Camera pose estimation giving wrong resultsCamera pose estimation: How do I interpret rotation and translation matrices?OpenCV: Camera Pose EstimationOpencv camera pose estimationCamera pose estimationCamera displacement estimation in OpenCV - incorrect pose outputHow can I estimate the camera pose with 3d-to-2d-point-correspondences (using opencv)3D reconstruction from two calibrated cameras - where is the error in this pipeline?Epipolar geometry pose estimation: Epipolar lines look good but wrong poseCamera pose estimation is invertedReal Time pose estimation of a textured objectStereo-calibration with virtual camera
Going to get married soon, should I do it on Dec 31 or Jan 1?
Sir Alex Ferguson advice OR Sir Alex Ferguson's advice
Can a US president have someone sent to prison?
Why is the Turkish president's surname spelt in Russian as Эрдоган, with г?
How come I was asked by a CBP officer why I was in the US when leaving?
Should I hide continue button until tasks are completed?
Why does the A-4 Skyhawk sit nose-up when on ground?
Do sudoku answers always have a single minimal clue set?
Find smallest index that is identical to the value in an array
What shortcut does ⌦ symbol in Camunda macOS app indicate and how to invoke it?
Should I tell my insurance company I have an unsecured loan for my new car?
Why isn’t the tax system continuous rather than bracketed?
Alphabet completion rate
Was "I have the farts, again" broadcast from the Moon to the whole world?
can’t run a function against EXEC
Cross over of arrows in a complex diagram
Why does the numerical solution of an ODE move away from an unstable equilibrium?
Should I report a leak of confidential HR information?
Children's short story about material that accelerates away from gravity
Set vertical spacing between two particular items
If protons are the only stable baryons, why do they decay into neutrons in positron emission?
What's the difference between にしては、 わりに and くせに?
Are there any vegetarian astronauts?
Confusion about multiple information Sets
Camera pose estimation giving wrong results
Camera pose estimation: How do I interpret rotation and translation matrices?OpenCV: Camera Pose EstimationOpencv camera pose estimationCamera pose estimationCamera displacement estimation in OpenCV - incorrect pose outputHow can I estimate the camera pose with 3d-to-2d-point-correspondences (using opencv)3D reconstruction from two calibrated cameras - where is the error in this pipeline?Epipolar geometry pose estimation: Epipolar lines look good but wrong poseCamera pose estimation is invertedReal Time pose estimation of a textured objectStereo-calibration with virtual camera
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I am trying to estimate relative camera movement based on matching points in two different images. Much like described here:
Camera pose estimation: How do I interpret rotation and translation matrices?
But the estimated translation and rotation do not make sense.
I use synthetic input to make sure all points are valid and perfectly positioned.
10 x 10 x 10 points evenly spread within a cube.
(Cube plotted with blue front face, red back face, lighter top and darker bottom)
zeroProjection
Camera is in front of the cube pointing into to the front face.
rotate90projection
Camera to the left of the cube pointing into the left side face.
I plot the two projections. You can easily verify visually that the camera has panned 90 degrees and moved diagonally in the x-z plane between the two projections.
In the code, the rotation (in degrees) is given as (0, -90, 0)
The translation is (0.7071, 0, 0.7071), camera movement distance is exactly 1.
I then do findEssentialMat() and recoverPose() on the 2d point sets to get translation and rotation estimates.
I expect to see the same translation and rotation I used to generate the images, but the estimates are completely wrong:
rotation estimate: (-74.86565284711004, -48.52201867665918, 121.26023708879158)
translation estimate: [[0.96576997]
[0.17203598]
[0.19414426]]
How can recover the actual (0, -90, 0), (0.7071, 0, 0,7071) transformation?
Complete code that displays the two cube images and prints out estimates:
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
E, _ = cv2.findEssentialMat(projectionA, projectionB, focal = f)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
print('rotation estimate:', angles)
print('translation estimate:', t)
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[0], [-90], [0]], dtype=np.float32)
translation = np.array([[math.sqrt(.5)], [0], [math.sqrt(.5)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
rotate90projection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, rotate90projection)
estimate_pose(zeroProjection, rotate90projection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
opencv 3d-reconstruction opencv-python
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
add a comment |
I am trying to estimate relative camera movement based on matching points in two different images. Much like described here:
Camera pose estimation: How do I interpret rotation and translation matrices?
But the estimated translation and rotation do not make sense.
I use synthetic input to make sure all points are valid and perfectly positioned.
10 x 10 x 10 points evenly spread within a cube.
(Cube plotted with blue front face, red back face, lighter top and darker bottom)
zeroProjection
Camera is in front of the cube pointing into to the front face.
rotate90projection
Camera to the left of the cube pointing into the left side face.
I plot the two projections. You can easily verify visually that the camera has panned 90 degrees and moved diagonally in the x-z plane between the two projections.
In the code, the rotation (in degrees) is given as (0, -90, 0)
The translation is (0.7071, 0, 0.7071), camera movement distance is exactly 1.
I then do findEssentialMat() and recoverPose() on the 2d point sets to get translation and rotation estimates.
I expect to see the same translation and rotation I used to generate the images, but the estimates are completely wrong:
rotation estimate: (-74.86565284711004, -48.52201867665918, 121.26023708879158)
translation estimate: [[0.96576997]
[0.17203598]
[0.19414426]]
How can recover the actual (0, -90, 0), (0.7071, 0, 0,7071) transformation?
Complete code that displays the two cube images and prints out estimates:
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
E, _ = cv2.findEssentialMat(projectionA, projectionB, focal = f)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
print('rotation estimate:', angles)
print('translation estimate:', t)
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[0], [-90], [0]], dtype=np.float32)
translation = np.array([[math.sqrt(.5)], [0], [math.sqrt(.5)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
rotate90projection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, rotate90projection)
estimate_pose(zeroProjection, rotate90projection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
opencv 3d-reconstruction opencv-python
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03
add a comment |
I am trying to estimate relative camera movement based on matching points in two different images. Much like described here:
Camera pose estimation: How do I interpret rotation and translation matrices?
But the estimated translation and rotation do not make sense.
I use synthetic input to make sure all points are valid and perfectly positioned.
10 x 10 x 10 points evenly spread within a cube.
(Cube plotted with blue front face, red back face, lighter top and darker bottom)
zeroProjection
Camera is in front of the cube pointing into to the front face.
rotate90projection
Camera to the left of the cube pointing into the left side face.
I plot the two projections. You can easily verify visually that the camera has panned 90 degrees and moved diagonally in the x-z plane between the two projections.
In the code, the rotation (in degrees) is given as (0, -90, 0)
The translation is (0.7071, 0, 0.7071), camera movement distance is exactly 1.
I then do findEssentialMat() and recoverPose() on the 2d point sets to get translation and rotation estimates.
I expect to see the same translation and rotation I used to generate the images, but the estimates are completely wrong:
rotation estimate: (-74.86565284711004, -48.52201867665918, 121.26023708879158)
translation estimate: [[0.96576997]
[0.17203598]
[0.19414426]]
How can recover the actual (0, -90, 0), (0.7071, 0, 0,7071) transformation?
Complete code that displays the two cube images and prints out estimates:
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
E, _ = cv2.findEssentialMat(projectionA, projectionB, focal = f)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
print('rotation estimate:', angles)
print('translation estimate:', t)
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[0], [-90], [0]], dtype=np.float32)
translation = np.array([[math.sqrt(.5)], [0], [math.sqrt(.5)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
rotate90projection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, rotate90projection)
estimate_pose(zeroProjection, rotate90projection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
opencv 3d-reconstruction opencv-python
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
I am trying to estimate relative camera movement based on matching points in two different images. Much like described here:
Camera pose estimation: How do I interpret rotation and translation matrices?
But the estimated translation and rotation do not make sense.
I use synthetic input to make sure all points are valid and perfectly positioned.
10 x 10 x 10 points evenly spread within a cube.
(Cube plotted with blue front face, red back face, lighter top and darker bottom)
zeroProjection
Camera is in front of the cube pointing into to the front face.
rotate90projection
Camera to the left of the cube pointing into the left side face.
I plot the two projections. You can easily verify visually that the camera has panned 90 degrees and moved diagonally in the x-z plane between the two projections.
In the code, the rotation (in degrees) is given as (0, -90, 0)
The translation is (0.7071, 0, 0.7071), camera movement distance is exactly 1.
I then do findEssentialMat() and recoverPose() on the 2d point sets to get translation and rotation estimates.
I expect to see the same translation and rotation I used to generate the images, but the estimates are completely wrong:
rotation estimate: (-74.86565284711004, -48.52201867665918, 121.26023708879158)
translation estimate: [[0.96576997]
[0.17203598]
[0.19414426]]
How can recover the actual (0, -90, 0), (0.7071, 0, 0,7071) transformation?
Complete code that displays the two cube images and prints out estimates:
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
E, _ = cv2.findEssentialMat(projectionA, projectionB, focal = f)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
print('rotation estimate:', angles)
print('translation estimate:', t)
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[0], [-90], [0]], dtype=np.float32)
translation = np.array([[math.sqrt(.5)], [0], [math.sqrt(.5)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
rotate90projection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, rotate90projection)
estimate_pose(zeroProjection, rotate90projection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
opencv 3d-reconstruction opencv-python
opencv 3d-reconstruction opencv-python
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
edited Mar 27 at 13:53
Ragnvald Tullenavn
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
asked Mar 25 at 11:26
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
13 bronze badges
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03
add a comment |
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03
add a comment |
1 Answer
1
active
oldest
votes
Turned out to be a few minor bugs in my code (like wrong principal point).
Working code below shows 3 images.
First is a cube displayed in front of the camera.
Second is the same cube, but different projection. Camera has been moved 1 unit and rotated around all 3 axes.
Camera translation and rotation is estimated from the two projections.
Third shows the cube projected using the rotation and translation estimates.
The code works since the second and third images are similar.
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
principal_point = (w/2,h/2)
E, m = cv2.findEssentialMat(projectionA, projectionB, focal = f, pp = principal_point, method=cv2.RANSAC, threshold=1, prob=0.999)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB, focal = f, pp = principal_point, mask = m)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
return angles, t
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[10], [-30], [5]], dtype=np.float32)
translation = np.array([[math.sqrt(.7)], [0], [math.sqrt(.3)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
movedprojection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, movedprojection)
estRot, estTra= estimate_pose(zeroProjection, movedprojection)
print('rotation estimate:', estRot)
print('translation estimate:', estTra)
rotation = np.array([[estRot[0]], [estRot[1]], [estRot[2]]], dtype=np.float32)
translation = np.array([[estTra[0]], [estTra[1]], [estTra[2]]], dtype=np.float32)
estimateProjection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, estimateProjection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55336759%2fcamera-pose-estimation-giving-wrong-results%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Turned out to be a few minor bugs in my code (like wrong principal point).
Working code below shows 3 images.
First is a cube displayed in front of the camera.
Second is the same cube, but different projection. Camera has been moved 1 unit and rotated around all 3 axes.
Camera translation and rotation is estimated from the two projections.
Third shows the cube projected using the rotation and translation estimates.
The code works since the second and third images are similar.
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
principal_point = (w/2,h/2)
E, m = cv2.findEssentialMat(projectionA, projectionB, focal = f, pp = principal_point, method=cv2.RANSAC, threshold=1, prob=0.999)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB, focal = f, pp = principal_point, mask = m)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
return angles, t
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[10], [-30], [5]], dtype=np.float32)
translation = np.array([[math.sqrt(.7)], [0], [math.sqrt(.3)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
movedprojection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, movedprojection)
estRot, estTra= estimate_pose(zeroProjection, movedprojection)
print('rotation estimate:', estRot)
print('translation estimate:', estTra)
rotation = np.array([[estRot[0]], [estRot[1]], [estRot[2]]], dtype=np.float32)
translation = np.array([[estTra[0]], [estTra[1]], [estTra[2]]], dtype=np.float32)
estimateProjection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, estimateProjection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
add a comment |
Turned out to be a few minor bugs in my code (like wrong principal point).
Working code below shows 3 images.
First is a cube displayed in front of the camera.
Second is the same cube, but different projection. Camera has been moved 1 unit and rotated around all 3 axes.
Camera translation and rotation is estimated from the two projections.
Third shows the cube projected using the rotation and translation estimates.
The code works since the second and third images are similar.
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
principal_point = (w/2,h/2)
E, m = cv2.findEssentialMat(projectionA, projectionB, focal = f, pp = principal_point, method=cv2.RANSAC, threshold=1, prob=0.999)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB, focal = f, pp = principal_point, mask = m)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
return angles, t
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[10], [-30], [5]], dtype=np.float32)
translation = np.array([[math.sqrt(.7)], [0], [math.sqrt(.3)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
movedprojection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, movedprojection)
estRot, estTra= estimate_pose(zeroProjection, movedprojection)
print('rotation estimate:', estRot)
print('translation estimate:', estTra)
rotation = np.array([[estRot[0]], [estRot[1]], [estRot[2]]], dtype=np.float32)
translation = np.array([[estTra[0]], [estTra[1]], [estTra[2]]], dtype=np.float32)
estimateProjection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, estimateProjection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
add a comment |
Turned out to be a few minor bugs in my code (like wrong principal point).
Working code below shows 3 images.
First is a cube displayed in front of the camera.
Second is the same cube, but different projection. Camera has been moved 1 unit and rotated around all 3 axes.
Camera translation and rotation is estimated from the two projections.
Third shows the cube projected using the rotation and translation estimates.
The code works since the second and third images are similar.
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
principal_point = (w/2,h/2)
E, m = cv2.findEssentialMat(projectionA, projectionB, focal = f, pp = principal_point, method=cv2.RANSAC, threshold=1, prob=0.999)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB, focal = f, pp = principal_point, mask = m)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
return angles, t
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[10], [-30], [5]], dtype=np.float32)
translation = np.array([[math.sqrt(.7)], [0], [math.sqrt(.3)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
movedprojection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, movedprojection)
estRot, estTra= estimate_pose(zeroProjection, movedprojection)
print('rotation estimate:', estRot)
print('translation estimate:', estTra)
rotation = np.array([[estRot[0]], [estRot[1]], [estRot[2]]], dtype=np.float32)
translation = np.array([[estTra[0]], [estTra[1]], [estTra[2]]], dtype=np.float32)
estimateProjection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, estimateProjection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
Turned out to be a few minor bugs in my code (like wrong principal point).
Working code below shows 3 images.
First is a cube displayed in front of the camera.
Second is the same cube, but different projection. Camera has been moved 1 unit and rotated around all 3 axes.
Camera translation and rotation is estimated from the two projections.
Third shows the cube projected using the rotation and translation estimates.
The code works since the second and third images are similar.
import cv2
import numpy as np
import math
def cameraMatrix(f, w, h):
return np.array([
[f, 0, w/2],
[0, f, h/2],
[0, 0, 1]])
n = 10
f = 300
w = 640
h = 480
K = cameraMatrix(f, w, h)
def cube(x=0, y=0, z=0, radius=1):
c = np.zeros((n * n * n, 3), dtype=np.float32)
for i in range(0, n):
for j in range(0, n):
for k in range(0, n):
index = i + j * n + k * n * n
c[index] = [i, j, k]
c = 2 * c / (n - 1) - 1
c *= radius
c += [x, y, z]
return c
def project3dTo2dArray(points3d, K, rotation, translation):
imagePoints, _ = cv2.projectPoints(points3d,
rotation,
translation,
K,
np.array([]))
p2d = imagePoints.reshape((imagePoints.shape[0],2))
return p2d
def estimate_pose(projectionA, projectionB):
principal_point = (w/2,h/2)
E, m = cv2.findEssentialMat(projectionA, projectionB, focal = f, pp = principal_point, method=cv2.RANSAC, threshold=1, prob=0.999)
_, r, t, _ = cv2.recoverPose(E, projectionA, projectionB, focal = f, pp = principal_point, mask = m)
angles, _, _, _, _, _ = cv2.RQDecomp3x3(r)
return angles, t
def main():
c = cube(0, 0, math.sqrt(.5), 0.1)
rotation = np.array([[0], [0], [0]], dtype=np.float32)
translation = np.array([[0], [0], [0]], dtype=np.float32)
zeroProjection = project3dTo2dArray(c, K, rotation, translation)
displayCube(w, h, zeroProjection)
rotation = np.array([[10], [-30], [5]], dtype=np.float32)
translation = np.array([[math.sqrt(.7)], [0], [math.sqrt(.3)]], dtype=np.float32)
print('applying rotation: ', rotation)
print('applying translation: ', translation)
movedprojection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, movedprojection)
estRot, estTra= estimate_pose(zeroProjection, movedprojection)
print('rotation estimate:', estRot)
print('translation estimate:', estTra)
rotation = np.array([[estRot[0]], [estRot[1]], [estRot[2]]], dtype=np.float32)
translation = np.array([[estTra[0]], [estTra[1]], [estTra[2]]], dtype=np.float32)
estimateProjection = project3dTo2dArray(c, K, rotation * math.pi / 180, translation)
displayCube(w, h, estimateProjection)
def displayCube(w, h, points):
img = np.zeros((h, w, 3), dtype=np.uint8)
plotCube(img, points)
cv2.imshow('img', img)
k = cv2.waitKey(0) & 0xff
if k == ord('q'):
exit(0)
def plotCube(img, points):
# Red back face
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n-1]), (0, 0, 255), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 128), 2)
cv2.line(img, tuple(points[n*n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (0, 0, 200), 2)
cv2.line(img, tuple(points[n*n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (0, 0, 200), 2)
# gray connectors
cv2.line(img, tuple(points[0]), tuple(points[n*n*(n-1)]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*n*(n-1)+n-1]), (150, 150, 150), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*n*(n-1)+n*(n-1)]), (100, 100, 100), 2)
cv2.line(img, tuple(points[n*(n-1)+n-1]), tuple(points[n*n*(n-1)+n*(n-1)+n-1]), (100, 100, 100), 2)
# Blue front face
cv2.line(img, tuple(points[0]), tuple(points[n-1]), (255, 0, 0), 2)
cv2.line(img, tuple(points[n*(n-1)]), tuple(points[n*(n-1)+n-1]), (128, 0, 0), 2)
cv2.line(img, tuple(points[0]), tuple(points[n*(n-1)]), (200, 0, 0), 2)
cv2.line(img, tuple(points[n-1]), tuple(points[n*(n-1)+n-1]), (200, 0, 0), 2)
main()
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
edited Apr 9 at 13:33
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
answered Apr 9 at 8:37
Ragnvald TullenavnRagnvald Tullenavn
13 bronze badges
13 bronze badges
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55336759%2fcamera-pose-estimation-giving-wrong-results%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Have you verified the results are wrong and not your interpretation of the values according to my answer in the linked question?
– oarfish
Mar 26 at 10:05
8 points may not be enough. Updated code to use 1000 points. Also updated the code to use translation distance = 1, since recoverPose() only provides the translation direction unit vector. (Distance is unknown) This is the information I want to recover: translation = (0.7071, 0, 0.7071), rotation = (0, -90, 0)
– Ragnvald Tullenavn
Mar 27 at 14:03