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How to use _get_origin_result_with_two_points method in Airtest

Best Python code snippet using Airtest

keypoint_base.py

Source:keypoint_base.py

...139        pts_sch1 = int(kp_sch[good[0].queryIdx].pt[0]), int(kp_sch[good[0].queryIdx].pt[1])140        pts_sch2 = int(kp_sch[good[1].queryIdx].pt[0]), int(kp_sch[good[1].queryIdx].pt[1])141        pts_src1 = int(kp_src[good[0].trainIdx].pt[0]), int(kp_src[good[0].trainIdx].pt[1])142        pts_src2 = int(kp_src[good[1].trainIdx].pt[0]), int(kp_src[good[1].trainIdx].pt[1])143        return self._get_origin_result_with_two_points(pts_sch1, pts_sch2, pts_src1, pts_src2)144    def _handle_three_good_points(self, kp_sch, kp_src, good):145        """å¤çä¸å¯¹ç¹å¾ç¹çæåµ."""146        # æ¿åºschåsrcçä¸¤ä¸ªç¹(ç¹1)å(ç¹2ç¹3çä¸ç¹)ï¼147        # ç¶åæ ¹æ®ä¸¤ä¸ªç¹ååè¿è¡åå¤ç(æ³¨æke_schåkp_srcä»¥åqueryIdxåtrainIdx):148        pts_sch1 = int(kp_sch[good[0].queryIdx].pt[0]), int(kp_sch[good[0].queryIdx].pt[1])149        pts_sch2 = int((kp_sch[good[1].queryIdx].pt[0] + kp_sch[good[2].queryIdx].pt[0]) / 2), int(150            (kp_sch[good[1].queryIdx].pt[1] + kp_sch[good[2].queryIdx].pt[1]) / 2)151        pts_src1 = int(kp_src[good[0].trainIdx].pt[0]), int(kp_src[good[0].trainIdx].pt[1])152        pts_src2 = int((kp_src[good[1].trainIdx].pt[0] + kp_src[good[2].trainIdx].pt[0]) / 2), int(153            (kp_src[good[1].trainIdx].pt[1] + kp_src[good[2].trainIdx].pt[1]) / 2)154        return self._get_origin_result_with_two_points(pts_sch1, pts_sch2, pts_src1, pts_src2)155    def _many_good_pts(self, kp_sch, kp_src, good):156        """ç¹å¾ç¹å¹éç¹å¯¹æ°ç®>=4ä¸ªï¼å¯ä½¿ç¨åç©éµæ å°,æ±åºè¯å«çç®æ åºå."""157        sch_pts, img_pts = np.float32([kp_sch[m.queryIdx].pt for m in good]).reshape(158            -1, 1, 2), np.float32([kp_src[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)159        # Mæ¯è½¬åç©éµ160        M, mask = self._find_homography(sch_pts, img_pts)161        matches_mask = mask.ravel().tolist()162        # ä»goodä¸é´çéåºæ´ç²¾ç¡®çç¹(åè®¾goodä¸å¤§é¨åç¹ä¸ºæ£ç¡®çï¼ç±ratio=0.7ä¿é)163        selected = [v for k, v in enumerate(good) if matches_mask[k]]164        # éå¯¹ææçselectedç¹åæ¬¡è®¡ç®åºæ´ç²¾ç¡®çè½¬åç©éµMæ¥165        sch_pts, img_pts = np.float32([kp_sch[m.queryIdx].pt for m in selected]).reshape(166            -1, 1, 2), np.float32([kp_src[m.trainIdx].pt for m in selected]).reshape(-1, 1, 2)167        M, mask = self._find_homography(sch_pts, img_pts)168        # è®¡ç®åä¸ªè§ç©éµåæ¢åçåæ ï¼ä¹å°±æ¯å¨å¤§å¾ä¸çç®æ åºåçé¡¶ç¹åæ :169        h, w = self.im_search.shape[:2]170        h_s, w_s = self.im_source.shape[:2]171        pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)172        dst = cv2.perspectiveTransform(pts, M)173        # trans numpy arrary to python list: [(a, b), (a1, b1), ...]174        def cal_rect_pts(dst):175            return [tuple(npt[0]) for npt in dst.astype(int).tolist()]176        pypts = cal_rect_pts(dst)177        # æ³¨æï¼è½ç¶4ä¸ªè§ç¹æå¯è½è¶åºsourceå¾è¾¹çï¼ä½æ¯(æ ¹æ®ç²¾ç¡®åæ å°åæ å°ç©éµMçº¿æ§æºå¶)ä¸ç¹ä¸ä¼è¶åºè¾¹ç178        lt, br = pypts[0], pypts[2]179        middle_point = int((lt[0] + br[0]) / 2), int((lt[1] + br[1]) / 2)180        # èèå°ç®åºçç®æ ç©éµæå¯è½æ¯ç¿»è½¬çæåµï¼å¿é¡»è¿è¡ä¸æ¬¡å¤çï¼ç¡®ä¿æ å°åçâå·¦ä¸è§âå¨å¾çä¸ä¹æ¯å·¦ä¸è§ç¹ï¼181        x_min, x_max = min(lt[0], br[0]), max(lt[0], br[0])182        y_min, y_max = min(lt[1], br[1]), max(lt[1], br[1])183        # æéåºç®æ ç©å½¢åºåå¯è½ä¼æè¶çæåµï¼è¶çæ¶ç´æ¥å°å¶ç½®ä¸ºè¾¹çï¼184        # è¶åºå·¦è¾¹çå0ï¼è¶åºå³è¾¹çåw_s-1ï¼è¶åºä¸è¾¹çå0ï¼è¶åºä¸è¾¹çåh_s-1185        # å½x_minå°äº0æ¶ï¼å0ã  x_maxå°äº0æ¶ï¼å0ã186        x_min, x_max = int(max(x_min, 0)), int(max(x_max, 0))187        # å½x_minå¤§äºw_sæ¶ï¼åå¼w_s-1ã  x_maxå¤§äºw_s-1æ¶ï¼åw_s-1ã188        x_min, x_max = int(min(x_min, w_s - 1)), int(min(x_max, w_s - 1))189        # å½y_minå°äº0æ¶ï¼å0ã  y_maxå°äº0æ¶ï¼å0ã190        y_min, y_max = int(max(y_min, 0)), int(max(y_max, 0))191        # å½y_minå¤§äºh_sæ¶ï¼åå¼h_s-1ã  y_maxå¤§äºh_s-1æ¶ï¼åh_s-1ã192        y_min, y_max = int(min(y_min, h_s - 1)), int(min(y_max, h_s - 1))193        # ç®æ åºåçè§ç¹ï¼æå·¦ä¸ãå·¦ä¸ãå³ä¸ãå³ä¸ç¹åºï¼(x_min,y_min)(x_min,y_max)(x_max,y_max)(x_max,y_min)194        pts = np.float32([[x_min, y_min], [x_min, y_max], [195                         x_max, y_max], [x_max, y_min]]).reshape(-1, 1, 2)196        pypts = cal_rect_pts(pts)197        return middle_point, pypts, [x_min, x_max, y_min, y_max, w, h]198    def _get_origin_result_with_two_points(self, pts_sch1, pts_sch2, pts_src1, pts_src2):199        """è¿åä¸¤å¯¹ææå¹éç¹å¾ç¹æå½¢ä¸çè¯å«ç»æ."""200        # åç®åºä¸å¿ç¹(å¨self.im_sourceä¸çåæ )ï¼201        middle_point = [int((pts_src1[0] + pts_src2[0]) / 2), int((pts_src1[1] + pts_src2[1]) / 2)]202        pypts = []203        # å¦æç¹å¾ç¹åxè½´æåyè½´(æ è®ºsrcè¿æ¯schä¸)ï¼åä¸è½è®¡ç®åºç®æ ç©å½¢åºåæ¥ï¼æ¤æ¶è¿åå¼ågood=1æå½¢204        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]:205            return None206        # è®¡ç®x,yè½´çç¼©æ¾æ¯ä¾ï¼x_scaleãy_scaleï¼ä»middleç¹æ©å¼ åºç®æ åºå:(æ³¨ææ´æ°è®¡ç®è¦è½¬ææµ®ç¹æ°ç»æ!)207        h, w = self.im_search.shape[:2]208        h_s, w_s = self.im_source.shape[:2]209        x_scale = abs(1.0 * (pts_src2[0] - pts_src1[0]) / (pts_sch2[0] - pts_sch1[0]))210        y_scale = abs(1.0 * (pts_src2[1] - pts_src1[1]) / (pts_sch2[1] - pts_sch1[1]))211        # å¾å°scaleåéè¦å¯¹middle_pointè¿è¡æ ¡æ£ï¼å¹¶éç¹å¾ç¹ä¸ç¹ï¼èæ¯æ å°ç©éµçä¸ç¹ã212        sch_middle_point = int((pts_sch1[0] + pts_sch2[0]) / 2), int((pts_sch1[1] + pts_sch2[1]) / 2)...

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