How to use _handle_two_good_points method in Airtest

Best Python code snippet using Airtest Github


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...16 # 匹配点对为1,可信度赋予设定值,并直接返回:17 return _handle_one_good_points(kp_src, good, threshold) if ONE_POINT_CONFI >= threshold else None18 elif len(good) == 2:19 # 匹配点对为2,根据点对求出目标区域,据此算出可信度:20 origin_result = _handle_two_good_points(im_source, im_search, kp_src, kp_sch, good)21 if isinstance(origin_result, dict):22 return origin_result if ONE_POINT_CONFI >= threshold else None23 else:24 middle_point, pypts, w_h_range = _handle_two_good_points(im_source, im_search, kp_src, kp_sch, good)25 elif len(good) == 3:26 # 匹配点对为3,取出点对,求出目标区域,据此算出可信度:27 origin_result = _handle_three_good_points(im_source, im_search, kp_src, kp_sch, good)28 if isinstance(origin_result, dict):29 return origin_result if ONE_POINT_CONFI >= threshold else None30 else:31 middle_point, pypts, w_h_range = _handle_three_good_points(im_source, im_search, kp_src, kp_sch, good)32 else:33 # 匹配点对 >= 4个,使用单矩阵映射求出目标区域,据此算出可信度:34 middle_point, pypts, w_h_range = _many_good_pts(im_source, im_search, kp_sch, kp_src, good)35 # 第四步:根据识别区域,求出结果可信度,并将结果进行返回:36 # 对识别结果进行合理性校验: 小于5个像素的,或者缩放超过5倍的,一律视为不合法直接raise.37 _target_error_check(w_h_range)38 # 将截图和识别结果缩放到大小一致,准备计算可信度...

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