5
içinde çözmePnP çalışırken Hata, OpenCV'de artırılmış bir gerçeklik programı yapmaya çalışıyorum. Ama ben solvePnP'yi çağırdığımda her zaman bir hata alıyorum.OpenCV Python
Yapmaya çalıştığım şey, seçili kırpılmış görüntünün homografi noktalarını tüm görüntüye alarak ve bu homografi noktalarını pozPnP'ye pozlama tahmini için besleyerek OpenCV'de artırılmış bir gerçeklik programı yapmaktır.
Başarılı bir şekilde homografi noktalarını uygulayabiliyorum, ancak bir nedenden dolayı çözmek için solvePnP'yi kullanamıyorum. Girişlerimin doğru biçimlendirilmediğinden şüpheleniyorum, ancak emin değilim.
, kodu sizin çalıştırmak isterseniz, git clone bu (sırayla gerekli dosyalar bu çalıştırmak için vardır): (https://github.com/vanstorm9/SLAM-experiments.git)
Ve çalıştırma dosyası:/artırılmış gerçeklik/örnek-komut/test .pyBirisi bu sorunu çözebilir mi?
Hata:
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 girişleri ve büyüklüğü
(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.]]]
Kod Bunu düzeltmek veya vermedi bilmiyorum
#!/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'