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