![](http://www.fwqtg.net/wp-content/uploads/2023/06/frc-cf0d92129138e2c138e143696168013a.png)
1 Airtest简介
Airtest是一个跨平台的、基于图像识别的UI自动化测试框架,适用于游戏和App,支持平台有Windows、Android和iOS。Airtest框架基于一种图形脚本语言Sikuli,引用该框架后,不再需要一行行的写代码,通过截取按钮或输入框的图片,用图片组成测试场景,这种方式学习成本低,简单易上手。
2 Airtest实践
APP接入流水线过程中,赛博平台只支持air脚本,因此需要对京管家APP的UI自动化脚本进行的改造。如截图可见,AirtestIDE的主界面由菜单栏、快捷工具栏和多个窗口组成,初始布局中的“设备窗口”是工具的设备连接交互区域。
air脚本生成步骤:
- 通过adb连接手机或模拟器
- 安装应用APK
- 运行应用并截图
- 模拟用户输入(点击、滑动、按键)
- 卸载应用
通过以上步骤自动生成了 .air脚本,调试过程中我们可以在IDE中运行代码,支持多行运行以及单行运行,调试通过后可在本地或服务器以命令行的方式运行脚本:
.air脚本运行方式:airtest run “path to your .air dir” —device Android
.air脚本生成报告的方式:airtest report “path to your .air dir”
3 Airtest定位方式解析
IDE的log查看窗口会时时打印脚本执行的日志,从中可以看出通过图片解析执行位置的过程。下面就以touch方法为例,解析Airtest如何通过图片获取到元素位置从而触发点击操作。
@logwrap
def touch(v, times=1, **kwargs):
"""
Perform the touch action on the device screen
:param v: target to touch, either a ``Template`` instance or absolute coordinates (x, y)
:param times: how many touches to be performed
:param kwargs: platform specific `kwargs`, please refer to corresponding docs
:return: finial position to be clicked, e.g. (100, 100)
:platforms: Android, Windows, iOS
"""
if isinstance(v, Template):
pos = loop_find(v, timeout=ST.FIND_TIMEOUT)
else:
try_log_screen()
pos = v
for _ in range(times):
G.DEVICE.touch(pos, **kwargs)
time.sleep(0.05)
delay_after_operation()
return pos
click = touch # click is alias of t
该方法通过loop_find获取坐标,然后执行点击操作 G.DEVICE.touch(pos, kwargs),接下来看loop_find如何根据模板转换为坐标。
@logwrap
def loop_find(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: %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:
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:
t
首先截取手机屏幕match_pos = query.match_in(screen),然后对比传参图片与截屏来获取图片所在位置match_pos = query.match_in(screen)。接下来看match_in方法的逻辑:
def match_in(self, screen):
match_result = self._cv_match(screen)
G.LOGGING.debug("match result: %s", match_result)
if not match_result:
return None
focus_pos = TargetPos().getXY(match_result, self.target_pos)
return focus_pos
里面有个关键方法:match_result = self._cv_match(screen)
@logwrap
def _cv_match(self, screen):
# in case image file not exist in current directory:
ori_image = self._imread()
image = self._resize_image(ori_image, screen, ST.RESIZE_METHOD)
ret = None
for method in ST.CVSTRATEGY:
# get function definition and execute:
func = MATCHING_METHODS.get(method, None)
if func is None:
raise InvalidMatchingMethodError("Undefined method in CVSTRATEGY: '%s', try 'kaze'/'brisk'/'akaze'/'orb'/'surf'/'sift'/'brief' instead." % method)
else:
if method in ["mstpl", "gmstpl"]:
ret = self._try_match(func, ori_image, screen, threshold=self.threshold, rgb=self.rgb, record_pos=self.record_pos,
resolution=self.resolution, scale_max=self.scale_max, scale_step=self.scale_step)
else:
ret = self._try_match(func, image, screen, threshold=self.threshold, rgb=self.rgb)
if ret:
break
return ret
首先读取图片调整图片尺寸,从而提升匹配成功率:
image = self._resize_image(ori_image, screen, ST.RESIZE_METHOD)
接下来是循环遍历匹配方法for method in ST.CVSTRATEGY。而ST.CVSTRATEGY的枚举值:
CVSTRATEGY = ["mstpl", "tpl", "surf", "brisk"]
if LooseVersion(cv2.__version__) > LooseVersion('3.4.2'):
CVSTRATEGY = ["mstpl", "tpl", "sift", "brisk"]
func = MATCHING_METHODS.get(method, None),func可能的取值有mstpl、tpl、surf、shift、brisk,无论哪种模式都调到了共同的方法_try_math
if method in ["mstpl", "gmstpl"]:
ret = self._try_match(func, ori_image, screen, threshold=self.threshold, rgb=self.rgb, record_pos=self.record_pos,
resolution=self.resolution, scale_max=self.scale_max, scale_step=self.scale_step)
else:
ret = self._try_match(func, image, screen, threshold=self.threshold, rgb=self.rgb)
而_try_math方法中都是调用的func的方法find_best_result()
@staticmethod
def _try_match(func, *args, **kwargs):
G.LOGGING.debug("try match with %s" % func.__name__)
try:
ret = func(*args, **kwargs).find_best_result()
except aircv.NoModuleError as err:
G.LOGGING.warning("'surf'/'sift'/'brief' is in opencv-contrib module. You can use 'tpl'/'kaze'/'brisk'/'akaze'/'orb' in CVSTRATEGY, or reinstall opencv with the contrib module.")
return None
except aircv.BaseError as err:
G.LOGGING.debug(repr(err))
return None
else:
return ret
以TemplateMatching类的find_best_result()为例,看一下内部逻辑如何实现。
@print_run_time
def find_best_result(self):
"""基于kaze进行图像识别,只筛选出最优区域."""
"""函数功能:找到最优结果."""
# 第一步:校验图像输入
check_source_larger_than_search(self.im_source, self.im_search)
# 第二步:计算模板匹配的结果矩阵res
res = self._get_template_result_matrix()
# 第三步:依次获取匹配结果
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
h, w = self.im_search.shape[:2]
# 求取可信度:
confidence = self._get_confidence_from_matrix(max_loc, max_val, w, h)
# 求取识别位置: 目标中心 + 目标区域:
middle_point, rectangle = self._get_target_rectangle(max_loc, w, h)
best_match = generate_result(middle_point, rectangle, confidence)
LOGGING.debug("[%s] threshold=%s, result=%s" % (self.METHOD_NAME, self.threshold, best_match))
return best_match if confidence >= self.threshold else Non
重点看第二步:计算模板匹配的结果矩阵res,res = self._get_template_result_matrix()
def _get_template_result_matrix(self):
"""求取模板匹配的结果矩阵."""
# 灰度识别: cv2.matchTemplate( )只能处理灰度图片参数
s_gray, i_gray = img_mat_rgb_2_gray(self.im_search), img_mat_rgb_2_gray(self.im_source)
return cv2.matchTemplate(i_gray, s_gray, cv2.TM_CCOEFF_NORMED)
可以看到最终用的是openCV的方法,cv2.matchTemplate,那个优先匹配上就返回结果。
4 总结
使用过程中可以发现Airtest框架有两个缺点:一是对于背景透明的按钮或者控件,识别难度大;二是无法获取文本内容,但这一缺点可通过引入文字识别库解决,如:pytesseract。
对不能用UI控件定位的部件,使用图像识别定位还是非常方便的。UI自动化脚本编写过程中可以将几个框架结合使用,uiautomator定位速度较快,但对于flutter语言写的页面经常有一些部件无法定位,此时可以引入airtest框架用图片进行定位。每个框架都有优劣势,组合使用才能更好的实现目的。
作者:京东物流 范文君
来源:京东云开发者社区
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