How to use _many_good_pts method in Airtest

Best Python code snippet using Airtest

sift_test.py

Source:sift_test.py Github

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...57 if mask is None:58 raise Exception("In _find_homography(), find no mask...")59 else:60 return M, mask61def _many_good_pts(im_source, im_search, kp_sch, kp_src, good):62 """特征点匹配点对数目>=4个，可使用单矩阵映射,求出识别的目标区域."""63 sch_pts, img_pts = np.float32([kp_sch[m.queryIdx].pt for m in good]).reshape(64 -1, 1, 2), np.float32([kp_src[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)65 # M是转化矩阵66 M, mask = _find_homography(sch_pts, img_pts)67 matches_mask = mask.ravel().tolist()68 # 从good中间筛选出更精确的点(假设good中大部分点为正确的，由ratio=0.7保障)69 selected = [v for k, v in enumerate(good) if matches_mask[k]]70 # 针对所有的selected点再次计算出更精确的转化矩阵M来71 sch_pts, img_pts = np.float32([kp_sch[m.queryIdx].pt for m in selected]).reshape(72 -1, 1, 2), np.float32([kp_src[m.trainIdx].pt for m in selected]).reshape(-1, 1, 2)73 M, mask = _find_homography(sch_pts, img_pts)74 # print(M, mask)75 # 计算四个角矩阵变换后的坐标，也就是在大图中的目标区域的顶点坐标:76 h, w = im_search.shape[:2]77 h_s, w_s = im_source.shape[:2]78 pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)79 dst = cv2.perspectiveTransform(pts, M)80 # trans numpy arrary to python list: [(a, b), (a1, b1), ...]81 def cal_rect_pts(dst):82 return [tuple(npt[0]) for npt in dst.astype(int).tolist()]83 pypts = cal_rect_pts(dst)84 # 注意：虽然4个角点有可能越出source图边界，但是(根据精确化映射单映射矩阵M线性机制)中点不会越出边界85 lt, br = pypts[0], pypts[2]86 middle_point = int((lt[0] + br[0]) /​ 2), int((lt[1] + br[1]) /​ 2)87 # 考虑到算出的目标矩阵有可能是翻转的情况，必须进行一次处理，确保映射后的“左上角”在图片中也是左上角点：88 x_min, x_max = min(lt[0], br[0]), max(lt[0], br[0])89 y_min, y_max = min(lt[1], br[1]), max(lt[1], br[1])90 # 挑选出目标矩形区域可能会有越界情况，越界时直接将其置为边界：91 # 超出左边界取0，超出右边界取w_s-1，超出下边界取0，超出上边界取h_s-192 # 当x_min小于0时，取0。 x_max小于0时，取0。93 x_min, x_max = int(max(x_min, 0)), int(max(x_max, 0))94 # 当x_min大于w_s时，取值w_s-1。 x_max大于w_s-1时，取w_s-1。95 x_min, x_max = int(min(x_min, w_s - 1)), int(min(x_max, w_s - 1))96 # 当y_min小于0时，取0。 y_max小于0时，取0。97 y_min, y_max = int(max(y_min, 0)), int(max(y_max, 0))98 # 当y_min大于h_s时，取值h_s-1。 y_max大于h_s-1时，取h_s-1。99 y_min, y_max = int(min(y_min, h_s - 1)), int(min(y_max, h_s - 1))100 # 目标区域的角点，按左上、左下、右下、右上点序：(x_min,y_min)(x_min,y_max)(x_max,y_max)(x_max,y_min)101 pts = np.float32([[x_min, y_min], [x_min, y_max], [102 x_max, y_max], [x_max, y_min]]).reshape(-1, 1, 2)103 pypts = cal_rect_pts(pts)104 return middle_point, pypts, [x_min, x_max, y_min, y_max, w, h]105# 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：106middle_point, pypts, w_h_range = _many_good_pts(im_source, im_search, kp_sch, kp_src, good)107print(middle_point)108print(pypts)109print(w_h_range)110# best_match = generate_result(middle_point, pypts, confidence)111#112# print("[sift] result=%s" % (best_match))113# matchesMask = [[0, 0] for i in range(len(matches))]114# coff = 0.2115# for i,(m,n) in enumerate(matches):116# if m.distance < coff * n.distance:117# matchesMask[i]=[1,0]118#119# print(matchesMask)120# draw_params = dict(matchColor = (0,255,0),...

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sift.py

Source:sift.py Github

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...30 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39 x_min, x_max, y_min, y_max, w, h = w_h_range40 target_img = im_source[y_min:y_max, x_min:x_max]41 resize_img = cv2.resize(target_img, (w, h))42 confidence = _cal_sift_confidence(im_search, resize_img, rgb=rgb)43 best_match = generate_result(middle_point, pypts, confidence)44 print("[aircv][sift] threshold=%s, result=%s" % (threshold, best_match))45 return best_match if confidence >= threshold else None46def _get_key_points(im_source, im_search, good_ratio):47 """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."""48 # 准备工作: 初始化sift算子...

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