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| import os.path as osp | |
| import numpy as np | |
| import torch | |
| from dkm.utils import * | |
| from PIL import Image | |
| from tqdm import tqdm | |
| class ScanNetBenchmark: | |
| def __init__(self, data_root="data/scannet") -> None: | |
| self.data_root = data_root | |
| def benchmark(self, model, model_name = None): | |
| model.train(False) | |
| with torch.no_grad(): | |
| data_root = self.data_root | |
| tmp = np.load(osp.join(data_root, "test.npz")) | |
| pairs, rel_pose = tmp["name"], tmp["rel_pose"] | |
| tot_e_t, tot_e_R, tot_e_pose = [], [], [] | |
| pair_inds = np.random.choice( | |
| range(len(pairs)), size=len(pairs), replace=False | |
| ) | |
| for pairind in tqdm(pair_inds, smoothing=0.9): | |
| scene = pairs[pairind] | |
| scene_name = f"scene0{scene[0]}_00" | |
| im1_path = osp.join( | |
| self.data_root, | |
| "scans_test", | |
| scene_name, | |
| "color", | |
| f"{scene[2]}.jpg", | |
| ) | |
| im1 = Image.open(im1_path) | |
| im2_path = osp.join( | |
| self.data_root, | |
| "scans_test", | |
| scene_name, | |
| "color", | |
| f"{scene[3]}.jpg", | |
| ) | |
| im2 = Image.open(im2_path) | |
| T_gt = rel_pose[pairind].reshape(3, 4) | |
| R, t = T_gt[:3, :3], T_gt[:3, 3] | |
| K = np.stack( | |
| [ | |
| np.array([float(i) for i in r.split()]) | |
| for r in open( | |
| osp.join( | |
| self.data_root, | |
| "scans_test", | |
| scene_name, | |
| "intrinsic", | |
| "intrinsic_color.txt", | |
| ), | |
| "r", | |
| ) | |
| .read() | |
| .split("\n") | |
| if r | |
| ] | |
| ) | |
| w1, h1 = im1.size | |
| w2, h2 = im2.size | |
| K1 = K.copy() | |
| K2 = K.copy() | |
| dense_matches, dense_certainty = model.match(im1_path, im2_path) | |
| sparse_matches, sparse_certainty = model.sample( | |
| dense_matches, dense_certainty, 5000 | |
| ) | |
| scale1 = 480 / min(w1, h1) | |
| scale2 = 480 / min(w2, h2) | |
| w1, h1 = scale1 * w1, scale1 * h1 | |
| w2, h2 = scale2 * w2, scale2 * h2 | |
| K1 = K1 * scale1 | |
| K2 = K2 * scale2 | |
| offset = 0.5 | |
| kpts1 = sparse_matches[:, :2] | |
| kpts1 = ( | |
| np.stack( | |
| ( | |
| w1 * (kpts1[:, 0] + 1) / 2 - offset, | |
| h1 * (kpts1[:, 1] + 1) / 2 - offset, | |
| ), | |
| axis=-1, | |
| ) | |
| ) | |
| kpts2 = sparse_matches[:, 2:] | |
| kpts2 = ( | |
| np.stack( | |
| ( | |
| w2 * (kpts2[:, 0] + 1) / 2 - offset, | |
| h2 * (kpts2[:, 1] + 1) / 2 - offset, | |
| ), | |
| axis=-1, | |
| ) | |
| ) | |
| for _ in range(5): | |
| shuffling = np.random.permutation(np.arange(len(kpts1))) | |
| kpts1 = kpts1[shuffling] | |
| kpts2 = kpts2[shuffling] | |
| try: | |
| norm_threshold = 0.5 / ( | |
| np.mean(np.abs(K1[:2, :2])) + np.mean(np.abs(K2[:2, :2]))) | |
| R_est, t_est, mask = estimate_pose( | |
| kpts1, | |
| kpts2, | |
| K1, | |
| K2, | |
| norm_threshold, | |
| conf=0.99999, | |
| ) | |
| T1_to_2_est = np.concatenate((R_est, t_est), axis=-1) # | |
| e_t, e_R = compute_pose_error(T1_to_2_est, R, t) | |
| e_pose = max(e_t, e_R) | |
| except Exception as e: | |
| print(repr(e)) | |
| e_t, e_R = 90, 90 | |
| e_pose = max(e_t, e_R) | |
| tot_e_t.append(e_t) | |
| tot_e_R.append(e_R) | |
| tot_e_pose.append(e_pose) | |
| tot_e_t.append(e_t) | |
| tot_e_R.append(e_R) | |
| tot_e_pose.append(e_pose) | |
| tot_e_pose = np.array(tot_e_pose) | |
| thresholds = [5, 10, 20] | |
| auc = pose_auc(tot_e_pose, thresholds) | |
| acc_5 = (tot_e_pose < 5).mean() | |
| acc_10 = (tot_e_pose < 10).mean() | |
| acc_15 = (tot_e_pose < 15).mean() | |
| acc_20 = (tot_e_pose < 20).mean() | |
| map_5 = acc_5 | |
| map_10 = np.mean([acc_5, acc_10]) | |
| map_20 = np.mean([acc_5, acc_10, acc_15, acc_20]) | |
| return { | |
| "auc_5": auc[0], | |
| "auc_10": auc[1], | |
| "auc_20": auc[2], | |
| "map_5": map_5, | |
| "map_10": map_10, | |
| "map_20": map_20, | |
| } | |