airTest 使用体验

image = self._resize_image(image, screen, ST.RESIZE_METHOD)

def find_template(im_source, im_search, threshold=0.8, rgb=False):
    """函数功能：找到最优结果."""
    # 第一步：校验图像输入
    check_source_larger_than_search(im_source, im_search)
    # 第二步：计算模板匹配的结果矩阵res
    res = _get_template_result_matrix(im_source, im_search)
    # 第三步：依次获取匹配结果
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    h, w = im_search.shape[:2]
    # 求取可信度:
    confidence = _get_confidence_from_matrix(im_source, im_search, max_loc, max_val, w, h, rgb)
    # 求取识别位置: 目标中心 + 目标区域:
    middle_point, rectangle = _get_target_rectangle(max_loc, w, h)
    best_match = generate_result(middle_point, rectangle, confidence)
    LOGGING.debug("threshold=%s, result=%s" % (threshold, best_match))
    return best_match if confidence >= threshold else None


def _get_template_result_matrix(im_source, im_search):
    """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(im_search), img_mat_rgb_2_gray(im_source)
    return cv2.matchTemplate(i_gray, s_gray, cv2.TM_CCOEFF_NORMED)

def find_sift(im_source, im_search, threshold=0.8, rgb=True, good_ratio=FILTER_RATIO):
    """基于sift进行图像识别，只筛选出最优区域."""
    # 第一步：检验图像是否正常：
    if not check_image_valid(im_source, im_search):
        return None
 
    # 第二步：获取特征点集并匹配出特征点对: 返回值 good, pypts, kp_sch, kp_src
    kp_sch, kp_src, good = _get_key_points(im_source, im_search, good_ratio)
 
    # 第三步：根据匹配点对(good),提取出来识别区域:
    if len(good) == 0:
        # 匹配点对为0,无法提取识别区域：
        return None
    elif len(good) == 1:
        # 匹配点对为1，可信度赋予设定值，并直接返回:
        return _handle_one_good_points(kp_src, good, threshold) if ONE_POINT_CONFI >= threshold else None
    elif len(good) == 2:
        # 匹配点对为2，根据点对求出目标区域，据此算出可信度：
        origin_result = _handle_two_good_points(im_source, im_search, kp_src, kp_sch, good)
        if isinstance(origin_result, dict):
            return origin_result if ONE_POINT_CONFI >= threshold else None
        else:
            middle_point, pypts, w_h_range = _handle_two_good_points(im_source, im_search, kp_src, kp_sch, good)
    elif len(good) == 3:
        # 匹配点对为3，取出点对，求出目标区域，据此算出可信度：
        origin_result = _handle_three_good_points(im_source, im_search, kp_src, kp_sch, good)
        if isinstance(origin_result, dict):
            return origin_result if ONE_POINT_CONFI >= threshold else None
        else:
            middle_point, pypts, w_h_range = _handle_three_good_points(im_source, im_search, kp_src, kp_sch, good)
    else:
        # 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：
        middle_point, pypts, w_h_range = _many_good_pts(im_source, im_search, kp_sch, kp_src, good)
 
    # 第四步：根据识别区域，求出结果可信度，并将结果进行返回:
    # 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.
    _target_error_check(w_h_range)
    # 将截图和识别结果缩放到大小一致,准备计算可信度
    x_min, x_max, y_min, y_max, w, h = w_h_range
    target_img = im_source[y_min:y_max, x_min:x_max]
    resize_img = cv2.resize(target_img, (w, h))
    confidence = _cal_sift_confidence(im_search, resize_img, rgb=rgb)
 
    best_match = generate_result(middle_point, pypts, confidence)
    print("[aircv][sift] threshold=%s, result=%s" % (threshold, best_match))
    return best_match if confidence >= threshold else None

# 如何找到特征点集
def _get_key_points(im_source, im_search, good_ratio):
    """根据传入图像,计算图像所有的特征点,并得到匹配特征点对."""
    # 准备工作: 初始化sift算子
    sift = _init_sift()
    # 第一步：获取特征点集，并匹配出特征点对: 返回值 good, pypts, kp_sch, kp_src
    kp_sch, des_sch = sift.detectAndCompute(im_search, None)
    kp_src, des_src = sift.detectAndCompute(im_source, None)
    # When apply knnmatch , make sure that number of features in both test and
    #       query image is greater than or equal to number of nearest neighbors in knn match.
    if len(kp_sch) < 2 or len(kp_src) < 2:
        raise NoSiftMatchPointError("Not enough feature points in input images !")
 
    # 匹配两个图片中的特征点集，k=2表示每个特征点取出2个最匹配的对应点:
    matches = FLANN.knnMatch(des_sch, des_src, k=2)
    good = []
    # good为特征点初选结果，剔除掉前两名匹配太接近的特征点，不是独特优秀的特征点直接筛除(多目标识别情况直接不适用)
    for m, n in matches:
        if m.distance < good_ratio * n.distance:
            good.append(m)
    # good点需要去除重复的部分，（设定源图像不能有重复点）去重时将src图像中的重复点找出即可
    # 去重策略：允许搜索图像对源图像的特征点映射一对多，不允许多对一重复（即不能源图像上一个点对应搜索图像的多个点）
    good_diff, diff_good_point = [], [[]]
    for m in good:
        diff_point = [int(kp_src[m.trainIdx].pt[0]), int(kp_src[m.trainIdx].pt[1])]
        if diff_point not in diff_good_point:
            good_diff.append(m)
            diff_good_point.append(diff_point)
    good = good_diff
 
    return kp_sch, kp_src, good

# sift对象
def _init_sift():
    """Make sure that there is SIFT module in OpenCV."""
    if cv2.__version__.startswith("3."):
        # OpenCV3.x, sift is in contrib module, you need to compile it seperately.
        try:
            sift = cv2.xfeatures2d.SIFT_create(edgeThreshold=10)
        except:
            print("to use SIFT, you should build contrib with opencv3.0")
            raise NoSIFTModuleError("There is no SIFT module in your OpenCV environment !")
    else:
        # OpenCV2.x, just use it.
        sift = cv2.SIFT(edgeThreshold=10)
 
    return sift　　　

touch(Template(r"tpl1551777086787.png", record_pos=(0.379, 0.922), resolution=(1080, 2160)))
wait(Template(r"tpl1551778382115.png", record_pos=(-0.003, -0.551), resolution=(1080, 2160)))
touch(Template(r"tpl1551775745377.png", record_pos=(-0.007, -0.547), resolution=(1080, 2160)))
text("cmq00002@qq.com")