How to use cal_rect_pts method in Airtest

Best Python code snippet using Airtest

sift.py

Source:sift.py Github

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...159 h_s, w_s = im_source.shape[:2]160 pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)161 dst = cv2.perspectiveTransform(pts, M)162 # trans numpy arrary to python list: [(a, b), (a1, b1), ...]163 def cal_rect_pts(dst):164 return [tuple(npt[0]) for npt in dst.astype(int).tolist()]165 pypts = cal_rect_pts(dst)166 # 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167 lt, br = pypts[0], pypts[2]168 middle_point = int((lt[0] + br[0]) / 2), int((lt[1] + br[1]) / 2)169 # 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：170 x_min, x_max = min(lt[0], br[0]), max(lt[0], br[0])171 y_min, y_max = min(lt[1], br[1]), max(lt[1], br[1])172 # 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：173 # 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-1174 # 当x_min小于0时，取0。 x_max小于0时，取0。175 x_min, x_max = int(max(x_min, 0)), int(max(x_max, 0))176 # 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。177 x_min, x_max = int(min(x_min, w_s - 1)), int(min(x_max, w_s - 1))178 # 当y_min小于0时，取0。 y_max小于0时，取0。179 y_min, y_max = int(max(y_min, 0)), int(max(y_max, 0))180 # 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。181 y_min, y_max = int(min(y_min, h_s - 1)), int(min(y_max, h_s - 1))182 # 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)183 pts = np.float32([[x_min, y_min], [x_min, y_max], [184 x_max, y_max], [x_max, y_min]]).reshape(-1, 1, 2)185 pypts = cal_rect_pts(pts)186 return middle_point, pypts, [x_min, x_max, y_min, y_max, w, h]187def _two_good_points(pts_sch1, pts_sch2, pts_src1, pts_src2, im_search, im_source):188 """返回两对匹配特征点情形下的识别结果."""189 # 先算出中心点(在im_source中的坐标)：190 middle_point = [int((pts_src1[0] + pts_src2[0]) / 2), int((pts_src1[1] + pts_src2[1]) / 2)]191 pypts = []192 # 如果特征点同x轴或同y轴(无论src还是sch中)，均不能计算出目标矩形区域来，此时返回值同good=1情形193 if pts_sch1[0] == pts_sch2[0] or pts_sch1[1] == pts_sch2[1] or pts_src1[0] == pts_src2[0] or pts_src1[1] == pts_src2[1]:194 confidence = ONE_POINT_CONFI195 one_match = generate_result(middle_point, pypts, confidence)196 return one_match197 # 计算x,y轴的缩放比例：x_scale、y_scale，从middle点扩张出目标区域:(注意整数计算要转成浮点数结果!)198 h, w = im_search.shape[:2]199 h_s, w_s = im_source.shape[:2]...

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sift_test.py

Source:sift_test.py Github

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...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]...

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keypoint.py

Source:keypoint.py Github

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...54 -1, 1, 255 )56 dst = cv2.perspectiveTransform(pts, M)57 # trans numpy arrary to python list: [(a, b), (a1, b1), ...]58 def cal_rect_pts(dst):59 return [tuple(npt[0]) for npt in dst.astype(int).tolist()]60 pypts = cal_rect_pts(dst)61 # 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62 lt, br = pypts[0], pypts[2]63 middlePoint = int((lt[0] + br[0]) / 2), int((lt[1] + br[1]) / 2)64 # 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：65 xMin, xMax = min(lt[0], br[0]), max(lt[0], br[0])66 yMin, yMax = min(lt[1], br[1]), max(lt[1], br[1])67 # 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：68 # 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-169 # 当x_min小于0时，取0。 x_max小于0时，取0。70 xMin, xMax = int(max(xMin, 0)), int(max(xMax, 0))71 # 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。72 xMin, xMax = int(min(xMin, w_s - 1)), int(min(xMax, w_s - 1))73 # 当y_min小于0时，取0。 y_max小于0时，取0。74 yMin, yMax = int(max(yMin, 0)), int(max(yMax, 0))75 # 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。76 yMin, yMax = int(min(yMin, h_s - 1)), int(min(yMax, h_s - 1))77 # 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)78 pts = np.float32(79 [[xMin, yMin], [xMin, yMax], [xMax, yMax], [xMax, yMin]]80 ).reshape(-1, 1, 2)81 pypts = cal_rect_pts(pts)82 return middlePoint, pypts, [xMin, xMax, yMin, yMax, w, h]83def KAZEMatching(filename, device, threshold=0.9, targetPos=5):84 # 1.读取图片85 imSearch = cv2.imread("img/{name}".format(name=filename))86 imSource = device.screenshot(format="opencv")87 # 2.获取特征点集并匹配出特征点对88 kpSch, kpSrc, good = getKeyPoints(imSearch, imSource)89 # 3.根据匹配点对(good),提取出来识别区域:90 originResult = handleGoodPoints(kpSch, kpSrc, good, imSearch, imSource)91 # 某些特殊情况下直接返回None作为匹配结果:92 if originResult is None:93 return None94 else:95 middlePoint, pypts, posRange = originResult...

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