def _logged_ocr(image, *args, **kwargs):
logger = richlog.get_logger(os.path.join(config.SCREEN_SHOOT_SAVE_PATH, 'ocrlog.html'))
logger.loghtml('<hr/>')
logger.logimage(image)
ocrresult = ocr.engine.recognize(image, *args, **kwargs)
logger.logtext(repr(ocrresult.text))
return ocrresult
def tell_group(
groupimg,
session,
bartop,
barbottom,
):
logger = get_logger(LOGFILE)
logger.logimage(groupimg)
grouptext = groupimg.crop((0, barbottom, groupimg.width, groupimg.height))
thim = imgops.enhance_contrast(grouptext.convert('L'), 60)
thim = imgops.crop_blackedge(thim)
logger.logimage(thim)
# groupname = recozh.recognize(thim)
# logger.logtext(recozh.recognize(thim))
# if len(recognized_groups) == 0 and any_in('首次', groupname):
# groupname = '首次掉落'
# elif any_in('声望', groupname) and '声望&龙门币奖励' not in recognized_groups:
# groupname = '声望&龙门币奖励'
# elif any_in('常规', groupname) and '常规掉落' not in recognized_groups:
# groupname = '常规掉落'
# elif any_in('特殊', groupname) and '特殊掉落' not in recognized_groups:
# groupname = '特殊掉落'
# elif any_in('幸运', groupname) and '幸运掉落' not in recognized_groups:
# groupname = '幸运掉落'
# elif any_in('额物资', groupname) and '额外物资' not in recognized_groups:
# groupname = '额外物资'
# elif any_in('报酬', groupname) and '报酬' not in recognized_groups:
# groupname = '报酬'
# elif any_in('理智返还', groupname) and '理智返还' not in recognized_groups:
# groupname = '理智返还'
comparsions = [(x[0],
imgops.compare_ccoeff(*imgops.uniform_size(thim, x[1])))
for x in grouptemplates
if x[0] not in session.recognized_groups]
comparsions.sort(key=lambda x: x[1], reverse=True)
logger.logtext(repr(comparsions))
groupname = comparsions[0][0]
if comparsions[0][1] < 0.6:
session.low_confidence = True
if groupname == '幸运掉落':
return (groupname, [('(家具)', 1)])
vw, vh = session.vw, session.vh
itemcount = roundint(groupimg.width / (20.370 * vh))
result = []
for i in range(itemcount):
itemimg = groupimg.crop(
(20.370 * vh * i, 0.000 * vh, 40.741 * vh, 18.981 * vh))
# x1, _, x2, _ = (0.093*vh, 0.000*vh, 19.074*vh, 18.981*vh)
itemimg = itemimg.crop((0.093 * vh, 0, 19.074 * vh, itemimg.height))
result.append(item.tell_item(itemimg, session))
return (groupname, result)
def recognize(img):
logger = get_logger(LOGFILE)
vw, vh = util.get_vwvh(img.size)
apimg = img.crop((100 * vw - 22.917 * vh, 2.917 * vh, 100 * vw,
8.194 * vh)).convert('L')
reco_Noto, reco_Novecento = load_data()
apimg = imgops.enhance_contrast(apimg, 80, 255)
logger.logimage(apimg)
aptext = reco_Noto.recognize(apimg)
logger.logtext(aptext)
# print("AP:", aptext)
opidimg = img.crop((100 * vw - 55.694 * vh, 11.667 * vh,
100 * vw - 44.028 * vh, 15.139 * vh)).convert('L')
opidimg = imgops.enhance_contrast(opidimg, 80, 255)
logger.logimage(opidimg)
opidtext = reco_Novecento.recognize(opidimg)
if opidtext.endswith('-'):
opidtext = opidtext[:-1]
opidtext = opidtext.upper()
logger.logtext(opidtext)
# print('operation:', opidtext)
delegateimg = img.crop((100 * vw - 32.778 * vh, 79.444 * vh,
100 * vw - 4.861 * vh, 85.417 * vh)).convert('L')
logger.logimage(delegateimg)
score = np.count_nonzero(np.asarray(delegateimg) > 127) / (
delegateimg.width * delegateimg.height)
delegated = score > 0.5
# print('delegated:', delegated)
consumeimg = img.crop((100 * vw - 14.306 * vh, 94.028 * vh,
100 * vw - 7.222 * vh, 97.361 * vh)).convert('L')
consumeimg = imgops.enhance_contrast(consumeimg, 80, 255)
logger.logimage(consumeimg)
consumetext = reco_Noto.recognize(consumeimg)
consumetext = ''.join(c for c in consumetext if c in '0123456789')
logger.logtext(consumetext)
return {
'AP': aptext,
'operation': opidtext,
'delegated': delegated,
'consume': int(consumetext) if consumetext.isdigit() else None
}
def find_jumping(ary, threshold):
logger = get_logger(LOGFILE)
ary = np.array(ary, dtype=np.int16)
diffs = np.diff(ary)
shit = [x for x in enumerate(diffs) if abs(x[1]) >= threshold]
groups = [[shit[0]]]
for x in shit[1:]:
lastgroup = groups[-1]
if np.sign(x[1]) == np.sign(lastgroup[-1][1]):
lastgroup.append(x)
else:
groups.append([x])
logger.logtext(repr(groups))
pts = []
for group in groups:
pts.append(int(np.average(
tuple(x[0] for x in group), weights=tuple(abs(x[1]) for x in group))) + 1)
return pts
def tell_item(itemimg, session):
logger = get_logger(LOGFILE)
logger.logimage(itemimg)
# l, t, r, b = scaledwh(80, 146, 90, 28)
# print(l/itemimg.width, t/itemimg.height, r/itemimg.width, b/itemimg.height)
# numimg = itemimg.crop(scaledwh(80, 146, 90, 28)).convert('L')
numimg = imgops.scalecrop(itemimg, 0.39, 0.71, 0.82, 0.84).convert('L')
numimg = imgops.crop_blackedge2(numimg, 220)
recodata, textreco = load_data()
if numimg is not None:
numimg = imgops.enhance_contrast(numimg)
logger.logimage(numimg)
numtext: str = textreco.recognize(numimg)
logger.logtext('amount: ' + numtext)
amount = int(numtext) if numtext.isdigit() else None
else:
amount = None
# scale = 48/itemimg.height
img4reco = np.array(
itemimg.resize((48, 48), Image.BILINEAR).convert('RGB'))
img4reco[itemmask] = 0
scores = []
for name, templ in recodata.items():
if templ.size != (48, 48):
templ = templ.resize((48, 48), Image.BILINEAR)
scores.append((name, imgops.compare_mse(img4reco, templ)))
scores.sort(key=lambda x: x[1])
itemname, score = scores[0]
# maxmatch = max(scores, key=lambda x: x[1])
logger.logtext(repr(scores[:5]))
diffs = np.diff([a[1] for a in scores])
if score < 800 and np.any(diffs > 600):
logger.logtext('matched %s with mse %f' % (itemname, score))
name = itemname
else:
logger.logtext('no match')
session.low_confidence = True
name = None
return (name, amount)
def recognize(im):
import time
t0 = time.monotonic()
vw, vh = util.get_vwvh(im.size)
logger = get_logger(LOGFILE)
lower = im.crop((0, 61.111 * vh, 100 * vw, 100 * vh))
logger.logimage(lower)
operation_id = lower.crop(
(0, 4.444 * vh, 23.611 * vh, 11.388 * vh)).convert('L')
logger.logimage(operation_id)
operation_id = imgops.enhance_contrast(imgops.crop_blackedge(operation_id),
80, 220)
logger.logimage(operation_id)
operation_id_str = reco_novecento_bold.recognize(operation_id).upper()
# FIXME: recognizer can't recognize [0o], [-i] well (the game uses sᴍᴀʟʟ ᴄᴀᴘs and the font has sᴍᴀʟʟ ᴄᴀᴘs in ASCII range)
# FIXME: currently, we have no 'o' and 'i' in recognizer data as '0' and '-' are used more frequently
# operation_name = lower.crop((0, 14.074*vh, 23.611*vh, 20*vh)).convert('L')
# operation_name = imgops.enhance_contrast(imgops.crop_blackedge(operation_name))
# logger.logimage(operation_name)
stars = lower.crop((23.611 * vh, 6.759 * vh, 53.241 * vh, 16.944 * vh))
logger.logimage(stars)
stars_status = tell_stars(stars)
level = lower.crop((63.148 * vh, 4.444 * vh, 73.333 * vh, 8.611 * vh))
logger.logimage(level)
exp = lower.crop((76.852 * vh, 5.556 * vh, 94.074 * vh, 7.963 * vh))
logger.logimage(exp)
items = lower.crop((68.241 * vh, 10.926 * vh, lower.width, 35.000 * vh))
logger.logimage(items)
x, y = 6.667 * vh, 18.519 * vh
linedet = items.crop((x, y, x + 1, items.height)).convert('L')
d = np.asarray(linedet)
linetop, linebottom, *_ = find_jumping(d.reshape(linedet.height), 32)
linetop += y
linebottom += y
grouping = items.crop((0, linetop, items.width, linebottom))
grouping = grouping.resize((grouping.width, 1), Image.BILINEAR)
grouping = grouping.convert('L')
logger.logimage(grouping.resize((grouping.width, 16)))
d = np.array(grouping, dtype=np.int16)[0]
points = [0, *find_jumping(d, 32)]
assert (len(points) % 2 == 0)
finalgroups = list(zip(*[iter(points)] * 2)) # each_slice(2)
logger.logtext(repr(finalgroups))
imggroups = [
items.crop((x1, 0, x2, items.height)) for x1, x2 in finalgroups
]
items = []
session = RecognizeSession()
session.vw = vw
session.vh = vh
for group in imggroups:
result = tell_group(group, session, linetop, linebottom)
session.recognized_groups.append(result[0])
items.append(result)
t1 = time.monotonic()
if session.low_confidence:
logger.logtext('LOW CONFIDENCE')
logger.logtext('time elapsed: %f' % (t1 - t0))
return {
'operation': operation_id_str,
'stars': stars_status,
'items': items,
'low_confidence': session.low_confidence
}