def assert_file_exist(file, flag=None):
if flag is None:
if not os.path.exists(file):
raise TargetNotFoundError("%s is not exist" % file)
else:
index = file.rindex('/')
start = file[:index]
end = file[index + 1:]
for fi in os.listdir(start):
if end in fi:
index = -1
if index != -1:
raise TargetNotFoundError("%s is not exist" % file)
def assert_ocr_true(start_x,
start_y,
end_x,
end_y,
pre,
flag=1,
val=1,
language=None):
img = ImageGrab.grab(bbox=(start_x, start_y, end_x, end_y))
real = ""
if language is None:
real = pytesseract.image_to_string(
cv2.threshold(cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY), 0,
255, cv2.THRESH_OTSU)[1]).strip()
else:
img = cv2.threshold(cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY), 0,
255, cv2.THRESH_OTSU)[1]
img = cv2.resize(img, None, fx=8, fy=8)
real = pytesseract.image_to_string(img, lang='chi_sim').strip()
if real == pre:
return
elif pre in real and flag == 2:
return
elif Levenshtein.distance(pre, real) < val and flag == 3:
return
raise TargetNotFoundError("The OCR result is{}, not {}".format(real, pre))
def assert_word_exist(file, row, words_given):
f = open(file, 'rb')
offset = -50
file_size = os.stat(file).st_size
assert_word_exist_flag = False
beginning_of_file_flag = False
while True:
if -offset > file_size:
f.seek(0, 0)
beginning_of_file_flag = True
else:
f.seek(offset, 2)
lines = f.readlines()
if len(lines) >= row + 1 or (len(lines) == row
and beginning_of_file_flag):
if row > 1:
words_in_file = lines[-row:]
words_in_file_co = ''
for j in range(len(words_in_file)):
words_in_file_co += str(words_in_file[j], encoding='gbk')
words_in_file_co = words_in_file_co.replace('\r\n', '')
if words_given in words_in_file_co:
assert_word_exist_flag = True
else:
words_in_file = lines[-1]
words_in_file = str(words_in_file, encoding='gbk')
if words_given in words_in_file:
assert_word_exist_flag = True
break
else:
offset *= 2
if assert_word_exist_flag is False:
raise TargetNotFoundError(
"{} is not on the last {} lines of {}".format(
words_given, row, file))
def loop_find_vanish(query,
timeout=ST.FIND_TIMEOUT,
threshold=None,
interval=0.5,
intervalfunc=None):
G.LOGGING.info("Try finding: %s", query)
start_time = time.time()
while True:
screen = G.DEVICE.snapshot(filename=None, quality=ST.SNAPSHOT_QUALITY)
if screen is None:
G.LOGGING.warning("Screen is None, may be locked")
else:
if threshold:
query.threshold = threshold
match_pos = query.match_in(screen)
if match_pos:
time.sleep(interval)
else:
try_log_screen(screen)
return match_pos
if intervalfunc is not None:
intervalfunc()
# 超时则raise，未超时则进行下次循环:
if (time.time() - start_time) > timeout:
try_log_screen(screen)
raise TargetNotFoundError('Picture %s not vanish in screen' %
query)
else:
time.sleep(interval)
def wati_util(v, condition, timeout=60, interval=0.5):
"""等待符合条件的对象
"""
start_time = time.time()
while True:
ret = find_all(v)
if condition(ret):
return ret
if (time.time() - start_time) > timeout:
raise TargetNotFoundError('Continue %s not found in screen' % v)
time.sleep(interval)
def select(vs, timeout=60, interval=0.5):
"""等待多个对象，返回第一个匹配到的对象
"""
start_time = time.time()
while True:
for idx, v in enumerate(vs):
ret = find_all(v)
if ret:
return idx, ret
if (time.time() - start_time) > timeout:
raise TargetNotFoundError('Continue %s not found in screen' % v)
time.sleep(interval)
def assert_exists(msg=""):
try:
r = self.exists()
if r is False:
raise TargetNotFoundError("找不到对象:{0}".format(self.kw))
except TargetNotFoundError:
if self.is_airtest:
raise AssertionError(
"%s does not exist in screen, message: %s" %
(self.airtest_ui, msg))
else:
v = self.poco_ui.query
raise AssertionError(
"%s does not exist in screen, message: %s" % (v, msg))
def assert_client_exist(order):
data = sock.recv(1024)
message = data.decode()
client_message = data
if len(message) == 18:
client_message = data[12:18]
sock.send(client_message)
else:
client_not_message = data
sock.send(client_not_message)
if client_message.decode() == "CCCCCC":
pass
else:
raise TargetNotFoundError(order)
def loop_find4(total_pic,
query,
timeout=ST.FIND_TIMEOUT,
threshold=None,
interval=0.5,
intervalfunc=None):
"""
Search for image template in the screen until timeout
Args:
query: image template to be found in screenshot
timeout: time interval how long to look for the image template
threshold: default is None
interval: sleep interval before next attempt to find the image template
intervalfunc: function that is executed after unsuccessful attempt to find the image template
Raises:
TargetNotFoundError: when image template is not found in screenshot
Returns:
TargetNotFoundError if image template not found, otherwise returns the position where the image template has
been found in screenshot
"""
G.LOGGING.info("Try finding:\n%s", query)
start_time = time.time()
while True:
# screen = G.DEVICE.snapshot(filename=None, quality=ST.SNAPSHOT_QUALITY)
screen = aircv.imread(total_pic)
if screen is None:
G.LOGGING.warning("Screen is None, may be locked")
else:
if threshold:
query.threshold = threshold
match_pos = query.match_in(screen)
if match_pos:
try_log_screen(screen)
return match_pos
if intervalfunc is not None:
intervalfunc()
# 超时则raise，未超时则进行下次循环:
if (time.time() - start_time) > timeout:
try_log_screen(screen)
raise TargetNotFoundError('Picture %s not found in screen' % query)
else:
time.sleep(interval)
def loop_find(query,
driver=None,
timeout=10,
threshold=None,
interval=0.5,
intervalfunc=None):
"""
Search for image template in the screen until timeout
Args:
query: image template to be found in screenshot
timeout: time interval how long to look for the image template
threshold: default is None
interval: sleep interval before next attempt to find the image template
intervalfunc: function that is executed after unsuccessful attempt to find the image template
Raises:
TargetNotFoundError: when image template is not found in screenshot
Returns:
TargetNotFoundError if image template not found, otherwise returns the position where the image template has
been found in screenshot
"""
threshold = 0.6
start_time = time.time()
while True:
screen = driver.screenshot()
query.resolution = get_resolution(screen)
if screen is None:
print("Screen is None, may be locked")
else:
if threshold:
query.threshold = threshold
match_pos = query.match_in(screen)
if match_pos:
try_log_screen(screen)
return match_pos
if intervalfunc is not None:
intervalfunc()
# 超时则raise，未超时则进行下次循环:
if (time.time() - start_time) > timeout:
try_log_screen(screen)
raise TargetNotFoundError('Picture %s not found in screen' % query)
else:
time.sleep(interval)
def assert_exists(v, msg="", timeout=ST.FIND_TIMEOUT):
"""
Assert target exists on device screen
:param v: target to be checked
:param msg: short description of assertion, it will be recorded in the report
:raise AssertionError: if assertion fails
:return: coordinates of the target
:platforms: Android, Windows, iOS
:Example:
>>> assert_exists(Template(r"tpl1607324047907.png"), "assert exists")
"""
try:
pos = loop_find(v,
timeout=timeout,
threshold=ST.THRESHOLD_STRICT or v.threshold)
return pos
except TargetNotFoundError:
raise TargetNotFoundError("%s does not exist in screen, message: %s" %
(v, msg))
def assert_lcd_true(start_x, start_y, end_x, end_y, pre, flag=1, val=1):
img = ImageGrab.grab(bbox=(start_x, start_y, end_x, end_y))
# 定义每一个数字对应的字段
DIGITS_LOOKUP = {
(0, 1, 1, 0, 1, 1, 0): '0凹',
(1, 1, 1, 0, 1, 1, 0): '0反凹',
(1, 1, 1, 0, 1, 1, 1): 0,
(0, 0, 1, 0, 0, 1, 0): 1,
(1, 0, 1, 1, 1, 0, 1): 2,
(1, 0, 1, 1, 0, 1, 1): 3,
(0, 1, 1, 1, 0, 1, 0): 4,
(1, 1, 0, 1, 0, 1, 1): 5,
(1, 1, 0, 1, 1, 1, 1): 6,
(1, 0, 1, 0, 0, 1, 0): 7,
(1, 1, 1, 1, 1, 1, 1): 8,
(1, 1, 1, 1, 0, 1, 1): 9,
(0, 0, 0, 0, 0, 0, 0): '',
(1, 1, 1, 1, 0, 0, 0): '0之横',
(1, 1, 1, 0, 0, 0, 0): '0之横'
}
# 将输入转换为灰度图片
gray = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY)
# 使用阈值进行二值化
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)[1]
non_zero = cv2.countNonZero(thresh)
# 如果不是黑底白字就转为黑底白字
if non_zero > img.size * 0.5:
for i in range(img.shape[0]):
for j in range(img.shape[1]):
thresh[i, j] = 255 - thresh[i, j]
kernel = (3, 3)
if end_x - start_x < 150:
kernel = (1, 1)
median = cv2.medianBlur(thresh, 7)
close = cv2.dilate(median, kernel, iterations=7)
# 在阈值图像中查找轮廓，然后初始化数字轮廓列表
cnts = cv2.findContours(close.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
digitCnts = []
w_sum = 0.
h_sum = 0.
# 循环遍历所有的候选区域
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
w_sum += w
h_sum += h
digitCnts.append(c)
# 从左到右对这些轮廓进行排序
w_ave = w_sum / len(digitCnts)
h_ave = h_sum / len(digitCnts)
digitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0]
digits = ""
dc = cv2.drawContours(np.array(img).copy(), digitCnts, -1, (0, 0, 255), 2)
# 循环处理每一个数字
i = 0
for index, c in enumerate(digitCnts):
# 获取ROI区域
(x, y, w, h) = cv2.boundingRect(c)
roi = thresh[y:y + h, x:x + w]
if index is 0 and w > h * 2:
digits += '-'
if h / w > 6:
digits += '1'
continue
elif 3 < h / w < 5 and h > (h_ave * 3 / 4):
digits += '半1'
continue
elif w <= (w_ave / 2) and h < (h_ave / 2):
digits += '.'
continue
# 分别计算每一段的宽度和高度
(roiH, roiW) = roi.shape
(dW, dH) = (int(roiW * 0.25), int(roiH * 0.15))
dHC = int(roiH * 0.05)
# 定义一个7段数码管的集合
segments = [
((0, 0), (w, dH)), # 上
((0, 0), (dW, h // 2)), # 左上
((w - dW, 0), (w, h // 2)), # 右上
((0, (h // 2) - dHC), (w, (h // 2) + dHC)), # 中间
((0, h // 2), (dW, h)), # 左下
((w - dW, h // 2), (w, h)), # 右下
((0, h - dH), (w, h)) # 下
]
on = [0] * len(segments)
# 循环遍历数码管中的每一段
for (i, ((xA, yA),
(xB,
yB))) in enumerate(segments): # 检测分割后的ROI区域，并统计分割图中的阈值像素点
segROI = roi[yA:yB, xA:xB]
total = cv2.countNonZero(segROI)
area = (xB - xA) * (yB - yA)
# 如果非零区域的个数大于整个区域的一半，则认为该段是亮的
if area == 0:
continue
if total / float(area) > 0.5:
on[i] = 1
if total / float(area) == 1.0:
on[i] = 0
# 进行数字查询并显示结果
digit = DIGITS_LOOKUP[tuple(on)]
digits += str(digit)
digits = digits.replace('0凹0反凹', '0').replace(
'半1半10之横0之横半1半1',
'0').replace('半1半1', '1').replace('0凹',
'0').replace('0反凹',
'0').replace('半1', '')
if digits == pre:
return
elif digits in pre and flag == 2:
return
elif Levenshtein.distance(pre, digits) < val and flag == 3:
return
raise TargetNotFoundError(
"The result of LCD recognization is{}, not {}".format(digits, pre))