File size: 4,515 Bytes
404d2af
 
 
 
8b973ee
404d2af
 
 
 
 
 
8b973ee
404d2af
 
 
8b973ee
404d2af
 
8b973ee
404d2af
 
 
 
8b973ee
404d2af
 
 
 
 
 
 
 
 
 
 
 
 
8b973ee
404d2af
 
 
 
8b973ee
 
404d2af
 
8b973ee
404d2af
 
 
 
 
8b973ee
404d2af
 
 
8b973ee
404d2af
 
 
 
 
 
8b973ee
 
404d2af
 
8b973ee
404d2af
 
 
 
8b973ee
404d2af
 
 
 
 
 
 
 
 
 
 
8b973ee
 
 
 
 
 
 
 
 
 
404d2af
 
8b973ee
 
 
 
404d2af
 
 
 
 
8b973ee
404d2af
 
 
 
 
 
 
 
 
 
8b973ee
404d2af
 
 
 
 
 
8b973ee
404d2af
 
 
 
 
 
 
 
 
 
8b973ee
 
404d2af
8b973ee
404d2af
8b973ee
404d2af
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import math
import numpy as np
import cv2


def extract_ORB_keypoints_and_descriptors(img):
    # gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    detector = cv2.ORB_create(nfeatures=1000)
    kp, desc = detector.detectAndCompute(img, None)
    return kp, desc


def match_descriptors_NG(kp1, desc1, kp2, desc2):
    bf = cv2.BFMatcher()
    try:
        matches = bf.knnMatch(desc1, desc2, k=2)
    except:
        matches = []
    good_matches = []
    image1_kp = []
    image2_kp = []
    ratios = []
    try:
        for (m1, m2) in matches:
            if m1.distance < 0.8 * m2.distance:
                good_matches.append(m1)
                image2_kp.append(kp2[m1.trainIdx].pt)
                image1_kp.append(kp1[m1.queryIdx].pt)
                ratios.append(m1.distance / m2.distance)
    except:
        pass
    image1_kp = np.array([image1_kp])
    image2_kp = np.array([image2_kp])
    ratios = np.array([ratios])
    ratios = np.expand_dims(ratios, 2)
    return image1_kp, image2_kp, good_matches, ratios


def match_descriptors(kp1, desc1, kp2, desc2, ORB):
    if ORB:
        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
        try:
            matches = bf.match(desc1, desc2)
            matches = sorted(matches, key=lambda x: x.distance)
        except:
            matches = []
        good_matches = []
        image1_kp = []
        image2_kp = []
        count = 0
        try:
            for m in matches:
                count += 1
                if count < 1000:
                    good_matches.append(m)
                    image2_kp.append(kp2[m.trainIdx].pt)
                    image1_kp.append(kp1[m.queryIdx].pt)
        except:
            pass
    else:
        # Match the keypoints with the warped_keypoints with nearest neighbor search
        bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
        try:
            matches = bf.match(desc1.transpose(1, 0), desc2.transpose(1, 0))
            matches = sorted(matches, key=lambda x: x.distance)
        except:
            matches = []
        good_matches = []
        image1_kp = []
        image2_kp = []
        try:
            for m in matches:
                good_matches.append(m)
                image2_kp.append(kp2[m.trainIdx].pt)
                image1_kp.append(kp1[m.queryIdx].pt)
        except:
            pass

    image1_kp = np.array([image1_kp])
    image2_kp = np.array([image2_kp])
    return image1_kp, image2_kp, good_matches


def compute_essential(matched_kp1, matched_kp2, K):
    pts1 = cv2.undistortPoints(
        matched_kp1,
        cameraMatrix=K,
        distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
    )
    pts2 = cv2.undistortPoints(
        matched_kp2,
        cameraMatrix=K,
        distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
    )
    K_1 = np.eye(3)
    # Estimate the homography between the matches using RANSAC
    ransac_model, ransac_inliers = cv2.findEssentialMat(
        pts1, pts2, K_1, method=cv2.FM_RANSAC, prob=0.999, threshold=0.001
    )
    if ransac_inliers is None or ransac_model.shape != (3, 3):
        ransac_inliers = np.array([])
        ransac_model = None
    return ransac_model, ransac_inliers, pts1, pts2


def compute_error(R_GT, t_GT, E, pts1_norm, pts2_norm, inliers):
    """Compute the angular error between two rotation matrices and two translation vectors.
    Keyword arguments:
    R -- 2D numpy array containing an estimated rotation
    gt_R -- 2D numpy array containing the corresponding ground truth rotation
    t -- 2D numpy array containing an estimated translation as column
    gt_t -- 2D numpy array containing the corresponding ground truth translation
    """

    inliers = inliers.ravel()
    R = np.eye(3)
    t = np.zeros((3, 1))
    sst = True
    try:
        cv2.recoverPose(E, pts1_norm, pts2_norm, np.eye(3), R, t, inliers)
    except:
        sst = False
    # calculate angle between provided rotations
    #
    if sst:
        dR = np.matmul(R, np.transpose(R_GT))
        dR = cv2.Rodrigues(dR)[0]
        dR = np.linalg.norm(dR) * 180 / math.pi

        # calculate angle between provided translations
        dT = float(np.dot(t_GT.T, t))
        dT /= float(np.linalg.norm(t_GT))

        if dT > 1 or dT < -1:
            print("Domain warning! dT:", dT)
            dT = max(-1, min(1, dT))
        dT = math.acos(dT) * 180 / math.pi
        dT = np.minimum(dT, 180 - dT)  # ambiguity of E estimation
    else:
        dR, dT = 180.0, 180.0
    return dR, dT