def search_index(index, img_hash, max_distance):
hashes_with_dist = map(lambda h: (phash.hamming_distance(img_hash, int(h, 16)), h),
index.keys())
in_threshold = filter(lambda t: t[0] <= max_distance, hashes_with_dist)
ordered = sorted(in_threshold, key=lambda t: t[0])
return [index[key] for [_, key] in ordered]
def detectCard(self):
"""Detect the card from the active frame
"""
# The phash python bindings operate on files, so we have to write our
# current frame to a file to continue
cv2.imwrite('frame.jpg', self.frame)
# Use phash on our frame
ihash = phash.dct_imagehash('frame.jpg')
idigest = phash.image_digest('frame.jpg')
candidates = {}
hashes = self.referencedb.get_hashes()
for MultiverseID in hashes:
if (MultiverseID in self.blacklist):
continue
hamd = phash.hamming_distance(ihash, int(hashes[MultiverseID]))
if (hamd <= self.threshold):
candidates[MultiverseID] = hamd
if (not len(candidates)):
print('No matches found')
return None
finalists = []
minV = min(candidates.values())
for MultiverseID in candidates:
if (candidates[MultiverseID] == minV):
finalists.append(MultiverseID)
bestMatch = None
correlations = {}
for MultiverseID in finalists:
hamd = candidates[MultiverseID]
digest = phash.image_digest(self.referencedb.IMAGE_FILE %
MultiverseID)
corr = phash.cross_correlation(idigest, digest)
if (bestMatch is None or corr > correlations[bestMatch]):
bestMatch = MultiverseID
correlations[MultiverseID] = corr
return bestMatch
def detectCard(self):
"""Detect the card from the active frame
"""
# The phash python bindings operate on files, so we have to write our
# current frame to a file to continue
cv2.imwrite("frame.jpg", self.frame)
# Use phash on our frame
ihash = phash.dct_imagehash("frame.jpg")
idigest = phash.image_digest("frame.jpg")
candidates = {}
hashes = self.referencedb.get_hashes()
for MultiverseID in hashes:
if MultiverseID in self.blacklist:
continue
hamd = phash.hamming_distance(ihash, int(hashes[MultiverseID]))
if hamd <= self.threshold:
candidates[MultiverseID] = hamd
if not len(candidates):
print("No matches found")
return None
finalists = []
minV = min(candidates.values())
for MultiverseID in candidates:
if candidates[MultiverseID] == minV:
finalists.append(MultiverseID)
bestMatch = None
correlations = {}
for MultiverseID in finalists:
hamd = candidates[MultiverseID]
digest = phash.image_digest(self.referencedb.IMAGE_FILE % MultiverseID)
corr = phash.cross_correlation(idigest, digest)
if bestMatch is None or corr > correlations[bestMatch]:
bestMatch = MultiverseID
correlations[MultiverseID] = corr
return bestMatch
def detectCard(self):
"""Detect the card from the active frame
"""
# The phash python bindings operate on files, so we have to write our
# current frame to a file to continue
cv2.imwrite('frame.jpg', self.frame)
# Use phash on our frame
ihash = phash.dct_imagehash('frame.jpg')
idigest = phash.image_digest('frame.jpg')
#print('1a')
candidates = {}
hashes = self.referencedb.get_hashes()
#print('1a1')
#print(hashes)
for MultiverseID in hashes:
#print('id %i' % MultiverseID)
if (MultiverseID in self.blacklist):
continue
hamd = phash.hamming_distance(ihash, int(hashes[MultiverseID]))
#print('1a11')
#print('ham: %i tresh: %i id: %i' % (hamd, self.threshold, MultiverseID))
#print(hamd <= self.threshold)
if (hamd <= self.threshold):
#print('X')
candidates[MultiverseID] = hamd
#print('1a2')
if (not len(candidates)):
print('No matches found')
return None
#print('1a3')
finalists = []
minV = min(candidates.values())
#print('1a4')
for MultiverseID in candidates:
if (candidates[MultiverseID] == minV):
finalists.append(MultiverseID)
#print('1b')
bestMatch = None
correlations = {}
for MultiverseID in finalists:
hamd = candidates[MultiverseID]
#print(self.ROOT_PATH % self.referencedb.IMAGE_FILE % MultiverseID)
s = self.referencedb.IMAGE_FILE % MultiverseID
s = '%s/%s' % (self.ROOT_PATH, s)
#print(s)
#digest = phash.image_digest(self.referencedb.IMAGE_FILE % MultiverseID)
digest = phash.image_digest(s)
corr = phash.cross_correlation(idigest, digest)
if (bestMatch is None or corr > correlations[bestMatch]):
bestMatch = MultiverseID
correlations[MultiverseID] = corr
name, code, rarity = self.referencedb.get_card_info(MultiverseID)
print('Candidate: ' + name + ' [' + code + '] ' + str(corr))
#print('1d')
bestMatches = []
ACCURACY = 1000
print('Finalists:')
print(finalists)
for MultiverseID in finalists:
if correlations[MultiverseID] + ACCURACY > correlations[bestMatch]:
bestMatches.append(MultiverseID)
return bestMatches
#return more finallist
return bestMatch
def distance(self, other: 'DctImageHash'):
return phash.hamming_distance(self._hash, other._hash) / 64
