def _match_auto(self, screen, search_img, threshold):
"""Maybe not a good idea
"""
# 1. try template first
ret = ac.find_template(screen, search_img)
if ret and ret['confidence'] > threshold:
return FindPoint(ret['result'], ret['confidence'], consts.IMAGE_MATCH_METHOD_TMPL, matched=True)
# 2. try sift
ret = ac.find_sift(screen, search_img, min_match_count=10)
if ret is None:
return None
matches, total = ret['confidence']
if 1.0*matches/total > 0.5: # FIXME(ssx): sift just write here
return FindPoint(ret['result'], ret['confidence'], consts.IMAGE_MATCH_METHOD_SIFT, matched=True)
return None
def match(self, pattern, screen=None, rect=None, offset=None, threshold=None, method=None):
"""Check if image position in screen
Args:
- pattern: Image file name or opencv image object
- screen (PIL.Image): optional, if not None, screenshot method will be called
- threshold (float): it depends on the image match method
- method (string): choices on <template | sift>
Returns:
None or FindPoint, For example:
FindPoint(pos=(20, 30), method='tmpl', confidence=0.801, matched=True)
Only when confidence > self.image_match_threshold, matched will be True
Raises:
TypeError: when image_match_method is invalid
"""
pattern = self.pattern_open(pattern)
search_img = pattern.image
pattern_scale = self._cal_scale(pattern)
if pattern_scale != 1.0:
search_img = cv2.resize(search_img, (0, 0),
fx=pattern_scale, fy=pattern_scale,
interpolation=cv2.INTER_CUBIC)
screen = screen or self.region_screenshot()
threshold = threshold or pattern.threshold or self.image_match_threshold
# handle offset if percent, ex (0.2, 0.8)
dx, dy = offset or pattern.offset or (0, 0)
dx = pattern.image.shape[1] * dx # opencv object width
dy = pattern.image.shape[0] * dy # opencv object height
dx, dy = int(dx*pattern_scale), int(dy*pattern_scale)
# image match
screen = imutils.from_pillow(screen) # convert to opencv image
if rect and isinstance(rect, tuple) and len(rect) == 4:
(x0, y0, x1, y1) = [int(v*pattern_scale) for v in rect]
(dx, dy) = dx+x0, dy+y0
screen = imutils.crop(screen, x0, y0, x1, y1)
#cv2.imwrite('cc.png', screen)
match_method = method or self.image_match_method
ret = None
confidence = None
matched = False
position = None
if match_method == consts.IMAGE_MATCH_METHOD_TMPL: #IMG_METHOD_TMPL
ret = ac.find_template(screen, search_img)
if ret is None:
return None
confidence = ret['confidence']
if confidence > threshold:
matched = True
(x, y) = ret['result']
position = (x+dx, y+dy) # fix by offset
elif match_method == consts.IMAGE_MATCH_METHOD_SIFT:
ret = ac.find_sift(screen, search_img, min_match_count=10)
if ret is None:
return None
confidence = ret['confidence']
matches, total = confidence
if 1.0*matches/total > 0.5: # FIXME(ssx): sift just write here
matched = True
(x, y) = ret['result']
position = (x+dx, y+dy) # fix by offset
elif match_method == consts.IMAGE_MATCH_METHOD_AUTO:
fp = self._match_auto(screen, search_img, threshold)
if fp is None:
return None
(x, y) = fp.pos
position = (x+dx, y+dy)
return FindPoint(position, fp.confidence, fp.method, fp.matched)
else:
raise TypeError("Invalid image match method: %s" %(match_method,))
(x, y) = ret['result']
position = (x+dx, y+dy) # fix by offset
if self.bounds:
x, y = position
position = (x+self.bounds.left, y+self.bounds.top)
return FindPoint(position, confidence, match_method, matched=matched)
def match(self,
pattern,
screen=None,
rect=None,
offset=None,
threshold=None,
method=None):
pattern = self.pattern_open(pattern)
search_img = pattern.image
pattern_scale = self._cal_scale(pattern)
if pattern_scale != 1.0:
search_img = cv2.resize(search_img, (0, 0),
fx=pattern_scale,
fy=pattern_scale,
interpolation=cv2.INTER_CUBIC)
screen = screen or self.region_screenshot()
threshold = threshold or pattern.threshold or self.image_match_threshold
# handle offset if percent, ex (0.2, 0.8)
dx, dy = offset or pattern.offset or (0, 0)
dx = pattern.image.shape[1] * dx # opencv object width
dy = pattern.image.shape[0] * dy # opencv object height
dx, dy = int(dx * pattern_scale), int(dy * pattern_scale)
# image match
screen = imutils.from_pillow(screen) # convert to opencv image
if rect and isinstance(rect, tuple) and len(rect) == 4:
(x0, y0, x1, y1) = [v * pattern_scale for v in rect]
(dx, dy) = dx + x0, dy + y0
screen = imutils.crop(screen, x0, y0, x1, y1)
# cv2.imwrite('cc.png', screen)
match_method = method or self.image_match_method
ret = None
confidence = None
matched = False
if match_method == consts.IMAGE_MATCH_METHOD_TMPL: # IMG_METHOD_TMPL
ret = ac.find_template(screen, search_img)
if ret is None:
return None
confidence = ret['confidence']
if confidence > threshold:
matched = True
(x, y) = ret['result']
position = (x + dx, y + dy) # fix by offset
else:
ret_all = ac.find_all_template(screen, search_img, maxcnt=10)
if not ret_all:
return None
for ret in ret_all:
confidence = ret['confidence']
if confidence > threshold:
(x, y) = ret['rectangle'][0]
color_screen = screen[y, x, 2]
color_img = search_img[0, 0, 2]
if -10 < int(color_img) - int(color_screen) < 10:
matched = True
break
(x, y) = ret['result']
position = (x + dx, y + dy) # fix by offset
if self.bounds:
x, y = position
position = (x + self.bounds.left, y + self.bounds.top)
return FindPoint(position, confidence, match_method, matched=matched)