我正在尝试在OpenCV中制作增强现实程序。但是当我调用solvePnP时，我似乎总是得到一个错误。
我想做的是在OpenCV中制作一个增强现实程序，方法是将所选裁剪图像的同形点输入整个图像，并将这些同形点输入到solvePnP中以获得姿态估计。
我成功地实现了同形点，但由于某种原因，我似乎无法使solvePnP正确工作。我怀疑我的输入格式不正确，但我不确定。
如果你想自己运行代码，git克隆它(运行它需要一些文件):(https://github.com/vanstorm9/SLAM-experiments.git)
并运行file:/augmented-reality/sample-scripts/test.py
有人能解决这个问题吗?
错误:Traceback (most recent call last):
File "/augmented-reality/sample-scripts/test.py", line 315, in <module>
(ret, rvecs, tvecs) = cv2.solvePnP(objp, corners2, mtx, dist)
error: /opencv/modules/calib3d/src/solvepnp.cpp:61: error: (-215) npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) in function solvePnP
solvePnP的输入和大小
(42, 3) # objp
(143, 2, 1) # corners2
(3, 3) # mtx
(1, 5) # dist
# objp
[[ 0. 0. 0.]
[ 1. 0. 0.]
[ 2. 0. 0.]
[ 3. 0. 0.]
[ 4. 0. 0.]
[ 5. 0. 0.]
[ 6. 0. 0.]
[ 0. 1. 0.]
[ 1. 1. 0.]
[ 2. 1. 0.]
[ 3. 1. 0.]
[ 4. 1. 0.]
[ 5. 1. 0.]
[ 6. 1. 0.]
[ 0. 2. 0.]
[ 1. 2. 0.]
[ 2. 2. 0.]
[ 3. 2. 0.]
[ 4. 2. 0.]
[ 5. 2. 0.]
[ 6. 2. 0.]
[ 0. 3. 0.]
[ 1. 3. 0.]
[ 2. 3. 0.]
[ 3. 3. 0.]
[ 4. 3. 0.]
[ 5. 3. 0.]
[ 6. 3. 0.]
[ 0. 4. 0.]
[ 1. 4. 0.]
[ 2. 4. 0.]
[ 3. 4. 0.]
[ 4. 4. 0.]
[ 5. 4. 0.]
[ 6. 4. 0.]
[ 0. 5. 0.]
[ 1. 5. 0.]
[ 2. 5. 0.]
[ 3. 5. 0.]
[ 4. 5. 0.]
[ 5. 5. 0.]
[ 6. 5. 0.]]
#corners2
[[[ 0.]
[ 0.]]
[[ 1.]
[ 1.]]
[[ 2.]
[ 2.]]
[[ 3.]
[ 3.]]
[[ 4.]
[ 4.]]
[[ 5.]
[ 5.]]
[[ 6.]
[ 6.]]
[[ 7.]
[ 7.]]
[[ 8.]
[ 8.]]
[[ 9.]
[ 9.]]
[[ 10.]
[ 10.]]
[[ 11.]
[ 11.]]
[[ 12.]
[ 12.]]
[[ 13.]
[ 13.]]
[[ 14.]
[ 14.]]
[[ 15.]
[ 18.]]
[[ 16.]
[ 19.]]
[[ 17.]
[ 20.]]
[[ 18.]
[ 28.]]
[[ 19.]
[ 29.]]
[[ 20.]
[ 30.]]
[[ 21.]
[ 31.]]
[[ 22.]
[ 32.]]
[[ 23.]
[ 33.]]
[[ 24.]
[ 35.]]
[[ 25.]
[ 36.]]
[[ 26.]
[ 39.]]
[[ 27.]
[ 40.]]
[[ 28.]
[ 41.]]
[[ 29.]
[ 42.]]
[[ 31.]
[ 52.]]
[[ 32.]
[ 53.]]
[[ 33.]
[ 54.]]
[[ 34.]
[ 56.]]
[[ 35.]
[ 57.]]
[[ 36.]
[ 59.]]
[[ 37.]
[ 60.]]
[[ 38.]
[ 61.]]
[[ 40.]
[ 69.]]
[[ 41.]
[ 70.]]
[[ 42.]
[ 71.]]
[[ 43.]
[ 72.]]
[[ 44.]
[ 75.]]
[[ 45.]
[ 76.]]
[[ 47.]
[ 78.]]
[[ 49.]
[ 79.]]
[[ 50.]
[ 86.]]
[[ 51.]
[ 87.]]
[[ 52.]
[ 88.]]
[[ 53.]
[ 89.]]
[[ 54.]
[ 90.]]
[[ 55.]
[ 94.]]
[[ 48.]
[ 95.]]
[[ 56.]
[ 101.]]
[[ 42.]
[ 105.]]
[[ 61.]
[ 109.]]
[[ 62.]
[ 110.]]
[[ 57.]
[ 111.]]
[[ 58.]
[ 112.]]
[[ 61.]
[ 113.]]
[[ 59.]
[ 115.]]
[[ 58.]
[ 116.]]
[[ 63.]
[ 117.]]
[[ 60.]
[ 118.]]
[[ 70.]
[ 119.]]
[[ 71.]
[ 120.]]
[[ 74.]
[ 125.]]
[[ 75.]
[ 126.]]
[[ 76.]
[ 128.]]
[[ 77.]
[ 129.]]
[[ 78.]
[ 131.]]
[[ 66.]
[ 133.]]
[[ 67.]
[ 134.]]
[[ 69.]
[ 135.]]
[[ 79.]
[ 136.]]
[[ 72.]
[ 137.]]
[[ 80.]
[ 139.]]
[[ 73.]
[ 140.]]
[[ 83.]
[ 141.]]
[[ 82.]
[ 142.]]
[[ 91.]
[ 143.]]
[[ 92.]
[ 144.]]
[[ 93.]
[ 145.]]
[[ 94.]
[ 146.]]
[[ 85.]
[ 147.]]
[[ 86.]
[ 148.]]
[[ 87.]
[ 149.]]
[[ 95.]
[ 150.]]
[[ 101.]
[ 153.]]
[[ 102.]
[ 154.]]
[[ 103.]
[ 155.]]
[[ 104.]
[ 156.]]
[[ 105.]
[ 157.]]
[[ 106.]
[ 158.]]
[[ 107.]
[ 159.]]
[[ 108.]
[ 160.]]
[[ 109.]
[ 161.]]
[[ 110.]
[ 163.]]
[[ 111.]
[ 164.]]
[[ 112.]
[ 165.]]
[[ 113.]
[ 166.]]
[[ 114.]
[ 167.]]
[[ 99.]
[ 168.]]
[[ 100.]
[ 169.]]
[[ 118.]
[ 171.]]
[[ 119.]
[ 172.]]
[[ 120.]
[ 173.]]
[[ 121.]
[ 174.]]
[[ 122.]
[ 175.]]
[[ 123.]
[ 176.]]
[[ 102.]
[ 177.]]
[[ 103.]
[ 178.]]
[[ 106.]
[ 180.]]
[[ 107.]
[ 181.]]
[[ 124.]
[ 182.]]
[[ 115.]
[ 183.]]
[[ 116.]
[ 184.]]
[[ 117.]
[ 187.]]
[[ 150.]
[ 188.]]
[[ 128.]
[ 192.]]
[[ 127.]
[ 194.]]
[[ 129.]
[ 197.]]
[[ 130.]
[ 198.]]
[[ 131.]
[ 199.]]
[[ 135.]
[ 200.]]
[[ 136.]
[ 201.]]
[[ 137.]
[ 202.]]
[[ 138.]
[ 203.]]
[[ 134.]
[ 204.]]
[[ 113.]
[ 205.]]
[[ 141.]
[ 206.]]
[[ 142.]
[ 207.]]
[[ 145.]
[ 208.]]
[[ 143.]
[ 212.]]
[[ 144.]
[ 213.]]
[[ 149.]
[ 214.]]
[[ 157.]
[ 216.]]
[[ 159.]
[ 218.]]
[[ 131.]
[ 220.]]
[[ 112.]
[ 221.]]
[[ 163.]
[ 223.]]
[[ 164.]
[ 224.]]
[[ 157.]
[ 228.]]]
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import glob
# Load previously saved data
mtx = np.load('calib-matrix/mtx.npy')
dist = np.load('calib-matrix/dist.npy')
rect = (0, 0, 0, 0)
startPoint = False
endPoint = False
selectedPoint = False
def on_mouse(
event,
x,
y,
flags,
params,
):
global rect, startPoint, endPoint, selectedPoint
# get mouse click
if event == cv2.EVENT_LBUTTONDOWN:
if startPoint == True and endPoint == True:
# Resets and delete box once you are done
startPoint = False
endPoint = False
rect = (0, 0, 0, 0)
if startPoint == False:
# First click, waits for final click to create box
rect = (x, y, 0, 0)
startPoint = True
elif endPoint == False:
# creates the box (I think}
rect = (rect[0], rect[1], x, y)
print '________________'
print 'Rectangle location: ', rect[0], ' ', rect[1], ' ',
x, ' ', y
endPoint = True
return
def drawCube(img, corners, imgpts):
imgpts = np.int32(imgpts).reshape(-1, 2)
# draw ground floor in green
img = cv2.drawContours(img, [imgpts[:4]], -1, (0, 255, 0), -3)
# draw pillars in blue color
for (i, j) in zip(range(4), range(4, 8)):
img = cv2.line(img, tuple(imgpts[i]), tuple(imgpts[j]), 255, 3)
# draw top layer in red color
img = cv2.drawContours(img, [imgpts[4:]], -1, (0, 0, 255), 3)
return img
def draw(img, corners, imgpts):
corner = tuple(corners[0].ravel())
img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255, 0, 0),
5)
img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0, 255, 0),
5)
img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0, 0, 255),
5)
return img
def detectAndDescribe(image):
# convert the image to grayscale
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# convert the keypoints from KeyPoint objects to NumPy
# arrays
kps = np.float32([kp.pt for kp in kps])
# return a tuple of keypoints and features
return (kps, features)
def matchKeypoints(
kpsA,
kpsB,
featuresA,
featuresB,
ratio,
reprojThresh,
):
# compute the raw matches and initialize the list of actual
# matches
matcher = cv2.DescriptorMatcher_create('BruteForce')
rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
matches = []
match_ar = []
i = 0
# loop over the raw matches
for m in rawMatches:
# ensure the distance is within a certain ratio of each
# other (i.e. Lowe's ratio test)
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
matches.append((m[0].trainIdx, m[0].queryIdx))
if i == 0:
match_ar = np.array([[[m[0].trainIdx],
[m[0].queryIdx]]], dtype=np.float)
match_ar = match_ar.transpose()
match_ar = list(match_ar)
print type(match_ar)
i = i + 1
else:
m_add = np.array([[m[0].trainIdx],
[m[0].queryIdx]])[None, :]
m_add = m_add.transpose()
match_ar = np.concatenate([match_ar, m_add])
# computing a homography requires at least 4 matches
if len(matches) > 4:
# construct the two sets of points
ptsA = np.float32([kpsA[i] for (_, i) in matches])
ptsB = np.float32([kpsB[i] for (i, _) in matches])
# compute the homography between the two sets of points
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
reprojThresh)
# return the matches along with the homograpy matrix
# and status of each matched point
# return (matches, H, status)
return (matches, match_ar, H, status)
# otherwise, no homograpy could be computed
return None
def drawMatches(
imageA,
imageB,
kpsA,
kpsB,
matches,
status,
):
# initialize the output visualization image
(hA, wA) = imageA.shape[:2]
(hB, wB) = imageB.shape[:2]
vis = np.zeros((max(hA, hB), wA + wB, 3), dtype='uint8')
print imageA.shape
print imageB.shape
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
# loop over the matches
for ((trainIdx, queryIdx), s) in zip(matches, status):
# only process the match if the keypoint was successfully
# matched
if s == 1:
# draw the match
ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
# return the visualization
return vis
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30,
0.001)
objp = np.zeros((6 * 7, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
axis = np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
cubeAxis = np.float32([
[0, 0, 0],
[0, 3, 0],
[3, 3, 0],
[3, 0, 0],
[0, 0, -3],
[0, 3, -3],
[3, 3, -3],
[3, 0, -3],
])
# Here we are going to try to define our corners
#fname = 'images/left01.jpg'
fname = 'images/checkerboard4.jpg'
img = cv2.imread(fname)
cv2.namedWindow('Label')
cv2.setMouseCallback('Label', on_mouse)
'''
while 1:
if selectedPoint == True:
break
if startPoint == True and endPoint == True:
cv2.rectangle(img, (rect[0], rect[1]), (rect[2], rect[3]),
(255, 0, 255), 2)
cv2.imshow('Label', img)
if cv2.waitKey(20) & 255 == 27:
break
cv2.destroyAllWindows()
'''
'''
reference_color = img[rect[1]:rect[3], rect[0]:rect[2]]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
reference_img = gray[rect[1]:rect[3], rect[0]:rect[2]]
'''
reference_color = img[101:400, 92:574]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
reference_img = gray[101:400, 92:574]
####### Attempting to preform homography #######
sift = cv2.xfeatures2d.SIFT_create()
(kp1, des1) = detectAndDescribe(gray)
(kp2, des2) = detectAndDescribe(reference_img)
(M, M_ar, H, status) = matchKeypoints(kp1,kp2,des1,des2,0.75,4.0,)
if M is None:
print 'No matches found'
exit()
print 'Matches found'
# vis = drawMatches(gray, reference_img, kp1, kp2, M, status)
vis = drawMatches(img,reference_color,kp1,kp2,M,status)
cv2.imshow('vis', vis)
cv2.waitKey(0)
corners2 = M_ar
if 1 == 1:
# Find the rotation and translation vectors.
print 'here'
(ret, rvecs, tvecs) = cv2.solvePnP(objp, corners2, mtx, dist)
# project 3D points to image plane
(imgpts, jac) = cv2.projectPoints(cubeAxis, rvecs, tvecs, mtx, dist)
img = drawCube(img, corners2, imgpts)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 255
else:
print 'No corners were detected'
我不知道你有没有修好它。也许你可以用_,rvecs, tvecs, inliers = cv2.solvePnPRansac(objp, corners2, mtx, dist)来修复它。因为solvePnP改变了，但它还没有在他们自己的教程中记录。