def __init__(self, helper):
super().__init__()
self.helper: BaseAutomator = helper
self.logger = logging.getLogger(type(self).__name__)
self.richlogger = richlog.get_logger(type(self).__name__)
self._sync_handler = None
self.on_attach()
def tell_item(itemimg, with_quantity=True, learn_unrecognized=False):
logger = get_logger(__name__)
logger.logimage(itemimg)
from . import itemdb
# 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')
low_confidence = False
quantity = None
if with_quantity:
numimg = imgops.scalecrop(itemimg, 0.39, 0.71, 0.82, 0.84).convert('L')
numimg = imgops.crop_blackedge2(numimg, 120)
if numimg is not None:
numimg = imgops.clear_background(numimg, 120)
logger.logimage(numimg)
numtext, score = itemdb.num_recognizer.recognize2(
numimg, subset='0123456789万')
logger.logtext('quantity: %s, minscore: %f' % (numtext, score))
if score < 0.2:
low_confidence = True
quantity = int(numtext) if numtext.isdigit() else None
# scale = 48/itemimg.height
img4reco = np.array(
itemimg.resize((48, 48), Image.BILINEAR).convert('RGB'))
img4reco[itemdb.itemmask] = 0
scores = []
for name, templ in itemdb.itemmats.items():
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])
item_type = None
if score < 800 and np.any(diffs > 600):
logger.logtext('matched %s with mse %f' % (itemname, score))
name = itemname
else:
prob, item_id, name, item_type = get_item_id(
common.convert_to_cv(itemimg))
logger.logtext(f'dnn matched {name} with prob {prob}')
if prob < 0.8 or item_id == 'other':
logger.logtext('no match')
low_confidence = True
name = None
if name is None and learn_unrecognized:
name = itemdb.add_item(itemimg)
return RecognizedItem(name, quantity, low_confidence, item_type)
def tell_item(itemimg, with_quantity=True):
logger = get_logger(__name__)
logger.logimage(itemimg)
cached = load_data()
# 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')
low_confidence = False
quantity = None
if with_quantity:
numimg = imgops.scalecrop(itemimg, 0.39, 0.71, 0.82, 0.84).convert('L')
numimg = imgops.crop_blackedge2(numimg, 120)
if numimg is not None:
numimg = imgops.clear_background(numimg, 120)
logger.logimage(numimg)
numtext, score = cached.num_recognizer.recognize2(
numimg, subset='0123456789万')
logger.logtext('quantity: %s, minscore: %f' % (numtext, score))
if score < 0.2:
low_confidence = True
quantity = int(numtext) if numtext.isdigit() else None
# scale = 48/itemimg.height
img4reco = np.array(
itemimg.resize((48, 48), Image.BILINEAR).convert('RGB'))
img4reco[cached.itemmask] = 0
scores = []
for name, templ in cached.itemmats.items():
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')
low_confidence = True
name = None
return RecognizedItem(name, quantity, low_confidence)
from fractions import Fraction
import numpy as np
from PIL import Image
from util.richlog import get_logger
from . import imgops
from . import resources
from . import util
logger = get_logger(__name__)
def check_main(img):
vw, vh = util.get_vwvh(img.size)
gear1 = img.crop(
(3.148 * vh, 2.037 * vh, 9.907 * vh, 8.796 * vh)).convert('L')
gear2 = resources.load_image_cached('main/gear.png', 'L')
gear1, gear2 = imgops.uniform_size(gear1, gear2)
result = imgops.compare_ccoeff(gear1, gear2)
# result = np.corrcoef(np.asarray(gear1).flat, np.asarray(gear2).flat)[0, 1]
logger.logimage(gear1)
logger.logtext('ccoeff=%f' % result)
return result > 0.9
def get_ballte_corners(img):
"""
:returns: [0][1]
[3][2]
"""
