def _match_auto(self, screen, search_img, threshold):
"""Maybe not a good idea
"""
# 1. try template first
ret = ac.find_template(screen, search_img)
print "template/conficence:", ret
if ret and ret['confidence'] > threshold:
return FindPoint(ret['result'],
ret['confidence'],
'template',
matched=True)
# 2. try sift
ret = ac.find_sift(screen, search_img, min_match_count=10)
print "sift/conficence:", ret
if ret is None:
return None
matches, total = ret['confidence']
print "other/conficence:", matches, total
if 1.0 * matches / total > 0.5: # FIXME(ssx): sift just write here
return FindPoint(ret['result'],
ret['confidence'],
'sift',
matched=True)
return None
def click(self, imgsrc, imgobj):
'''
@Time : 2018/9/17
@Author : 诸葛小心
@File : Locate_Image_Click.py
instruction:
基于aircv图像识别，传入待查找的图片、原图，则定位到带查找图片在原图上的坐标点
usage:
imgsr = 'imgsr.png'
imgobj = 'imgobj.png'
self.Locate_Image_Click.click(imgsrc, imgobj)
'''
imsrc = ac.imread(imgsrc) # 原始图像
imsch = ac.imread(imgobj) # 待查找的部分
position = ac.find_sift(imsrc, imsch)
# print(position)
x, y = position['result']
print("x = ", x)
print("y = ", y)
self.driver.swipe(x, y, x, y, 50)
def sift_test():
t1 = ac.imread("testdata/1s.png")
t2 = ac.imread("testdata/2s.png")
print ac.sift_count(t1), ac.sift_count(t2)
result = ac.find_sift(t1, t2, min_match_count=ac.sift_count(t1)*0.4) # after many tests, 0.4 may be best
if result:
print 'Same'
else:
print 'Not same'
def get_money_pos(pic):
im = ac.imread(pic)
im2 = ac.imread('standard/money.png')
try:
match_result = ac.find_sift(im, im2)
if match_result and match_result['result'][0] > 0 and match_result[
'confidence'][1] > 10:
return match_result['result']
except Exception as e:
print('get_money_pos exception', e)
return
def sift_test():
t1 = ac.imread("tests/testdata/1s.png")
t2 = ac.imread("tests/testdata/2s.png")
print((ac.sift_count(t1), ac.sift_count(t2)))
result = ac.find_sift(t1, t2, min_match_count=ac.sift_count(t1) *
0.4) # after many tests, 0.4 may be best
if result:
print('Same')
else:
print('Not same')
def findImage():
imgfound = [None, None, None, None]
# imsrc = ac.imread(self.d.screenshot(os.getcwd() + "/tmp.png"))
# src = self.minicap_ins.crop_image()
# src.save(os.getcwd() + "/tmp.png")
imgsrc = ac.imread("tmp.png")
imobj = ac.imread('w1.720x1512_480x960.png')
rt = ac.find_template(imgsrc, imobj)
print "rt:", rt
rts = ac.find_sift(imgsrc, imobj)
print "snift:", rts
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, threshold=None):
"""Check if image position in screen
Args:
- pattern: Image file name or opencv image object
- screen: opencv image, optional, if not None, screenshot method will be called
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 self.image_match_threshold
dx, dy = pattern.offset
dx, dy = int(dx*pattern_scale), int(dy*pattern_scale)
# image match
screen = imutils.from_pillow(screen) # convert to opencv image
match_method = self.image_match_method
ret = None
if match_method == consts.IMAGE_MATCH_METHOD_TMPL:
ret = ac.find_template(screen, search_img)
elif match_method == consts.IMAGE_MATCH_METHOD_SIFT:
ret = ac.find_sift(screen, search_img, min_match_count=10)
else:
raise TypeError("Invalid image match method: %s" %(match_method,))
if ret is None:
return None
(x, y) = ret['result']
# fix by offset
position = (x+dx, y+dy)
if self.bounds:
x, y = position
position = (x+self.bounds.left, y+self.bounds.top)
confidence = ret['confidence']
matched = True
if match_method == consts.IMAGE_MATCH_METHOD_TMPL:
if confidence < threshold:
matched = False
elif match_method == consts.IMAGE_MATCH_METHOD_SIFT:
matches, total = confidence
if 1.0*matches/total > 0.5: # FIXME(ssx): sift just write here
matched = True
return FindPoint(position, confidence, match_method, matched=matched)
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)
import aircv as ac
import cv2
import matplotlib.pyplot as plt
imsrc = ac.imread('test_images/img_0001_new.png')
imsch = ac.imread('test_images/button.png')
result = ac.find_sift(imsrc, imsch)
result_positon = result['rectangle']
src_img = cv2.imread('test_images/img_0001_new.png', cv2.COLOR_RGB2BGR)
cv2.line(src_img, result_positon[0], result_positon[1], (255, 0, 0), 5)
cv2.line(src_img, result_positon[1], result_positon[2], (255, 0, 0), 5)
cv2.line(src_img, result_positon[2], result_positon[3], (255, 0, 0), 5)
cv2.line(src_img, result_positon[3], result_positon[0], (255, 0, 0), 5)
plt.imshow(src_img)
plt.show()
def main():
bg = aircv.imread('testdata/bg.png')
search = aircv.imread('testdata/guard.png')
print aircv.find_sift(bg, search)
def main():
bg = aircv.imread('testdata/bg.png')
search = aircv.imread('testdata/guard.png')
print aircv.find_sift(bg, search)
def get_image_cv_pos(img_res_path=None,
image=None,
pos_curson="Center",
pos_offsetY=0,
pos_offsetX=0,
confidence_df=0.85,
waitfor=WAIT_FOR_IMG):
'''
截屏并根据图片计算其位置
'''
__logger.debug(r'ready OpenCV Recognition operation')
pos = None
try:
starttime = time.time()
image = img_res_path + '\\res\\' + image
while True:
try:
pic = TEMP_PATH + 'ScreenCapture.png'
screen_capture(pic)
imsrc = ac.imread(pic) # 原始图像
imsch = ac.imread(image)
# imsch = ac.imread('d:/1.png') # 带查找的部分
# print(str(ac.sift_count(r'd:\1.png')))
sift_json = ac.find_sift(imsrc, imsch)
if sift_json == None:
print(
u'sift_json return null，OpenCV cannot recognize picture'
)
else:
sift_dict = json.loads(json.dumps(sift_json))
__logger.warning(
u'OpenCV Identifying return information :' +
str(sift_dict))
#误取信心度
confidence = get_cv_confidence_percent(
sift_dict['confidence'])
if confidence >= confidence_df:
#根据相关信息计算真正位置
pos = cal_position(sift_dict['result'],
sift_dict['rectangle'], pos_curson,
pos_offsetY, pos_offsetX)
if pos[0] < 0 or pos[1] < 0:
__logger.debug(
u'Location information is negative, error')
pos = None
continue
__logger.warning(u'OpenCV confidence :' +
str(confidence))
break
else:
__logger.error(u'OpenCV confidence too low:' +
str(confidence))
except Exception as ex:
print(str(ex), traceback.format_exc())
pass
runtime = time.time() - starttime
if runtime >= waitfor:
break
__logger.debug(
r'OpenCV Recognize failure and wait for the next time ')
time.sleep(TRY_INTERVAL)
except Exception as e:
print(str(e), traceback.format_exc())
finally:
return pos
import aircv as ac
imsrc = ac.imread('tests/testdata/g18/screen_big.png') # 原始图像
imsch = ac.imread('tests/testdata/g18/task.png') # 带查找的部分
print((ac.find_sift(imsrc, imsch)))
def find_sift(source, target):
result = ac.find_sift(source, target)
print(1, 'SIFT匹配结果：')
print(result)
def match(self, pattern, screen=None, threshold=None):
"""Check if image position in screen
Args:
- pattern: Image file name or opencv image object
- screen: opencv image, optional, if not None, screenshot method will be called
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 self.image_match_threshold
dx, dy = pattern.offset
dx, dy = int(dx * pattern_scale), int(dy * pattern_scale)
# image match
screen = imutils.from_pillow(screen) # convert to opencv image
match_method = self.image_match_method
ret = None
if match_method == consts.IMAGE_MATCH_METHOD_TMPL:
ret = ac.find_template(screen, search_img)
elif match_method == consts.IMAGE_MATCH_METHOD_SIFT:
ret = ac.find_sift(screen, search_img, min_match_count=10)
else:
raise TypeError("Invalid image match method: %s" %
(match_method, ))
if ret is None:
return None
(x, y) = ret['result']
# fix by offset
position = (x + dx, y + dy)
if self.bounds:
x, y = position
position = (x + self.bounds.left, y + self.bounds.top)
confidence = ret['confidence']
matched = True
if match_method == consts.IMAGE_MATCH_METHOD_TMPL:
if confidence < threshold:
matched = False
elif match_method == consts.IMAGE_MATCH_METHOD_SIFT:
matches, total = confidence
if 1.0 * matches / total > 0.5: # FIXME(ssx): sift just write here
matched = True
return FindPoint(position, confidence, match_method, matched=matched)
