nerf_plus_plus/data_verifier.py

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2020-10-12 03:33:31 +02:00
import cv2
import numpy as np
## pip install opencv-python=3.4.2.17 opencv-contrib-python==3.4.2.17
def skew(x):
return np.array([[0, -x[2], x[1]],
[x[2], 0, -x[0]],
[-x[1], x[0], 0]])
def two_view_geometry(intrinsics1, extrinsics1, intrinsics2, extrinsics2):
'''
:param intrinsics1: 4 by 4 matrix
:param extrinsics1: 4 by 4 W2C matrix
:param intrinsics2: 4 by 4 matrix
:param extrinsics2: 4 by 4 W2C matrix
:return:
'''
relative_pose = extrinsics2.dot(np.linalg.inv(extrinsics1))
R = relative_pose[:3, :3]
T = relative_pose[:3, 3]
tx = skew(T)
E = np.dot(tx, R)
F = np.linalg.inv(intrinsics2[:3, :3]).T.dot(E).dot(np.linalg.inv(intrinsics1[:3, :3]))
return E, F, relative_pose
def drawpointslines(img1, img2, lines1, pts2, color):
'''
draw corresponding epilines on img1 for the points in img2
'''
r, c = img1.shape
img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)
img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)
for r, pt2, cl in zip(lines1, pts2, color):
x0, y0 = map(int, [0, -r[2]/r[1]])
x1, y1 = map(int, [c, -(r[2]+r[0]*c)/r[1]])
cl = tuple(cl.tolist())
img1 = cv2.line(img1, (x0,y0), (x1,y1), cl, 1)
img2 = cv2.circle(img2, tuple(pt2), 5, cl, -1)
return img1, img2
def epipolar(coord1, F, img1, img2):
# compute epipole
pts1 = coord1.astype(int).T
color = np.random.randint(0, high=255, size=(len(pts1), 3))
# Find epilines corresponding to points in left image (first image) and
# drawing its lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,1,2), 1,F)
lines2 = lines2.reshape(-1,3)
img3, img4 = drawpointslines(img2,img1,lines2,pts1,color)
## print(img3.shape)
## print(np.concatenate((img4, img3)).shape)
## cv2.imwrite('vis.png', np.concatenate((img4, img3), axis=1))
return np.concatenate((img4, img3), axis=1)
def verify_data(img1, img2, intrinsics1, extrinsics1, intrinsics2, extrinsics2):
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
E, F, relative_pose = two_view_geometry(intrinsics1, extrinsics1,
intrinsics2, extrinsics2)
# sift = cv2.xfeatures2d.SIFT_create(nfeatures=20)
# kp1 = sift.detect(img1, mask=None)
# coord1 = np.array([[kp.pt[0], kp.pt[1]] for kp in kp1]).T
# Initiate ORB detector
orb = cv2.ORB_create()
# find the keypoints with ORB
kp1 = orb.detect(img1, None)
coord1 = np.array([[kp.pt[0], kp.pt[1]] for kp in kp1[:20]]).T
return epipolar(coord1, F, img1, img2)
if __name__ == '__main__':
from data_loader import load_data
from run_nerf import config_parser
from nerf_sample_ray import parse_camera
import os
parser = config_parser()
args = parser.parse_args()
print(args)
data = load_data(args.datadir, args.scene, testskip=1)
all_imgs = data['images']
all_cameras = data['cameras']
all_intrinsics = []
all_extrinsics = [] # W2C
for i in range(all_cameras.shape[0]):
W, H, intrinsics, extrinsics = parse_camera(all_cameras[i])
all_intrinsics.append(intrinsics)
all_extrinsics.append(np.linalg.inv(extrinsics))
#### arbitrarily select 10 pairs of images to verify pose
out_dir = os.path.join(args.basedir, args.expname, 'data_verify')
print(out_dir)
os.makedirs(out_dir, exist_ok=True)
def calc_angles(c2w_1, c2w_2):
c1 = c2w_1[:3, 3:4]
c2 = c2w_2[:3, 3:4]
c1 = c1 / np.linalg.norm(c1)
c2 = c2 / np.linalg.norm(c2)
return np.rad2deg(np.arccos(np.dot(c1.T, c2)))
images_verify = []
for i in range(10):
while True:
idx1, idx2 = np.random.choice(len(all_imgs), (2,), replace=False)
angle = calc_angles(np.linalg.inv(all_extrinsics[idx1]),
np.linalg.inv(all_extrinsics[idx2]))
if angle > 5. and angle < 10.:
break
im = verify_data(np.uint8(all_imgs[idx1]*255.), np.uint8(all_imgs[idx2]*255.),
all_intrinsics[idx1], all_extrinsics[idx1],
all_intrinsics[idx2], all_extrinsics[idx2])
cv2.imwrite(os.path.join(out_dir, '{:03d}.png'.format(i)), im)