def watch_for_card(camera):
has_moved = False
been_to_base = False
global captures
global font
captures = []
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1.0, 1.0)
img = cv.QueryFrame(camera)
size = cv.GetSize(img)
n_pixels = size[0] * size[1]
grey = cv.CreateImage(size, 8, 1)
recent_frames = [cv.CloneImage(grey)]
base = cv.CloneImage(grey)
cv.CvtColor(img, base, cv.CV_RGB2GRAY)
# cv.ShowImage('card', base)
tmp = cv.CloneImage(grey)
while True:
img = cv.QueryFrame(camera)
cv.CvtColor(img, grey, cv.CV_RGB2GRAY)
biggest_diff = max(sum_squared(grey, frame) / n_pixels for frame in recent_frames)
# display the cam view
cv.PutText(img, "%s" % biggest_diff, (1, 24), font, (255, 255, 255))
cv.ShowImage('win', img)
recent_frames.append(cv.CloneImage(grey))
if len(recent_frames) > 3:
del recent_frames[0]
# check for keystroke
c = cv.WaitKey(10)
# if there was a keystroke, reset the last capture
if c == 27:
return captures
elif c == 32:
has_moved = True
been_to_base = True
elif c == 114:
base = cv.CloneImage(grey)
# if we're stable-ish
if biggest_diff < 10:
# if we're similar to base, update base
# else, check for card
# base_diff = max(sum_squared(base, frame) / n_pixels for frame in recent_frames)
base_corr = 0
try:
base_corr = min(ccoeff_normed(base, frame) for frame in recent_frames)
except:
print ("Unable to calculate the base_corr")
pass
# cv.ShowImage('debug', base)
"""for i, frame in enumerate(recent_frames):
tmp = cv.CloneImage(base)
cv.Sub(base, frame, tmp)
cv.Pow(tmp, tmp, 2.0)
cv.PutText(tmp, "%s" % (i+1), (1,24), font, (255, 255, 255))
#my_diff = sum_squared(base, frame) / n_pixels
my_diff = ccoeff_normed(base, frame) #score(base, frame, cv.CV_TM_CCOEFF_NORMED)
cv.PutText(tmp, "%s" % my_diff, (40, 24), font, (255, 255, 255))
cv.ShowImage('dbg%s' % (i+1), tmp)"""
# print "stable. corr = %s. moved = %s. been_to_base = %s" % (base_corr, has_moved, been_to_base)
if base_corr > 0.75 and not been_to_base:
base = cv.CloneImage(grey)
# cv.ShowImage('debug', base)
has_moved = False
been_to_base = True
print "STATE: been to base. waiting for move"
elif has_moved and been_to_base:
corners = detect_card(grey, base)
if corners is not None:
card = get_card(grey, corners)
cv.Flip(card, card, -1)
captures.append(card)
update_windows()
# cv.ShowImage('card', card)
has_moved = False
been_to_base = False
print "STATE: detected. waiting for go to base"
else:
if not has_moved:
print "STATE: has moved. waiting for stable"
has_moved = True
def watch_for_card(camera):
has_moved = False
been_to_base = False
global captures
global font
captures = []
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1.0, 1.0)
img = cv.QueryFrame(camera)
size = cv.GetSize(img)
n_pixels = size[0] * size[1]
grey = cv.CreateImage(size, 8, 1)
recent_frames = [cv.CloneImage(grey)]
base = cv.CloneImage(grey)
cv.CvtColor(img, base, cv.CV_RGB2GRAY)
#cv.ShowImage('card', base)
tmp = cv.CloneImage(grey)
while True:
img = cv.QueryFrame(camera)
cv.CvtColor(img, grey, cv.CV_RGB2GRAY)
biggest_diff = max(
sum_squared(grey, frame) / n_pixels for frame in recent_frames)
#display the cam view
cv.PutText(img, "%s" % biggest_diff, (1, 24), font, (255, 255, 255))
cv.ShowImage('win', img)
recent_frames.append(cv.CloneImage(grey))
if len(recent_frames) > 3:
del recent_frames[0]
#check for keystroke
c = cv.WaitKey(10)
#if there was a keystroke, reset the last capture
if c == 27:
return captures
elif c == 32:
has_moved = True
been_to_base = True
elif c == 114:
base = cv.CloneImage(grey)
#if we're stable-ish
if biggest_diff < 10:
#if we're similar to base, update base
#else, check for card
#base_diff = max(sum_squared(base, frame) / n_pixels for frame in recent_frames)
base_corr = min(
ccoeff_normed(base, frame) for frame in recent_frames)
#cv.ShowImage('debug', base)
"""for i, frame in enumerate(recent_frames):
tmp = cv.CloneImage(base)
cv.Sub(base, frame, tmp)
cv.Pow(tmp, tmp, 2.0)
cv.PutText(tmp, "%s" % (i+1), (1,24), font, (255, 255, 255))
#my_diff = sum_squared(base, frame) / n_pixels
my_diff = ccoeff_normed(base, frame) #score(base, frame, cv.CV_TM_CCOEFF_NORMED)
cv.PutText(tmp, "%s" % my_diff, (40, 24), font, (255, 255, 255))
cv.ShowImage('dbg%s' % (i+1), tmp)"""
#print "stable. corr = %s. moved = %s. been_to_base = %s" % (base_corr, has_moved, been_to_base)
if base_corr > 0.75 and not been_to_base:
base = cv.CloneImage(grey)
# cv.ShowImage('debug', base)
has_moved = False
been_to_base = True
print "STATE: been to base. waiting for move"
elif has_moved and been_to_base:
corners = detect_card(grey, base)
if corners is not None:
card = get_card(grey, corners)
cv.Flip(card, card, -1)
captures.append(card)
update_windows()
#cv.ShowImage('card', card)
has_moved = False
been_to_base = False
print "STATE: detected. waiting for go to base"
else:
if not has_moved:
print "STATE: has moved. waiting for stable"
has_moved = True
def calc_background_similarity(self): return min(ccoeff_normed(self.background_gray, frame) for frame in self.recent_frames_gray)
def match_card(card, known_set, cache):
mag, grad = gradient(card)
phash = dct_hash(card)
#fetch the twenty candidates with the lowest hamming distance on the phash
#there's a 99% chance that the matching card is one of the first 20
candidate_matches = sorted([
(name, set_name, hamming_dist(h, phash))
for name, set_name, h in known_set
], key = lambda (n,s,dist): dist)
#we want the first 20
candidate_matches = candidate_matches[:20]
#calculate the correlation score,
#and also find the 'place' of each phash score
#(multiple candidates can tie a phash score, so we rank by count of
#distances < our distance)
candidate_scores= [
(
name, set_name,
dist, len([d for _,_,d in candidate_matches if d < dist]),
ccoeff_normed(grad, cache.getCard(set_name, name))
) for name,set_name,dist in candidate_matches
]
#sort by score, and add a rank
candidate_scores = sorted(candidate_scores,
key = lambda (n,s,d,hr,ccoeff): ccoeff,
reverse = True)
norm_factor = 0 - candidate_scores[-1][4]
total_score = sum([ccoeff + norm_factor for n,s,d,hr,ccoeff in candidate_scores])
#for each score, compute the normaized features (- mean, / std_median)
#for correlation, we want the share of normalized score
features = [
(
name, set_name,
(corr_rank - 9.5) / 5.766,
(h_rank - 6.759942) / 4.522550,
(dist - 17.374153) / 3.014411,
(((corr + norm_factor) / total_score)- 0.050000) / 0.040183,
) for corr_rank, (name, set_name, dist, h_rank, corr)
in enumerate(candidate_scores)
]
#compute the score (based on fancy machine learning.)
#todo: make more automatic and configurable
scores = [
(
name, set_name,
1.0 / (1 + math.e ** -(-6.48728 + 0.53659 * cr + -0.11304 * hr + -3.06121 * d + 2.94122 * corr))
) for name, set_name, cr, hr, d, corr in features
]
#consider the scores in order
scores = sorted(scores, key=lambda (n,s,score): score, reverse=True)
#each score is a probability 0.0-1.0 of how likely it is that
#that name, set_name is the correct card. we'll consider <= 0.50 a no
# >= 0.60 a yes, and 0.5..0.6 a maybe (todo: adjust?)
yes_cards = [(n, s) for n, s, score in scores if score >= 0.6]
maybe_cards = [(n, s) for n, s, score in scores if 0.6 > score > 0.5]
#if we have one or more 'yes' cards
if len(yes_cards) > 0:
#if they're all the same card...
if len(set([n for n,s in yes_cards])) == 1:
#then we're sure it's that card (unsure on set, but it's the same art, so hard to tell
return (yes_cards[0], True)
elif len(maybe_cards) > 0:
#we have no 'yes' cards at all. if we have any maybe cards...
#if they're all the same card
if len(set([n for n,s in maybe_cards])) == 1:
#it *could* be this card, but we're not confidant
return (maybe_cards[0], False)
#we can't really say what it is with any sort of confidence
return (('',''),False)
def match_card(card, known_set, cache):
mag, grad = gradient(card)
phash = dct_hash(card)
#fetch the twenty candidates with the lowest hamming distance on the phash
#there's a 99% chance that the matching card is one of the first 20
candidate_matches = sorted([
(name, set_name, hamming_dist(h, phash))
for name, set_name, h in known_set
], key = lambda (n,s,dist): dist)
# Not even a chance, return
if len(candidate_matches) is 0:
return (('',''),False)
#we want the first 20
candidate_matches = candidate_matches[:20]
#calculate the correlation score,
#and also find the 'place' of each phash score
#(multiple candidates can tie a phash score, so we rank by count of
#distances < our distance)
candidate_scores= [
(
name, set_name,
dist, len([d for _,_,d in candidate_matches if d < dist]),
ccoeff_normed(grad, cache.getCard(set_name, name))
) for name,set_name,dist in candidate_matches
]
'''
print "candidate_scores"
for name, set_name, dist, length, coef_norm in candidate_scores:
print "\tname %s set_name %s dist %d length %d coef_nrom %d" % (name, set_name, dist, length, coef_norm)
'''
#sort by score, and add a rank
candidate_scores = sorted(candidate_scores,
key = lambda (n,s,d,hr,ccoeff): ccoeff,
reverse = True)
norm_factor = 0 - candidate_scores[-1][4]
total_score = sum([ccoeff + norm_factor for n,s,d,hr,ccoeff in candidate_scores])
#for each score, compute the normaized features (- mean, / std_median)
#for correlation, we want the share of normalized score
features = [
(
name, set_name,
(corr_rank - 9.5) / 5.766,
(h_rank - 6.759942) / 4.522550,
(dist - 17.374153) / 3.014411,
(((corr + norm_factor) / total_score)- 0.050000) / 0.040183,
) for corr_rank, (name, set_name, dist, h_rank, corr)
in enumerate(candidate_scores)
]
#compute the score (based on fancy machine learning.)
#todo: make more automatic and configurable
scores = [
(
name, set_name,
1.0 / (1 + math.e ** -(-6.48728 + 0.53659 * cr + -0.11304 * hr + -3.06121 * d + 2.94122 * corr))
) for name, set_name, cr, hr, d, corr in features
]
#consider the scores in order
scores = sorted(scores, key=lambda (n,s,score): score, reverse=True)
'''
print "score"
for name, set_name, score in scores:
print "\tname %s set_name %s score %d" % (name, set_name, score)
'''
#each score is a probability 0.0-1.0 of how likely it is that
#that name, set_name is the correct card. we'll consider <= 0.50 a no
# >= 0.60 a yes, and 0.5..0.6 a maybe (todo: adjust?)
yes_cards = [(n, s) for n, s, score in scores if score >= 0.6]
maybe_cards = [(n, s) for n, s, score in scores if 0.6 > score > 0.5]
'''
print "maybe cards"
for name, set_name in maybe_cards:
print "\tname %s set_name %s" % (name, set_name)
print "Done maybe"
print "yes cards"
for name, set_name in yes_cards:
print "\tname %s set_name %s" % (name, set_name)
print "Done maybe"
'''
#if we have one or more 'yes' cards
if len(yes_cards) > 0:
#if they're all the same card...
if len(set([n for n,s in yes_cards])) == 1:
#then we're sure it's that card (unsure on set, but it's the same art, so hard to tell
return (yes_cards[0], True)
elif len(maybe_cards) > 0:
#we have no 'yes' cards at all. if we have any maybe cards...
#if they're all the same card
if len(set([n for n,s in maybe_cards])) == 1:
#it *could* be this card, but we're not confidant
return (maybe_cards[0], False)
#we can't really say what it is with any sort of confidence
return (('',''),False)
