.argv[2])
print 'Hamming distance: %d (%08x / %08x)' % (pHash.hamming_distance(
hash1, hash2), hash1, hash2)
digest1 = pHash.image_digest(sys.argv[1], 1.0, 1.0, 180)
digest2 = pHash.image_digest(sys.argv[2], 1.0, 1.0, 180)
print(pHash.crosscorr(digest1, digest2))
