(self, query, reference, conf_thr=-1, vis_fig=None)
| 118 | return valid_pts3d, matches_im_query, matches_im_map, matches_conf |
| 119 | |
| 120 | def match(self, query, reference, conf_thr=-1, vis_fig=None): |
| 121 | if query.shape != reference.shape: |
| 122 | raise ValueError("Two images need to have the same shape.") |
| 123 | |
| 124 | orig_h, orig_w = query.shape[1], query.shape[2] |
| 125 | images = convert_images([query, reference], size=512) |
| 126 | output = inference([tuple(images)], self.model, self.device, batch_size=1, verbose=False) |
| 127 | |
| 128 | # at this stage, you have the raw dust3r predictions |
| 129 | view1, pred1 = output['view1'], output['pred1'] |
| 130 | view2, pred2 = output['view2'], output['pred2'] |
| 131 | conf1, conf2 = pred1['desc_conf'].squeeze(0).cpu().numpy(), pred2['desc_conf'].squeeze(0).cpu().numpy() |
| 132 | desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach() |
| 133 | |
| 134 | # find 2D-2D matches between the two images |
| 135 | matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8, |
| 136 | device=self.device, dist='dot', block_size=2**13) |
| 137 | |
| 138 | # ignore small border around the edge |
| 139 | new_h, new_w = view1['true_shape'][0] |
| 140 | new_h = int(new_h) |
| 141 | new_w = int(new_w) |
| 142 | valid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < new_w - 3) & ( |
| 143 | matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < new_h - 3) |
| 144 | |
| 145 | valid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < new_w - 3) & ( |
| 146 | matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < new_h - 3) |
| 147 | |
| 148 | valid_matches = valid_matches_im0 & valid_matches_im1 |
| 149 | matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches] |
| 150 | |
| 151 | if conf_thr >= 0: |
| 152 | matches_confs = np.minimum( |
| 153 | conf2[matches_im1[:, 1], matches_im1[:, 0]], |
| 154 | conf1[matches_im0[:, 1], matches_im0[:, 0]] |
| 155 | ) |
| 156 | valid_matches = matches_confs >= conf_thr |
| 157 | matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches] |
| 158 | |
| 159 | if vis_fig is not None: |
| 160 | self.visualize_matches(view1, view2, matches_im0, matches_im1, vis_fig) |
| 161 | |
| 162 | resize_func, to_resize, to_orig_1 = get_resize_function(self.maxdim, |
| 163 | self.model.patch_embed.patch_size, |
| 164 | orig_h, orig_w) |
| 165 | |
| 166 | resize_func, to_resize, to_orig_2 = get_resize_function(self.maxdim, |
| 167 | self.model.patch_embed.patch_size, |
| 168 | orig_h, orig_w) |
| 169 | |
| 170 | matches_im_query = matches_im0.astype(np.float64) |
| 171 | matches_im_map = matches_im1.astype(np.float64) |
| 172 | |
| 173 | # if orig_h == new_h: |
| 174 | # if orig_w != new_w: |
| 175 | # matches_im_query[:, 0] += (orig_w - new_w) / 2 |
| 176 | # matches_im_map[:, 0] += (orig_w - new_w) / 2 |
| 177 | # else: |
no test coverage detected