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from PIL import Image |
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import numpy as np |
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import os |
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from tqdm import tqdm |
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from dkm.utils import pose_auc |
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import cv2 |
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class HpatchesHomogBenchmark: |
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"""Hpatches grid goes from [0,n-1] instead of [0.5,n-0.5]""" |
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def __init__(self, dataset_path) -> None: |
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seqs_dir = "hpatches-sequences-release" |
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self.seqs_path = os.path.join(dataset_path, seqs_dir) |
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self.seq_names = sorted(os.listdir(self.seqs_path)) |
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self.ignore_seqs = set( |
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[ |
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"i_contruction", |
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"i_crownnight", |
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"i_dc", |
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"i_pencils", |
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"i_whitebuilding", |
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"v_artisans", |
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"v_astronautis", |
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"v_talent", |
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] |
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) |
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def convert_coordinates(self, query_coords, query_to_support, wq, hq, wsup, hsup): |
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offset = 0.5 |
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query_coords = ( |
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np.stack( |
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( |
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wq * (query_coords[..., 0] + 1) / 2, |
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hq * (query_coords[..., 1] + 1) / 2, |
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), |
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axis=-1, |
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) |
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- offset |
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) |
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query_to_support = ( |
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np.stack( |
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( |
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wsup * (query_to_support[..., 0] + 1) / 2, |
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hsup * (query_to_support[..., 1] + 1) / 2, |
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), |
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axis=-1, |
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) |
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- offset |
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) |
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return query_coords, query_to_support |
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def benchmark(self, model, model_name = None): |
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n_matches = [] |
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homog_dists = [] |
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for seq_idx, seq_name in tqdm( |
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enumerate(self.seq_names), total=len(self.seq_names) |
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): |
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if seq_name in self.ignore_seqs: |
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continue |
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im1_path = os.path.join(self.seqs_path, seq_name, "1.ppm") |
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im1 = Image.open(im1_path) |
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w1, h1 = im1.size |
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for im_idx in range(2, 7): |
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im2_path = os.path.join(self.seqs_path, seq_name, f"{im_idx}.ppm") |
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im2 = Image.open(im2_path) |
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w2, h2 = im2.size |
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H = np.loadtxt( |
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os.path.join(self.seqs_path, seq_name, "H_1_" + str(im_idx)) |
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) |
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dense_matches, dense_certainty = model.match( |
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im1_path, im2_path |
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) |
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good_matches, _ = model.sample(dense_matches, dense_certainty, 5000) |
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pos_a, pos_b = self.convert_coordinates( |
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good_matches[:, :2], good_matches[:, 2:], w1, h1, w2, h2 |
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) |
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try: |
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H_pred, inliers = cv2.findHomography( |
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pos_a, |
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pos_b, |
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method = cv2.RANSAC, |
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confidence = 0.99999, |
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ransacReprojThreshold = 3 * min(w2, h2) / 480, |
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) |
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except: |
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H_pred = None |
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if H_pred is None: |
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H_pred = np.zeros((3, 3)) |
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H_pred[2, 2] = 1.0 |
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corners = np.array( |
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[[0, 0, 1], [0, h1 - 1, 1], [w1 - 1, 0, 1], [w1 - 1, h1 - 1, 1]] |
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) |
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real_warped_corners = np.dot(corners, np.transpose(H)) |
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real_warped_corners = ( |
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real_warped_corners[:, :2] / real_warped_corners[:, 2:] |
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) |
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warped_corners = np.dot(corners, np.transpose(H_pred)) |
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warped_corners = warped_corners[:, :2] / warped_corners[:, 2:] |
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mean_dist = np.mean( |
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np.linalg.norm(real_warped_corners - warped_corners, axis=1) |
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) / (min(w2, h2) / 480.0) |
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homog_dists.append(mean_dist) |
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n_matches = np.array(n_matches) |
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thresholds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
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auc = pose_auc(np.array(homog_dists), thresholds) |
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return { |
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"hpatches_homog_auc_3": auc[2], |
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"hpatches_homog_auc_5": auc[4], |
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"hpatches_homog_auc_10": auc[9], |
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} |
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