def snapshot(self, filename=None):
"""
Take a screenshot and save it in ST.LOG_DIR folder
Args:
filename: name of the file to give to the screenshot, {time}.jpg by default
Returns:
display the screenshot
"""
if self.handle:
screen = screenshot(filename, self.handle)
else:
screen = screenshot(filename)
if self.app:
rect = self.get_rect()
screen = aircv.crop_image(screen, [rect.left, rect.top, rect.right, rect.bottom])
if not screen.any():
if self.app:
rect = self.get_rect()
screen = aircv.crop_image(screenshot(filename), [rect.left, rect.top, rect.right, rect.bottom])
if self._focus_rect != (0, 0, 0, 0):
height, width = screen.shape[:2]
rect = (self._focus_rect[0], self._focus_rect[1], width + self._focus_rect[2], height + self._focus_rect[3])
screen = aircv.crop_image(screen, rect)
if filename:
aircv.imwrite(filename, screen)
return screen
def snapshot(self, filename=None, quality=10, max_size=None):
"""
Take a screenshot and save it in ST.LOG_DIR folder
Args:
filename: name of the file to give to the screenshot, {time}.jpg by default
quality: The image quality, integer in range [1, 99]
max_size: the maximum size of the picture, e.g 1200
Returns:
display the screenshot
"""
if self.handle:
screen = screenshot(filename, self.handle)
else:
screen = screenshot(filename)
if self.app:
rect = self.get_rect()
screen = aircv.crop_image(
screen, [rect.left, rect.top, rect.right, rect.bottom])
if not screen.any():
if self.app:
rect = self.get_rect()
screen = aircv.crop_image(
screenshot(filename),
[rect.left, rect.top, rect.right, rect.bottom])
if self._focus_rect != (0, 0, 0, 0):
height, width = screen.shape[:2]
rect = (self._focus_rect[0], self._focus_rect[1],
width + self._focus_rect[2], height + self._focus_rect[3])
screen = aircv.crop_image(screen, rect)
if filename:
aircv.imwrite(filename, screen, quality, max_size=max_size)
return screen
def snapshot(self, filename="tmp.png"):
"""
Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
"""
if not filename:
filename = "tmp.png"
if self.handle:
screen = screenshot(filename, self.handle)
else:
screen = screenshot(filename)
if self.app:
rect = self.get_rect()
screen = aircv.crop_image(screen, [rect.left, rect.top, rect.right, rect.bottom])
if self._focus_rect != (0, 0, 0, 0):
height, width = screen.shape[:2]
rect = (self._focus_rect[0], self._focus_rect[1], width + self._focus_rect[2], height + self._focus_rect[3])
screen = aircv.crop_image(screen, rect)
if filename:
aircv.imwrite(filename, screen)
return screen
def _find_keypoint_result_in_predict_area(self, func, image, screen):
G.LOGGING.debug("teststtsstt")
if not self.record_pos:
return None
# calc predict area in screen
image_wh, screen_resolution = aircv.get_resolution(
image), aircv.get_resolution(screen)
xmin, ymin, xmax, ymax = Predictor.get_predict_area(
self.record_pos, image_wh, self.resolution, screen_resolution)
G.LOGGING.debug("111111111111111111111try match with %s" % xmin)
# crop predict image from screen
predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
if not predict_area.any():
return None
# keypoint matching in predicted area:
ret_in_area = func(image,
predict_area,
threshold=self.threshold,
rgb=self.rgb)
# calc cv ret if found
if not ret_in_area:
return None
ret = deepcopy(ret_in_area)
if "rectangle" in ret:
for idx, item in enumerate(ret["rectangle"]):
ret["rectangle"][idx] = (item[0] + xmin, item[1] + ymin)
ret["result"] = (ret_in_area["result"][0] + xmin,
ret_in_area["result"][1] + ymin)
return ret
def find_best_result(self):
"""函数功能：找到最优结果."""
if self.resolution!=():
# 第一步：校验图像输入
check_source_larger_than_search(self.im_source, self.im_search)
if self.resolution[0]<self.im_search.shape[1] or self.resolution[1]<self.im_search.shape[0]:
raise TemplateInputError("error: resolution is too small.")
# 第二步：计算模板匹配的结果矩阵res
if not self.record_pos is None:
area, self.resolution = self._get_area_scope(self.im_source, self.im_search, self.record_pos, self.resolution)
self.im_source = aircv.crop_image(self.im_source, area)
check_source_larger_than_search(self.im_source, self.im_search)
r_min, r_max = self._get_ratio_scope(
self.im_source, self.im_search, self.resolution)
s_gray, i_gray = img_mat_rgb_2_gray(self.im_search), img_mat_rgb_2_gray(self.im_source)
confidence, max_loc, w, h, _ = self.multi_scale_search(
i_gray, s_gray, ratio_min=r_min, ratio_max=r_max, step=self.scale_step, threshold=self.threshold)
if not self.record_pos is None:
max_loc = (max_loc[0] + area[0], max_loc[1] + area[1])
# 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 None
else:
return None
def _find_sift_in_predict_area(self, image, screen):
if not self.record_pos:
return None
# calc predict area in screen
image_wh, screen_resolution = aircv.get_resolution(
image), aircv.get_resolution(screen)
xmin, ymin, xmax, ymax = Predictor.get_predict_area(
self.record_pos, image_wh, self.resolution, screen_resolution)
# crop predict image from screen
predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
if not predict_area.any():
return None
# find sift in that image
ret_in_area = aircv.find_sift(predict_area,
image,
threshold=self.threshold,
rgb=self.rgb)
# calc cv ret if found
if not ret_in_area:
return None
ret = deepcopy(ret_in_area)
if "rectangle" in ret:
for idx, item in enumerate(ret["rectangle"]):
ret["rectangle"][idx] = (item[0] + xmin, item[1] + ymin)
ret["result"] = (ret_in_area["result"][0] + xmin,
ret_in_area["result"][1] + ymin)
return ret
def crop_image(self, rect: list):
"""局部截图
:param rect = [x_min, y_min, x_max ,y_max].
:return filepath 图片路径
"""
# 局部截图
img = G.DEVICE.snapshot()
crop_screen = crop_image(img, rect)
# 生成截图路径
filename = "%(time)d.jpg" % {'time': timestamp() * 1000}
filepath = os.path.join(ST.LOG_DIR, filename)
# 保存局部截图到logs文件夹中
pil_image = cv2_2_pil(crop_screen)
pil_image.save(filepath, quality=99, optimize=True)
return filepath
def Record(self, bbox):
import time
import airtest.core.win.screen as screen
threading.current_thread.name = 'recording'
while True:
if not self.m_bRecording:
break
self.m_Lock.acquire()
print('Recording...', (bbox[2] - bbox[0], bbox[3] - bbox[1]))
try:
im = aircv.crop_image(screen.screenshot(None), bbox)
self.video.write(numpy.array(im)) # 将img convert ndarray
except:
pass
self.m_Lock.release()
time.sleep(INTERVAL)
def _find_sift_in_predict_area(self, image, screen):
if not self.record_pos:
return None
# calc predict area in screen
screen_resolution = aircv.get_resolution(screen)
xmin, ymin, xmax, ymax = Predictor.get_predict_area(self.record_pos, screen_resolution)
# crop predict image from screen
predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
# aircv.show(predict_area)
# find sift in that image
ret_in_area = aircv.find_sift(predict_area, image, threshold=self.threshold, rgb=self.rgb)
# calc cv ret if found
if not ret_in_area:
return None
ret = deepcopy(ret_in_area)
ret["result"] = (ret_in_area["result"][0] + xmin, ret_in_area["result"][1] + ymin)
return ret
def loop_find3(total_pic,
query,
posi,
tag=None,
timeout=ST.FIND_TIMEOUT,
threshold=None,
interval=0.5,
intervalfunc=None):
# print("ST.FIND_TIMEOUT",ST.FIND_TIMEOUT)
G.LOGGING.info("Try finding:\n%s", query.record_pos)
G.LOGGING.info(query._imread().shape)
# screen_resolution = aircv.get_resolution(screen)
part_size_width = query._imread().shape[0]
part_size_height = query._imread().shape[1]
part_resolution_width = query.resolution[0]
part_resolution_height = query.resolution[1]
# pairt_origin_xmin_relative = posi[0];
# pairt_origin_xmax_relative = posi[2] ;
pairt_origin_ymin_relative = posi[0] / part_resolution_height
pairt_origin_ymax_relative = posi[1] / part_resolution_height
# print("香菇：",(pairt_origin_ymin_relative,pairt_origin_ymax_relative))
posi_x = query.record_pos[0]
posi_y = query.record_pos[1]
# print("posi_x",posi_x)
start_time = time.time()
screen = aircv.imread(total_pic)
G.LOGGING.debug("query path %s" % query.filepath)
# predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
screen_resolution = aircv.get_resolution(screen)
# print("总图片的分辨率",screen_resolution);
total_resolution_width = screen_resolution[0]
total_resolution_height = screen_resolution[1]
width_scale = round(total_resolution_width / part_resolution_width, 2)
height_scale = round(total_resolution_height / part_resolution_height, 2)
# print("width_scale",width_scale)
# print("width_scale",height_scale)
# print("总图的分辨率",(total_resolution_width,total_resolution_height))
# print("缩放之后的相对坐标",(pairt_origin_xmin_relative * width_scale,pairt_origin_ymin_relative * height_scale,pairt_origin_xmax_relative * width_scale,pairt_origin_ymax_relative * height_scale))
# pairt_end_xmin_absolute = round( (pairt_origin_xmin_relative * width_scale) * total_resolution_width );
pairt_end_xmin_absolute = 0
# pairt_end_xmax_absolute = round( (pairt_origin_xmax_relative * width_scale) * total_resolution_width );
pairt_end_xmax_absolute = total_resolution_width
pairt_end_ymin_absolute = round(
(pairt_origin_ymin_relative * height_scale) *
total_resolution_height) - 30
pairt_end_ymax_absolute = round(
(pairt_origin_ymax_relative * height_scale) *
total_resolution_height) + 30
# print("enddddd",(pairt_end_xmin_absolute,pairt_end_ymin_absolute,pairt_end_xmax_absolute,pairt_end_ymax_absolute))
# part_resolution_width_scaled = part_size_width * width_scale;
# part_resolution_height_scaled = part_size_height * height_scale;
# part_end_xmax = ((posi_x + 0.5) if (posi_x + 0.5)<=1 else 1)*total_resolution_width
# part_end_ymax = ((posi_y + 0.5) if (posi_y + 0.5)<=1 else 1)*total_resolution_height
# part_end_xmin = part_end_xmax - part_resolution_width_scaled
# part_end_ymin = part_end_ymax - part_resolution_height_scaled
predict_area = aircv.crop_image(
screen, (pairt_end_xmin_absolute, pairt_end_ymin_absolute,
pairt_end_xmax_absolute, pairt_end_ymax_absolute))
screen[0:pairt_end_ymin_absolute, 0:total_resolution_width] = 0
screen[pairt_end_ymax_absolute:total_resolution_height,
0:total_resolution_width] = 0
# print("黑素区域",(pairt_end_ymin_absolute,pairt_end_ymax_absolute))
# screen[200:250,0:50] = 0;
aircv.imwrite("part_black.png", screen, 99)
# predict_area = aircv.crop_image(screen, (0, 0, 1126, 2436));
# print("hahahahah")
# print((part_end_xmin, part_end_ymin, part_end_xmax, part_end_ymax))
# print(predict_area.shape)
aircv.imwrite("predict_area3.png", predict_area, 99)
while True:
if predict_area is None:
G.LOGGING.warning("预测区域有问题！")
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)
G.LOGGING.warning("图片查找超时！")
break
else:
time.sleep(interval)
def loop_find2(total_pic,
query,
timeout=ST.FIND_TIMEOUT,
threshold=None,
interval=0.5,
intervalfunc=None):
G.LOGGING.info("Try finding:\n%s", query.record_pos)
G.LOGGING.info(query._imread().shape)
part_size_width = query._imread().shape[0]
part_size_height = query._imread().shape[1]
part_resolution_width = query.resolution[0]
part_resolution_height = query.resolution[1]
# print("part_resolution_width:" + str(part_resolution_width))
# print("part_resolution_height:" + str(part_resolution_height))
posi_x = query.record_pos[0]
posi_y = query.record_pos[1]
print("posi_x", posi_x)
start_time = time.time()
screen = aircv.imread(total_pic)
G.LOGGING.debug("query path %s" % query.filepath)
# predict_area = aircv.crop_image(screen, (xmin, ymin, xmax, ymax))
screen_resolution = aircv.get_resolution(screen)
total_resolution_width = query.resolution[0]
total_resolution_height = query.resolution[1]
width_scale = total_resolution_width / part_resolution_width
height_scale = total_resolution_height / part_resolution_height
print("test_scale:" + str(1 / 2))
print("width_scale:" + str(width_scale))
print("height_scase:" + str(height_scale))
part_resolution_width_scaled = part_size_width * width_scale
part_resolution_height_scaled = part_size_height * height_scale
print("part_resolution_width_scaled:" + str(part_resolution_width_scaled))
print("part_resolution_height_scaled:" +
str(part_resolution_height_scaled))
G.LOGGING.debug("图片的分辨率")
G.LOGGING.debug(screen_resolution)
part_end_xmax = ((posi_x + 0.5) if
(posi_x + 0.5) <= 1 else 1) * total_resolution_width
part_end_ymax = ((posi_y + 0.5) if
(posi_y + 0.5) <= 1 else 1) * total_resolution_height
part_end_xmin = part_end_xmax - part_resolution_width_scaled
part_end_ymin = part_end_ymax - part_resolution_height_scaled
predict_area = aircv.crop_image(
screen, (part_end_xmin, part_end_ymin, part_end_xmax, part_end_ymax))
print("hahahahah")
print((part_end_xmin, part_end_ymin, part_end_xmax, part_end_ymax))
print(predict_area.shape)
aircv.imwrite("predict_area.png", screen, 99)
while True:
if predict_area is None:
G.LOGGING.warning("预测区域有问题！")
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(predict_area)
# raise TargetNotFoundError('Picture %s not found in screen' % query)
G.LOGGING.warning("图片查找超时！")
break
else:
time.sleep(interval)
