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# Copyright 2020 Toyota Research Institute. All rights reserved.
# Adapted from: https://github.com/rpautrat/SuperPoint/blob/master/superpoint/evaluations/descriptor_evaluation.py
import random
from glob import glob
from os import path as osp
import cv2
import numpy as np
from utils import warp_keypoints
def select_k_best(points, descriptors, k):
"""Select the k most probable points (and strip their probability).
points has shape (num_points, 3) where the last coordinate is the probability.
Parameters
----------
points: numpy.ndarray (N,3)
Keypoint vector, consisting of (x,y,probability).
descriptors: numpy.ndarray (N,256)
Keypoint descriptors.
k: int
Number of keypoints to select, based on probability.
Returns
-------
selected_points: numpy.ndarray (k,2)
k most probable keypoints.
selected_descriptors: numpy.ndarray (k,256)
Descriptors corresponding to the k most probable keypoints.
"""
sorted_prob = points[points[:, 2].argsort(), :2]
sorted_desc = descriptors[points[:, 2].argsort(), :]
start = min(k, points.shape[0])
selected_points = sorted_prob[-start:, :]
selected_descriptors = sorted_desc[-start:, :]
return selected_points, selected_descriptors
def keep_shared_points(keypoints, descriptors, H, shape, keep_k_points=1000):
"""
Compute a list of keypoints from the map, filter the list of points by keeping
only the points that once mapped by H are still inside the shape of the map
and keep at most 'keep_k_points' keypoints in the image.
Parameters
----------
keypoints: numpy.ndarray (N,3)
Keypoint vector, consisting of (x,y,probability).
descriptors: numpy.ndarray (N,256)
Keypoint descriptors.
H: numpy.ndarray (3,3)
Homography.
shape: tuple
Image shape.
keep_k_points: int
Number of keypoints to select, based on probability.
Returns
-------
selected_points: numpy.ndarray (k,2)
k most probable keypoints.
selected_descriptors: numpy.ndarray (k,256)
Descriptors corresponding to the k most probable keypoints.
"""
def keep_true_keypoints(points, descriptors, H, shape):
"""Keep only the points whose warped coordinates by H are still inside shape."""
warped_points = warp_keypoints(points[:, [1, 0]], H)
warped_points[:, [0, 1]] = warped_points[:, [1, 0]]
mask = (
(warped_points[:, 0] >= 0)
& (warped_points[:, 0] < shape[0])
& (warped_points[:, 1] >= 0)
& (warped_points[:, 1] < shape[1])
)
return points[mask, :], descriptors[mask, :]
selected_keypoints, selected_descriptors = keep_true_keypoints(
keypoints, descriptors, H, shape
)
selected_keypoints, selected_descriptors = select_k_best(
selected_keypoints, selected_descriptors, keep_k_points
)
return selected_keypoints, selected_descriptors
def compute_matching_score(data, keep_k_points=1000):
"""
Compute the matching score between two sets of keypoints with associated descriptors.
Parameters
----------
data: dict
Input dictionary containing:
image_shape: tuple (H,W)
Original image shape.
homography: numpy.ndarray (3,3)
Ground truth homography.
prob: numpy.ndarray (N,3)
Keypoint vector, consisting of (x,y,probability).
warped_prob: numpy.ndarray (N,3)
Warped keypoint vector, consisting of (x,y,probability).
desc: numpy.ndarray (N,256)
Keypoint descriptors.
warped_desc: numpy.ndarray (N,256)
Warped keypoint descriptors.
keep_k_points: int
Number of keypoints to select, based on probability.
Returns
-------
ms: float
Matching score.
"""
shape = data["image_shape"]
real_H = data["homography"]
# Filter out predictions
keypoints = data["prob"][:, :2].T
keypoints = keypoints[::-1]
prob = data["prob"][:, 2]
keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1)
warped_keypoints = data["warped_prob"][:, :2].T
warped_keypoints = warped_keypoints[::-1]
warped_prob = data["warped_prob"][:, 2]
warped_keypoints = np.stack(
[warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1
)
desc = data["desc"]
warped_desc = data["warped_desc"]
# Keeps all points for the next frame. The matching for caculating M.Score shouldnt use only in view points.
keypoints, desc = select_k_best(keypoints, desc, keep_k_points)
warped_keypoints, warped_desc = select_k_best(
warped_keypoints, warped_desc, keep_k_points
)
# Match the keypoints with the warped_keypoints with nearest neighbor search
# This part needs to be done with crossCheck=False.
# All the matched pairs need to be evaluated without any selection.
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
matches = bf.match(desc, warped_desc)
matches_idx = np.array([m.queryIdx for m in matches])
m_keypoints = keypoints[matches_idx, :]
matches_idx = np.array([m.trainIdx for m in matches])
m_warped_keypoints = warped_keypoints[matches_idx, :]
true_warped_keypoints = warp_keypoints(
m_warped_keypoints[:, [1, 0]], np.linalg.inv(real_H)
)[:, ::-1]
vis_warped = np.all(
(true_warped_keypoints >= 0) & (true_warped_keypoints <= (np.array(shape) - 1)),
axis=-1,
)
norm1 = np.linalg.norm(true_warped_keypoints - m_keypoints, axis=-1)
correct1 = norm1 < 3
count1 = np.sum(correct1 * vis_warped)
score1 = count1 / np.maximum(np.sum(vis_warped), 1.0)
matches = bf.match(warped_desc, desc)
matches_idx = np.array([m.queryIdx for m in matches])
m_warped_keypoints = warped_keypoints[matches_idx, :]
matches_idx = np.array([m.trainIdx for m in matches])
m_keypoints = keypoints[matches_idx, :]
true_keypoints = warp_keypoints(m_keypoints[:, [1, 0]], real_H)[:, ::-1]
vis = np.all(
(true_keypoints >= 0) & (true_keypoints <= (np.array(shape) - 1)), axis=-1
)
norm2 = np.linalg.norm(true_keypoints - m_warped_keypoints, axis=-1)
correct2 = norm2 < 3
count2 = np.sum(correct2 * vis)
score2 = count2 / np.maximum(np.sum(vis), 1.0)
ms = (score1 + score2) / 2
return ms
def compute_homography(data, keep_k_points=1000):
"""
Compute the homography between 2 sets of Keypoints and descriptors inside data.
Use the homography to compute the correctness metrics (1,3,5).
Parameters
----------
data: dict
Input dictionary containing:
image_shape: tuple (H,W)
Original image shape.
homography: numpy.ndarray (3,3)
Ground truth homography.
prob: numpy.ndarray (N,3)
Keypoint vector, consisting of (x,y,probability).
warped_prob: numpy.ndarray (N,3)
Warped keypoint vector, consisting of (x,y,probability).
desc: numpy.ndarray (N,256)
Keypoint descriptors.
warped_desc: numpy.ndarray (N,256)
Warped keypoint descriptors.
keep_k_points: int
Number of keypoints to select, based on probability.
Returns
-------
correctness1: float
correctness1 metric.
correctness3: float
correctness3 metric.
correctness5: float
correctness5 metric.
"""
shape = data["image_shape"]
real_H = data["homography"]
# Filter out predictions
keypoints = data["prob"][:, :2].T
keypoints = keypoints[::-1]
prob = data["prob"][:, 2]
keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1)
warped_keypoints = data["warped_prob"][:, :2].T
warped_keypoints = warped_keypoints[::-1]
warped_prob = data["warped_prob"][:, 2]
warped_keypoints = np.stack(
[warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1
)
desc = data["desc"]
warped_desc = data["warped_desc"]
# Keeps only the points shared between the two views
keypoints, desc = keep_shared_points(keypoints, desc, real_H, shape, keep_k_points)
warped_keypoints, warped_desc = keep_shared_points(
warped_keypoints, warped_desc, np.linalg.inv(real_H), shape, keep_k_points
)
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = bf.match(desc, warped_desc)
matches_idx = np.array([m.queryIdx for m in matches])
m_keypoints = keypoints[matches_idx, :]
matches_idx = np.array([m.trainIdx for m in matches])
m_warped_keypoints = warped_keypoints[matches_idx, :]
# Estimate the homography between the matches using RANSAC
H, _ = cv2.findHomography(
m_keypoints[:, [1, 0]],
m_warped_keypoints[:, [1, 0]],
cv2.RANSAC,
3,
maxIters=5000,
)
if H is None:
return 0, 0, 0
shape = shape[::-1]
# Compute correctness
corners = np.array(
[
[0, 0, 1],
[0, shape[1] - 1, 1],
[shape[0] - 1, 0, 1],
[shape[0] - 1, shape[1] - 1, 1],
]
)
real_warped_corners = np.dot(corners, np.transpose(real_H))
real_warped_corners = real_warped_corners[:, :2] / real_warped_corners[:, 2:]
warped_corners = np.dot(corners, np.transpose(H))
warped_corners = warped_corners[:, :2] / warped_corners[:, 2:]
mean_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1))
correctness1 = float(mean_dist <= 1)
correctness3 = float(mean_dist <= 3)
correctness5 = float(mean_dist <= 5)
return correctness1, correctness3, correctness5
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