def dino():
img1 = cv2.imread(img1_path)
img2 = cv2.imread(img2_path)
pts1, pts2 = features.find_correspondence_points(
img1, img2, feature, limit)
points1 = processor.cart2hom(pts1)
points2 = processor.cart2hom(pts2)
fig, ax = plt.subplots(2, 1)
ax[0].autoscale_view('tight')
ax[0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax[0].plot(points1[0],
points1[1],
'ro',
markerfacecolor='none',
markersize=2)
ax[1].autoscale_view('tight')
ax[1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
ax[1].plot(points2[0],
points2[1],
'ro',
markerfacecolor='none',
markersize=2)
fig.show()
height, width, ch = img1.shape
intrinsic = np.array([ # for dino
[2360, 0, width / 2], [0, 2360, height / 2], [0, 0, 1]
])
return points1, points2, intrinsic
def house():
input_path = 'imgs/house/'
camera_filepath = 'imgs/house/3D/house.00{0}.P'
cameras = [
Camera(processor.read_matrix(camera_filepath.format(i)))
for i in range(9)
]
[c.factor() for c in cameras]
points3d = processor.read_matrix(input_path + '3D/house.p3d').T # 3 x n
points4d = np.vstack((points3d, np.ones(points3d.shape[1]))) # 4 x n
points2d = [
processor.read_matrix(input_path + '2D/house.00' + str(i) + '.corners')
for i in range(9)
]
index1 = 2
index2 = 4
img1 = cv2.imread(input_path + 'house.00' + str(index1) +
'.pgm') # left image
img2 = cv2.imread(input_path + 'house.00' + str(index2) + '.pgm')
# fig = plt.figure()
# ax = fig.gca(projection='3d')
# ax.plot(points3d[0], points3d[1], points3d[2], 'b.')
# ax.set_xlabel('x axis')
# ax.set_ylabel('y axis')
# ax.set_zlabel('z axis')
# ax.view_init(elev=135, azim=90)
# x = cameras[index2].project(points4d)
# plt.figure()
# plt.plot(x[0], x[1], 'b.')
# plt.show()
corner_indexes = processor.read_matrix(
input_path + '2D/house.nview-corners', np.int)
corner_indexes1 = corner_indexes[:, index1]
corner_indexes2 = corner_indexes[:, index2]
intersect_indexes = np.intersect1d(np.nonzero([corner_indexes1 != -1]),
np.nonzero([corner_indexes2 != -1]))
corner_indexes1 = corner_indexes1[intersect_indexes]
corner_indexes2 = corner_indexes2[intersect_indexes]
points1 = processor.cart2hom(points2d[index1][corner_indexes1].T)
points2 = processor.cart2hom(points2d[index2][corner_indexes2].T)
height, width, ch = img1.shape
intrinsic = np.array([ # for imgs/house
[2362.12, 0, width / 2], [0, 2366.12, height / 2], [0, 0, 1]
])
return points1, points2, intrinsic
def dino(a, b):
# Dino
#img1 = cv2.imread('imgs/dinos/viff.003.ppm')
#img2 = cv2.imread('imgs/dinos/viff.001.ppm')
img1 = cv2.imread(a)
img2 = cv2.imread(b)
pts1, pts2 = features.find_correspondence_points(img1, img2)
points1 = processor.cart2hom(pts1)
points2 = processor.cart2hom(pts2)
height, width, ch = img1.shape
intrinsic = np.array([ # for dino
[2360, 0, width / 2], [0, 2360, height / 2], [0, 0, 1]
])
return points1, points2, intrinsic
def test():
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
# load points
points = processor.read_matrix('testsets/house.p3d').T # 3 x n
points = processor.cart2hom(points) # 4 x n
img = cv2.imread('testsets/house1.jpg')
height, width, ch = img.shape
K = np.array([[width * 30, 0, width / 2], [0, height * 30, height / 2],
[0, 0, 1]])
R = np.array([ # No rotation
[1, 0, 0], [0, 1, 0], [0, 0, 1]
])
t = np.array([[0], [0], [100]]) # t != 0 to be away from image plane
# Setup cameras
cam = Camera(K=K, R=R, t=t)
x = cam.project(points)
rotation_angle = 20
rotation_mat = transformers.rotation_3d_from_angles(rotation_angle)
cam = Camera(K=K, R=rotation_mat, t=t)
x2 = cam.project(points)
# Plot actual 3d points
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect('equal')
ax.plot(points[0], points[1], points[2], 'b.')
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
ax.view_init(elev=140, azim=0)
# Plot 3d to 2d projection
f, ax = plt.subplots(2, sharex=True, sharey=True)
plt.subplots_adjust(left=0.08,
bottom=0.08,
right=0.99,
top=0.95,
wspace=0,
hspace=0.01)
ax[0].set_aspect('equal')
ax[0].set_title(
'3D to 2D projection. Bottom x-axis rotated by {0}°'.format(
rotation_angle))
ax[0].plot(x[0], x[1], 'k.')
ax[1].plot(x2[0], x2[1], 'k.')
plt.show()
def dino():
img1 = cv2.imread(directory_name + "/" + filename)
img2 = cv2.imread(directory_name1 + "/" + filename)
pts1, pts2 = features.find_correspondence_points(img1, img2)
points1 = processor.cart2hom(pts1)
points2 = processor.cart2hom(pts2)
fig, ax = plt.subplots(1, 2)
ax[0].autoscale_view('tight')
ax[0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax[0].plot(points1[0], points1[1], 'r.')
ax[1].autoscale_view('tight')
ax[1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
ax[1].plot(points2[0], points2[1], 'r.')
fig.show()
height, width, ch = img1.shape
intrinsic = np.array([ # for dino
[2360, 0, width / 2], [0, 2360, height / 2], [0, 0, 1]
])
return points1, points2, intrinsic
def dino():
# Dino
img1 = cv2.imread('../../../Desktop/imgs/dinos/viff.003.ppm')
img2 = cv2.imread('../../../Desktop/imgs/dinos/viff.001.ppm')
pts1, pts2 = features.find_correspondence_points(img1, img2)
points1 = processor.cart2hom(pts1)
points2 = processor.cart2hom(pts2)
fig, ax = plt.subplots(1, 2)
ax[0].autoscale_view('tight')
ax[0].imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax[0].plot(points1[0], points1[1], 'r.')
ax[1].autoscale_view('tight')
ax[1].imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
ax[1].plot(points2[0], points2[1], 'r.')
fig.show()
height, width, ch = img1.shape
intrinsic = np.array([ # for dino
[2360, 0, width / 2], [0, 2360, height / 2], [0, 0, 1]
])
return points1, points2, intrinsic
intrinsic = np.array([[size, 0, center], [0, size, center], [0, 0, 1]])
# Points of on the surface of the cube
points3d = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 2],
[2, 1, 0, 2, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, -1, -1, -1, -2, -2, -2, 0, 0, -1, -1, -2, -2],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
# Define pose of cube with respect to camera1 in world view
rotation_mat = transformers.rotation_3d_from_angles(120, 0, 60)
translation_mat = np.matrix([0, 0, 5]).T
c = camera.Camera(K=intrinsic, R=rotation_mat, t=translation_mat)
# Project 3d points to camera1 on the left
points1 = c.project(points3d)
points1 = processor.cart2hom(points1)
# Get 4x4 homogenous extrinsic parameters of camera 1
H_c1 = np.vstack([c.extrinsic, [0, 0, 0, 1]])
# Define rotation of camera1 wrt camera2 and
# translation of camera2 wrt camera1
rotation_mat_wrt_c1 = transformers.rotation_3d_from_angles(0, -25, 0)
translation_mat_wrt_c1 = np.matrix([3, 0, 1]).T
H_c2_c1 = np.hstack([rotation_mat_wrt_c1, translation_mat_wrt_c1])
H_c1_c2 = extrinsic_from_camera_pose(H_c2_c1)
# Calculate pose of model wrt to camera2 in world view
H_c2 = np.dot(H_c1_c2, H_c1)
# Project 3d points to camera 2 on the right
